Advanced Analytics for Green and Sustainable Economic Development: Supply Chain Models and Financial Technologies (Premier Reference Source)

Advanced Analytics for Green and Sustainable Economic Development: Supply Chain Models and Financial Technologies Zongwei Luo University of Hong Kong, China

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Library of Congress Cataloging-in-Publication Data

Advanced analytics for green and sustainable economic development: supply chain models and financial technologies / Zongwei Luo, editor. p. cm. Includes bibliographical references and index. ISBN 978-1-61350-156-6 (hbk.) -- ISBN 978-1-61350-157-3 (ebook) -- ISBN 978-1-61350-158-0 (print & perpetual access) 1. Sustainable development--Environmental aspects. 2. Sustainable development--Finance. 3. Economic development--Environmental aspects. 4. Industries--Environmental aspects. I. Luo, Zongwei, 1971- II. Title. HC79.E5A34 2012 338.9’27--dc23 2011027885

British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher.

Table of Contents

Preface..................................................................................................................................................... v Chapter 1 Low Carbon Economy-Finance and Technology Models........................................................................ 1 S. Sureshkukar, National Institute for Interdisciplinary Science and Technology, India Chapter 2 Carbon Markets and Investments: EcoSecurities Investment Case Analysis........................................ 15 J. Zambujal-Oliveira, Technical University of Lisbon, Portugal Chapter 3 Firms’ Banking and Pooling in the EU ETS (2005-2007)..................................................................... 34 Julien Chevallier, Université Paris Dauphine, France Johanna Etner, Université Paris Descartes & ESG Management School, France Pierre-André Jouvet, Université Paris Ouest Nanterre La Défense, France Chapter 4 Mind the Gap Please! Contrasting Renewable Energy Investment Strategies between the World Bank and Poor Customers in Developing Countries.................................................................. 50 Sam Wong, University of Liverpool, UK Chapter 5 Alternatives to the Global Financial Sector: Local Complementary Currencies, LETS, and Time Backed Currencies........................................................................................................................ 64 Carl Adams, University of Portsmouth, UK Simon Mouatt, Southampton Solent University, UK Chapter 6 Low Carbon Economy and Developing Countries: A Case of Neplese Forest...................................... 79 Raghu Bir Bista, Tribhuvan University, Nepal

Chapter 7 Transition to Low-Carbon Hydrogen Economy in America: The Role of Transition Management........................................................................................................................................... 92 Jacqueline C.K. Lam, The University of Hong Kong, China Peter Hills, The University of Hong Kong, China Chapter 8 Operational Hedging Strategies to Overcome Financial Constraints during Clean Technology Start-Up and Growth............................................................................................................................ 112 S. Sinan Erzurumlu, Babson College, USA Fehmi Tanrisever, Eindhoven University of Technology, The Netherlands Nitin Joglekar, Boston University, USA Chapter 9 Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading............. 132 Ying Yin, The University of Hong Kong, China Zongwei Luo, The University of Hong Kong, China Chapter 10 Modeling Closed Loop Supply Chain Systems................................................................................... 157 Roberto Poles, University of Melbourne, Australia Chapter 11 Bike Transportation System Design.................................................................................................... 187 Avninder Gill, Thompson Rivers University, Canada Chapter 12 Data Center Technology Roadmap...................................................................................................... 202 Tugrul Daim, Portland State University, USA Timothy R. Anderson, Portland State University, USA Mukundan Thirumalai, Portland State University, USA Ganesh Subramanian, Portland State University, USA Nitin Katarya, Portland State University, USA Dhanabal Krishnaswamy, Portland State University, USA Neelu Singh, Portland State University, USA Compilation of References................................................................................................................ 231 About the Contributors..................................................................................................................... 246 Index.................................................................................................................................................... 251

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Preface

INTRODUCTION Traditional Supply Chain Management (SCM) aims at movement of goods and services from one end of this chain to the other through different stages so as to improve the efficiency, productivity and profitability of the entire process. As SCM spans across the economic functions of the entire value chain of a product or service, it is vital for a company to join in, form, or coordinate its business related supply chains, forming various kinds of business relationships. Supply chain relationship management, or relationship management in supply chains, increasingly becomes one of the core functions in today’s market place for companies to strive for business competiveness.

ENTERPRISES IN TRANSITION Examples always help understanding. Pearl River Delta (PRD), a south region in China, is a region where the world’s largest manufacturing base is located. In PRD, various economic functions of the entire value chain of a product or service are conveniently located together in a close geographical area. The enterprises have established various smoothly running industrial clusters with various business relationships formed. With the emphasis now on environmental protection and high technology development in China’s trade policy, many of those enterprises, small and medium sized and labor-intensive, have been losing their competitiveness. They are in low-end industries, with low capitalization, in relatively low technological conditions. Transformation, upgrade, and relocation are the only way out for them, which have now become the national encouraged policy, being enforced in China. Hong Kong government is urged to take pro-active role in helping them access financial resources, technology know-how, and market intelligence information. Now it is a critical moment to innovate technologies and solutions for those enterprises to transform and upgrade while in consistency with China’s new processing trade policy. Market potential and financial resources are the major two concerns for them. On the financial resources side, cross border financing technology and solution innovation is particularly important to improving the financial situations for Hong Kong invested enterprises in PRD and to help them retain the employment in the middle of a current financial tsunami.

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On the hard hand, upgrade, transform, and relocation present challenges on the supply chain management for those enterprises. The supply chains would become even longer and more complex with more diverse transportation networks across different and sparse regions. This would put serious threats on enterprises’ products and services market potentials as it hinders the market observation and feedback. Strong demand is there for market information disclosing and sharing leading to the discovery of market demand and feedback, especially during the transform period. The information often is rather expensive to obtain in the long and complex supply chains, as they tend to span across the economic functions of the entire value chain of a product or service. It is vital for a company to join in, form, or coordinate its business related supply chains, forming close business relationships with business partners. Supply chain relationship management, thus, increasingly becomes one of the core functions in today’s market place for companies to strive for business competiveness. The supply chain relationship modeling and analysis will lead to informed decision making and better market adaptation capabilities in the fast changing business environment.

SUPPLY CHAIN RELATIONSHIP MANAGEMENT FOR SUSTAINABLE DEVELOPMENT Supply chain relationship management emerges to be a key business capability to help address these challenges in the upgrade, transform and relocation of these enterprises, especially small ones. SCM, spanning across the economic functions of the entire value chain of a product or service, presents challenges and opportunities for relationship management to enhance enterprises’ capability for market adaptation. Traditional SCM, aiming at movement of goods and services from one end of this chain to the other through different stages so as to improve the efficiency, productivity and profitability of the entire process, often widen the distance of an enterprise to the market. Supply chain relationship management, on the other hand, helps narrow the distance for agile market adaptation, studying the business interconnections of how a company can join in, form, or coordinate its business related supply chains by establishing various business relationships with its partners. Supply chain relationship management increasingly becomes one of the core functions in today’s market place for companies to strive for business competiveness. Supply chain relationship management presents the following characteristics in order to help enterprises’ decision intelligences for dynamic market adaptation:

ADVANCED ANALYTICS WITH SUPPLY CHAIN MODELS AND FINANCIAL TECHNOLOGIES The green and sustainable development trend has been centric in all the hearts in major economies. Sophisticated green analysis for sustainability demands advanced analytics to cope with large data volume dispersed in every corner and to help deal with the risks and identify opportunities in the sustainable economy development. Advanced analytics are essential to high-value decision management towards building a sustainable competitive advantage in the green economy. Advanced analytics will provide innovative concepts, methods, tools, and application development to drive better decision makings with practical relevance to the green and sustainable economy development.

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This book on Advanced Analytics would contain a set of contribution with various focuses on the development of innovative techniques and tools to help clarify/answer some urgent questions in this global trend for sustainable economy development. The book contains 12 selected chapters with abstract following their titles for your easy reading through. I hope you enjoy your reading. Chapter 1, Low Carbon Economy – Finance and Technology Models, by S. Sureshkukar; The climate change is forcing a low carbon growth model not only for the developed nations but also for the developing countries, and particularly the emerging major emitters belonging to the emerging economies like china and India. New types of policies, partnerships and instruments, which dramatically scale up present climate change efforts, will be needed, if efforts to mitigate climate change and adapt to its effects are to succeed. The focus of this chapter will be on these and related issues pertaining to financial and technological aspects of the challenges confronting us in this context. The methodology used is essentially based on current literature and tacit knowledge arising from related experience along with its explicit accounts. Chapter 2, Carbon Markets and Investments: VAM’s Case Analysis, by J. Zambujal-Oliveira; In a world where greenhouse gases (GHG) carry a price, organizations can create financial instruments that are tradable on the carbon market by investing in projects that reduce GHG emissions. The purpose of this study is to critically analyze an investment project from EcoSecurities to mitigate the emissions of methane from a coalmine located in China’s Sichuan province. This project generates carbon credits that are later sold to governments and organizations under the Kyoto Protocol. In order to evaluate this investment, we conducted an analysis centered in its net present value, and we take into consideration a set of external variables and the financial and economic situation of EcoSecurities. This study concludes that Ecosecurities project investment, since project’s net present value is positive, it has a relevant impact on EcoSecurities strategy and improves the company’s financial situation as it increases revenues and improves assets using efficiency. Chapter 3, Firms’ Banking and Pooling in the EU ETS (2005-2007), by Julien Chevallier, Johanna Etner, and Pierre-André Jouvet; This article investigates firms’ banking and pooling behaviors in the context of the EU Emissions Trading Scheme (EU ETS) during Phase I (2005-2007). It provides an overview of the questions raised at the firm-level by the introduction and implementation of the EU trading system in terms of allowances management. More specifically, the article details the banking behavior at the installation level, and the pooling of risks at the group level attached to allowance trading between the parent company and its subsidiaries. Based on case-studies of the most significant patterns in terms of allowances management among firms, the empirical analyses underline the efficiency of the banking instrument as a risk-management tool. Chapter 4, Mind the Gap Please! – Contrasting Renewable Energy Investment Strategies between the World Bank and Poor Customers in Developing Countries, by Sam Wong; This chapter scrutinizes the World Bank’s nine guiding principles for investment strategies on renewable energy in developing countries. Drawing on two World Bank-funded solar lighting projects in Bangladesh and India as examples, it demonstrates a wide gap in investment strategies between the Bank and local people. It suggests that a rigid distinction of renewable and non-renewable options risks restricting poor people to adopt an energy-mix approach to cope with poverty. The economic assumptions of the strategic choice for renewable energy investment pay inadequate attention to the cultural norms that shape people’s preferences for energy sharing. A lack of participation of NGOs and local communities in shaping the Bank’s investment strategies also undermines the effectiveness of its renewable energy policies in the long term.

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This chapter suggests that the World Bank re-conceptualises the complex relationships between energy and poverty and seeks a better understanding of local people’s daily energy consumption practices. Chapter 5, Alternatives to the Global Financial Sector: Local Complementary Currencies LETS and Time backed Currencies, by Carl Adams and Simon Mouatt; This chapter explores complementary currencies and exchange systems and how they can provide some stability and competition to the vulnerability of the financial markets. The social economy, or 3rd sector, already plays a significant part in many societies. This is becoming more so as many governments and nations are facing decades of debt inevitably resulting in cut backs in key social and health services. In addition, the existing formal economic activity does not capture, value or support the full range of social and economic interaction within a nation. The chapter examines timebank systems, a particular type of complementary currencies and exchange system, and provides guidance on issues to consider in develop them. One of the finding from the evaluation is that as the number of people in the timebank system increases then more formality is needed to moderate the system and reduce potential for misuse. Chapter 6, Low Carbon Economy and Developing Countries: A Case of Nepalese Forest, by Raghu Bir Bista; In forest, reduction of emission from deforestation and forest degradation (REDD) is considered as low carbon instrument. Financial Incentive scheme of this new climate change mitigation approach generates query about REDD’s economic implication in developing country. This study is to examine empirically low carbon potential from avoided deforestation in Nepal. The case study is the Kafle community forest of Nepal. We used 10 meter radius circle sample plot for carbon inventory data collection. In addition, we conducted household survey through 48 households for data set collection. This study finds that community forest contributes 45 percent livelihood income (fire wood, leaf litter, grass, water) to the forest dependent stakeholder’s total income. This labor incentive based on labor contribution in forest management is distributed among the member households. This study further finds huge carbon income potentials. Annually, KCF can earn carbon income Rs. 39, 81,196, if KCF enters in REDD. It is 41 times higher than the present mean income Rs 24, 549.55 from the forest product sale. In mixed familiarity about REDD, the study finds only 44 percent households expecting that REDD will be a better livelihood alternative to the poor. 63 percent responds need and use of carbon income for livelihood objectives. From estimation, household stakeholders who have good asset holdings (land and Rlivestock) think that REDD will be not a better livelihood alternative to the poor. However, the household stakeholders who have literacy, different food sufficiency level, land holding (1>), different earning per day, Rsex, per day earning and age think that REDD will be a better alternative. Thus, the poor households expects livelihood role from REDD in Nepal. Therefore, REDD should be more beneficial to the poor household stakeholders and their livelihoods. Chapter 7, Transition to Low-Carbon Hydrogen Economy in America: The Role of Transition Management, by Jacqueline C.K. LAM and Peter HILLS; This chapter describes the process of transition to low-carbon hydrogen economy in America and the role of transition management (TM) in such process. Focussing on the transition process of hydrogen-based energy and transport systems in America, especially California, this study outlines the key characteristics of TM that have been employed in managing the low-carbon transition of hydrogen economy. Several characteristics of TM have been noted in America’s hydrogen transition, including: (a) the complementation of the long-term vision with incremental targets, (b) the integration of top-down and bottom-up planning, (c) system innovations and gradualism, (d) multi-level approach and interconnectedness, and (e) reflexivity by learning and experimenting,. These characteristics are instrumental in bringing about the development and initial commercialization of HFCVs and energy infrastructure in America.

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Chapter 8, Operational Hedging Strategies to Overcome Financial Constraints during Clean Technology Start-up and Growth, by S. Sinan Erzurumlu, Fehmi Tanrisever, Nitin Joglekar; Clean technology startups face multiple sources of uncertainty, and require specialized knowhow and longer periods for revenue growth than their counterparts in other industries. These startups require large investments and have been hit hard during the current credit squeeze. On the other hand, clean technologies create important positive externalities for the economy. Hence, loan guarantees and other incentive schemes are being developed that are conditioned upon operational benchmarks. We offer a framework to establish the extent wherein operational hedging can reduce risk and increase the probability of obtaining financing. We examine a variety of evidence, ranging from production outsourcing to creation of joint ventures, to posit that operational hedging may affect both the marginal cost of capital and the marginal return on investment through mitigating the informational problems in the market. However, operational hedging may not be an effective strategy in all settings: the decision for creation of such hedges ought to weigh the benefits of reduced marginal cost of capital and the opportunity cost of reduced future growth potential against a status quo. Chapter 9, Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading, by Ying Yin, Zongwei Luo; Warehouse financing has been emerged as one of the most effective financing approaches for small and medium-sized enterprises (SME). Its basic working mechanism is to transfer the company’s assets to collaterals which are more acceptable by the bank. As a logistics service provider, the 3rd Party Logistics (3PL) coordinates and controls the whole financing process. With the professional 3PL’s help, it is easier for SMEs to get loan from the bank. In the meantime, the 3PL’s profit margin has also been increased by providing financing service in addition to their traditional logistics based functions. This chapter explains the basic working mechanism of warehouse financing, applies SCOR reference model to identify financing activities and the risks caused by them. Then this paper synthesizes four relevant risk analysis / management frameworks from previous literatures, and proposes a new risk framework and evaluation measures aimed specifically for warehouse financing. Finally, a case of carbon trading in China is studied using the previous framework. Chapter 10, Modeling Closed Loop Supply Chain Systems, by Roberto Poles; In the past, many companies were concerned with managing activities primarily along the traditional supply chain to optimize operational processes and thereby economic benefits, without considering new economic or environmental opportunities in relation to the reverse supply chain and the use of used or reclaimed products. In contrast, companies are now showing increased interest in reverse logistics and closed loop supply chains (CLSCs) and their economic benefits and environmental impacts. In this chapter, our focus is the study of remanufacturing activity, which is one of the main recovery methods applied to closed loop supply chains. Specifically, we investigate and evaluate strategies for effective management of inventory control and production planning of a remanufacturing system. To pursue this objective, we model a production and inventory system for remanufacturing using the System Dynamics (SD) simulation modeling approach. Our primary interest is in the returns process of such a system. Case studies will be referred to in this chapter to support some of the findings and to further validate the developed model. Chapter 11, Bike Transportation System Design, by Avninder Gill; The main objective of this chapter is to address the facility design and location issues in a public bike transportation system. The major decisions in introducing a public bike transportation system include determining the number of bike facilities and their locations. The present chapter considers a case study from city of Vancouver bike transportation system to demonstrate the importance of these decisions through a real world application. The city intends to decide the number and location of bike terminals. Addressing these two decisions is

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the main focus of the present chapter and the chapter employs linear programming and center of gravity approaches to arrive at the solutions. The chapter also provides a basic introduction to bike facilities and discusses the sustainability benefits of bike transportation mode. Chapter 12, Data Center Technology Roadmap, by Tugrul Daim, Timothy R. Anderson, Mukundan Thirumalai, Ganesh Subramanian, Nitin Katarya, Dhanabal Krishnaswamy, and Neelu Singh; Datacenters have been in existence all over the world for the past several decades. In today’s dynamic world, especially with most of the businesses being heavily dependent on Information Technology, interconnecting various systems within the organization and the outside world is a mandatory requirement for the success of any business. Datacenters all around the world perform this role to some level of satisfaction. Since datacenters started to play a significant factor in any organization’s success, companies realize the value of having a datacenter oriented strategy as one of the strategic initiatives for the success of their organization. Despite the agreement that the value of having such an initiative for datacenters is important, there is a lack of clarity in terms of the technical know-how involved in datacenters. Our objective here in this study is to fill that gap in the Industry. We wanted to portray the different facets of datacenters in terms of how can they be classified, what are the underlying technologies, what are the current challenges faced by the industry and where the industry is headed in the next 10years. We illustrate the evolution of the datacenter industry in the last decade and how it is going to continue in the next 10 years graphically in the form of a Technology Roadmap. We based our research on going through existing industry literature, analyze challenges and develop a technology roadmap for data center industry with emphasis on energy efficiency and cost reduction. The wide audience for this roadmap would include IT professionals, datacenter managers, company strategists, the Government as well as environmentalists. Our intention is to present the audience with a single-stop snap shot of the data center industry on how the industry has evolved over the time and where it is heading in the future. We present our findings based on analyzing the data obtained from literature research and expert knowledge. The key research areas of our study were challenges, market trends, technological innovation, energy efficiency, cost reduction and government involvement. In this report, we take you through the general roadmap architecture starting with market drivers, products, technology and its components followed by our recommendations and inference from the study. Zongwei Luo University of Hong Kong, China

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Chapter 1

Low Carbon Economy-Finance and Technology Models S. Sureshkukar National Institute for Interdisciplinary Science and Technology, India

ABSTRACT The climate change is forcing a low carbon growth model not only for the developed nations but also for the developing countries, and particularly the emerging major emitters belonging to the emerging economies like China and India. New types of policies, partnerships and instruments, which dramatically scale up present climate change efforts, will be needed, if efforts to mitigate climate change and adapt to its effects are to succeed. The focus of this chapter will be on these and related issues pertaining to financial and technological aspects of the challenges confronting us in this context. The methodology used is essentially based on current literature and tacit knowledge arising from related experience along with its explicit accounts.

INTRODUCTION The IEA estimates that limiting GHG concentrations to 450 ppm CO2eq would require US$550 billion to be invested in clean energy from now to 2030. UNDP estimates the cost of adaptation at US$86billion. Most of the financing in the coming years will have to come from private sources, or from innovative funding mechanisms DOI: 10.4018/978-1-61350-156-6.ch001

currently available or being developed. Current levels of ODA, while significant, are unlikely to be sufficient to finance the necessary investments. For example, for energy-related activities, ODA, at present, provides US$5-7 billion per year, which is only 1% of the total amount required. The international community is currently piloting a number of public policies, new market-based instruments and innovative financial mechanisms, to attract and drive direct investment towards lower-carbon and climate- resilient technologies

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Low Carbon Economy-Finance and Technology Models

and practices. In 2007, the private sector invested nearly US$150 billion of new money in clean energy technologies in response to these new policy and financial incentives. However, these financial flows often remain restricted to OECD countries and a small number of rapidly developing countries; barriers still need to be removed before they can be widely disseminated for easier access by other developing countries. For example, the Kyoto Protocol created the Clean Development Mechanism (CDM) to promote both sustainable development and GHG emission reduction in developing countries. The CDM is a global cap-and-trade mechanism, which allows developing countries to earn credits for their emission reduction projects and sell these cheaper credits to industrialised countries. Despite its potential, there is strong concern that only a limited number of countries will benefit from the CDM, and that this mechanism could bypass Africa entirely. (UNEP, 2009) Only five countries–China, India, Brazil, South Korea, and Mexico–are expected to generate over 80 percent of CDM credits by 2012. Current market rules all too often fail to attract investors into lower-carbon technologies and sustainable land-use projects. The specific market conditions of developing countries will need to be incorporated into the design of new market-based and innovative financial mechanisms. A number of reforms to the CDM are currently being discussed to achieve this objective (programme approaches, etc.). At the same time, developing countries will need assistance to put into place an enabling environment (e.g. public policies, institutions, human resources) so that they are in a better position to leverage these new sources of finance. A new order of partnership is needed between developed and developing economies–one that supports the development needs of developing countries but assists them onto a low carbon trajectory that leap-frogs the 20th century development patterns of the North.

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Encouraging financial flows between rich and less well off countries is key as is the involvement of the private finance sector and global investment community. UNEP Finance Initiative and the UNEP Sustainable Energy Finance Initiative are some examples. More recently, the interaction between UNEP’s Initiatives and other private finance networks has intensified. Combating climate change is not about costs to the economy but an investment in the kinds of renewable, clean-tech and natural resource management economies able to generate low-footprint wealth and employment for over one billion people unemployed or under employed. The total investment required to avoid dangerous climate change is more than USD 1 trillion per annum, according to the International Energy Agency (IEA). Half of this amount could be redirected from business-asusual investment in conventional technologies to low-carbon alternatives. The remainder (USD 530 billion) is required in the form of additional investment. World Bank estimates suggest that around USD 475 billion of the total annual investment must occur within developing countries. Around USD 400 billion per annum of investment will be required for mitigation investment. A further USD 75 billion per annum will be required for adaptation investment. (UNDP, 2009) Developing countries will be most advantaged if public finance contributions are designed to maximise the leverage of additional private finance. Institutional investors could provide much of the capital, if an appropriate risk-reward balance is offered. Institutional investors, such as pension funds, insurance companies and sovereign wealth funds, are in a position to provide some of the required capital. It is estimated that pension funds alone control assets worth more than $12 trillion and that sovereign wealth funds have a further $3.75 trillion under management. However, to stimulate their engagement the expected returns on climate-change mitigation investment need to be commensurate with the perceived level of risk. This is not currently the case.

Low Carbon Economy-Finance and Technology Models

Public Finance Mechanisms (PFMs), which could deliver between $3 and $15 of private investment for every $1 of public money, are part of the solution. Public money can be used to increase returns or reduce risks, and can be an efficient way of mobilising institutional investor capital. Alongside efforts to reform carbon markets and to create the conditions needed for ‘nationally appropriate mitigation actions’ (NAMAs), PFMs also need to be examined and optimised if they are to facilitate the required scale and speed of private capital injection. The guiding principles of the Financial Mechanism under the UN Framework Convention on Climate Change should recognise the potential for use of public funds to leverage private finance. Much of the required capital will be directed via specialised low-carbon funds, such as those recently proposed by the World Economic Forum. However, it is likely that big listed firms, largely owned by institutional investors, may implement individual large-scale low-carbon projects. PFMs should be available to institutional investors in both contexts. Developing countries should be heavily involved in the development and application of PFMs. A pre-requisite of PFM success is host-country commitment to the investment. Reflecting this, developing countries should be heavily involved in the process of determining the outcome(s) of the competition between investors for the use of PFMs. Competition to provide PFMs might also be introduced. There is a marked difference in the extent to which Development Finance Institutions (DFIs) attempt to, and succeed in, engaging with the private sector. To create incentives to encourage this engagement, institutions providing successful PFMs could, over time, receive more resources from relevant national governments. (UNEP & Partners, 2009; IEA, 2009) Drawing on World Bank research, climatechange mitigation investment in the developing world needs is estimated to be around USD 400 billion per annum. A further USD 75 billion per annum of investment may be required for adaptation.

Both the public and the private sector have roles to play in meeting this challenge. In comparison to the USD 475 billion per annum investment required, the World Bank reports existing commitments of USD 9 billion per annum. This is less than 2 percent of the required amount. Public sector commitments may increase following the Copenhagen conference, but will still fall short of the required level. Demands on public finance are acute and this has been exacerbated by the current recession. The public sector commitments for the developing world currently under negotiation, if delivered to their maximum ambition, total around USD 110 billion per annum. The shortfall, in excess of USD 350 billion per annum, could be met by the private sector. (World Bank, 2009) Part of the answer is to deploy Public Finance Mechanisms (PFMs). PFMs are financial commitments made by the public sector which alter the risk-reward balance of private sector investments. They include grants, concessional finance, risk mitigation instruments and market aggregation activities. UNEP’s prior research provides more information on the range of PFMs available. (UNEP-SEFI, 2008) PFMs can leverage significant private capital. Previous research suggests that $1 of public investment spent through a welldesigned PFM can leverage between $3 and $15 of private sector money Energy production and consumption patterns: Reducing world carbon dioxide emissions by 50% by 2050, compared to 1990 levels, will require revolutionary changes in our energy production and consumption patterns. Notably, we will have to rapidly introduce mitigation technologies that are commercially viable and that have immediate impacts on GHG reduction at a negative cost. According to the IEA, many clean-energy technologies (renewable energy and energy efficiency) are ready to launch. Moreover, the bulk of end-use energy efficiency measures can be implemented at a negative cost. The McKinsey Global Institute has estimated that we could cut the projected growth of global energy demand up

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Low Carbon Economy-Finance and Technology Models

to 2020 by at least half, by capturing opportunities which increase energy productivity—the level of output we achieve from the energy we consume. Additional annual investments of US$170 billion for the next 13 years would be sufficient to capture the energy productivity opportunity among all end users. (McKinsey Global Institute, 2007) The economics of such investments are very attractive, with an average internal rate of return (IRR) of 17%, and are calculated to collectively generate energy savings, which can reach up to US$900 billion annually by 2020. In this scenario, 57% of the investments would occur in developing countries, notably China. Similarly, the IEA has shown that, on average, an additional one dollar invested in more efficient electrical equipment, appliances and buildings, avoids more than two dollars in investment in electricity supply. This ratio is highest in non-OECD countries. (World Energy Outlook, 2006) Achieving this transformational exercise will require a dramatic shift in public and private investments from traditional energy supply sources and technologies to more sustainable climate-friendly alternatives. The IEA estimates that US$550 billion/year needs to be invested in clean energy, from now to 2030, if we are to limit GHG concentrations to 450 ppm CO2e. (World Energy Outlook,2008) Therefore, a number of new market-based instruments and innovative financial mechanisms are currently being piloted to attract and drive direct investment towards lower-carbon technologies and practices and to cut the costs of adaptation. Climate change has the potential to affect many companies in both positive and negative ways and is likely to result in a Schumpeter’s cycle of “destruction-creation.” The degree to which a company is exposed to climate change will depend on a variety of factors, including their business model and geographical location. Government policies to manage the climate can create new markets for low-GHG and climate-resilient products and services, and profoundly alter costs and companies’ current comparative advantages.

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For example, a recent energy-efficiency standard, introduced for existing buildings in France, has the market potential of €350 billion by 2012. Companies and investors are quickly realising that climate change is not merely a social, political, or moral issue - it is an economic and business issue/ opportunity as well. (Deutsche Bank Group,2007) There is a range of the new financing mechanisms for mitigation, at the international and national/sub-national level. Some examples: international schemes, national and sub-national schemes, public funds, ODA (multilateral, bilateral, and decentralized cooperation), multilateral funds, green economic stimulus, environmental fiscal reforms, export credits, rebates and subsidies, tax credits and tax free bonds, low interest loans, private funds, green equity finance, private investment funds, foundations, non-governmental organisations, global philanthropic foundations, corporate social responsibility (MNCs), national philanthropic foundations, corporate social responsibility (national corporations), market-based mechanisms, tradable renewable energy certificates, carbon cap-and-trade mechanisms (CDM, JI, voluntary), tradable renewable energy certificates, green insurance contracts, progressive approaches (NAMA, etc.), tradable renewable energy certificates, utility DSM, green mortgages, tax free climate change bonds, domestic carbon projects, innovative instruments transaction taxes (TOBIN), international CC finance initiative, air travel levy, global carbon tax, debt-for-efficiency swaps, international carbon auction funds, international noncompliance fees, efficiency penny, carbon taxes, energy taxes, auction of emission allowances, national non-compliance fees, green investment schemes, efficiency penny. These schemes can be divided into four main categories: 1. public funds providing either grant or loan assistance;

Low Carbon Economy-Finance and Technology Models

2. private funds providing either grant or loan assistance; 3. market-based instruments; and 4. innovative financing instruments. Market-based instruments and innovative financing instruments are two fairly recent developments in international finance. Market-based mechanisms, such as the cap-and-trade system, rely on markets to provide financial incentives to steer funding towards lower-carbon and climate resilient investments. Cap-and-trade schemes are intended to minimise the cost of a given level of pollution abatement by creating property rights to emit, administratively limiting the supply of permits to ensure the emissions target level is not exceeded and distributing permits (either by auction or by direct allocation). Subsequently a trade in permits is allowed so that emitters lacking permits are forced to buy them from those with a surplus because of abatement. (Deutsche Bank Group, 2007) However, a key issue with a number of these new and innovative sources of finance is their acute regional and technological unevenness, with the bulk of these funds going to a few large emerging economies and to a small selection of technologies. The EU and the US currently receive the greatest share of both the new investment and the acquisition activity. Developing countries shared 22% of new investment (venture capital/ private equity, public markets and asset finance) in 2007, up from 12% in 2004. However, most of this investment was in China and Brazil, which together represented 17%. In actual financial terms, developing countries attracted US$26 billion in new investment in 2007, double 2006’s total of US$13 billion (and 14 times 2004’s US$1.8 billion). In 2007, investment in the least developed regions, such as Africa, was limited to asset financing of US$1.3 billion—mainly for biofuel plants. Although an estimated 575 million people still rely on traditional biomass in Africa16, the

region accounted for less than 1% of the total private investment in clean energy in 2007. Private sector investment in clean energy is strongly biased towards certain technologies. Wind was once again the leading sector in 2007, accounting for US$50.2 billion (43%) of new investment and extending its 2006 lead, when it received 38%. Solar and biofuels, respectively, attracted the second and third largest investment volumes. Together, all three technologies accounted for nearly 85% of new investment in 2007. In contrast, energy efficiency technologies, whose immediate deployment is critical to avoid dangerous climate change attracted only 2% of financing. (UNEP, 2008) The CDM has huge potential in terms of allowing developing countries to earn credits for their emission reduction projects and to sell these credits to industrialised countries. The UNFCCC estimated that the CDM could range from US$10 and US$100 billion per year by 2030, depending on emission reduction targets and the price of carbon credits. A recent World Bank study on the potential for CDM in Africa concluded that 170 GW of additional power-generation capacity could be created in Sub-Saharan Africa through low-carbon projects eligible for CDM (i.e. projects recognised by the international community as reducing GHG emissions). This would equal roughly four times the region’s current modernenergy production. However, the analysis of the existing CDM pipeline reveals that only a limited number of countries are benefiting, and that the mechanism could bypass Africa entirely. Just five countries—China, India, Brazil, South Korea and Mexico—are expected to generate over 80 percent of CDM credits by 2012. Almost half of these credits will come from non-CO2 industrial gas emissions (such as HFC-23 destruction and N2O emissions capture) that are characterised by a high return on investment but have very limited sustainable development Benefits. (World Energy Outlook, 2006; World Energy Outlook, 2008;

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Deutsche Bank Group, 2007; UNEP, 2008; World Bank, 2008) The specific market conditions of developing countries will need to be incorporated into the design of the new market-based and innovative finance mechanisms. In addition, developing countries will need assistance to establish an enabling environment (e.g. public policies, institutions, human resource capacities) at all levels, so that countries are in a better position to leverage these new sources of finance to obtain better access to clean energy services. Furthermore, the potential of many of these instruments can be maximised by appropriately combining and sequencing different instruments. For example, the additional carbon revenues generated through the CDM for wind energy projects are not substantial enough to change the underlying profitability. In such a case, the use of feed-in tariffs in combination with carbon revenues can serve as the critical tipping point. Another example is when introducing regulations that require energy-efficient and climateresilient building codes. Such regulations would be far easier to implement if they were combined with interest-free loans. Therefore another critical requirement will be to enhance the capacity of decision-makers at the local, regional and national level, to consider different options as part of an integrated climate change strategy. The UNFCCC estimates that the additional investment and financial flows needed worldwide for adaptation will be US$60-182 billion in 2030. The largest component in this estimate is the cost of adapting infrastructure, which may require US$8-130 billion in 2030, one-third of which would be for developing countries. The UNFCCC also estimates that an additional US$52-62 billion would be needed for agriculture, water, health, ecosystem protection and coastal-zone protection, most of which again would be used in developing countries. In total, it is estimated that US$28-67 billion in additional investment and financial flows will be required in 2030 for adaptation in developing

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countries. Others arrive at similar estimates for adaptation. The World Bank concludes that the incremental costs of adapting to the projected impacts of climate change in developing countries are likely to be approximately US$10-40 billion per year, while Oxfam International estimates this number to be over US$50 billion per year. UNDP suggests aid financing for adaptation could amount to US$86 billion per year by 2015. (Stockholm Environment Institute, 2008) The current levels of official development assistance (ODA) for adaptation in developing countries are extremely low (less than US$100 million per year). Even if increased significantly, they will probably be insufficient. Similar to mitigation, the past few years have witnessed an extremely rapid development of new sources of funding for adaptation and climate change management In terms of market instruments, weather derivatives and CAT bonds are particularly important for mobilizing financing. As with mitigation, financial markets and the insurance industry can also play an important role in supporting adaptation to climate change, specifically through cutting the costs of adaptation—that is, how economies respond to climate change—by reallocating capital to newly productive sectors and regions and hedging weather-related risks. However, while market-based instruments are expected to play the leading role in mitigation, innovative financial instruments are likely to account for a larger share of funding in adaptation. One example of an innovative financial instrument is the Adaptation Fund. This Fund is unique in that it generates revenue through a two percent levy on emission permits—‘Certified Emission Reductions’ (CERs)—generated by the emission reduction projects under the Kyoto Protocol’s Clean Development Mechanism (CDM). The international community’s ability to transform energy systems to meet future demands for growth and lower GHG emissions will ultimately depend on a burst of technological innovation over the next few decades. The potential of key

Low Carbon Economy-Finance and Technology Models

low-carbon technologies is now well known—the latest microeconomic analysis suggests they can offer up to 11% of GHG abatement potential to 2030; and up to 27% by 205016. Technology’s biggest contribution to a low-carbon future will be its ability to expand low-carbon choices and make the options ever cheaper. This requires driving technologies down the cost curve through advancements in science, engineering and mass deployment. The long-term, risky and often very costly nature of research, development and deployment of potentially revolutionary technologies requires intensified and better coordinated public and private sector efforts.

ACCELERATING INVESTMENT IN LOW-CARBON TECHNOLOGIES There is a need for public-private partnership investment and risk sharing via a series of proof point demonstration projects to help transcend the current challenges facing the smart grid industry and to clearly illustrate the value proposition to investors and governments. Linkages to programmes designed to reform utility pricing incentives and implement building standards and electrified transportation networks could also be pursued, creating a set of integrated low-carbon city demonstration projects. The development and testing of competing CCS technologies could be accelerated through a coordinated series of large-scale targeted demonstration projects over the coming decade. These demonstration projects would be jointly funded by governments and companies, with the financing of the incremental cost for CCS being supported by developing countries, multilateral development banks and available carbon financing mechansims. Once the technical viability of various CCS approaches is better established through this global initiative, developed and developing country governments could consider whether to

establish a comprehensive global strategy to deploy the best technologies at scale by introducing into the post-Kyoto framework a sector-based approach on coal-fired power plants and/or including various financing mechanisms such as an international carbon sequestration unit within the Clean Development Mechanism (CDM). The Consultative Group on International Energy Research (CGIER) would facilitate applied research programmes on locally-relevant low-carbon energy solutions through open source collaboration among academics, businesses and other actors, similar to the multistakeholder GreenTech model in China. In addition, they could develop full life-cycle views on regional technology innovation, offering regional “pull” models for technology diffusion; facilitate regional intellectual property rights mechanisms, such as patent trading platforms; stimulate research and dialogue on pathways to reductions in harmful energy subsidies; and promote efforts to bring to scale solar photovoltaic (PV) technology (especially across the US, Japan, EU, India, and China), distributed models of solar PV (especially across India, the Middle East/North African, and subSaharan Africa) and advanced wind and biofuel technologies. Funding for the centres would be drawn from a range of public, private and philanthropic sources. Their main purpose would be to support nationally appropriate mitigation action plans through the mobilization of multistakeholder networks of expertise both inside and outside the region in question Mechanisms are needed to leverage the climate-related increases in ODA that developed countries do provide with larger amounts of long-dated debt and patient equity from private investors, allowing for flows from an international offset market to grow over time. By far the largest potential source of such long-term private investment is institutional investors, such as public and private pension funds, insurance

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companies, sovereign wealth funds, endowments and private banks. Most institutional investors invest in funds managed by private investment management firms. This allows them to access a wide variety of investible projects in markets far from their centre of operations, exercise effective governance, achieve targeted “exit” returns and, most importantly, diversify their risk. There is growing interest among such investors in low-carbon infrastructure in developing countries, but the volume of investment by them remains low because of the considerable risks and uncertainties involved and the related fact that few large, diversified funds exist for this purpose. The investor community has confidence in multilateral and bilateral development finance institutions and values in particular their ability to enhance the creditworthiness of transactions by participating in or providing credit enhancement to investments. Private investors sometimes require the involvement of the World Bank or regional multilateral development banks (MDBs) before they enter new markets and investment classes in a material way. Accordingly, MDBs and bilateral development finance agencies have an opportunity to play a transformational role in stimulating private energy investment in emerging and developing economies if they find a way to scale their credit support for such transactions. A public-private investment model in which public credit enhancement and regulatory capacity building is combined with private institutional capital has the potential to unlock significant investment flows for low-carbon energy systems in developing countries, far beyond what can be financed directly from foreign aid budgets. An initial, streamlined model (MDB Low-Carbon Challenge Funds) could catalyse up to US$ 10 billion per region per three-year cycle, ready for business by 2011. A second, more ambitious model (Regional Low-Carbon Cornerstone Funds) could catalyse US$ 50-75 billion per region each three years and

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could be ready for business before the start of the second commitment period in early 2013. In this way, the increased official development assistance that developed countries provide in connection with a new agreement under the UNFCCC could be structured to mobilize the maximum possible amount of low-carbon financing for developing countries. MDB-Multilateral and -Bilateral development finance institutions would bid out preferential access to regional packages of their public finance mechanisms. Leading global (or regional) und management firms would tender for the bids, explaining how they would leverage the mechanisms on offer to create a new fund (or strengthen an existing one) and generate enhanced investment flows as a result. The credit support packages of development finance institutions would improve the risk/return ratio of projects within these lowcarbon infrastructure funds. Regional cornerstone funds for low-carbon infrastructure would be created and administered through establishment of specialized institutions modelled on the US Overseas Private Investment Corporation. They would raise anchor equity (e.g. US$ 5 billion) from major institutional investors as well as official and philanthropic donors and then invite leading global and regional fund management firms to establish low-carbon energy funds, clean infrastructure funds, low-carbon building funds, green-tech funds, etc. by bidding for a distribution of part of the anchor equity. These firms would then galvanize their investor network to raise a further US$ 4 billion each from the wider universe of secondary institutional investors who invest in global emerging markets. Since most of the funds’ investments would have infrastructure-style investment characteristics, they could then borrow from banks and debt capital markets to secure at least a 66% debt-toequity ratio for their project portfolios

Low Carbon Economy-Finance and Technology Models

ACCOUNTING AND MARKET MECHANISMS A lack of comparable, comprehensive and reliable climate-related information from corporate emitters is a significant impediment to the transition to a low-carbon model of economic growth. Fortunately, a de facto standard for the preparation of corporate/entity level GHG inventories has already emerged from the cooperation of the business and environmental NGO communities in the form of the GHG Protocol. And work is already underway in these communities through the Climate Disclosure Standards Board to create a generally accepted framework for the disclosure of emission inventories, carbon-related risks and management strategies in the annual reports of corporations. The direct emissions component of this framework is based on the GHG Protocol. Governments should direct their securities and accounting regulatory bodies to engage in these path breaking processes with the ultimate goal of creating a generally accepted set of international accounting principles that can be adopted by securities and other regulators for inclusion in policy responses to climate change that require monitoring and reporting of climate risks, opportunities, strategies and GHG emissions. A new international framework should allow national governments to employ market-based domestic policies best suited to their own national circumstances; however, it should also facilitate the linkage of explicit or implicit carbon values established at various national and regional levels. This would enhance the economic efficiency of efforts to combat climate change and stimulate low-carbon investment, especially in developing countries. A global carbon market will need to be broad, deep and liquid to be effective. This is best achieved through ambitious and coherent national emissions reduction targets; early and effective linking of national and regional schemes; and the development and scaling up of systems for the

crediting of project-based and sectoral emissions reductions. Governments need to set a target date for linking existing and emerging emissions trading systems. They must agree on a broad set of principles to ensure that the system design does not impede subsequent linking, and that will ensure the environmental integrity of the system. The most important areas for policy harmonization are target-setting, the use of international and domestic carbon credits, rules for monitoring reporting and verification, mechanisms for avoiding excessive price fluctuations and the role of financial intermediaries. A new framework needs to encourage greater participation in the carbon markets from unrepresented regions, and should set out the path for participating CDM countries to transition to sector- and national-level targets. Approaches beyond the existing mechanisms could, if well designed, help to deliver emissions reductions in sectors (e.g. reforestation, avoided deforestation, energy efficiency) and projects (e.g. carbon capture and storage) currently not effectively targeted by international climate policies. The most promising ideas that have emerged include: •

•

•

Sectoral Approaches: where emission targets are agreed at a sector level; targets could be set at a national or international level Simplified Programmatic CDM: where establishing additionality is no longer on a case-by-case basis, thereby reducing the project development costs to participants Inclusion of Forestry Credits (REDD+): as forest-based mitigation becomes a vital part of a global deal on climate change, incorporating the forestry sector into carbon markets will be important to drive investments into this area Any new mechanisms should be designed to stimulate and scale-up private sector flows of finance. It

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Low Carbon Economy-Finance and Technology Models

is clear that public financing will be necessary to build the foundations at the international and national levels for the largescale implementation of REDD+ activities. Several policies are required to attract private sector finance to REDD+ activities: •

•

•

•

•

Parties should include forest carbon in the new climate agreement through a mechanism such as REDD+ and ensure adequate stability of such regulation over the long term Within such an agreement, these projects must produce carbon credits of compliance grade that are tradable as offsets with other credits in international carbon markets Measurement, reporting and verification (MRV) procedures must be robust and include the use of systems that can ensure reliable calculations of the carbon value of projects Forest-based mitigation efforts should be made available for investment at a project level, but placed within the context of national baselines and forest nations’ Nationally Appropriate Mitigation Actions (NAMA) Plans A risk management framework for this new asset class will be required to mitigate risks such as unforeseen reversal The development of each of these policies will require a process of extensive dialogue between the public and private sectors.

THE FINANCING CONTINUUM Both private- and public-sector financing are essential in bringing an array of new technologies to market to achieve a level of technological transformation sufficient to avoid the worst impacts of global climate change. Early-stage financing in basic R&D (technology research) must come

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from the public sector to the extent that the market potential is too uncertain to interest private-sector funders. In contrast, development of technologies with more predictable market potential attracts the interest of venture capitalists and private-equity funders with an appetite for higher potential risk and returns. As technologies are proven, financing through public-equity markets as well as mergers and acquisitions becomes possible. Large-scale rollouts of well-established technologies can obtain additional financing through access to debt financing and carbon financing. Other types of activities, such as large infrastructure projects, education and other so-called public goods with low immediate profit potential may require public financing even in situations where technologies are well developed and ready for deployment. These infrastructural improvements, too, can provide an important platform for growth in the private sector, opening new opportunities. Overall, the vast majority of investment in sustainable energy must come from the private sector, which government policy can stimulate by providing appropriate incentive frameworks. China, Europe, and North America together will account for over half of the world’s expected need for capital investment in renewable energy and improved efficiency. In recent years, understanding of current global investments in sustainable energy has also improved dramatically, and a newly published study authored jointly by the United Nations Environment Programme (UNEP) and New Energy Finance (NEF) provides regional investment and capital data for many investment categories over the past seven years. The primary purpose of comparing recent investments across regions is to put their investment initiatives into context with one another. An improved data basis could enable a robust comparison of investment levels with investment needs within each region as a means of identifying specific investment gaps. (www.gmfus.org, 2011) Available policies and measures to limit the release of greenhouse gases into the atmosphere include regulations and

Low Carbon Economy-Finance and Technology Models

standards, taxes and charges, tradable permits, voluntary agreements, informational instruments, subsidies and incentives, and research and development Governments can implement a price signal through either carbon markets or taxes. Carbon markets have garnered increasing attention as a central mitigation policy and are particularly suited for GHG abatement in sectors where emissions come from a limited number of large point sources that are easy to measure and monitor. A carbon market is created by setting a cap on emissions and allowing companies to either reduce their emissions to meet the cap or to buy tradable emission allowances from other companies. Generally, the tighter the emissions cap is, the higher the carbon price and the greater the incentive to reduce emissions. (OECD, 2009) By allowing use of offsets as an alternative compliance option, carbon markets can also create an incentive for substantial investment in sectors and regions not covered under the emissions trading system. By contrast, an emission tax requires individual emitters to pay for every ton of GHGs released into the atmosphere. As a result, emitters will weigh the cost of emissions control against the cost of emitting and paying the tax; the end result is that polluters undertake to implement those emission reductions that are cheaper than paying the tax. Both taxes and carbon markets introduce a price signal for greenhouse gas emissions, attaching a cost to polluting behavior. Over upcoming decades, such a price tag will transform the economics not only of the energy sector, but of all energy-using sectors. Currently, however, the price signals created by existing carbon markets and fiscal mechanisms have been too low, too volatile, or too fraught with longer-term policy uncertainty to catalyze low-carbon investment on the scale required for a fundamental shift toward clean energy. (WEF, 2009) Nor can carbon prices be realistically expected to achieve sufficiently high levels in the short to medium term: political opposition and institutional

inertia will initially limit the stringency of pricing mechanisms and it will require time and political effort before carbon prices alone can provide an economic rationale for the large-scale deployment of high-cost abatement options, such as renewable energy or carbon capture and sequestration. A broader portfolio of policy instruments is hence required in addition to carbon pricing, tailored to specific geographical and socioeconomic circumstances. The two biggest economies in the EU took very different pathways in promoting the deployment of renewable energy in the electricity sector. Germany introduced guaranteed prices for each kWh produced from renewable sources. In contrast to the German feed-in tariff, the UK’s renewable energy support system has primarily relied on the Renewables Obligation (RO)—a quota system comparable to the Renewable Portfolio Standard (RPS) widely used in the United States. The UK scheme obliges utilities to supply a certain share of their power generation from renewable sources or to pay a buy-out price. Compliance is proved through freely traded certificates (Renewable Obligation Certificates), which are the main source of income for operators of renewable-energy installations. Even though a direct comparison of the schemes is subject to some caveats (most importantly a much later starting point of the UK scheme), current installation rates suggest that feed-in tariffs have fuelled more dynamic growth in Germany than the RO policy has in the United Kingdom.(The German Marshall Fund Of The Us, 2011)

INFRASTRUCTURE AND SUSTAINABLE CITIES A significant impediment to reducing green house gas emissions is the state of current transportation infrastructure. Public investment is needed to both maintain and improve current conditions.

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First, public investment in the transportation infrastructure should shift toward low-carbon alternatives, with a greater focus on increasing public transportation capacity instead of increasing road capacity. Where investments in public transportation are made, new systems should rely on low- or no-carbon inputs and vehicles such as hybrid technology and biofuels. Second, immediate public investment can be made in the areas of sustainable urban planning practices. Planning authorities should create opportunities for transit-oriented development by changing density and zoning regulations to ensure that the areas surrounding transit stations can be developed to meet growing demand for dense, walkable neighborhood options. Planning practices should carefully align public transportation routes with existing travel patterns and eliminate current restrictions that prevent private real estate developers from building up desirable land closest to transit stations. This will allow more people to travel from home to work, shopping, and entertainment using low-carbon public systems rather than carbon-intensive private vehicles. As in other sectors, public investment to upgrade and expand infrastructure and regulatory changes to remove disincentives for private real-estate investment can, in combination, create new opportunities for profitable private investment. (Pew Center on Global Climate Change, 2003) In order to meet future global emissions targets, significantly reducing emissions attributable to buildings is imperative. This sector already accounts for a large share of global CO2 emissions, and the built environment is expected to grow considerably in coming decades. Based on McKinsey estimates, investment in buildings should therefore constitute roughly 40 percent of capital investment above BAU starting in 2011 in North America, Western Europe and China. The buildings sector is the largest single investment category in the near term, but does not see nearly the same dramatic growth over the 2011–2030 time period as the power and transport sectors. This is

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consistent with the need to undertake large-scale retrofitting of existing building stock in the nearterm. By 2030, needed capital investment in the building sector is expected to grow in absolute terms, but in relative terms it will decrease from 39 percent of capital investment in GHG abatement in 2011 to 24 percent of the total by 2030. (McKinsey & Company, 2009)

CONCLUSION These and other challenges reinforce the absolutely central role of effective national policy in the markets and geographies that financiers do find attractive—this was strongly emphasized in all jurisdictions. There is no ‘one size fits all’ policy formula: the overall policy environment, including incentives such as feed-in tariffs, need to translate into commercial investment options. To build an environment for attractive investment opportunities, characteristics of policy include: clear objectives, coverage of issues from planning and permitting to delivery and grid regulations, enforcement, a time horizon consistent with underlying finance needs, and stability: described as ‘investment grade’ policy. (Hamilton, 2009) Embedding RE policy in wider utility and energy sector policy, and tackling risk factors in the broader energy sector is a central issue at the on-grid end of the market. In developing countries, a robust social policy, and clear economic policy, can contribute to the sense of market stability. Gaps, failures, or lack of integration in policy and regulation that build risk for investors are likely to result in a greater need for public finance to enable commercial investment in developed and developing countries; or put another way, a well designed policy environment can be one of the most effective ways of reducing risk for investors. Notwithstanding existing capital flows to emerging markets, and even in the context of strong national policies, developing countries

Low Carbon Economy-Finance and Technology Models

present a range of risks for financiers that can make it difficult or impossible to invest. In this context multilateral banks and public financing (export credit agencies and national development banks, etc.) have a key role in enabling commercial activity or accelerating it. Public finance has arguably increased in importance to plug gaps in the provision of capital linked to the financial crisis; and to facilitate the very significant scale-up of private finance into RE in a much wider range of developing markets, as this has risen to the centre of the policy agenda. As yet, private financiers do not see government allocations to this agenda, through the multilaterals and public institutions, commensurate with the scale of financing requirements implied by climate scenarios. Private financiers are looking for well-targeted, well-designed and scaled public finance that fits actual gaps on the ground. Areas for attention include: smaller-scale projects; support for developers; accelerated commercial scale-up of key technologies to facilitate cost reduction; and delivery infrastructure. Greater integration between national policy development and availability of well-designed public risk reduction tools for commercial investment (e.g. around PPA payment security) is required. Board level mandates are likely to be required for public institutions in order to provide longer-term, more strategic provision to this sector.

REFERENCES Deutsche Bank Group. (2011). Investing in Climate Change – An Asset Management Perspective. Frankfurt, Germany: Deutsche Bank Group. Hamilton, K. (2009). Unlocking Finance for Clean Energy: the Need for ‘Investment Grade’ Policy. London: Chatham House. RE Finance Project.

IEA. (2008a). Energy Technology Perspectives: Scenarios and Strategies to 2050. Paris: International Energy Agency. IEA. (2008b). World Energy Outlook 2008. Paris: International Energy Agency. McKinsey and Company. (2009). Pathways to a Low-Carbon Economy. Green Economy Initiative. Retrieved from http://www.greeneconomyinitiative.com /news/160/ARTICLE/1390/2009-01-21. html. McKinsey Global Institute. (2008, February). The case for Investing in Energy Productivity. The Energy Blog. Mehling, M., Best, A., Marcellano, D., Perry, M., & Umpfenbach, K. (2010).Transforming Economies through Green Investment: Needs, Progress, and Policies. Climate and Energy paper series. Retrieved from www.gmfus.org. Frankfurt, Germany: The German Marshall Fund of the US. OECD. (2009). Climate Change Mitigation: What Do We Do? Paris: OECD. Retrieved from http:// www.oecd.org/ dataoecd/30/41/41753450.pdf. Pew Center on Global Climate Change. (2003). Reducing Greenhouse Gas Emissions From U.S. Transportation. Retrieved from http://www.pewclimate.org /docUploads/ustransp.pdf. Stockholm Environment Institute. (2008). International Climate Policy. Policy brief for the International Commission on Climate Change and Development. UNDP. (2009).Low carbon route to development. New York: United Nations Development Programme. UNEP and Partners. (2009).Catalysing low-carbon growth in developing economies: Public Finance Mechanisms to scale up private sector investment in climate solutions - Case Study Analysis. October.

IEA. (2006). World Energy Outlook 2006. Paris: International Energy Agency.

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UNEP. (2008). Global Trends in Sustainable Energy Investment 2008: Analysis of Trends and Issues in the Financing of Renewable Energy and Energy Efficiency. UNEP. (2009).Catalysing low-carbon growth in developing economies. Retrieved from http:// www.unep.org. UNEP-SEFI. (2008). Public Finance Mechanisms to Mobilise Investment in Climate Change Mitigation. Final report by the United Nations Environment Programme Sustainable Energy Finance Initiative.

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WEF. (2009).Task force on low carbon prosperity. Retrieved from www.weforum.org. World Bank. (2008). Low-carbon Energy Projects for Development in Sub-Saharan Africa. Washington, D.C.: World Bank. World Bank. (2009). World Development Report: Development and Climate Change. Washington, D.C: World Bank. World Economic Forum. (2009). Green Investing: Towards a Clean Energy Infrastructure. Retrieved from http://www.weforum.org /pdf/ climate/Green.pdf.

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Chapter 2

Carbon Markets and Investments:

EcoSecurities Investment Case Analysis J. Zambujal-Oliveira Technical University of Lisbon, Portugal

ABSTRACT In a world where greenhouse gasses (GHG) carry a price, organizations can create financial instruments that are tradable on the carbon market by investing in projects that reduce GHG emissions. The purpose of this study is to analyse critically an investment project from EcoSecurities to mitigate the emissions of methane from a coalmine located in China’s Sichuan province. This project generates carbon credits that are later sold to governments and organizations under the Kyoto Protocol. In order to evaluate this investment, we conduct an analysis centred on its net present value, and we take into consideration a set of external variables and the financial and economic situation of EcoSecurities. This study concludes that EcoSecurities’ project investment, since the project’s net present value is positive, has a relevant impact on EcoSecurities’ strategy and improves the company’s financial situation as it increases revenues and improves assets using efficiency.

INTRODUCTION Interest in the voluntary carbon markets and carbon offsets has accelerated dramatically with the global climate change (EIA, 2008). The Kyoto Protocol was the first international treaty to adDOI: 10.4018/978-1-61350-156-6.ch002

dress global climate change by directly regulating human-caused greenhouse gas (GHG) emissions. Hence, the developing countries that ratified the Kyoto Protocol had to cut 5.2% of their GHG emissions (Janssen, 2000). Regulated governments and firms can fulfil emissions reduction obligations by purchasing credits generated by

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Carbon Markets and Investments

projects that reduce emissions in industrialized nations (Reinaud, 2004). These projects can be implemented through the Kyoto Protocol’s Clean Development Mechanism (CDM) (Janssen, 2000). Once approved by the United Nations (UN), they can earn one carbon credit called a Certified Emission Reduction (CER) for each ton of carbon dioxide (or its equivalent of another GHG) reduced (Kollmuss & Zink, 2008). One striking result of the Kyoto Protocol is the market opportunity to source, develop, and trade carbon credits from greenhouse gas emission reduction projects. Foreseeing this trend, EcoSecurities (ECO) was formed in the same year that the Kyoto Protocol was adopted (1997) with the purpose of facilitating the acquisition of carbon credits by firms. This was achieved by steering projects through the UN approval process and purchasing the resultant CERs from the project owners. The purpose of this text is to analyse an investment project from ECO named Ventilation Air Methane (VAM). VAM would generate carbon credits by mitigating the emissions of methane from a coalmine located in China’s Sichuan province. Later these carbon credits would be sold to governments and organizations under the Kyoto Protocol (Perold, Reinhardt, & Hyman, 2008). This leads to the following research question: Should EcoSecurities invest in the Ventilation Air Methane project? Projects with the objective of trading carbon credits by sequestering, storing, or preventing the release of GHG to the atmosphere will tend to increase in the near future (Ambrosi & Capoor, 2007). Despite the interest, investment analyses in the carbon market have been lacking. Therefore, our contribution is to offer some guidance to companies or institutions that want to invest in similar projects. It is expected that this study will offer a critical reasoning on how to invest in projects that create financial instruments that are tradable on the carbon market.

16

This study is organized in three main parts. The first part examines the financial and economic situation of ECO. The study evaluates the firm’s situation according to an economic and financial analysis, using ratios that provide information about the firm from five aspects (liquidity, profitability, debt, market, and activity ratios). These ratios will be benchmarked against the past performance of the company and against the correspondent industry ratios (the environmental services industry). The second part appraises the project investment. It focuses on profit value, incorporates risk into the decision, and performs a sensitivity and scenario analysis in order to understand the decision maturity. Finally, the study analyses the impact on ECO’s strategy and how ECO’s capital structure and financing properties affect the investment decision.

THE PROJECT ENVIRONMENT AND CONTEXT Cheap coal improved the Chinese economy, but thousands of miners were injured or killed by coalmine explosions caused by methane accumulation. To control the methane levels, mine owners used ventilators (fresh air introduction) and drainage (collecting methane via boreholes drilled into the earth surrounding a mine) systems (Perold et al., 2008). Removing methane from coalmines is essential to miners’ safety; however, polluting the air with methane contributes to global climate change (EPA, 2005).1 Nevertheless, the methane dumped from mines in high concentrations could be used to produce energy.2 Therefore, a few projects using drained methane have been submitted to the UN. However, the Ventilation Air Methane (VAM) project in Sichuan province would be the first project submitted using ventilated methane (Perold, Reinhardt, & Hyman, 2008). Ventilated and drained methane exit the Sichuan coalmine in similar quantities, totalling approximately 20,000 tons of methane per year. To enrich the ventilation

Carbon Markets and Investments

stream and increase the efficiency of the equipment used, the drained gas would be mixed with the ventilated air stream. The VAM project involves a three-way collaboration between ECO, the coalmine owner, and Tecterra, a leading international producer of industrial machinery,3 which would provide innovative technology, such as a giant machine called the FOVOC that would oxidize the mixed methane stream to convert the gas into CO2 and water. The FOVOC would emit 2.95 tons of CO2 for every ton of methane it destroyed. It was estimated that only 50% of the potential emission reductions would be realized in order to comprise the methane that would escape from the FOVOC due to a combination of downtime for scheduled maintenance, performance uncertainty, and methane concentrations that could not be processed by the FOVOC. It was forecast that the project would abate about 10,000 tons of methane per year, and that 2% of all the CERs generated would be drawn into a climate change adaptation fund for communities in countries that are especially vulnerable to climate change risks. ECO would require several activities, such as setting up a power and water supply, constructing gas pipes to transport the drained methane to the FOVOC, clearing away a small coal processing factory, and leveling the land near the ventilation shaft to construct a pump room, a low-voltage distribution room, a control room, and a janitor’s room. These works would cost $750,000. The cost of drawing the document containing the description, implementation, and impact of the project would amount to $55,000. After the official approval by the Chinese Government, ECO would pay $15,000 for a UN-accredited organization to screen and validate the project before submission to the UN. Once the UN has approved and registered the project, it can start earning credits. ECO would have to pay a registration fee based on the expected annual credits to cover the UN’s costs: for the first 15,000 tons of reduced CO2 it would

pay $0.10 per ton and for each ton thereafter it would pay $0.20. The costs of electricity, maintenance, annual operations, and ensuring that the machinery and subsequent data are properly monitored would amount to $362,500. In order to verify the accuracy of the reported emission reductions a second independent UN-approved organization to monitor and verify the data would be needed and would cost $50,000. There are various uncertainties associated with the VAM project; in particular, the approval by the Chinese Government and the UN. There is also uncertainty about the timing of approval, and delays would be costly. It is possible that the UN would view the drained gas supply as ineligible for certification and, thus, an estimated 20% chance that the UN would issue credits only for reductions of ventilated emissions and not for reductions of drained emissions. There is also a risk associated with the CER price. By the time of the project analysis, CERs were trading at prices of around $26 per ton of CO2. However, it is hard to estimate the CER price after the Kyoto Protocol expires in 2012. Finally, a contingency of $500,000 was reserved for the risk related to cost overruns. The following assumptions were applied during the project evaluation: 1. 12.5% corporate income tax on profits; 2. 15% discount rate when valuing cash flows.

PROJECT APPRAISAL AND THE FINANCIAL SITUATION The VAM project ventilates and drains methane from the Sichuan province coalmine. Removing methane from the coalmine would control the methane that would be dumped from the mine and thus would be essential to miners’ safety (EPA, 2005). This project would be the first submitted to the UN using ventilated methane. The VAM

17

Carbon Markets and Investments

project involves a three-way partnership between ECO, the coalmine owner, and Tecterra, which provides a machine to convert the mixed methane stream into CO2 and water. The parties involved in the project tentatively negotiated a ten-year contract with the possibility of renewal beyond 2018. They also negotiated the amount that ECO would pay to the mine owner and Tecterra. There are several costs related to building activities in the coalmine, project management, independent UN-accredited organizations, UN registration fees, electricity, maintenance, annual operations, and project data monitoring. There are also various uncertainties associated with the Chinese Government and UN approvals, certification eligibility of the drained gas supply, the CER price, and cost overruns. For the analysis of the economic and financial situation of ECO, the study took into consideration the financial reporting of the company (Figure 4 and Figure 5 in the Appendix). Since the evaluation of the VAM project investment took place in late 2007, the study uses financial information from December 2007 and the two previous years. To quantify the different aspects of the ECO business we will use diverse ratios. These ratios are benchmarked against: 1. the two previous years and

Figure 1. Liquidity and debt ratios

18

2. the correspondent ratios of the industry. According to the Reuters Group, a former financial market data provider, ECO belongs to the Industrials sector and it operates in the Environmental Services Industry 4. Reuters (2010) provides financial ratios for this industry; however, it just provides this information for the last financial year (2008) (Figure 6 in the Appendix).4 Therefore, the text assumes that the environmental services industry ratios (Figure 1) remained constant during the four years of analysis. Not all the financial ratios are intensively exploited but just a few that express interesting financial information about the company (useful to support our research decision) or that cover different aspects of ECO’s economic situation (e.g. financial strength, management effectiveness, firm efficiency). It should be noted that the working capital is positive for the years, which means that ECO, using the traditional equilibrium approach, is in a good financial situation. Since the working capital level is elevated it may also indicate that the current assets are not being used efficiently. The liquidity ratios levels are about 20% above the industry average ratios (a current ratio of 2.43 and a quick ratio of 2.75). This means that ECO should not have problems meeting its short-term debt needs. It may also indicate that ECO is not

Carbon Markets and Investments

using its inventory resources proficiently. In 2005, the liquidity ratios were approximately four times bigger than in 2005 and 2006. The study can also observe that receivables affect the liquidity (quick ratios minus cash ratios) more than the inventory (current ratios minus quick ratios). However, both the receivables and the inventory are still small values in comparison with the cash. The environmental services industry is aggressive in financing its growth with debt (debt to equity ratio of 26.69). The same is not happening with ECO, which presents a very inferior debt to equity ratio over the years (see Figure 2). A similar analysis can be performed regarding the long-term debt to equity. There are two interesting topics to analyse in this case. First, the long-term debt to equity is lower than the current debt to equity, which means that the financing of ECO is mainly achieved with short-term debt. Second, long-term debt has been decreasing over the years, which may be a good sign of prosperity since the debt is shrinking and the cash is increasing. The assets–turnover ratio is below the industry ratio (0.09) (Figure 2), meaning that ECO is probably not using its assets efficiently in generating revenue. However, the efficiency has been improving over the last two years. On the contrary, the accounts receivable turnover has been decreasing, having in 2006 and 2007 a lower value than

the respective industry ratio value (0.7). This may indicate that ECO’s payment terms are too lenient or that ECO’s extension of credit and collection of accounts receivable are not efficient. Regarding the market ratios, the dividend yields are zero between 2007 and 2009 since the enterprise is not able to generate profits. EcoSecurities has a high P/S ratio relative to the industry average (0.17), which is not attractive for the investor since the investor is paying more for each unit of sales. However, sales do not reveal the whole picture, especially if we take into consideration that EcoSecurities is unprofitable. On the contrary, EcoSecurities presents low P/B ratios (Figure 3) in comparison with the industry average (1.84). This may mean that the stock is undervalued or that something is fundamentally wrong with the company. The price-to-cash-flow ratio provides an indication about the effects of depreciation and other non-cash factors. As the value of the ratio has been increasing over the years, the market’s expectations of future financial health are increasing. However, it is still a negative value and much lower than the respective industry ratio (low market expectations). ECO’s revenue has grown since 2005 due to an increase in CER commercialization. However, this increase in revenue is not reflected in the gross margin of 2007. This can be explained by

Figure 2. Activity and market ratios

19

Carbon Markets and Investments

Figure 3. Profitability ratios

the price of CERs allocated to the cost of sales. The operating profit and net profit in 2006 and 2007 decreased in comparison with 2005 due to an expansion in the headcount and an increase in the number of offices and in administration costs. Administrative expenses growth can help to explain these negative profits, which differ from the low positive net operating and net profit values of the industry. Since ECO’s net profit is negative, the ROA and ROE of the firm are also negative and significantly below the industry ROA and ROE values. This leads to two important conclusions. First, ECO is not efficiently managing its assets to generate profit (negative ROA). Second, ECO has low profitability, since it cannot generate value for the shareholders (negative ROE). Additionally, the ROA and ROE values decreased between 2005 and 2007 due to the sharp net profit decreases. From this evidence the following conclusions can be drawn. First, the liquidity and activity ratios indicate that ECO is not using its operational assets efficiently. In particular, the high cash ratio reveals that cash can be used immediately to invest in projects. Second, the debt ratios indicate that ECO should reassess its credit policies in order to ensure the timely collection of imparted credit that is not earning interest for the firm. Third, ECO seems not to be adequately managed, since

20

the shareholders’ money is not generating positive net income. This possibly indicates that the firm needs to perform new investments in order to create additional value. The next section is structured as follows. First, the project cash-flow statement considering an assumptions set is provided. Second, the study evaluates the investment decision using a relevant set of indicators.

THE INVESTMENT DECISION The analysis of VAM’s project decision is based on information provided by Perold, Reinhardt, and Hyman (2008). The maturity evaluation of ECO’s investment decision is conducted separately from VAM’s financing concerns (i.e., the study analyses the VAM project as if it was all equity financed). The initial investments comprise the acquisition of a FOVOC machine and a local factory construction to supply the FOVOC machine (Figure 7 in the Appendix). The FOVOC development requires an investment of $5,000,000 by Tecterra, which will only accept the project for a 20% pre-tax internal rate of return (IRR). Nothing is referred to regarding the required payment conditions for this deal; however, according to Perold et al (2008), ECO had to propose a lease fee to Tecterra that would

Carbon Markets and Investments

be charged over constant payments until 2018. Assuming a lease fee of 10%, a cost of capital of $6m (5,000,000 × [1 + 02]) will be paid over an 11-year period (2008–2018), i.e., (6,000,000/11) × 1, 1i with i = 0 (2008), 1 (2009),..., 10 (2018). A limitation of the data used concerns the upfront costs to install the FOVOC machine. These costs are mixed with the factory development by ECO, which renders the calculus of the plant depreciation difficult. Thus, the costs sum $750,000, assuming that both the local factory and the FOVOC installation efforts are inputted in the ECO assets. The process of obtaining the number of CERs per ton of methane emitted per year is presented in Table 1. To reach this value, the paper estimates the coalmine emissions of methane and then subtracts the following aspects: the machine emissions, the efficiency of combustion, and the tons of methane not handled due to maintenance times and huge concentrations of methane. Additionally, a percentage of the total number of CERs is subtracted to cover the support countries with climate risk vulnerabilities (in line with Kyoto’s regulation (Janssen, 2000)), for the coalmine owner and for the Chinese Government. From the initial number of 21 CERs per ton of methane, the study obtains a liquid value of 13.689 CERs per ton of methane, and from the initial 20,000 tons of methane emis-

sions only 50% is considered, resulting in a total of 136,890 CERs per year. The annual fee per CER necessary to cover the United Nations administration costs depends on the accumulated number of CERs. Thus, it was necessary to separate the 2008 and 2009 – 2012 fees, with the 2008 UN fee being 15000 tons × 0.1 + (136890 – 15000) tons × 0.2 = 25878 and the 2009 – 2012 UN fee being 136,890 × 0.2 = 27378. In 2007, the prices of CERs for 2008 – 2012 and 2012–2018 were uncertain. The estimation of the CER price for 2008–2018 assumes those values based on two variables: 1. trends from 1998–2007 and 2. the ECO estimated value for 2012 (CER price = $20). In order to obtain the values we defined a function based on the discrete set of price values 1998, 1999,.., 2006, 2007, 2012, and we retrieved the price values for the 2008–2011 range of years. Not so clear is the value of CERs from 2012 to 2018 due to Kyoto expiration. Will there be a new regulation? Will it preserve the actual design? News from 2007 provided some information on this topic, claiming an approximated continuation to justify the project’s efforts. We used the

Table 1. Revenues and variable costs #CERs per (ton of methane ÷ year)

Ton of methane ÷ year

#CERs (or #CO2 tons)

Coalmine emissions of methane

21

20000

420000

Machine emissions4

(1.950)

-

(39000)

18.05

20000

361000

Tons of methane not handled

-

(10000)

(180500)

Overall efficiency

18.05

10000

180500

Support to countries vulnerable to climate risks

(0.361)

-

(3610)

Coalmine owner accords

(3.8)

-

(38000)

Chinese government accords

(0.2)

-

(2000)

Total

13.689

10000

136890

Efficiency of combustion 5

21

Carbon Markets and Investments

information contained in Ambrosi and Capoor (2007) as the input to support our choice of values. In order to derive CER profits there are two assumptions. First, all the CERs acquired are sold. Second, all the CERs obtained during a year are priced with the value estimated at the beginning of that year. The estimated value per CER and the total CER profits (CERs*price – fee) are summarized in Table 1. The initial costs of the project comprise technical documentation and negotiations with the Chinese Government ($55,000) and a UNaccredited organization validation ($15,000). The annual costs are divided into two main topics: 1. annual operations (monitoring and data collection) and maintenance (totalizing $362,500/year) and 2. payment to a UN-accredited company to monitor and verify the reductions (totalizing $50,000/year). Figure 10 presents all the fixed costs per year. Assuming a 5-year straight-line depreciation, and having investments of 750,000+6,000,000=6,750,000, we have a yearly depreciation of 6,750,000/11=$613,636.36. The possibility of renovating the contract with Tecterra and the coalmine owner continuing to use the FOVOC machine gives to the FOVOC a considerable salvage value. That will be close to $2,000,000 in 2018. Applying a modified accelerated cost recovery system with the use of 10-year depreciation rate information as it is presented in Brealey et al. (2008), there are no significant differences relative to the investment final evaluation (the NPV increases by only 3%).

Risk Analysis The investment project has five main risks. 1. The first reflects the possibility of the VAM project not being registered by the UN or

22

2.

approved due to Chinese Government issues. This would lead ECO not to issue any more CERs from the VAM project. Nevertheless, in a voluntary market, a new type of credit can be sold from $5 to $13 (Perold, Reinhardt, & Hyman, 2008). The probability of this occurrence rounds to a percentage of 20%. The study assumes that this new value for VAM’s credits can be obtained from a Gaussian curve with a mean value of $9. The second risk comprises the fact that VAM uses a drained methane component (50%) for which the available technology exists. This could lead to the possibility of the UN only financing ventilated emissions, which would represent less than 50% of the emissions and, consequently, of the CER profits. Our assumption is a 30% probability of occurrence of this scenario. Figure 11 summarizes the calculation of the value of CER profits based on this information and recurring to the formula (Equation 1):

CERs × (probprice1 ×CERprice1 + probprice2 ×CERprice2 + probprice 3 ×CERprice 3) = CERs × (0.8 × p1 + 0.2 × p2 + 0.8 × 0.3 × p 3) (1) 3. Third, there is a risk that comes from the possibility of selling the energy produced by the FOVOC. Selling the FOVOC energy represents an opportunity to increase the project value, but could make the UN approval more difficult (because the project’s revenues must only come from the sale of carbon credits). Also, if this energy was used to supply hot water for the coalmine and the employees who live on site, not selling the energy could facilitate the Chinese Government approval. Therefore, we assume that energy-selling benefits must not be considered for the NPV calculation.

Carbon Markets and Investments

4.

Fourth, there is the possibility that the negotiations and approvals will delay the process substantially, leading to a clear loss of CERs in the first year as a result. The project considers an average of the probability of each delay (1 month, 2 months...) to occur and its impact on the CERs (loss of 1/12 CERs, 2/12 CERs...) and assumes that the probability of these delays occurring is 30%. Therefore, the impact of such delays comprises the loss of 1/6 CERs in the first year. The new value for the profits of the first year is (Equation 2):

0.3 × (1 − 1 / 6) × 2919994 + 0.7 × 2919994 = 2773995

(2)

5. Finally, the project allocates an initial $50,000 contingency reserve to hedge financial risk associated cost overruns. As with any other project, VAM entails an additional investment in working capital. Nevertheless, here we do not have an investment in inventories, because ECO only issues CERs and we can assume that the FOVOC does not significantly consume any raw material. Moreover, from the opposite perspective, ECO customers are the buyers of CERs. Therefore, they cannot delay payments because to acquire a CER they need to pay promptly. In order to define this value accurately, we consider that the time for ECO to obtain a CER and sell it on the market is 6 months (i.e., half of the CERs obtained in a year will be sold only in the next year). This assumption defines the values considered for working capital as presented in Figure 12. Thus, using all the information collected, it is possible to determine the tax to pay (based on: CER profits, other costs, depreciation). The tax rate used is 12.5% in accordance with Perold, Reinhardt, and Hyman (2008). It is important to note that a negative tax payment means a cash inflow

(i.e. the statement assumes that ECO can use this tax loss to shield income from other projects. See Figure 13 in the Appendix). After setting the cash flows from the operations, the process determines the net cash flow (by comprising the amounts in capital investment, disposal, and working capital), and computes the discount factors. The considered interest rate for ECO shareholders is 15% (Perold, Reinhardt, & Hyman, 2008), so the discount factors will simply be 1/(1,15i), with i = 0(2007),1(2008),..,12(2019)) (Figure 14 in the Appendix). With an NPV of $2,569,083, the study shows evidence that leads to a concordance with ECO’s decision (i.e., the VAM project should be accepted). However, knowing all the NPV limitations, our analysis needs to be enhanced with additional concerns and perspectives.

THE MAIN FACTORS AFFECTING THE INVESTMENT We need to analyse ECO’s decision from an isolated point of view, disregarding other projects, which comprises the study of the other factors that could influence ECO’s decision: 1. Demand for CER: although the demand is characterized by moderate volatility, there is a clear upward trend (which surpasses our estimations based on historical data); 2. UE and USA administration commitment to maintaining long-term values: if this happens, it would substantially increase the CER value; 3. Congestion of other sectors: this could lead to the development of the emergent ventilated sector. It supports the choice of the VAM project in the case of NPV>0, as projects in other areas may have more difficulty in succeeding; 4. Partnership evolution with Tecterra: in the case of success this partnership could be used strategically;

23

Carbon Markets and Investments

5. The possibility to renew the contract: if the project extends its life duration, there will be continuous profits generated from credits and a reduced value of FOVOC’s depreciation that are traduced in cash flows with positive values; 6. First-mover advantage: it is easy to operate in this market and seizing contracts could represent a big impact on ECO’s overall strategy.

Analysis of Other Indicators of Investment The profitability index (PI) adds information to the NPV since it clarifies its magnitude. The VAM project has a PI of (Equation 3): PI =

(2, 569, 083 + 6, 750, 000) = 1.38 6, 750, 000

(3)

As it is not too close to 1, the project can sustain a higher interest rate than the one considered of 15%. Moreover, it is an important metric, as the company ECO has “hard rationing” to finance its projects. It is possible to observe that ECO has a level of liabilities much greater than its equity level, when compared with other companies of its sector. This can denote some difficulties, although it is relatively easy for companies in the energy sector to contract loans. If this is the case, ECO’s choice decision must not be based only on the NPV value but also on the PI and on the project’s time horizon. The payback period ratio is a trap for the VAM project as the market knows that from 2012 to 2018 the price of credits could decrease due to Kyoto Protocol expiration while the company has to support the linear amortization of the initial investments in the FOVOC and in the on-side factory. Thus, we cannot expect an extraordinary performance when the payback period ends in 2011 (Figure 15).

24

A BRIEF LOOK AT ECO’S PROJECT PORTFOLIO Looking at ECO’s range of projects under the Kyoto Protocol (13) and at their dimension measured by the number of CERs (approximately 3195/85 = 37,5 KCERs), the VAM project (which produces 10 KCERs per year) is not a source of as many credits as other projects (although we do not know clearly their credit profit margin and capital needs). However, if VAM was approved by the UN it would represent, more or less, 20% (10/10+42)) of the total portfolio of ECO in China. Considering all the NPV limitations, ECO should continue with the VAM project as it allows ECO to expand the company visibility in the Chinese sector (ECO’s position is still small when compared with other companies (Ambrosi & Capoor, 2007)). Table 2 shows the values estimated for 2008 (based on the 2007 running projects) and forecasts for 2012. A closer look at the ECO projects portfolio (Table 2), despite ECO being accustomed to methane initiatives, reveals that ECO is not only immature in using the ventilated technology (although in line with other companies) but also in running projects in the coalmine sector, where it only has three projects. However, this analysis also leads to the importance of this move in seizing the benefits from three variables: the congestion of other sectors, Tecterra’s partnership interests, and ventilated technology first-mover advantages (Table 3).

Table 2. CER forecast Country

Projs

KCERs 2008

KCERs 2012

China

148

36295

527663

Total

883

113672

1175754

Country

ECO Projs

ECO KCERs 2008

ECO KCERs 2012

China

13

42

9241

Total

85

3195

46056

Carbon Markets and Investments

Table 3. Sizing methane in the ECO projects portfolio Country

Total

Registered

Pipeline

Carbon Dioxide

92

25

67

Methane

104

59

45

Nitrous Oxide

16

0

16

Hydrofluorocarbons

1

1

0

Coalmine

3

0

3

Total

213

85

128

Sensitivity and Scenario Analysis As the NPV is a function of several variables, it is relevant to exercise consistently variations of those variables and analyse the degree to which they impact on the NPV level. The framework was set using the approval probabilities, the CER market value, and the lease fee to accord with Tecterra as the three main variables to study a set of possible scenarios. 1. Investment timing analysis. The decision rule for investment timing, according to Brealey et al. (2008), is to choose the investment date that results in the highest net present value. There are two options: to run the project immediately or delay its beginning. Delays have several impacts on the CERs issued (before 2012), thus only degrade the project’s overall performance. Therefore, assuming the immediate beginning of the project, two new options appear: increase or decrease the maturity of the project. Note that if we decrease the time horizon, Tecterra’s payments will not be updated; only the annual costs and CER profits (greater than the annual costs) will be reduced by n years, leading to a lower NPV. Increasing the time horizon implies a renegotiation of the FOVOC contractual

values with Tecterra and increases the NPV vulnerability due to CER price uncertainty. 2. Hard or soft rationing regime. On one hand, the ECO balance statement reveals dependency on loans to finance projects that can degrade a soft rationing scenario. On the other hand, the amount of projects being executed by ECO (previous section) together with the increasing ease of financing projects in the green energies sector makes the project appraisal independent of other projects decisions (Ambrosi & Capoor, 2007). Thus, it can be assumed that ECO evaluates the project according to a nearly approximated soft rationing regime. 3. Approval probabilities. The probability of the UN approving the project is 80%. Admitting half of the CERs, the probability decreases to 30% (excluding the drained methane). Fixing the second percentage, the project can sustain a positive NPV, even if the approval percentages change to 40% (admitting that CERs can be acquired in the free market for $9 each). The second factor refers to the partial approval and has a different effect. Here this variable can increase to 70% and continue obtaining a positive NPV. If we use data-mining techniques with all of these factors, we can obtain relations among those variables that sustain a positive NPV. 4. CER price estimation. Two ranges of prices must be considered: if the UN approves the project or not. In the first case, for the project still being approved (positive NPV), the decrease in the CER prices can only go to 15% (meaning that a $20 CER can only decrease on average to $17). Note that in the second hypothesis, which is less likely to happen, CER prices in the free market can decrease to 50%. However, this hypothesis strongly increases the project risk, because if the UN does not give its approval, it will result in a bigger loss for ECO.

25

Carbon Markets and Investments

5. Tecterra’s lease fee. This rate is of primordial importance to the NPV calculation. Considering an initial rate of 10%, if we increase this rate to 17%, ECO will no longer be able to sustain an interest rate of return of 15%. This is an important variable and, consequently, must be carefully defined during ECO and Tecterra negotiations.

THE FINANCING DECISION Two major aspects will be briefly reviewed in this section: first, the way financing approximations can be incorporated into VAM’s valuation; and second, assuming that the investment decision is made, it is necessary to focus on the best way to finance VAM. In order to incorporate financing issues into the VAM valuation, there is just the need to calculate the NPV with an adjusted discount rate (after-tax weight cost of capital, awccrate). Defining rE and rD as the expected rates of return demanded by investors in equity securities and in ECO’s debt (e.g. 11% as a result of a bank loan), and considering that the project is 20% financed by ECO’s equity and 80% by debt, the new discounting rate is (Equation 4): rD × (1 − tax ) × 0.4 + rE × 0.6 = 0.11 × (1 − 0.125) × 0.4 + 0.15 × 0.6 = 0.107 (4) The percentages of the capital structure result from other ECO projects (see Figure 8 in Perold, Reinhardt, & Hyman, 2008). This capital structure and rD value lead to an increase in the NPV (since awccrate = 10.7% < interestrate = 15%). However, the equity level exposed to the defined shareholders’ interest rate would be lower. Since the usual rD×(1−0,125) taxes for short-term financing are lower than rE = 15%, this example illustrates well what would be the most probable conclusion: using debt to finance the project increases

26

the NPV but limits the exposure of equity to the shareholders’ interest rate. To study all the financing options available to the VAM project would require a detailed awareness of the major financial institutions that provide loans (and for each one the range of alternatives) and of the wide variety of securities that ECO can issue. Thus, the goal of maximizing the VAM financing structure is complex due to the huge number of possible combinations. Also, modern capital markets are highly competitive, efficient (prices of stocks, bonds, and other securities react quickly and accurately when new information arrives), and demand fair terms. Moreover, most of the time spreads for loan rates are variable and not anticipatively revealed. Thus, we leave a deeper analysis of financing options and choice for possible future research.

CONCLUSION The analysis conducted in this paper reveals that ECO’s decision was mature (i.e., the VAM project should be accepted). The study used the NPV criterion as the main argument to support this statement. However, there are several truly variable aspects of the investment decision. Therefore, the project risk, timing options, subcontracting and financing dependencies, and strategic factors were studied as well as ECO’s rationing role. Using the theory of probabilities to deal with the risk, the main risk sources for the VAM project come from the United Nations, the Chinese Government, the coalmine owner, ECO’s customers, and governments’ grants. The UN determines the certification of credits for emission reductions and the quantity of reductions considered. The Chinese Government and coalmine owner can determine the project execution and their interests add volatility to the potential profit margin. ECO’s customers determine the changes in working capital and their will directly affects the price of credits (either certified or acquired in the free

Carbon Markets and Investments

market). Governments’ initiatives can also affect the prices of CERs after the Kyoto expiration. The options of delaying or reducing the project maturity were refuted and the benefits from a possible extension of the project time horizon were irrelevant and quite volatile. A strong dependency between the NPV and the equipment supplier interest rate were detected during the sensitivity analysis. This means that the investment decision must be balanced with this expected rate (17%). This study also observed that the project NPV depends on the capital structure and on the debt interest rate. However, it was stated that the market values for the debt interest rate would not affect the NPV negatively. Additionally, there is a set of factors that could benefit ECO’s strategy, such as the congestion of other sectors, the first-mover advantage, and Tecterra’s partnership potentialities. Although these aspects have not been contemplated in the NPV determination, they created pressure on ECO’s managers to decide in favour of the project investment. The research conducted in this paper has also revealed the ease of ECO’s financing of its projects, which leads to an approximation of a soft rationing scenario. In such a scenario, project alternatives are under-considered when the project under analysis has a positive NPV. This argument supports our evaluation. However, if it is not clear that this is ECO’s scenario, we presented VAM’s profitability index and demonstrated its good performance when compared with other projects from ECO’s company. The analysis of the financial situation of EcoSecurities showed that ECO is using its assets inefficiently and has not been able to generate net profits from shareholders’ money. Therefore, this investment should promote a better financial situation for EcoSecurities by increasing the revenues (continuous generation and selling of CERs) and reducing the inventories assets, as well as the receivable conversion period since VAM requiring low investments in working capital.

REFERENCES Ambrosi, P., & Capoor, K. (2007). State and trends of the carbon market. Washington, DC: The World Bank. Brealey, R., Myers, A., & Stewart, C. (2008). Principles of Corporate Finance (9th ed.). Boston: McGraw-Hill/Irwin. EGP. (2007). Annual report 2006. EcoSecurities Group PLC. EGP. (2008a). 2007 preliminary results presentation. EcoSecurities Group PLC. EGP. (2008b). Annual report 2007. EcoSecurities Group PLC. EIA. (2008). International Energy Outlook, Technical Report. Washington: Energy Information Administration, United States Department of Energy. EPA. (2005). Identifying opportunities for methane recovery at U.S. coal mines: Profiles of selected gassy underground coal mines 1999-2003. Washington: United States Environmental Protection Agency. Janssen, J. (2000). Implementing the Kyoto mechanisms: Potential contributions by banks and insurance companies. The Geneva Papers on Risk and Insurance, 25(4), 602–618. doi:10.1111/14680440.00085 Kollmuss, A., &. Zink, H. (2008). Making sense of the voluntary carbon market. A comparison of carbon offset standards. Stockholm: Stockholm Environment Institute. Perold, A., Reinhardt, F., & Hyman, M. (2008). International carbon finance and EcoSecurities. Harvard Business School Case, 208–151. Reinaud, J. (2004). Emissions trading and its possible impacts on investment decisions in the power sector. Paris: International Energy Agency (IEA).

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Carbon Markets and Investments

Reuters (2010). Financial ratios. ThomsonReuters. Retrieved 2010, from http://www.reuters. com/ sectors/industries/.

ENDNOTES 1



28

In 2000, coalmines accounted for 8% of all human-caused methane emissions; other sources included natural gas and petroleum production and land falls, among others (EPA, 2005).

2



3



4



Methane accounts for over 20% of global energy consumption (EIA, 2008). Retrieved November 25, 2009 from http:// www.ucalgary.ca/news/july2009/tecterra. Retrieved November 25, 2009 from http:// www.reuters.com/sectors/industries.

Carbon Markets and Investments

APPENDIX Figure 4. EcoSecurities’ Balance Sheet

29

Carbon Markets and Investments

Figure 5. Cash flow statement

30

Carbon Markets and Investments

Figure 6. Industry and sector ratios

Figure 7. Initial investments

Figure 8. Revenues and variable costs

31

Carbon Markets and Investments

Figure 9. CER portfolio

Figure 10. Fixed costs

Figure 11. Approval risk

Figure 12. Change in working capital

Figure 13. Profits after taxes

32

Carbon Markets and Investments

Figure 14. Present value of cash flows

Figure 15. Payback

33

34

Chapter 3

Firms’ Banking and Pooling in the EU ETS (2005-2007) Julien Chevallier Université Paris Dauphine, France Johanna Etner Université Paris Descartes & ESG Management School, France Pierre-André Jouvet Université Paris Ouest Nanterre La Défense,, France

ABSTRACT This chapter investigates firms’ banking and pooling behaviors in the context of the EU Emissions Trading Scheme (EU ETS) during Phase I (2005-2007). It provides an overview of the questions raised at the firm-level by the introduction and implementation of the EU trading system in terms of allowances management. More specifically, the article details the banking behavior at the installation level, and the pooling of risks at the group level attached to allowance trading between the parent company and its subsidiaries. Based on case-studies of the most significant patterns in terms of allowances management among firms, the empirical analyses underline the efficiency of the banking instrument as a risk-management tool.

INTRODUCTION On tradable permits markets, banking refers to the possibility for agents to save unused permits for future use, while borrowing represents the possibility to borrow permits from future allocations for use in current period1. Without uncertainty, firms with banking smooth their emissions between DOI: 10.4018/978-1-61350-156-6.ch003

trading periods (Rubin [1996], Kling and Rubin [1997], Leiby and Rubin [2001]). The introduction of uncertainty2 provides further incentives for firms to bank permits, and to consider collusion as a way of insurance (Von der Fehr [1993], Ehrhart et al. [2008]). In the context of the European Union Emissions Trading Scheme (EU ETS), unlimited banking and borrowing was allowed within Phase I (2005-2007), but not towards Phase II (2008-2012)3.

Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Firms’ Banking and Pooling in the EU ETS (2005-2007)

Pooling allowances implies the introduction of a cooperation agency between firms, which is responsible to maximize the sum of firms’ profits whatever the uncertainties. This agency may either correspond to a “parent agency” with N subsidiaries, or to a centralization of production decisions4. Alberola et al. (2009a) evoke the existence of the pooling behavior in the EU ETS, and emphasize that it appears as a fruitful area for future research. To our best knowledge, there are no previous empirical studies in the area of banking and pooling at the installation level in the EU ETS during 2005-2007. As a brief state of the art, we can only mention previous work by Ellerman and Buchner (2008) who provided a preliminary analysis of the 2005-2006 compliance data in the EU ETS. In this chapter, we use compliance data during the first three years of the scheme. We provide the first empirical analysis of the banking behavior at the installation level, and the pooling behavior at the group level. We identify the impact of different allocation rules and overall regulatory uncertainty on the variation of firms’ banked permits, and the existence of risks-pooling by parent companies to save penalty and permits purchases costs. The allocation of permits in the EU ETS during Phase I followed the grandfathering principle, which allocates permits freely based on business-as-usual emissions. If a firm encounters an uncertainty and goes beyond its emissions forecasts during the current allocation period, then it is basically left with two choices: either use its banked permits, or go on the market to buy permits (see Alberola et al. (2009a, 2009b)). We investigate the changes in banked permits at the installation level that occurred in a context of regulatory uncertainty on the EU ETS during 2005-2007. The remainder of the chapter is structured as follows. Section 2 provides background information on the EU ETS. Section 3 develops our empirical analysis. Section 4 discusses the policy relevance of the results. Section 5 concludes.

BACKGROUND INFORMATION ON THE EU ETS The EU ETS has been created on January 1, 2005 to reduce by 8% CO2 emissions in the European Union by 2012, relative to 1990 emissions levels. This aggregated emissions reduction target in the EU has been achieved following differentiated agreements, sharing efforts between Member States based on their potential of “decarbonisation” of their economy. The EU ETS covers energy intensive companies above the threshold of 20MW, in application of the Directive 2003/87/EC. Sectors covered include power generation, mineral oil refineries, coke ovens, iron and steel and factories producing cement, glass, lime, brick, ceramics, pulp and paper, which represents 10,600 installations (see Alberola et al. [2009a, 2009b]). Such markets have already been introduced at the country level, such as for lead in gasoline, SO2 and NOx in the U.S. For greenhouse gases, a domestic emission trading scheme has been introduced in the UK in 2002 and Denmark has created a market for CO2 in 2000. The European market covers 46% of European CO2 emissions. The EU ETS draws on the U.S. sulfur dioxide trading system for much of its inspiration (Ellerman et al. [2000]), but relies much more heavily on decentralized decision making for the allocation of emissions allowances and for the monitoring and management of sources (Kruger et al. [2007]). Each country is required to develop a National Allocation Plan (NAP), which, among other design features, addresses the national CO2 emissions target. The sum of NAPs determines the number of quotas distributed to installations in the EU ETS. In this institutional framework, 2.2 billion allowances per year have been distributed during 2005-2007. 2.08 billion allowances per year will be distributed during 2008-20125. The allocation methodology consisted in a free distribution of quotas in proportion of recent emissions. Some Member States also allowed for auctioning in Phase I (2005-2007) and II (2008-2012), but the

35

Firms’ Banking and Pooling in the EU ETS (2005-2007)

maximum shares of 5 and 10% allowed by the Directive were not reached. The risk underlying permits trading on the European carbon market is linked to increasing permits prices, against which installations need to hedge6, and to the firm’s net short/long position that need to be carefully managed to save penalty costs (Ellerman and Buchner [2008]). An installation is defined as net short (long) if the number of verified emissions is superior (inferior) to the number of allocated allowances during the compliance period. An installation with a short (long) position may buy (sell) allowances in order to be in compliance (make profits). If the installation does not match its mandatory emissions target with the corresponding number of allowances during the compliance period, it needs to pay back one permits plus a penalty7 during the next period. In the next section, we investigate empirically firms’ banking and pooling behavior during Phase I of the EU ETS.

EMPIRICAL EVIDENCE ON BANKING AND POOLING IN THE EU ETS This empirical section picks a few cases of firms in the EU ETS. The choice of these case-studies is motivated by the need to identify and characterize different firms’ behavior in terms of banking and pooling of allowances. By doing so, we aim at establishing a typology of firms’ behavior with respect to allowances management. Besides, the effects of uncertainty concerning the allocation of emission permits appears worthy of investigation for firms covered by the EU ETS. Between Phases I and II, allocation has been cut by 48 million tons of CO2, which corresponds to a 12 million tons cut per year over the four years covered by Phase II. Notwithstanding the changes in the perimeters of the scheme8, the magnitude of this effect may be estimated as being equal to 960€ million9. Thus, we are interested in assessing whether firms use

36

different banking strategies in presence of regulatory uncertainty on the European carbon market. We use Reuters Carbon Market Database10 to provide a qualitative discussion. The appropriateness of the data is guaranteed by Carbon Market Data, which offers a comprehensive analytical tool to investigate more than 12,000 installations data in the EU 27 directly from the European Commission DG Climate repository. Indeed, the aggregation of emissions data between installations and parent companies is still in its infancy11, which precludes us from exploiting econometrically this database. From the 800 companies included in this database, we identify seven companies that allow us to shed some light on the banking and pooling behaviors. These seven companies have been chosen as being the most representative of the typical behaviors that may be observed across firms in the EU ETS. The representativeness of the firms chosen may be evaluated against the criteria of size in each sector of activity, i.e. identified the largest firms and/or emitter in each category. Our case-studies are divided into three subsamples: 1. firms with the highest emission permits shortages at the group level, 2. firms with the highest emission permits surpluses at the group level, and 3. the highest emitter of CO2 on the market. We focus our comments on the number of allowances banked forward at the installation level, and on the presumed pooling behavior at the group level. Note that the interested reader may refer to the EconomiX Working Paper #2008-25 for the full database used in this article12. The method of calculation simply consists in descriptive statistics and visual analysis of the data reported in this working paper. For more formal quantitative tools, the reader is invited to the section on further research directions.

Firms’ Banking and Pooling in the EU ETS (2005-2007)

The Banking Behavior at the Installation Level On the EU ETS, the political uncertainty regarding the second period permits allocation is linked to the negotiation of the second National Allocation Plans (NAPs) between Member States (MS) and the European Commission (EC). However, the variation of banked allowances between Phases I and II corresponds to the 2008 compliance result, which was disclosed by the EC mid-May 200913. Thus, we choose to focus our comments on the variation of permits banked by firms during 20052007. By many aspects, Phase I may be considered as a ”warm-up” period for the EU ETS, during which several key provisions14 of this newly created permits market were characterized by abrupt decision changes. By using this approach, we intend to provide an empirical discussion of the banking of permits that corresponds to the early development of the European carbon market15. Banking behaviors vary greatly for the seven companies that belong to our case-studies. In each subsample, we have chosen the firm(s) which provides the best benchmark against which we can evaluate various banking behaviors. What economic variables influence that variation go beyond the scope of this chapter, while sectoral analysis may be found in Alberola et al. (2009a, 2009b). In the subsample of firms which record the highest permits surpluses, we remark net banking patterns for the largest installations in terms of allocation for ArcelorMittal (Figure 1, Figure 2, and Figure 3) and Dalkia (Figure 4, Figure 5, and Figure 6), as well as for Eesti Energia’s installation (Figure 7, Figure 8, and Figure 9). This comment applies especially for installations in the combustion sector, which seem to have benefited from windfall profits during the NAPs I allocation process. In the sub-sample of firms which record the highest permits shortages, we observe asymmetric banking (borrowing) patterns for Enel (Figure 10, Figure 11, Figure 12), E.ON (Figure 13, Figure 14, Figure 15) and Union Fenosa

(Figure 19, Figure 20, Figure 21) depending on whether those installations were characterized by a net long (short) position during 2005- 2007. The same comments apply to our last sub-sample of firms for RWE, the highest emitter of CO2 on the market (Figure 16, Figure 17, Figure 18). Based on the visual inspection of the data, we therefore highlight a wide variation in the amount of banked permits at the installation level during 2005-2007. Three kinds of arguments may explain these variations between firms. First, differentiated allocation rules were enforced by the regulator between EU ETS sectors that affect firms’ permits supply. Second, unforeseen economic activity events may impact firms’ production levels, and their permits demand. Third, these heterogeneous behaviors in terms of banked permits may come from the political uncertainty described earlier regarding banking provisions and NAPs II. This first step of the inspection of the data therefore brings us to a more detailed analysis of the potential for permits pooling by the parent company in the next section.

The Pooling Behavior at the Group Level Let us detail first the rationale behind permits pooling, and second the actual behavior of the firms contained in our sample. On the EU ETS, pooling behaviors may emerge at the group level in order to save the cost of purchasing permits. The economic logic behind such an argument unfolds as follows: if both types of net short and long subsidiaries exist, the parent company may transfer allowances internally between them, so that the net position of the parent company is globally in compliance. Thus, the exposure to the risk of permits shortage during compliance periods may be reduced by this intra re-allocation of permits. This type of behavior is close to the role of an agency pooling risks. Note this logic holds only if there is an alternate of net short and long installations at the

37

Firms’ Banking and Pooling in the EU ETS (2005-2007)

Figure 1. Distributed allowances and verified emissions for ArcelorMittal in 2005 from Reuters Carbon Market Data

Figure 2. Distributed allowances and verified emissions for ArcelorMittal in 2006 from Reuters Carbon Market Data

38

Firms’ Banking and Pooling in the EU ETS (2005-2007)

Figure 3. Distributed allowances and verified emissions for ArcelorMittal in 2007 from Reuters Carbon Market Data

Figure 4. Distributed allowances and verified emissions for Dalkia in 2005 from Reuters Carbon Market Data

39

Firms’ Banking and Pooling in the EU ETS (2005-2007)

Figure 5. Distributed allowances and verified emissions for Dalkia in 2006 from Reuters Carbon Market Data

Figure 6. Distributed allowances and verified emissions for Dalkia in 2007 from Reuters Carbon Market Data

40

Firms’ Banking and Pooling in the EU ETS (2005-2007)

Figure 7. Distributed allowances and verified emissions for Eesti Energia in 2005 from Reuters Carbon Market Data

Figure 10. Distributed allowances and verified emissions for Enel in 2005 from Reuters Carbon Market Data

Figure 8. Distributed allowances and verified emissions for Eesti Energia in 2006 from Reuters Carbon Market Data

Figure 11. Distributed allowances and verified emissions for Enel in 2006 from Reuters Carbon Market Data

Figure 9. Distributed allowances and verified emissions for Eesti Energia in 2007 from Reuters Carbon Market Data

Figure 12. Distributed allowances and verified emissions for Enel in 2007 from Reuters Carbon Market Data

41

Firms’ Banking and Pooling in the EU ETS (2005-2007)

Figure 13. Distributed allowances and verified emissions for Eon in 2005 from Reuters Carbon Market Data

Figure 14. Distributed allowances and verified emissions for Eon in 2006 from Reuters Carbon Market Data

42

Firms’ Banking and Pooling in the EU ETS (2005-2007)

Figure 15. Distributed allowances and verified emissions for Eon in 2007 from Reuters Carbon Market Data

Figure 16. Distributed allowances and verified emissions for RWE in 2005 from Reuters Carbon Market Data

43

Firms’ Banking and Pooling in the EU ETS (2005-2007)

Figure 17. Distributed allowances and verified emissions for RWE in 2006 from Reuters Carbon Market Data

Figure 18. Distributed allowances and verified emissions for RWE in 2007 from Reuters Carbon Market Data

44

Firms’ Banking and Pooling in the EU ETS (2005-2007)

Figure 19. Distributed allowances and verified emissions for Union Fenosa in 2005 from Reuters Carbon Market Data

Figure 20. Distributed allowances and verified emissions for Union Fenosa in 2006 from Reuters Carbon Market Data

Figure 21. Distributed allowances and verified emissions for Union Fenosa in 2007 from Reuters Carbon Market Data

group level, which is why we detail below several cases that may apply. Among the three firms in our sample that are in a net short position, Union Fenosa exhibits the largest shortage by 7.3M European Union Allowances (EUAs)16 in 2007. Out of twelve installations, nine are in a short position, which may only be compensated internally by 2M EUAs in surplus. Thus, the pooling of allowances by the parent company allows reducing the risk of permits shortage by 25% for some, but not all, subsidiaries. The visual inspection of the data in Figures 19 to 21 reveals that three installations are especially short of permits over 2005-2007. Next, we turn our attention to EON, which records a net short position of 2.7M EUAs. 31 out of 89 installations have a permits shortage, and the potential for permits transfer at the group level is equal to 1.6M EUAs. Hence, the risk-sharing strategy by the parent company may save the costs of permits purchases on the market by 60%. The distribution of subsidiaries in Figure 13 to 15 also reveals a strong dispersion in terms of size, with one installation of 1M allocated allowances being consistently short over 2005-2007. Enel records a net deficit of allowances by 1.5M in 2007. Five out of nine installations are net short, which may only be compensated by another subsidiary by 0.05M EUAs. Figures 10 to 12 confirms this analysis, with most installations being net short in 2007. From this sub-sample of firms with permits shortages, our analysis has confirmed the potential for risk-sharing, and thereby the pooling behavior at the group level. Let us now examine another subsample of firms with permits surpluses. Among the three firms in our sample that record a net long position, ArcelorMittal stands out as holding the largest surplus of allowances. Indeed, it is net long by 18.9M EUAs during the compliance year 2007. There appears to be little room for permits pooling within subsidiaries. Only 2 out of 25 installations are in a slightly short position, which may easily be counterbalanced by permits reallocation from other installations

45

Firms’ Banking and Pooling in the EU ETS (2005-2007)

within the group. This situation is confirmed by the visual inspection of the data in Figures 1 to 3. Overall, the parent company is a net seller on the permits market. Dalkia also exhibits a large surplus of 2.4M EUAs in 2007. Four out of 125 installations are net short, which supposes similarly that their deficit may be compensated internally by the parent company, thereby covering the risk of permits shortage for its subsidiaries. From Figures 4 to 6, one may remark that the distribution of installations is very heterogeneous, with two installations above 1M of allocated allowances holding substantive surpluses. On a smaller scale, Eesti Energia displays a net long position of 0.27M EUAs in 2007 for one installation being reported in the Reuters Carbon Market Database. Without commenting further the possibility of pooling risks, Figures 7 to 9 reveals that this permits surplus has been increasing from 2005 to 2007. This second sub-sample of firms has confirmed the liquidity of the permits market in terms of extra-allowances available for trading during each compliance period. Given this high level of heterogeneity between firms, if parent companies are still in a net short position after pooling allowances internally, they may buy allowances on the market to be globally in compliance. Finally, we comment the case of RWE, which is the largest CO2 emitter in the current European system, with 128M EUAs in 2007. Besides, we observe that RWE is in a net short position by 8.6 M EUAs. 21 out of 73 installations are in a situation of permits shortage, which may be compensated internally by the parent company by 2.8M EUAs, i.e. 33% of the total permits shortage. The distribution of installations displayed in Figures 16 to 18 reveals that RWE gathers very large installations, with 4 installations being allocated above 1.5M EUAs in 2007. One installation above 2M EUAs allocated records a net shortage of allowances in 2007. Our analysis has therefore confirmed the potential for permits pooling between subsidiaries for this large parent company.

46

POLICY IMPLICATIONS The policy implications of these empirical results shall be analyzed in conjunction with the well known properties of banking for use on emission permits markets. By allowing agents to arbitrate between actual and expected abatement costs over specific periods, banking forms another dimension of flexibility where agents can trade permits not only spatially but also through time (Rubin [1996], Kling and Rubin [1997], Leiby and Rubin [2001]). As a general rule-of-thumb, the regulator should strive to reduce or eliminate uncertainty in future permits allocation. More precisely, the task of the regulator consists in announcing and enforcing strict emissions target. If uncertainty arises concerning changes in allocation rules, firms may use banking in order to limit the level of risk attached to emissions trading. In absence of a credible commitment17 from the regulator in terms of allocation targets for future periods, banking therefore appears as an adequate tool for policy risk control. Besides, the existence of the pooling behavior raises the question of creating an agency at the sector level, since it appears missing between the government- and firm-levels. Such an agency may prove to be useful in order to pool allowances between firms with different technological characteristics. It therefore may be seen as an institutional “insurance” device against the variation of political decisions on emission permits markets.

FUTURE RESEARCH DIRECTIONS In brief, future research in this field includes the careful monitoring of banked and borrowed allowances based on the full public disclosure of the EU ETS 2005-2010 data. As the EU ETS develops, it is very likely that we will be able to further detail the firms’ banking and borrowing behaviours at the installation/group levels as de-

Firms’ Banking and Pooling in the EU ETS (2005-2007)

scribed in this chapter. Indeed, some panel data econometric analysis should become possible as soon as the European Commission publishes publicly the figures of banked and borrowed allowances during the first period until present. Such formal quantitative analysis will then translate into formal recommendations for decision-making in the policy arena. Besides, we have highlighted that pooling behaviours may emerge at the group level in order to save the cost of purchasing permits. While it would be surprising if a group did not in effect seek to have some co-ordination between the activities of its component parts, interesting questions appear as to whether that co-ordination reaches its full potential in terms of reducing the costs of purchasing permits and whether the production activities were in any way influenced. These research questions also need to be tackled using hard data (to be disclosed very soon at the time of writing this chapter). In sum, this chapter provides a first look at the 2005-2007 compliance data, which opens the way in return to a great deal of elaboration and extensions (in terms of econometric analysis especially).

We therefore observe a variation of the number of permits banked by firms in reaction to political uncertainty on the EU ETS. The latter result illustrates that the parent company acts as a cooperation agency to introduce an optimal risks sharing rule between its subsidiaries. From a regulatory viewpoint, the management of the environment through the introduction of emission permits implies that firms have the ability to bank permits in order to hedge against the risks of political decision changes. The use of banking is not motivated here by adaptation concerns to environmental constraints, but by the need to counter-balance political risks. Our analysis has therefore confirmed the key role played by banking provisions in order to cope with the potential political uncertainty related to the creation of emission permits markets. If unlimited banking and pooling are allowed towards Phase III (2013-2020) and beyond, firms’ observed trading strategies are to be expected in the future too.

REFERENCES

The recent development of the EU ETS during 2005-2007 allows us to study

Alberola, E., & Chevallier, J. (2009). European Carbon Prices and Banking Restrictions: Evidence from Phase I (2005-2007). The Energy Journal (Cambridge, Mass.), 30(3), 107–136. doi:10.5547/ ISSN0195-6574-EJ-Vol30-No3-3

1. the banking behavior at the installation level, and 2. the permits pooling behavior at the group level.

Alberola, E., Chevallier, J., & Chèze, B. (2009a). The EU Emissions Trading Scheme: The Effects of Industrial Production and CO2 Emissions on European Carbon Prices. Inter Economics, 116, 95–128.

CONCLUSION

The investigation of the banking behavior has revealed asymmetric banking (borrowing) patterns at the installation level as a consequence of varying net long (short) positions during 20052007. The investigation of the pooling behavior has confirmed the potential for internal permits transfers between subsidiaries at the group level.

Alberola, E., Chevallier, J., & Chèze, B. (2009b). Emissions Compliances and Carbon Prices under the EU ETS: A Country Specific Analysis of Industrial Sectors. Journal of Policy Modeling, 31, 446–462. doi:10.1016/j.jpolmod.2008.12.004

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Biglaiser, G., Horowitz, J., & Quiggin, J. (1995). Dynamic Pollution Regulation. Journal of Regulatory Economics, 8, 33–44. doi:10.1007/ BF01066598

Rubin, J. (1996). A model of Intertemporal Emission Trading, Banking, and Borrowing. Journal of Environmental Economics and Management, 31, 269–286. doi:10.1006/jeem.1996.0044

Chevallier, J. (2008). Strategic Manipulation on Emissions Trading Banking Program with Fixed Horizon. Economic Bulletin, 17(14), 1–9.

Von der Fehr, N. H. (1993). Tradeable Emissions Rights and Strategic Interaction. Environmental and Resource Economics, 3, 129–151. doi:10.1007/BF00338781

Ehrhart, K. M., Hoppe, C., & Loschel, R. (2008). Abuse of EU Emissions Trading for Tacit Collusion. Environmental and Resource Economics, 41, 347–361. doi:10.1007/s10640-008-9195-y Ellerman, A. D., & Buchner, B. K. (2008). OverAllocation or Abatement? A Preliminary Analysis of the EU ETS Based on the 2005-06 Emissions Data. Environmental and Resource Economics, 41, 267–287. doi:10.1007/s10640-008-9191-2 Ellerman, A. D., Joskow, P. L., Schmalensee, R., Montero, J. P., & Bailey, E. (2000). Markets for Clean Air: The U.S. Acid Rain Program. Cambridge University Press. doi:10.1017/ CBO9780511528576 Gollier, C. (2001). The economics of risk and time. Massachusetts Institute of Technology. Cambridge, MA: The MIT Press. Helm, D., Hepburn, C., & Mash, R. (2003). Credible Carbon Policy. Oxford Review of Economic Policy, 19, 438–450. doi:10.1093/oxrep/19.3.438 Kling, C., & Rubin, J. (1997). Bankable Permits for the Control of Environmental Pollution. Journal of Public Economics, 64, 101–115. doi:10.1016/ S0047-2727(96)01600-3 Kruger, J., Oates, W. E., & Pizer, W. A. (2007). Decentralization in the EU Emissions Trading Scheme and Lessons for Global Policy. Resources for the Future Discussion Paper 07-02. Leiby, P., & Rubin, J. (2001). Intertemporal Permit Trading for the Control of Greenhouse Gas Emissions. Environmental and Resource Economics, 19, 229–256. doi:10.1023/A:1011124215404

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ADDITIONAL READING Ellerman, A. D., Convery, F. J., & De Perthuis, C. (2010). Pricing Carbon: The European Union Emissions Trading Scheme. Cambridge, UK: Cambridge University Press.

KEY TERMS AND DEFINITIONS EU ETS: European Union Emissions Trading Scheme, which is a cap-and-trade program for greenhouse gases emissions developed in the EU 27. EUA: European Union Allowance exchanged under the European Union Emissions Trading Scheme, which is equal to one ton of CO2equivalent emitted in the atmosphere.

ENDNOTES 1



2



3



See Chevallier (2008) for market power concerns on tradable permits markets with banking. Stemming from various sources such as economic, financial or political uncertainties. See Alberola and Chevallier (2009) for a detailed analysis of these effects on CO2 prices.

Firms’ Banking and Pooling in the EU ETS (2005-2007)

4



5



6



7



8



9



10

11





The latter type of pooling corresponds to common practices for consumers’ mutual insurance companies (see Gollier (2001)). The data on allocation is available from the Website: http://ec.europa.eu/environment/ ets It is not our purpose to further comment this type of risk in the remainder of the chapter. Equal to 40€ per unit during 2005-2007, and 100€ per unit during 2008-2012. Bulgaria and Romania having integrated the trading scheme in 2007. This figure has been computed using a conservative estimate of 20€ per quota. Available at http://www.carbonmarketdata. com The Reuters Carbon Market Database exploits individual companies’ emissions reports during 2005-2007, which are the only source of public information available. Note the Community Independent Transaction Log (CITL), which oversees all national registries, displays extensive information at the installation level concerning allocation and verified emissions. However, not all

12



13



14



15



16



17



registries have been connected to date, and the CITL contains raw data that needs to be reorganized between subsidiaries and parent companies. Available at the following address: http:// economix.u-paris10.fr/pdf/dt/2008/WP_ EcoX_2008-25.pdf Note that, due to institutional fungibility between the EU ETS and the Kyoto Protocol as of 2008, the possibility to transfer banked allowances from Phase I to Phase II has been restricted by all MS. Such as allocation criteria or banking provisions. For an extensive discussion of the interperiod ban on banking in the EU ETS, see Alberola and Chevallier (2009). One EUA is equal to one ton of CO2 emitted in the atmosphere. We may also refer here to the notion of temporal consistency of public policies applied to the case of allowance trading. See Biglaiser et al. (1995) and Helm et al. (2003) for a discussion.

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Chapter 4

Mind the Gap Please!

Contrasting Renewable Energy Investment Strategies between the World Bank and Poor Customers in Developing Countries Sam Wong University of Liverpool, UK

ABSTRACT This chapter scrutinises the World Bank’s nine guiding principles for investment strategies on renewable energy in developing countries. Drawing on two World Bank-funded solar lighting projects in Bangladesh and India as examples, it demonstrates a wide gap in investment strategies between the Bank and local people. It suggests that a rigid distinction of renewable and non-renewable options risks restricting poor people to adopt an energy-mix approach to cope with poverty. The economic assumptions of the strategic choice for renewable energy investment pay inadequate attention to the cultural norms that shape people’s preferences for energy sharing. A lack of participation of NGOs and local communities in shaping the Bank’s investment strategies also undermines the effectiveness of its renewable energy policies in the long term. This chapter suggests that the World Bank re-conceptualises the complex relationships between energy and poverty and seeks a better understanding of local people’s daily energy consumption practices.

DOI: 10.4018/978-1-61350-156-6.ch004

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Mind the Gap Please!

INTRODUCTION The World Bank plays a multiple and indispensible role in promoting renewable energy in developing countries. It is a big investor—US$11 billion has been spent on renewable energy since 1990 (2007: ix). It has approved 366 renewable energy and energy efficiency projects in 90 countries between 2004 and 2009 (2009). It has spent billion dollars on research and development in order to find suitable renewable technologies that match with the needs of poor people. It is also an ambitious campaigner—the ‘Lighting Africa’ campaign alone, launched in 2008, was intended to provide solar lighting to more than 250 million Africans (Wamukonya, 2005). What are the driving forces underlying the World Bank’s very considerable interest in renewable energy? Sustainable development, concerns over climate change, and the Clean Development Mechanisms have provided the Bank with a continuous flow of funding and legitimate reasons for interventions. What is equally important is that renewable energy fits into the Bank’s overall plan of poverty reduction (World Bank, 2000). From the Bank’s perspective, solving energy-poverty by renewable energy is crucial to providing solutions to other poverty-related problems, such as economic stagnation, environmental degradation and gender inequalities. Based on this complex rationality, the World Bank has developed nine strategic investment principles for renewable energy in developing countries (World Bank, 2007a, 2004, 2002). They are: 1. use localised renewable energy supplies; 2. apply sustainable technologies to provide renewable energy; 3. give heavy subsidies to promote renewable energy market; 4. recognise that poor people are potential customers for renewable energy;

5. ensure that charges for renewable energy should not be as high as non-renewable options; 6. insist that customers pay for services, no matter how poor they are; 7. create different services for different customers; 8. decentralise energy supplies at local levels; and 9. work with NGOs and private companies to provide renewable energy services. How are these strategic investment principles actually translated on the ground? How effective are they? What impact have they made on poor people’s lives, and how will people respond to the changes? To provide answers to these questions, we will draw on two World Bank-funded solar lighting projects in South Asia: an individual solar home system in South Bangladesh and a solar lantern project in Rajasthan, India. We will compare the World Bank’s renewable energy strategies with local people’s energy consumption practices. In this chapter, we will expose a wide gap in the investment strategies between the World Bank, as the funder and project initiator, and local people, as users and customers. Firstly, it will suggest, with evidence, that, in contrast to the World Bank’s rigid distinction between renewable and non-renewable energy options, poor people take a pragmatic approach to energy use. Rather than considering renewable and non-renewable energy as either-or, they adopt a flexible energy-mix strategy which enables them to cope with their daily struggle against poverty. Secondly, it will suggest that, most of the World Bank’s guiding principles for renewable energy are over-economic and pay inadequate attention to socio-political factors, such as cultural norms of sharing collective resources and the right-tofree-electricity, that shape their preferences for energy uses. Thirdly, it will argue that the World Bank considering NGOs as purely an implementer

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of its renewable energy policies has missed the opportunity to hear the voice of local people. This chapter will suggest that the widening gaps raise doubt over the effectiveness of the Bank’s investment in renewable energy on the ground. In addressing the problems, it urges the Bank to reconceptualise the ‘energy-poverty’ relationships, to seek a better understanding of poor people’s energy consumption practices, and to encourage NGOs to be more critical of its own energy policies. This structure of this chapter is as follows: it will first discuss the nature of the World Bank’s nine guiding principles of strategic investment in renewable energy in developing countries. It will then provide contextual backgrounds of the two case studies and examine local customers’ actual investment strategies in solar lighting. By exposing the gaps in investment strategies between the World Bank and local people, it will bring out the problems that may undermine the effectiveness of the Bank’s renewable energy policies. It will conclude by offering solutions to overcoming the barriers.

WORLD BANK’S COMPLEX INVESTMENT STRATEGIES IN RENEWABLE ENERGY The nine principles (see Table 1), guiding the World Bank’s investment strategies in renewable energy, are derived from its 60 years’ engagement in developing countries. They are the lessons that the Bank has learnt from its success, as well as mistakes and failure, of its interventions and development reforms. From the Bank’s perspective, the multiple socio-economic-political-environmental problems that have baffled the developing world are the results of under-development. Securing constant, reliable, self-sufficient, and the recent idea of green, energy supplies have, therefore, been

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regarded as a solution to energy poverty. In the 1950’s and 60’s, driven by the agenda of modernisation, the Bank had introduced, predominantly, non-renewable energy projects, such as coal-based power stations, as the engine for economic growth. Some renewable energy-related programmes, such as building dams for hydro-electricity, had also been promoted. The outcomes of the interventions, however, were not all satisfactory (Wong, 2009a). Firstly, electricity, produced by coal-burning and hydrodriven power stations, was distributed via the national grids. The coverage of the grids was limited because of the high costs of construction. As a result, the grids failed to reach the majority of the population living rural areas. Secondly, the big energy projects were implemented without paying sufficient attention to environmental protection and costs to human lives, such as massive scale of deforestation and forced migration. At the same time, the ‘small is beautiful’ campaigns, led by Ernst Schumacher in the West in the 1970s, had placed the World Bank’s development programmes under a close scrutiny (Bortman, 2005). The 1980’s and the early 1990’s were considered as the ‘lost decade’ for many developing countries. Poverty worsened; many states went into bankruptcy. To cope with energy poverty, not-so-poor people relied on kerosene and oil lamps for lighting and cooking while the very poor used animal waste and firewood for survival. These coping strategies had, however, resulted in undesirable environmental and health problems. Cutting down trees in order to collect firewood resulted in deforestation and soil erosion, and land degradation led to declining farm productivity (Asian Development Bank, 2000). Burning animal waste and firewood created black smoke and affected people’s health. Huge human resources were also wasted since the dim lighting by kerosene had forced people to stop working in the evening. The emergence of the notion of sustainable development in the late 1980’s had raised aware-

Mind the Gap Please!

ness of the balance between social well-being, environmental conservation and economic growth. Concerns over the impact of the climate change, such as rising sea level and higher frequencies of extreme weather, in the mid-1990’s, have highlighted the role of renewable energy and sustainable technology in mitigating, and adapting to, changing climate, both in developed and developing countries (Paavola & Adger, 2002). Amid these socio-political-environmental changes, the World Bank pondered: how was it possible to help poor people obtain energy to improve their well-being, without compromising the long-term environment and social development? Since the late 1990s, the World Bank has integrated the energy-poverty relationships into its development framework. Renewable energy strategies are highlighted as key ‘pro-poor’ interventions to address wider problems (Chaurey et al., 2004). Firstly, renewable energy creates economic growth. Small-scale irrigation and mechanisation is possible, thus improving rural productivity. New job opportunities are created; technicians are trained to provide maintenance support. Secondly, renewable energy enhances well-being. Smoke-free environment improves

health. Artificial lighting raises literacy, and improving communication by radio and television makes the flow of information easy. Thirdly, renewable energy empowers women. Lighting provides women and girls with confidence, thus increasing their mobility.

Nine Guiding Principles Table 1 summarises the nine principles that shape the World Bank’s investment strategies in renewable energy in developing countries. Principles 1 and 2 touch on the sources of renewable energy and the role of sustainable technology in generating energy. The first principle highlights self-sufficiency of obtaining renewable energy from localised sources, rather than from outside. This principle is based on two observations: firstly, discussion of energy poverty in developing countries in the past, focused too much on what poor countries lacked, and too little on what they had. From the investment perspective, developing countries have huge potential for developing renewable energy since many of them own abundant and unlimited supplies of natural resources, such as sunlight and

Table 1. 9 principles guiding the World Bank’s investment strategies in renewable energy in developing countries Sources of energy and role of technology Principle 1

Use localised renewable energy supplies

Principle 2

Apply sustainable technologies to provide renewable energy Characteristics of the market and customers

Principle 3

Heavy subsidies needed to promote renewable energy market

Principle 4

Poor people are potential customers for renewable energy

Principle 5

Charges for renewable energy should not be as high as non-renewable

Principle 6

Customers pay for services, no matter how poor they are and how small a contribution they make

Principle 7

Create different services for different customers Service delivery and governance

Principle 8

Decentralise energy supplies at local levels

Principle 9

Work with NGOs and private companies to provide services

(sources: author’s own table, inspired by World Bank, 2007b, 2004, 2000; Martinot et al., 2001)

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wind, and they have large populations to support the market. Secondly, these clean and renewable sources are resources and assets that have not been fully tapped or utilised for development before (World Bank, 2000). Principle 2 ascertains the role of sustainable technology in generating renewable energy and in poverty reduction (Willis et al. 2006). Different types of renewable innovations have been adopted and applied in developing countries, such as reverse osmosis plants to provide clean water and solar mirrors for cooking. Favourable conditions, such as the Clean Development Mechanism and the agreed North-South technology transfer in the Kyoto Protocol, have made the introduction of sustainable technology in developing countries easier and bigger in scale. Principles 3 to 7 summarises the World Bank’s understanding of the characteristics and features of renewable energy markets in developing countries (World Bank, 2008; IEA International Energy, 2008). Principle 3 identifies an obvious fact that free market of provision of renewable energy, based on demand and supply, does not yet exist in poor countries (Robins, 2006). Hands-on policies, such as the provision of heavy subsidies, are considered necessary to incentivise both the suppliers and the users of the renewable energy sectors. Principle 4 may sound ironic because customers, by definition, are those who can afford for goods or services. Poor people in developing countries are usually not able to pay. From the World Bank’s perspective, however, people, suffering from poverty, still pay for non-renewable energy for living. The obvious option is kerosene. In light of this, Principle 4 asserts that each person, no matter how poor, should be treated as a potential customer for renewable energy. In order to tap into the big pool of potential customers, how much the renewable energy providers should charge for their services should depend on the customers’ affordability. Principle 5 suggests that the service providers can compare the price of the

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renewable energy with that of non-renewable options. In order to maintain the competitiveness and attractiveness of the renewable options, it should be set at the same, or even lower, price level of the non-renewable alternatives. Principle 6 is based on the user-pays principle that each customer should make some contributions for obtaining the renewable energy (Redwood & Eerikainen, 2009). The forms of contributions can be in cash or in kind, such as crops and animals. The World Bank argues that providing services free-of-charge to poor people would cause more harm than good. Making contributions, no matter how small, empowers poor customers. It creates a sense of ownership by generating demand for better services and monitoring the quality of services. Principle 7 deconstructs the potential customers into three groups: the not-so-poor, the poor, and the-very-poor. Each group is different, in terms of energy consumption patterns, affordability and needs. In order to tap into the potential of each group, renewable energy sectors, the World Bank argues, should provide tailor-made services for each group. The last two principles touch on service delivery and governance (World Bank, 2004; Havet et al. 2009). The World Bank considers the central and regional governments in developing countries weak, inefficient and corrupt. Principle 8 builds on the Bank’s good governance agenda, adopting a decentralised approach to provide funding and services directly to local communities. Principle 9 highlights the importance of building partners with local NGOs, grassroots community groups and the private sector. The assumptions are that NGOs have long been working with local people, so the sense of trust helps promote renewable energy. Driven by profit-making, the private sector is responsive to changing needs of customers and they should provide better services than civil servants.

Mind the Gap Please!

CASE STUDIES AND RESEARCH METHODS How effective are the World Bank’s investment strategies in renewable energy at local levels? How have these principles been put into practice? What impact have they made on poor people’s lives and how do people respond to the changes? In answering these questions, seeking a deeper understanding of local people’s investment decisions in energy use is crucial. By comparing and contrasting the investment strategies of renewable energy between the World Bank, as the donor and initiator, and local people living in small communities, as users and customers, it will be useful to examine the effectiveness of the World Bank’s renewable energy intervention on the ground. We draw on two World Bank-related solar lighting projects in South Asia as case studies: an individual solar home system in Char Kajal, South Bangladesh1 and a solar lantern system in Rajasthan, India. To promote individual solar home systems in Bangladesh, the World Bank assisted the Bangladeshi Central Government to set up a Dhaka-based company, called the Infrastructure Development Company. It oversaw all solar home projects in Bangladesh. It sub-contracted the projects to 15 NGOs. These NGOs were responsible for recruiting customers. They offered their clients three options: 50 watts, 40 watts and 20 watts. While the down-payment of the first option cost 5,200 taka2 which supported four bulbs, one TV and one mobile phone charger, the down-payment of the last option was 1,000 taka which supported two bulbs and one mobile phone charger. In addition, the owners of the first option needed to pay 1,000 taka for monthly subscription fees while the owners paid 400 taka for the third option. The down-payments might sound a lot to many poor households, yet the World Bank had already subsidised US$100 to each solar home system subscriber. The NGOs would provide ten years maintenance service support for the PV panels, five years for batteries and three years for elec-

trical circuits. They went to each household to collect payment monthly. In order to incentivise NGOs in promoting the solar home systems, they would obtain US$80 for each transaction. In order to achieve the goal of building 200,000 units of solar home systems in Bangladesh by the end of 2009, the Infrastructure Development Company deliberately arranged a number of NGOs to work in the same areas in order to create a sense of competition. We conducted our research in Char Kajal in January 2007. Char Kajal had 35,000 population, comprising 5,300 households. There were six NGOs selling the systems in that area. Through our personal networks, we successfully contacted two NGOs working in Char Kajal. There were no figures showing how many units of systems had been sold, but according to our interviews with the NGOs, totally 1,000 households in Char Kajal had bought the systems. This covered nearly one-fifth of the total population in the area. We found out that those without the solar home systems were largely poor or very poor households. Some of them had never been contacted by the NGOs. In their words, the NGOs only approached those families who could afford the ‘expensive’ systems. Considering the solar home systems as a ‘golden goose’, the NGOs were highly motivated to promote the systems in order to raise their revenues. However, the keen competition had caused bitterness amongst the NGOs. In addition, some NGO workers expressed their reservations about the success of the projects. They were worried that, when all free maintenance services expired in ten years, many solar home owners would not be financially capable of getting them replaced or repaired. Our second case study was solar lantern projects in East Rajasthan, India3. Partially funded by the World Bank, the Energy and Resource Institute (TERI), a Delhi-based energy research organisation, was responsible for implementing the solar lantern projects in Rajasthan. It then contracted the projects out to a Rajasthan-based

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NGO, called Humana People to People (HPTP). HPTP received funding from TERI and served nine communities, with each community being offered 50 free solar lanterns. Apart from building solar battery-recharging facilities, HPTP was responsible for recruiting an entrepreneur from each community. The entrepreneur would assist HPTP to provide services, such as running the battery-recharging shops and offering technical maintenance, for their local users. Regarding the payment, HPTP told the entrepreneurs that they could not charge more than 60 rupees4 for monthly subscribers. This amount of payment was based on estimation that each household paid 2 rupees for kerosene a day. For non-regular users, entrepreneurs were allowed to charge flexibly. The entrepreneurs could also expand the market by serving customers living in other communities. According to HPTP’s calculation, if all 50 lanterns were successfully rented out, each entrepreneur could make more than 3,000 rupees profit a month. In order to ensure financial viability of the projects, each entrepreneur was requested to set up a maintenance fund by reserving 1,000 rupees of their incomes (or one thirds of their profits) into a bank at the end of each month. We went to two villages, Banganga and Chhota Kantrana, to examine the impact of the solar lantern systems in April 2009. The former village was managed by a female entrepreneur while the latter by a male. The former system was regarded by HPTP as a ‘failure’ because of low subscription rate (only 15 out of 50 solar lanterns were regularly rented out). The latter, in contrast, had a 75% subscription rate. HPTP explained that the former received an older-generation of the solar lanterns which had suffered from many technical problems. The hands-off policy by HPTP worsened the situations. Assuming that the entrepreneurs and local users would develop a self-regulatory mechanism, HPTP did not provide immediate technical support to the entrepreneur in Banganga. The high break-down rate had first reduced the confidence of local users, and then undermined

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the profitability of the business. As a result, the vicious cycle had eroded the effectiveness of the solar lantern system in Banganga. During our fieldwork, we adopted qualitative research methods. Based on the stakeholder analysis, we targeted different community groups, including women, the elderly, NGO leaders, government officials and entrepreneurs. We raised questions as to what renewable and non-renewable energy options were available in their villages, and what factors affected their choices between the renewable and non-renewable energy. We interviewed in total thirteen males and twelve females in our two case studies.

GAPS IN INVESTMENT STRATEGIES Based on the nature of the nine design principles, we divide this section into three parts: sources of energy and role of technology, characteristics of the market and customers, and service delivery and governance. Our analysis will explore the potential gaps in investment strategies between the World Bank and local people over renewable energy.

Sources of Energy and Role of Technology The principle of self-sufficiency by obtaining clean energy from local sources is highly appreciated. However, while localised renewable energy is utilised, equipment to make renewable energy technology often comes from outside. Many components of sustainable innovations are built and transported from another side of the countries concerned or even from abroad. In the case studies of Bangladesh and India, while sunshine was tapped into for producing solar lighting on site, many parts of the solar home systems came from Germany. Nearly all solar lanterns were assembled and transported to Rajasthan from Delhi. The Chief Executive of the Infrastructure Development Company explained, in interviews, that it was

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much cheaper to buy in bulk components from Germany than produce them locally (interview, 10/01/08) because they lacked the right kind of technology and expertise in Bangladesh. Using sustainable technology to produce renewable energy gains support from the local population, and that helps promote public acceptance for clean energy. Villagers, from our case studies, considered solar home and solar lantern systems as a sign of modernisation and a source of community pride. Suffering from smoke and coughing by burning animal waste and firewood, they appreciated the smoke-free cooking environment. In the fishing community of Char Kajal, the solar lighting made it possible for women to make money by repairing fishing nets in the evening (Wong, 2009b). Children were also able to do their homework. Villagers exclaimed that this was not possible in the past because kerosene produced dim light, and they were forced to go to bed after 9pm. People in Rajasthan also considered the solar lanterns very useful. Because of the size and weight, they could take them out for outdoor activities, such as weddings, funerals, or harvesting in the field. A new ‘hierarchy of inferiority’, built along the use of different sources of lighting, has also created favourable conditions for promoting renewable energy in poor communities. In the case study of the solar lantern systems, at the bottom, the poorest could not afford anything and they lived in the darkness. A layer above was those who relied on clay lamps and kerosene. The next layer was solar lantern users, and at the top were the villagers connected to national grids. Our research reveals that the solar lanterns had provided the users with new social status. That said, they still considered the national grids more powerful since it provided more functions and supported irrigation and TVs. The preferences for energy use had shown the users’ pragmatic attitudes towards renewable and non-renewable energy. This point will be further elaborated on in the next section.

Characteristics of the Market and Customers These five principles could be found in our case studies. In the individual solar home systems, the World Bank offered US$100 subsidies for each household. Each NGO would also receive US$80 as a bonus for each installation. In the case study of Rajasthan, solar lanterns were supplied freeof-charge; the constructing costs of the batteryrecharging facilities were paid by the Bank and other donors. Setting the prices of renewable energy at the same, or lower, level of non-renewable options had been implemented in this solar lantern case study. HPTP had made estimation that each household, on average, spent 2 rupees a day for kerosene. In order to make solar lanterns competitive, HPTP set the rental fees at 2 rupees a day. To meet different demands for the solar home systems in Char Kajal, customers could choose between 50 watts, 40 watts and 20 watts. Those who could afford the down-payment of 5,200 taka and monthly subscription fees of 1,000 taka chose the first option. How effective are these principles in promoting and sustaining the renewable energy services? Do they match with the customers’ actual investment strategies? We make our analysis from four aspects: pragmatic decision-making, affordability, service reliability, and unintended social impact.

Pragmatism Villagers in our case studies showed a pragmatic approach to energy use. They considered energy as a means to achieve other economic and social goals. Whether the energy being produced was renewable or not did not bother them. Contrasting to the World Bank’s division of energy as renewable and non-renewable, poor people in our case studies did not regard these artificial divisions useful. Neither did they consider renewable energy more desirable than the non-renewable. What they were more concerned were the availability

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of the energy options as well as the suitability of the options to meet the needs (c.c. Practical Action, 2009). In the case study of Rajasthan, poor villagers had developed special energy-mix strategies. While solar lanterns were used for lighting, kerosene was still in demand for cooking and firewood was collected for creating warmth in winter. When harvests were good, farmers made use of the light and mobile design of the solar lanterns and took them to the fields to do extra work in the evening. Solar lanterns were also popular on occasions of weddings and funerals when external lighting was needed. For the not-so-poor customers, gaining access to various sources of energy supplies was desirable. Successful connections to the national grids might boost their social status in their villages, but the unreliability of the grids in electricity supplies had created inconvenience. So they got connected to the grids and also rented the solar lanterns if needed in order to maximise energy security. Both Indian and Bangladeshi governments have long been providing subsidies for using kerosene since it is still the most popular source of energy consumption. This policy may be changed because, according to a senior governmental official in Bangladesh, the government might consider reducing the subsidies on kerosene and divert the resources to support solar energy (interview, 12/01/07). Changes in the subsidy policies would only benefit the not-so-poor customers who could afford solar lighting. They would penalise the very poor population who still heavily rely on kerosene. This would push them to use more animal waste and firewood to cope with their daily needs.

Affordability The three options for the solar home systems (50, 30 and 20 watts) offered in Char Kajal seemed to offer sufficient choices to meet different demands. Yet, the cheapest option still required 1,000 taka for down-payment and 400 taka for monthly

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subscription fees. The capital costs were still too high for many poor Bangladeshi households. The Infrastructure Development Company had considered introducing the 10 watts systems to capture the unmet market. The NGO workers realised that heavy down-payments could reduce the attractiveness of the solar home systems, but they were reluctant to introduce micro-credit schemes to deal with financial exclusion. Theoretically, micro-credit schemes help by pulling the needy together and provide them with some money to pay for the down-payments. In practice, however, the NGOs in Char Kajal considered the solar home systems as consumptive, rather than productive, goods. Although the solar lighting provided more income-generating opportunities for the poor, the NGOs were worried that, unlike using the credits to buy fertilisers or to install irrigation, the solar home systems would not be able to help poor families raise sufficient money to repay the debts. How to pay for monthly subscription fees is another concern to the poor. In the case study of the solar lantern systems in Rajasthan, two rupees a day were charged in order to compete with kerosene. Yet, these two-rupee charges were only applicable for monthly subscribers. Each monthly subscriber was requested to pay 60 rupees in a lump sum. If villagers wanted to rent a lantern on a daily basis, they actually needed to pay up to five rupees. The NGO defended this policy, explaining that this would encourage more monthly subscription, and that would provide a better financial foundation for the systems in the long-run. Our research, however, shows that many households could not afford 60 rupees in a lump sum. Villagers explained that farming is a seasonal business and income generation was highly fluctuating. Even though some of them were able to afford the lump sum, they were reluctant to do so because they feared they might need the money for unanticipated events, such as health problems. Charging solar lighting at the same or lower prices of non-renewable options does not always

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make the renewable energy more attractive. Apart from pricing, customers’ investment decision lies in how energy is actually kept and saved. Take solar lantern and kerosene as examples for comparison. Solar lanterns are generally able to provide five to six hours lighting for each household. Once the family pays the rents, the fees would become the ‘sunk cost’ because no matter the household uses the lantern or not, they have already paid for the services. They cannot save the solar energy for future use since they need to return the lantern to the shopkeeper the day after. Kerosene, in contrast, is kept in liquid form. Villagers can control when, and how much, kerosene is used and for what purposes. Unlike solar lighting, the extra kerosene can be kept for future use. The practice of stealing electricity from the national grids also poses a threat to the demand for renewable energy. Some poor families, in interviews, suggested that this was their ‘human right’ to obtain free electricity from the government in order to alleviate poverty. Showing us how they connected to the national grids illegally, they defended their actions by saying that it was extremely unfair that the grids were built for the rich who were heavily subsidised by the government. The NGO workers believed that the ‘right-to-electricity’ thinking was inspired by the ‘right-to-water’ campaigns by other NGOs in the same region (c.c. Tully, 2006). Despite a heavy penalty being imposed to deter illegal connection, villagers suggested that corrupted government officials were willing to take bribes. In addition, villagers helped one another by setting up an early warning system in case of inspection.

Service Reliability The success of renewable energy also relies on efficient and robust technical support. Unlike non-renewable energy options, local users find renewable energy innovations interesting, but technologically incomprehensible. This does not necessarily create barriers for adoption of

renewable energy as long as the users receive quick service support in case of technical breakdown. The efficiency of obtaining support is, therefore, crucial to building public confidence and acceptance of the innovations. In the case study of Rajasthan, one village was still using the first-generation solar lantern system, and technical problems occurred frequently. Worse still, the slow responses of the NGO and the system manufacturers had made the situation worse. 15, out of 50, lanterns were broken. This high break-down rate and inefficient maintenance support created a bad reputation, thus reducing the demand for the services. The entrepreneur running the business was disincentivised as profits dropped. Once this vicious cycle was developed, it made the promotion of renewable energy difficult.

Unanticipated Social Impact Poor people tend to help one another to cope with the daily struggle against poverty. Yet the forms of mutual help and cooperation are different between renewable and non-renewable energy options. Before the introduction of solar lighting, villages in our case studies relied on kerosene for lighting. Kept in liquid form, kerosene plays a special social function: it can be consumed-individually but transferred easily from one family to another. This means that the needy families can obtain kerosene from other villagers and use it whenever, and wherever, they like. Sharing animal waste and firewood has similar facility. Solar lighting may produce better lighting as it covers a wider area, but sharing solar lighting is more geographically- and temporally-bound than kerosene. Unlike kerosene, people need to be close together at the same time and in the same place in order to enjoy the solar lighting. For example, households enjoying free lighting provided by their neighbours are those living next to ones who could afford solar lighting. Solar lighting would also add a new layer of social divide in the communities, between those

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who could afford the solar lighting and those couldn’t (c.c. Wong, 2009b). This inequality between the ‘haves’ and the ‘have-nots’ is built on other social differences, such as caste and land ownership. As mentioned before, the villagers had created a ‘hierarchy of inferiority’ with reference to different means of lighting. This psychological impact might offer strong incentives for poor people to improve their lives, but inequality could be further widened when renewable energy innovations become more technically advanced and provide more functions, such as radio and mobile phone charging. The poorest would feel they were further lagging behind. Applying the pay-for-service principle in renewable energy also risks creating tension in the villages, and that could affect cooperation amongst villagers in the long-run. The entrepreneurs in the solar lantern case study could earn more than 3,000 rupees a month if all 50 lanterns were successfully rented out. This could easily stir up jealousy and conflict because many poor villagers regarded the solar lanterns as a gift from the donors to the whole community. Perceived as communal, rather than private, goods, villagers condemned the entrepreneurs for making personal gains out of the communal resources. Secondly, they felt exploited by the entrepreneurs when they were in great need. On special occasions, such as their children preparing for exams, community members hired the solar lanterns for one day, and were charged 5 rupees a day. This meant that non-monthly subscribers paid 150% higher than the daily rate of their monthly counterparts.

Service Delivery and Governance NGOs play a strategic role in achieving the World Bank’s renewable energy policies in developing countries (World Bank, 2008; Bank Information Centre, 2006). Ideally, NGOs act as a bridge between the World Bank and local people. They work closely with local communities and understand poor people’s energy consumption patterns

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and habits. They then reflect their observations and concerns to the World Bank, which helps the Bank design more poverty-sensitive investment strategies in renewable energy. The reality, however, could be very different. The reasons for a lack of engagement of the NGOs in our case studies, which will be illustrated below, were that the intervention model of the World Bank remained top-down in nature. It took an instrumental perspective and used NGOs to gain legitimacy for its renewable energy policies on the ground. By considering NGOs as an implementer, rather than a partner, the Bank designed the whole investment strategies and set the targets, such as area coverage and number of solar panels being sold, and then asked the NGOs to achieve this. The problems of this approach were that: firstly, NGOs failed to reflect the voices of local communities to the Bank. Secondly, NGOs internalised their role as purely executors. They became less critical of the Bank’s energy policies, and that would further widen the gaps in investment strategies in renewable energy between the Bank and local people.

NGOs as Executors NGOs are one of the winners of recent trends for stronger renewable energy development. The continuous flow of funding has resulted in income generation and new job opportunities. New innovations and external expertise have also widened their scope of services. There is a wide range of NGOs in developing countries: campaigning for social and ecological justice from one side and simply following policies set by the donors from the other. Our two case studies belong to the latter. They considered working with the World Bank as a way to raise revenues. The financial reward was obvious in the Bangladeshi case study in which the NGO would earn US$80 for each transaction. HPTP in the solar lantern systems in Rajasthan was only involved in recruiting and training entrepreneurs. How the entrepreneurs worked with

Mind the Gap Please!

local people, and what level of quality of service had been achieved, were not their prime concerns. Using financial rewards to promote renewable energy policies could create pervasive incentives because only not-so-poor customers were served. The NGOs in the Bangladeshi case study targeted not-so-poor households because they knew they could afford the systems. The poor and very poor people were largely excluded in the promotion campaigns of the solar home systems. The conscious act of raising competition by putting six NGOs in a tiny area had made the situations worse. While the deliberate selection of customers had violated the World Bank’s strategic principle that poor people were potential customers for renewable energy, the NGOs defended their actions, arguing that they needed to target the audience in order to maximise the impact of their limited manpower. The hands-off attitudes of the NGO in Rajasthan resulted in a lack of vigilant monitoring of the quality of the service being delivered on the ground. The entrepreneurs kept changing the rules, without consulting HPTP and the customers. In Chhota Kantrana, for example, the entrepreneur imposed a new rule that no refund was allowed if the users did not report any technical faults within two hours after collecting the solar lanterns. The solar lantern users found the new rule unreasonable because they did not often check the batteries till they used the lanterns in the evening. To ensure the financial sustainability of the project, each entrepreneur was expected to set up a maintenance fund. The entrepreneur in our case study, however, admitted that she had not set it up even though the project had been operated for more than one year.

CONCLUSION Framing renewable energy from a wider developmentalist perspective, the World Bank has avoided a narrow focus on energy poverty. Instead, it has been successful in integrating renewable energy

policies into its global poverty-reduction strategies. In designing the guiding principles for the investment strategies on renewable energy in developing countries, the Bank has also drawn lessons from its previous mistakes, such as the over-reliance on the national grids to reach the poor. Recent ideas of maximising local natural resources to generate energy and decentralising the decision-making process have achieved some success on the ground. That said, the Bank’s investment strategies remain top-down in nature. The assumptions underlying the guiding principles are too economic. The use of the NGOs in implementing its renewable energy policies is too instrumental. Based on our detailed analysis of local people’s energy consumption practices in two of the World Bank-funded solar lighting projects in Bangladesh and India, our chapter has exposed a wide gap in investment strategies between the World Bank and local people. Poor people have adopted a pragmatic energymix in their daily lives because various sources of energy serve different purposes at different times. The distinctions between the renewable and nonrenewable energy options and the Bank’s explicit preferences for renewable energy risk reducing poor people’s flexibility in coping with poverty. The guiding principles concerning payment stress the importance of making renewable energy more competitive than non-renewable energy options. This assumes that local customers making decisions on energy consumption are based on rational choice. We have provided evidence to suggest that poor people’s decision-making process is socially-shaped. The right-to-free-electricity and the cultural norms of sharing collective resources are a few socio-political factors affecting their choice of energy use. The Bank’s instrumental use of the NGOs as an executor, rather than a genuine working partner, in its renewable energy policies is also problematic. Without the NGOs’ critical reflections of the needs of local people, our study has shown that

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the Bank has missed the opportunity to improve its investment strategies on renewable energy by making them more poverty-sensitive. In addressing these limitations, this chapter suggests that the World Bank should have a better understanding of the ‘energy-poverty’ relationships as well as poor people’s daily energy consumption practices. While renewable energy may help lessen poverty, poverty can undermine the actual impact of the renewable energy policies on the ground. People living in chronic poverty, for instance, may sell the components of the solar home systems in return for short-term benefits (Practical Action, 2009). The Bank should also make the design of its strategic investment policies more participatory and reflective (Bank Information Centre, 2006). Paying more attention to the voice of local people and encouraging the NGOs to be more critical of its energy policies could achieve better outcomes of its interventions on renewable energy in developing countries.

Havet, I., Chowdhury, S., Takada, M., & Cantano, A. (2009). Energy in National Decentralization Policies. United Nations Development Programs. IEA International Energy. (2008). Renewable Energy Services for Developing Countries. In support of the Millennium Development Goals: Recommended Practice & Key Lessons (available at www.ieapvps.org/products/ download/ REEEP%20RESDC% 20%20MDGs%20 v9%202.pdf; accessed on 7 July 2009) Infrastructure Development Company Ltd. (2007). Annual Report 2006-7. Kawranbazar, Dhaka. Martinot, E., Cabraal, A., & Mathur, S. (2001). World Bank/GEF Solar Home System Projects: Experiences and Lessons Learned 1993-2000. Renewable & Sustainable Energy Reviews, 5, 39–57. doi:10.1016/S1364-0321(00)00007-1 Paavola, J., & Adger, N. (2002). Justice and Adaptation to Climate Change. Tyndall Centre for Climate Change Research. Working Paper 23.

REFERENCES

Practical Action. (2009). Energy Poverty: the Hidden Energy Crisis. London.

Asian Development Bank. (2000). [Review of the Energy Policy. Manila.]. Energy, 2000.

Redwood, J., & Eerikainen, J. (2009). Climate Change and the World Bank Group: Phase I: An Evaluation of World Bank Win-Win Energy Policy Reforms. Washington, D.C: World Bank.

Bank Information Centre & Bretton Woods Project. (2006). How the World Bank’s Energy Framework Sells the Climate and Poor People Short. A Civil Society Response to the World Bank’s Investment Framework for Clean Energy and Development. (available at: www.bicusa. org/proxy /Document.9515.aspx; accessed on 10 May 2009) Bortman, J. (2005). Small Is Beautiful – Technology as if People Mattered. The Journal of Technology Transfer, 2, 77–82. doi:10.1007/BF02221571 Chaurey, A., Ranganathan, M., & Mohanty, P. (2004). Electricity Access for Geographically Disadvantaged Rural Communities – Technology and Policy Insights. Energy Policy, 32, 1693–1705. doi:10.1016/S0301-4215(03)00160-5 62

Robins, B. (2006). Subsidised Solar Lighting: The Only Option for 1 Billion People. Refocus, 7, 36–39. doi:10.1016/S1471-0846(06)70571-6 Tully, S. (2006). The Human Right to Access Electricity. The Electricity Journal, 19, 30–39. doi:10.1016/j.tej.2006.02.003 Wamukonya, N. (2005). Solar Home System Electrification as a Viable Technology Option for Africa’s Development. Energy Policy, 35, 6–14. doi:10.1016/j.enpol.2005.08.019

Mind the Gap Please!

Willis, M., Wilder, M., & Curnow, P. (2006), The Clean Development Mechanism: Special Considerations for Renewable Energy Projects. Renewable Energy and International Law Project (available at: http://www.reilproject.org/about. htm; accessed on 7 July 2009) Wong, S. (2009a). Linking Climate Change and Sustainable Energy Technologies to Develop a ‘People-Centred’ Intervention Framework. In W. Filho (ed.) Interdisciplinary Aspects of Climate Change, 449-468. New York: Peter Lang Scientific Publishers. Wong, S. (2009b). Climate Change and Sustainable Technology: Re-linking Nature, Governance and Gender. Gender and Development, 17, 95–108. doi:10.1080/13552070802696953 Wong, S. (forthcoming). Overcoming Obstacles Against Effective Solar Lighting Interventions in South Asia. Energy Policy. World Bank. (2000). Energy and Development Report. Washington: Washington DC. World Bank. (2004). World Development Report 2004: Making Services Work for Poor People. New York: Oxford University Press.

World Bank. (2007b). Catalyzing Private Investment for a Low-Carbon Economy - World Bank Group Progress on Renewable Energy and Energy Efficiency in Fiscal 2007. Washington, D.C.: World Bank. World Bank. (2008). Issues Note of the REToolkit: A Resource for Renewable Energy Development. Washington, D.C.: World Bank. World Bank. (2009). Renewable Energy & Energy Efficiency Financing by World Bank Group Hits All Time High. Press release on September 10, 2009 (available at: http://beta.worldbank.org/ climatechange /news/renewable-energy-energyefficiency-financing -world-bank-group-hits-alltime-high; accessed on 15 Sept 2009)

ENDNOTES 1



2



3

4



This project was funded by the British Council (2006-7). Currency rate: £1 = 114.9 taka (on 10/11/09) This project was funded by the British Academy (2007-09). Currency rate: £1 = 77.1 rupees (on 10/11/09)

World Bank. (2007a). Technical and Economic Assessment of Off-grid, Mini-grid and Grid Electrification Technologies. Energy Sector Management Assistance Program (ESMAP) Technical Paper 121/07. World Bank: Washington D.C.

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Chapter 5

Alternatives to the Global Financial Sector:

Local Complementary Currencies, LETS, and Time Backed Currencies Carl Adams University of Portsmouth, UK Simon Mouatt Southampton Solent University, UK

ABSTRACT This chapter explores complementary currencies and exchange systems and how they can provide some stability and competition to the vulnerability of the financial markets. The social economy, or 3rd sector, already plays a significant part in many societies. This is becoming more so as many governments and nations are facing decades of debt inevitably resulting in cut backs in key social and health services. In addition, the existing formal economic activity does not capture, value or support the full range of social and economic interaction within a nation. The chapter examines timebank systems, a particular type of complementary currencies and exchange system, and provides guidance on issues to consider in develop them. One of the finding from the evaluation is that as the number of people in the timebank system increases then more formality is needed to moderate the system and reduce potential for misuse.

INTRODUCTION The current turbulence within the financial sector raises issues for long-term stability and the corresponding wider impact across the economy: There are clearly systemic weaknesses in the DOI: 10.4018/978-1-61350-156-6.ch005

financial sector that have been growing over the last few decades. During this period the prime focus of attention in the banking sector has been on very large and quick flows of markets and money exchanges—casino banking, effectively making money from money (or commoditizing money). A truly vast amount of ‘virtual’ money

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Alternatives to the Global Financial Sector

was generated on the stock markets, currency exchanges, futures and derivatives markets and short selling. Yet at the same time the actual productive economy consisting of supplies of raw material, man-power based services and finished products saw only modest increases. This fostered the impression that the banking sector was becoming decoupled from the real economy. In addition the tsunami towards globalization has resulted in a focus in business and government policy aimed at multinational corporations and global markets. Consequently the result of these trends has been that local business, community and local interaction have often been at a disadvantage compared to big multinationals and ‘big money’. As the systemic weaknesses in the financial sector intensified over the last few decades, at the same time there has been a quite social revolution towards sustainable living, ‘triple bottom line’ economics thinking and interest in complementary money and exchange schemes. Perhaps the seemingly decoupling of the banking sector from the real economy has helped to hasten interest in these schemes. The general concern over wider issues such as the environment, global warming and the need for sustainable living have also likely increased interest in possible alternatives to the traditional banking sector view of the economy. Either way there is clearly an interest and given the weaknesses that persist in the traditional banking sector view of the world there is a need for real alternatives to the banking sector. This chapter addresses that need by examining some alternative schemes and complementary currencies that can support local interaction. The chapter particularly focuses on timebank type activity or time-backed currency systems, since these seem to offer much potential to encourage interaction at a local level. The chapter also provides a classification of timebank systems and guidance on setting systems up. The aim of this chapter is to show the possibility of having complementary systems to that of the dominant (and flawed) financial system.

First the chapter looks at non-banks, which mainly consists of the large retail organisations and technology organisations. Then the chapter examines some of the background to the concepts of Money and uses this to explore Local Exchange and Transfer Schemes (LETS), complementary currencies and timebank schemes.

NON-BANKS AND THEIR ROLE IN THE BANKING SECTOR The banking sector seems to be in a very dominant position controlling markets and dictating the direction of innovation in the financial sector. However, this dominance may not be as assured as first thought: Non-banks are playing an increasingly significant role in the financial world and are involved in all aspects of financial systems (Mouatt & Adams, 2010; Adams & Mouatt, 2010). For instance, Bradford et al (2002) examined the roles that non-banks play in payment activity and identified that:•

•

•

•

Non-banks are involved in a myriad of activities and roles, both in traditional and emerging payments types; Non-bank business relationships with banks and other participants in the payments systems are often highly complex and interrelated; Non-banks are rarely directly involved in settlement activities and, hence, appear to be associated with limited settlement and systemic risk; Both non-banks and banks appear to be increasingly susceptible to operational risk factors.

Similarly a study by the European Central Bank and Federal Reserve Bank of Kansas City (ECB & FRBK, 2007) shows the growing importance, and influence of non-banks, across the banking sector: “Retail payments systems throughout the world

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are undergoing fundamental change. Traditional paper-based forms of payment are giving way to electronic forms of payment. Technology advances are making possible new front-end payment instruments and new back-end processing methods. New products, business models, new markets, and new alliances are an everyday occurrence.... One key element of this new environment is the increased importance of non-banks in the payment system. Non-banks are making their presence felt at all stages of the payments chain. At this time, non-banks appear most prominent in the United States, but they are prominent in many European countries as well. And, most importantly, their presence appears to be increasing in virtually all countries.” (ECB and FRBKC 2007, p45) One significant area of non-banking influence and activity has come from the retail sector. Indeed, several of the large retailer chains have diversified into financial services resulting in significant competition that is challenging banks in their own core markets (Welch & Worthington, 2007). Competition from the retailer chains pose a significant threat to the financial sector’s control of ‘retail banking’ (i.e. handling deposits and transaction in the real economy): Retailers have strong brands and are responsive to customer needs—including financial. However, as Welch and Worthington identified, so far retailers have adopted a selective approach to the provision of financial services and do not cover the whole range of financial services offered by banks. Of course, by adopting a selective approach the large retail companies can cherry-pick the most lucrative services or the best-fit services to their existing market space (such as credit cards tied in with purchasing reward schemes). In addition, retail companies can also offer financial services not well covered by the traditional banking players, such as community banking and insurance. In the United States, for instance, community banking and out-reach banking provides access to financial services for people with very low income. A va-

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riety of different types of corporations are acting as community and out-reach banks: “The Independent Community Bankers of America, the nation’s voice for community banks, represents nearly 5,000 community banks of all sizes and charter types throughout the United States and is dedicated exclusively to representing the interests of the community banking industry and the communities and customers we serve. With nearly 5,000 members, representing more than 20,000 locations nationwide and employing nearly 300,000 Americans, ICBA members hold $1 trillion in assets, $800 billion in deposits, and $700 billion in loans to consumers, small businesses and the agricultural community.” (from http://www.icba.org/) In addition, retailer corporations have strong relationships with their customers, often providing ranges of services that tie-in customers with reward schemes and interlocking services. Customers regularly visit their preferred retailers for weekly food shopping which will likely include shopping for a variety of other goods such as medicines, mobile phones, kitchen items, white goods, electronics goods, books, games, CDs, DVDs, stationary and a variety of services such as credit cards, or pet and car insurance. In contrast, the trend in banking has been towards ‘distance-banking’ (Mouatt & Adams, 2010): this is the realm of online accounts, ATM machines and closing of high street branches, indeed customers rarely get to see banking personnel. The large retailer chains are encroaching into the core markets of the banking sector at the same time as having a very firm and growing foundation in their own markets. Further, many of the innovations in the banking and financial sector (such as ATMs, online banking, call-centres and general ‘distancing of the customer’) have been driven mostly by cost saving, whereas innovations from the retail sector have been mostly driven by developing new

Alternatives to the Global Financial Sector

services (Adams & Mouatt, 2010). Banks’business activity is mostly a mono-system revolving almost exclusively around financial services (Mouatt & Adams, 2010). Business activity for retail chains is typically more diverse involving a wider range of services. This diversification has been driven by fierce competition in the retail sector. The result being that if there are problems in the financial services markets, then banks are likely to be more heavily affected than retailers who can rely on their sustainable and diverse retailing activity to offset financial instabilities. In addition, in comparison to the high risk ‘casino banking’ dominating the banking sector, retailer corporations mostly practice a low-risk approach to business activity. The market space is based on a low-risk activity since whatever the economic climate people will still be purchasing their weekly food and household goods. This low-risk approach of retailers extends to other aspects of the supply chain relationship. Subramani (2004) identified, for instance, that the supplier-retailer relationships is complex and uneven, especially where there are technologydominated supply chains, since a small supplier will need relationship-specific investments and are effectively locked-in to a retailer. The larger retailers transfer much of the risk to their wider supplier network. They even transfer some of the risk to customers since large retailers often receive payment from customers for the popular daily and weekly shopping items often before the suppliers have been paid for the very same goods. Large retailers are likely to operate a monthly invoice and payment cycle where suppliers get paid on delivery of good over the previous month. Consequently a small bakery or dairy supplying fresh produce will have to wait potentially weeks for payment, yet the large food retailers would have received payments from customers for the bread and milk items on the day. A study from the Centre for the Study of Financial Innovation (a London based think-tank) into the “non-bank” phenomenon within Europe and

how this would impact the retail banking sector concluded that the new entrant retail players did pose a serious threat for the longer term, although the market-share impact made by retailers into the financial sector was currently small (Lascelles, 1999). It seems that the retail sector is poised and well placed to make an impact on the retail banking sector and its financial systems. Further competition from the retail sector may require extra stimulus, such as increased competition. However, often much of the stimulus for change has come from technological innovations, as well as technology based organisations. A significant part of the non-bank competition to the banking sector is coming from technology based companies. Edward De Bono (1993), while at the Centre for the Study of Financial Innovation, raised the concept of an asset-backed ‘IBM Dollar’ or large corporation currency that is directly linked to the commodity being produced. David Boyle (2000) also suggested something similar to the concept of corporate money, in the form of new money systems for large urban centres such as London. This would effectively form ‘regional corporation’ money and cover significant expenditure items within the region, such as transport and/or local economic exchanges. Examples already exist with the Oyster card system in London and the Octopus cards in Hong Kong, which can also be used to purchase non-transport items. Similar systems are been applied in other cities around the world such as in Holland and Dubai (Octopus 2007). An example of a significant complementary currency system operated by non-bank corporations is the ‘Wir’ system in Switzerland: “Wir, an acronym for Wirtschaftsring –‘economic circle’ Europe’s oldest bartering operation, is specifically aimed at smaller companies, and is now so widespread that it amounts to a virtual currency in parallel to the Swiss franc. Wir was started in 1934 by two followers of the economist Silvio Gesell (admired by Keynes), who had urged the creation of negative interest-rate currencies. By

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1993, it had a turnover of £12 billion and 65,000 corporate members.” (Boyle 2000, p14). A town, city or local region can have enough social participation and economic activity to generate its own complementary or duel currency that competes with the existing formal currency and financial system. This is not a new phenomenon, indeed in the time leading up to and during the Industrial Revolution there were many local tokencoin systems in operation across England (Dalton & Hamer, 1977). For instance in Chichester, a small city in the South of England, there was a variety of token coins used by different tradesmen in the 17th century and the Chichester penny and halfpenny in the 18th century (Steer, 1960). The use of some token exchange system may also be used for dispersed groups of people connected up over the Internet. For instance, the example of Microsoft points used for interaction between game players on the Xbox, or the virtual ‘Farm Coins’ used in the Farm Ville game popular on the Facebook social networking site (e.g. see http://www.squidoo.com/FarmVille-getcoinsinFarmVille, accessed May 2010). Similar token exchange systems operate in the retail and corporate world. For instance, a host of vouchers are used for books, music, groceries and travel (e.g. Airmiles). Some of these are direct representations of existing currency—such as the interesting collaboration of ‘vouchers’ used across 4000+ independent retail outlets in Ireland, the One4all® Gift Voucher’ (see http://www. one4all.ie/). Other voucher systems offer wider exchanges, such as the food retailers in the UK, including Tesco Clubcard points and the Nectar points system (see www.nectar.com) used by a network of retailers. Such loyalty vouchers can be exchanged for items more than the money equivalent of the vouchers—for instance, the Tesco Clubcard points can be exchanged for up to 4 times the money value of the points (see http://www. tesco.com/clubcard/deals/). These innovations in forms of money are driven by competition within

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the retail sectors and supported by technological innovations. ICT innovations are continuing to impact the banking and financial services sector. For instance, Towell et al (2007) shows that the move towards Web 2.0 technologies across the Internet is going to have a substantial impact on the range and type of financial services that will emerge. It will also facilitate more new entrants to the financial services market place, and the new entrant are likely to be dominated by technology companies, traditional retailers and virtual retailers rather than the more traditional banking institutions. To fully appreciate the impact and potential of non-banking entities to compete as alternative monetary and exchange systems, it will be helpful to examine what money actually is. This will be covered in the next section.

BACKGROUND TO MONEY CONCEPTS At a basic level, money functions as a means of exchange, a unit (and means) of account, a store of value and a standard for deferred payment (Mouatt and Adams 2010). Money also has other functions and attributes, as Davies notes (p.27) money provides a framework for the market allocation system (prices) and also the existence of monetary factors arguably provide an instigative impact on the productive economy in general. At a macro level money acts as the oil for an economy enabling exchanges to take place. Of course there are other features of money including cultural and psychological factors, and clearly a national currency is a source of national pride and identity. All sorts of things have been historically used as money, for instance Davies (1994) provides an alphabetical list of some of the more primitive examples of money, including: “Amber, beads, cowries (shells), drums, eggs, feathers, gongs, hoes, ivory, jade, kettles, leather, mats, nails, oxen, pigs, quartz, rice, salt, thimbles, umiacs, vodka,

Alternatives to the Global Financial Sector

wampum, yarns, and zappozats” (Davies, 1994, p27). Wampums are shells or beads used by the North American Indians as money. Zappozats are apparently decorated axes. Using a precious metal, like gold or silver, or a rare and precious item has provided a good base for money, and the weighing and assaying functions have usually been performed by the state. The relative scarcity and preciousness of the items embeds ‘value’ into those items (Adams & Mouatt, 2010). The channels of money transaction, in corporate and the traditional banking sector, are increasingly technology-based. Electronic digits have virtually replaced coins and paper. As Niebyl (1946) identifies in his monetary discourse of the classical economic period, empirical work on money has to be considered within the specific historical context of the time. The attributes of trust and value that was embedded in precious metal currency will be different to the trust and value in new forms of money (i.e. electronic money). Any item that is accepted as a value of exchange (and possibly fulfils some of the other functions of money, as above) can be use as money: So Microsoft points can be used for exchange of online games components, or within the 3D virtual world of Second Life they can use Lindens to exchange virtual items produced within Second Life (see http://secondlife.com/shop/).

A SOCIAL REVOLUTION AND NEW ECONOMICS The ‘formal’ economy does not capture the full range of economic or social interaction within a nation. The 3rd economy or 3rd sector—consisting of charities, voluntary organisations, not-for profit organisations and social enterprises—also operates across a society, and arguably is a measure of the health and maturity of a society. A society that has strong social and support structures along with good community spirit and engagement is likely to have a healthy and well balanced society.

The 3rd sector is a significant part of the economy for most nations. In traditional currency terms ($’s, Euro’s, £’s etc) if one collated together the amount of time and effort that people contribute to charities, voluntary organisations, social enterprises as well as individual careers and contributors across a society then this would be a very significant number. Kendall and Knapp’s (2000) study found that in 1995 in the UK the 3rd sector accounted for 1.5 million full-time equivalent paid workers, which effectively translated to over 6% of the economic activity in the UK at the time. The 3rd sector as an equivalent proportion of GDP varies from nation to nation, but typically can be in the range of 3% to 10%. The level of individual and unstructured contribution within the 3rd sector is difficult to calculate, however it is likely to be a significant, if not the most significant part of the sector: “Of course, there are social entrepreneurs, managers, employees, volunteers, users and clients in the third sector, but there is something perhaps even more basic at work: self-organisation – the capacity of citizens to organise around shared interests and needs outside the market and without being mandated to do so by the state. This is the civil society aspect of the third sector: the sum of private action in the public interest, serving the public good.” (Anheier, 2002, p8) The 3rd sector has a direct impact on the formal public and private sectors since it keeps people out of hospital and formal support services (e.g. see http://www.thirdsector.co.uk). For instance if the very older or disabled people are able to be cared for and supported in own homes then this will relieve overstretched hospital and social support systems. Indeed, within most nations the formal public sector would not be able to cope if it had to do all the extra work that the 3rd sector does. Bridge’s (2008) work on the social economy and 3rd sector identifies the wider political and policy context and the importance of that the wider 3rd sector. There is a tremendous amount of social capital across a nation that is contributing to the

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wellbeing of the society – not just in providing support complementing the public sector, but also in developing community, contributing to the environment and developing general wellbeing within society. There has been considerable interest in developing the wider 3rd sector given the changing demographics, particularly the growing older population. People are living longer and will need more care and support during their lifetime. Hard pressed public sector support mechanisms will not be able to cope at the current levels of funding when the proportion of old and very old people increase significantly. Having a strong 3rd sector is seen as one of the main safety nets for future generations in many nations. It is not just the impact on health and social care that is of interest. The New Economics Foundation (NEF) (see http://www.neweconomics.org/) is an independent think-and-do-tank that tries to capture the economics of things that matter or ‘real economic well-being’. For instance, one of the key approaches they take is a ‘triple bottom line’ analysis to investments where investment is evaluated by their longer-term social, environmental and economic returns. Arguably the triple ‘bottom line approach’ calculates the full cost, and the full benefits, of investments and human activity. The NEF apply a range of ‘triple bottom line’ evaluations across the public, private and third sectors. The NEF was founded in 1986 by the leaders of ‘The Other Economic Summit’ (TOES) which forced issues such as international debt onto the agenda of the G7 and G8 summits: “TOES promotes economics which incorporate the sustainable use of natural resources and the productive engagement of all people in the development of their communities and societies. It addresses the disarray in conventional economics by helping to bring such economic thinking into line with late twentieth century realities.” (http:// www.toes-usa.org/) The social economy and 3rd sector are a significant part of a society in both relation to the

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proportion of GDP and in the contribution to the wellbeing of that society. There has been a growing interest in the wider non-formal economies, and from many stances—long-term healthcare and support, the environment or the long-term wellbeing of a nation. This is an area that is set to be increasingly more important in the future. There is a growing interest in is new economics that along with traditional economic activity captures the wider and long term social economy, the environment, and the economy of things that are important for human communities. However, much of the existing formal economic activity does not capture or support the full range of economic and social interaction within a nation. This is where complementary currencies, Local Exchange and Transfer Schemes (LETS) and Timebank systems operate. The next section explores a range of complementary currencies and exchange systems that attempt to fill this gap.

COMPLEMENTARY CURRENCIES AND LETS There are various duel currency developments in the world today. Venezuela, for instance, has been going through a period of record high inflation, and currency devaluation, and is preparing to introduce a de facto dual currency system to develop some stability in the economy (Mander, 2008). In the global economy, the dollar or increasingly now the Euro (or any stable currency), is often used as a dual currency alternative to local (relatively unstable) currencies. Having a dual currency is not just about stability. Multiple currencies are not new phenomena and, some argue they perform a specific set of functions that substantially improve the quality of economic and social life. Bernard Lietaer, a recognized expert on complementary currencies, has provided convincing evidence from Bali where a successful dual currency operates (Lietaer, 2001). As Jacobs has argued (Jacobs, 2003, p29): “we need a range of currencies, time

Alternatives to the Global Financial Sector

banks to underpin the social economy, local currencies to keep money and resources circulating locally and regional currencies to provide low cost finance to small business. It is also argued we need a range of experimental (asset based—our emphasis) currencies based on anything from renewable energy to the value of local vegetables” (Douthwaite, 2003, p165). There is also a community aspect to sharing a duel currency. Ownership (with property rights to control) of a currency, or dual forms of money, can develop and maintain a sense of community, even between people dispersed over large areas such as with rural communities in Australia (Taylor & Marshall, 2002). The community aspect is often found in (social) monetary contexts, notably in Local Exchange and Trading Systems (LETS). David Boyle’s books ‘the Money Changers’ and ‘The little money book’ give several examples of successful LETS schemes (Boyle 2002, 2003), including Edgar Cahn’s Time Dollars (Boyle, 2002, p241) and a variety of LETS systems evolving from Michael Linton’s mutual credit system called ‘green dollars’ (Boyle, 2002, p262). Many of the LETS are based around towns, cities and their distinct communities. An example of a local exchange scheme over the internet is the ‘Geek Credit’ described as “a digital complementary currency for internet. It is decentralized, secure, interest and demurrage free and is backed by mutual credit (time). Since there is no central issuing and control authority, it is a true peer-topeer currency” (GNA). In the USA much of the momentum towards developing local currencies and LETS schemes has come from the E.F. Schumacher Society set up in the 1980’s (see. http://www.localcurrency. org). Several innovative example flourish and are seen as a way to develop sustainable regional economies. In 2004 the E.F. Schumacher Society hosted the ‘The Local Currencies in the 21st Century’ conference which brought together many examples of LETS, local currency systems and

timebanks used throughout the world alongside national currencies. Many organizations, and even individual people, have created their own money (Boyle, 2003). In Europe the EU Electronic Money directive gave member states guidelines to develop legislation for corporations to develop their own electronic monies. In the UK this has been translated in to the governance of electronic money issue, by the Financial Services Association (FSA) who issue licenses for corporations that wish to issue their own money. The licenses are granted under tight rules and monitoring activity. Interestingly, Paypal was an early example of a licensee under the new EU scheme, pre-dating the issuing of licenses to many of the telecoms companies. An amendment to the directive from the EU, directive 2007/64/EC, moves further to encourage competition in financial services and e-payment markets. Fundamentally, the amended directive encourages variation and competition, specifically incorporating supermarkets, retailers and other entities, and to encourage low value or micro payment mechanisms.

TIME BANK TYPE SYSTEMS The history of Time Banks and Time Dollars is tie up with Professor Edgar S. Cahn, as the US Timebank website details: “he dreamed up Time Dollars as a new currency to provide a solution to massive cuts in government spending on social welfare. If there was not going to be enough of the old money to fix all the problems facing our country and our society, Edgar reasoned, why not make a new kind of money to pay people for what needs to be done? Time Dollars value everyone’s contributions equally. One hour equals one service credit. Seven years later (in 1987) at the London School of Economics, Edgar developed his theoretical explanation for why the currency should work. He came back to the US and started putting service credits (not yet called Time Dol-

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lars) into operation.” (http://www.timebanks.org/ history-structure.htm). There are examples of timebanks around the globe—including Canada, Chile, Curacao, Dominican Republic, Israel, Italy, Japan, New Zealand, Portugal, Spain, South Korea, Taiwan, United Kingdom (http://www.timebanks.org/ international.htm); however the US and the UK have probably seen the most significant numbers of timebanks as well as the number of people involved in them. Timebank type systems aim to link people locally to share their time and skills. One of the main themes is that one hour of time of a person’s skills is equal to one hour of another person’s skills. There are many schemes and different types of schemes running both in the US, UK, and elsewhere, though not all of them are successful or gaining critical mass (yet). “Time Banking UK” is the national umbrella charity that links and supports time bank systems across the UK, though a time bank system does not have to be part of the scheme. The latest statistics for time banks in the UK are:• • • • •

110 active time banks 99 developing time banks 3 neighbourhood time banks 11420 participants actively involved in time banking 945504 hours traded between participants to date (from http://www.timebanks. co.uk/cgi-bin/hours_display.pl, accessed 25/09/09)

In the Timebanks USA is the national umbrella organisation that links and supports time bank systems across the US. Their mission is to “expand a movement that develops, supports, and promotes a network of Time Banks that rebuild community, and reforms economic and social systems, policies and practices so that they empower human beings to contribute to the well-being of each other through reciprocity.” (see http://www.timebanks.

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org/). A directory of timebanks in the USA can be found at http://www.timebanks.org/directory. htm#MA. At the start of the TimeBanks USA 2009 conference they capture the spirit of the timebank movement: “In more than 40 states and 32 countries, TimeBanking is a force for good and source of hope. It works because people create networks of support where everyone gives and receives. They activate untapped skills, build new capacities and uncover hidden talents to create a whole new sense of community. TimeBanks bridge the gaps between government and citizens, promoting justice and creating safe, healthy neighborhoods.” (http://tbusa.org/) Some of the main differences between Time banks and LETS are discussed by Time Bank UK, these include: •

•

•

“Time banks value everyone’s time equally. You give an hour and you get an hour back—no matter what service is required or skill needed to deliver it. This exchange rate never changes. LETS schemes sometimes work this way but each LETS group has its own way of deciding how much their currency is worth. In many LETS groups, one LETS credit is worth one pound sterling which enables them to relate to market values. Time banks aim to work alongside the mainstream agencies like Health and Social Services, or local authorities, whilst LETS work more closely towards building an alternative economy. More recently, however, as part of their anti-poverty initiatives, some local authorities are now working with LETS schemes. Both Time banks and LETS help strengthen a sense of community spirit and allow individuals to discover, develop and value their own abilities. Time banks match people using a central broker. LETS provide a directory of mem-

Alternatives to the Global Financial Sector

•

•

•

bers who contact each other directly to get the service they need. Time banks usually have at least one paid member of staff. Most LETS schemes work on a purely voluntary basis. Time banks have a local base, office or shop where participants can call in for a chat, get some advice, etc. LETS usually work without a base and use the telephone, a directory and local get-togethers as contact points. Time banks have been given a Benefits Disregard by the Government. At present, LETS have not been awarded this disregard—members on benefits have to declare their LETS earnings. LETS schemes, with the backing of several government ministers, are trying to get this position changed.” (from http://www.timebanking. org/faq.html#Q7?, accessed 25/09/09)

small group. If there is a problem then the group can quickly disband, and possibly reform with just the trusted or willing members. As the number of people in the timebank system increases then the vouchers will need to be more formal—they need to be recognised as a timebank voucher among the wider and growing number of people involved in the system. There will also need to be more formal recognising of the time hours (i.e. keeping a record of who has used the time-hours and who has a stock of timehours left). There are potential problems and areas for misuse with timebank systems including: •

Table 1 collates together some of the main attributes of different type of timebank systems. As the size of the timebank group increase so too does the need for more formal systems. When there are small numbers of people involved, say in a local baby sitting circle, then everyone knows each other and they can keep communal record between themselves of who has used up their time-hours and who has a stock left. They could also use an informal time voucher such as a matchstick or a hand written I.O.U. Fairness and trust can be maintained and controlled within the

Hoarding: If a few people start hoarding the timebank vouchers then the system starts to slow down as there will not be enough vouchers to stimulate exchanges. This can be overcome by incentives to stop hoarding, such as having a time limit or use by/exchange date, or possibly having a maximum number of timebank vouchers that can be hoarded by individuals (possibly a realistic option for central controlled systems). A further way to address hoard is some form of ‘quantitative easing’ or just distributing more timebank vouchers. Indeed, one of the issues of any timebank system is how many timebank vouchers each participant should start with. If the number is too small then it might limit the flow of time exchanges; if the number is

Table 1. Variation of timebank systems Central-Full

Central-moderated

Central-Self managed

Individual

Moderation / Control

high

medium-high

medium-low

high to low (depending on group cohesion)

Level of formality

high

medium

medium-low

low

Running costs / Overhead

high

medium

medium-low

low

Size of timebank group

large

large-medium

medium

small

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•

•

74

too high then it may lessen the incentive to exchange timebank tokens. Cheap labour misuse: The timebank vouchers could be viewed by some as a source of cheap labour. Having a (high) subscription may contribute to this view (i.e. participants have ‘paid’ for a number of timebank hours) as well as if participants start off will several timebank vouchers. This may need ‘policing’ or moderating to limit misuse in this area: moderating may be easier to do with more full central systems where there is a moderating or monitoring team. Small groups may also selfmoderate such behaviour (i.e. with a small group the member would know who is not being fair in the time exchanges and can then not do exchanges with those people accordingly). Exchange for money misuse: Similar to viewing the system as a means for cheap labour there may be a potential misuse around offering money or products for time spend. This goes against the concept of timebank systems (i.e. it is supposed to be an exchange of people’s time). It may also encroach upon employment regulations and the formal economy (and so attract tax etc). The purist view is that no exchange of time-for money or products is acceptable. The pragmatist view would be that there may be ‘exceptional’ times when some monetary or produce exchange makes sense. For instance, if there is a time voucher exchange to cover apple picking. The person with the apple trees and the apply picker may decide on an exchange that involves a mix of timebank vouchers and apples. Similarly, if someone has a situation that calls for the use of lots of timebank hours (say a large amount of garden work) but has not been able to accumulate enough timebank hours (possibly because of any hoarding rules) then some supple-

•

ment money or other produce exchange (say some of the apples or apple chutney) may be appropriate. If there is regular exchange of hours for money in a timebank system then this would be classed as an employment system and will need to be treated accordingly (such as being compliant with employment and tax legislation). Inappropriate exchanges: The exchanges have to be legal and acceptable within the timebank community. Particular care needs to be taken when dealing with venerable people, such as the young and old. This may be easier to do with centrally moderated and controlled timebank systems, though it would be difficult to guarantee no misuse. This highlights the need for formal rules and use policies that participants have to sign up to, especially for larger timebank groups. There should also be mechanisms to exclude people engaged in inappropriate exchanges.

Tony Warne and Keith Lawrence, from the University of Salford, evaluated the ‘The Salford Time Banking’ scheme and compared it to other schemes (see http://www.timebanking.org/ documents/Time-Banks-Literature/Time_Banking_evaluation_Salford_Time_Bank.doc). They identify several issues such as achieving critical mass, trust within the membership in order to increase activity (i.e. between individuals at a personal level, and in a members ability to perform a task competently) and for the Salford example they identify that an active coordinator would be required and consequently would need funding (either self funding or from appropriate local public agencies). Warne and Lawrence also identify a selection of good reference material to provide background to a variety of Time Bank schemes and corresponding issues, including Young (1997) and Seyfang (2003, 2004) work. The core principles of timebank systems are to encourage local exchange and community. It

Alternatives to the Global Financial Sector

operates as a complementary system to the formal economy and does not replace or compete with the formal economy. There are several groups that would particularly benefit from participation in a timebank system, such as time-rich people like retired people and unemployed (or not fully employed). Even if someone is unemployed and actively looking for work they can make a valuable contribution to a timebank system. While doing so they can keep active, develop skills and make a contribution to the local community. The same would be true for retired people. This ties in very much with Cain’s view of ‘no throw away people’: Every one has something they can contribute to the community. Equally though, the fully employed can benefit from both the time-rich contributors as well as contributing their own time in areas that are particularly of interest to them. For instance a car mechanic, accountant, or anyone else can contribute his or her non-professional skills in painting, gardening, reading or anything else that they are interested in or enjoy. It may also be an opportunity to give something back to the community or participate in the community. Large cities can be very lonely places: Given the mobility in the global workforce it can often be the case that mobile and semi-transient workers do not know their neighbours. Participating in a timebank system will enable people to be activity involved within their community. A successful timebank system may not need to operate indefinitely; indeed, a timebank system may run very successfully for a fixed or limited period of time to meet the needs of a local community at that time. In addition, the duration of a timebank may be affected by the participation and support of local champions or support agencies. Equally, a timebank system may split up and reform, possibly at a later date, to meet local needs within the community as they emerge. There are clearly a range of different timebank schemes, as to LETS schemes, and they provide

the bedrock of many complementary currencies and exchange systems.

CONCLUSION A complementary currency approach, based on non-banking activity, seems to offer realistic competition to the vulnerability of the financial markets. In addition, the social economy and 3rd sector are already a significant part of many societies and given the changes in demographics are set to be more important in future years. In addition, given recessions around the world following the 2008 collapse of the financial markets, many governments and nations are facing decades of debt inevitably resulting in cut backs in key social and health services. During this period societies will need to have strong social economies and 3rd sectors. At the same time there are pressing environmental needs (such as global warming) and corresponding interest in sustainability within economies. The existing formal economic activity does not capture, value or support the full range of social and economic interaction within a nation, the environment or the long-term wellbeing of a nation. These require new thinking which draws upon the development of complementary currencies, Local Exchange and Transfer Schemes (LETS) and timebank systems. In addition, providing support for more sustainable economic activity, there are clear benefits covering the development strong social and community aspects at the local level. This chapter has identified some of the systemic weaknesses within the existing formal banking sector and corresponding traditional economics. On one level the banking sector generates huge volumes of virtual wealth (thorough the ‘casino’ type banking) and wields significant power in shaping global markets and setting or currency rates. On another level the bedrock of the banking sector—retail banking where deposits are kept and the real economy transactions takes place—the

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banking sector looks less solid. Competition from the non-banks, including significant competition from large retail companies and technology companies, is already considerable and set to grow. Innovation in ICT is driving further competitive change and the banking sector is not well placed to be market leaders in this change. This chapter has also provided an evaluation on timebank systems and guidance on issues to consider in develop them. A key finding from the evaluation is that as the number of people in the timebank system increases then more formality is needed to moderate the system and reduce potential for misuse. One part of this formality is the need for a token, reward or voucher to formally recognise people’s contribution to the timebank system as well as a mechanism of account among the people involved in the system.

REFERENCES

Anheie,r H.K. (2002). The third sector in Europe: Five theses. Civil Society Working Paper 12, London School of Economics. Ashford, R., & Rodney, S. (1999). Binary Economics: The New Paradigm. Lanham, MD: University Press of America. Ashtom, T. S. (1968). The Industrial Revolution 1790-1830. Oxford, UK: Oxford University Press. Bello, W. B. Nicola & Malhotra, Kamal & Mezzera, Marco (2000). Notes on the Ascendancy and Regulation of Speculative Capital. In W. B. Bello, Nicola & Malhotra, Kamal. Malaysia, Zed (eds). Global Finance: New Thinking on Regulating Speculative Capital Markets. Boyle, D. (2000). Why London Needs its own Currency. New Economic Foundation. Retrieved from http://www.neweconomics.org/ gen/uploads/ wm1ldn45byfcr555pty xzs2506042005123501. pdf, Accessed 11/1/2008.

Adams, C., & Mouatt, S. (2008). Government Support for M-Payment Systems. The Third International Conference on Mobile Government, 15 - 16 September 2008 / Sheraton Voyager, Antalya, Turkey.

Boyle, D. (2002). The Money Changers. Ebbw Vale, UK: Earthscan.

Adams, C., & Mouatt, S. (2010). The rise of complementary currencies and corporafinance: e-commerce driven competition in the financial sector. Journal of Internet Banking and Commerce, 15(1), 1–13.

Boyle, D. (2004). The Little Money Book. Bristol, UK: Alastair Sawday Publishing.

Adams, C., & Ramos, I. (2010). The past, present and future of social networking and outsourcing: Impact on theory and practice. UKAIS 2010 conference, 23-24 March, Oxford. Adams, C., & Simon, M. (2010). Evolution of Mobile Business and Services: Government Support of M-Payment Services. International Journal of E-Services and Mobile Applications, 2(2). doi:10.4018/jesma.2010040104

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Boyle, D. (2003). The Money-Changers: Currency Reforms from Aristotle to e-cash. Sterling, UK: Earthscan Publications.

Bradford, T., Davies, M., & Weiner, S. E. (2002) Non-banks in the payments system. Payments System Research Working Paper PSR WP 02-02, Federal Reserve Bank of Kansas City. Available from ideas.repec.org/p/fip /fedkpw/psrwp02-02. html, Accessed 11/1/08. Bridge, S. (2008). Understanding the Social Economy and the Third Sector. Hampshire, UK: Palgrave Macmillan. Dalton, R., & Hamer, S. H. (1977). Provincial token-coinage of the 18th Century. Lincoln, MA: Quarterman Publications inc.

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De Bono, E. (1993). The IBM Dollar. Pamphlet published by The Centre for the Study of Financial Innovation, in London. Extracts also in Boyle (2002), page 168-170. See also http://www.csfi. org.uk/. Deane, P. (1965). The first industrial revolution. Cambridge, UK: Cambridge University Press. Douthwaite, R. (2003). A Multi-Currency World. In Boyle, D. (Ed.), The Little Money Book. Alastair Sawday publishing (pp. 164–165). Bristol, UK: The Future of Money. ECB and FRBKC [European Central Bank and Federal Reserve Bank of Kansas City] (2007). Non-banks in the payments system: European and U.S. Perspectives. Proceedings of Non-banks in the payments system: Innovation, Competition and Risk. Santa Fe, New Mexico, May 2-4, 2007. Available from http://www.kansascityfed. org /Publicat/PSR/Proceedings /2007/07prg.htm, accessed 3/1/08. Hayek, F. A. (1944). The Road to Serfdom. London: Routledge. Hayek, F. A. (1948). Individualism and Economic order. Chicago: University of Chicago press. Hilferding, R. (1910). Finance Capital. London: Routledge. Jacobs, J. (2003). Big currencies and the Euro. In Boyle D. The Little Money Book (pp28-29). Bristol, UK: Alastair Sawday Publishing. Jagger S. (2008). Hedge funds on the brink as Fed cash fails to ease crisis. The Times, Thursday March 13, p44. Kendall, J., & Knapp, M. (2000). The third sector and welfare state modernisation: Inputs, activities and comparative performance. Civil Society Working Paper 14, LSE. Available from http:// www.lse.ac.uk/ collections/CCS/pdf /CSWP_14. pdf, accessed May 2010)

Lascelles, D. (1999). Europe’s new banks: The “Non-Bank” Phenomenon. The Centre for the Study of Financial Innovation, in London. Also available from: http://www.csfi.org.uk/ Lietaer, B. (2001). The Future of Money: Creating New Wealth, Work and a Wise World. Guildford, UK: Random House. Marx, K. (1971). Theories of Surplus Value: Part Three. Moscow: Progress. Mouatt S. & Adams C. (2009). The rise of complementary currencies and corporafinance: e-commerce driven competition to the financial sector. Solent discussion papers, Solent University, May 2009 Mouatt, S., & Adams, C. (2010). Corporate and social transformation of money and banking: Breaking the serfdom. Hampshire, UK: PalgraveMacmillan. doi:10.1057/9780230298972 Niebyl, K. H. (1946). Studies in the Classical Theories of Money. New York: Columbia University Press. Octopus (2007). Octopus to Provide Major Transport Smartcard System in Dubai. Octopus press release 22 November 2007 Available from http://www.octopus.com.hk/ release/detail/ en/20071122.jsp, accessed 11/1/2008. Seyfang, G. (2003). Growing Cohesive Communities, One Favour At A Time: Social exclusion, active citizenship and Time Banks’. International Journal of Urban and Regional Research, 27(3), 699–706. doi:10.1111/1468-2427.00475 Seyfang, G. (2004). Working Outside The Box: Community currencies, Time Banks and social inclusion. Journal of Social Policy, 33(1), 49–71. doi:10.1017/S0047279403007232 Soros G. (1995). The Looming Crisis. Interview on the State and Future of the Financial Systems. Reprinted in Boyle (2003) (pp80-83)

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Steer, F. W. (1960). Some Chichester tradesmen 1652-1839. The Chichester Papers,17. UK: Chichester City Council. Towell, P., Scott, A., & Oates, C. (2007). Web 2.0: How the next generation Internet is changing financial services. The Centre for the Study of Financial Innovation, London. September, 2007. Available from http://www.csfi.org.uk/, accessed 11/1/08.

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Welch, P., & Worthington, S. (2007). Banking at the Checkout 2007-08: Evaluating the Provision of Financial Services by Retailers. ECR Publishing Partnership Worthington, S., Retailers and financial services in the United Kingdom. Journal of Financial Services Marketing, 2(3), 230–245. Young, S. (1997). Community-based partnerships and sustainable development: a third force in the social economy. In Baker, S., Kousis, M., Richardson, D., & Young, S. (Eds.), The Politics of Sustainable Development: Theory, Policy and Practice Within the European Union. Manchester, UK: Manchester University Press.

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Chapter 6

Low Carbon Economy and Developing Countries: A Case of Neplese Forest Raghu Bir Bista Tribhuvan University, Nepal

ABSTRACT In forest, reduction of emission from deforestation and forest degradation (REDD) is considered as low carbon instrument. Financial Incentive scheme of this new climate change mitigation approach generates query about REDD’s economic implication in developing country. This study is to examine empirically low carbon potential from avoided deforestation in Nepal. The case study is the Kafle community forest of Nepal. We used 10 meter radius circle sample plot for carbon inventory data collection. In addition, we conducted household survey through 48 households for data set collection. This study finds that community forest contributes 45 percent livelihood income (fire wood, leaf litter, grass, water) to the forest dependent stakeholder’s total income. This labor incentive based on labor contribution in forest management is distributed among the member households. This study further finds huge carbon income potentials. Annually, KCF can earn carbon income Rs. 39, 81,196, if KCF enters in REDD. It is 41 times higher than the present mean income Rs 24, 549.55 from the forest product sale. In mixed familiarity about REDD, the study finds only 44 percent households expecting that REDD will be a better livelihood alternative to the poor. 63 percent responds need and use of carbon income for livelihood objectives. From estimation, household stakeholders who have good asset holdings (land and Rlivestock) think that REDD will be not a better livelihood alternative to the poor. However, the household stakeholders who have literacy, different food sufficiency level, land holding (1>), different earning per day, Rsex, per day earning and age think that REDD will be a better alternative. Thus, the poor households expects livelihood role from REDD in Nepal. Therefore, REDD should be more beneficial to the poor household stakeholders and their livelihoods. DOI: 10.4018/978-1-61350-156-6.ch006

Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Low Carbon Economy and Developing Countries

INTRODUCTION Context It is now well evident that climate change is a major global threat. If climate change is not mitigated, there will be a huge damage cost as GDP loss of developing countries higher than developed countries (Stern, 2006). Eliasch (2008) estimates 5-20 percent loss of the global GDP. Further, a large size of the population in developing countries particularly in African and Asian countries will suffer from mal nutrition, food deficit, water scarcity, deaths and diseases in future. The climate change depends on past and present emissions of greenhouse gases (IPCC, 2007 and Royal Haskoning, 2007). Uniformly increasing GHG stocks are outcomes of population growth and human activities such as industrial activities, deforestation etc. Among these drivers, deforestation is major driver of GHG growth with 18-25 percent GHG emission in recent studies (Fry, 2008, Pagiola & Bosquet, 2009, Ramankutty et al. 2007, Stern’s Review, 2007, and Sohngena & Beach, 2006). Fry (2008) argues carbon dioxide dominant (i.e. 70 percent) in GHG emission. This context makes low carbon economy relevancy. Basic idea of low carbon economy is less carbon emission activities. Stern (2006), IPCC (2007) and UNFCCC (2007) explain this idea as climate change mitigation and prospective new service economy. Developed countries (US, EU etc) have implemented carbon emission output compliance policy and carbon intensity input substitution policy in polluted manufacturing industries. Clean Development Mechanism (CDM) as supplementary has been implemented for carbon emission compliance, carbon input substitution and developing carbon market. In addition, there are new economic activities such as development of efficient technology, alternative energy (wind energy, solar energy etc.) and service sector. UK and EU have given top policy preference on wind energy production as clean energy. In transport,

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Norway has tested hydrogen energy public bus. These countries have focused on service economy such as education, it etc. There is a claim a huge big market of efficient technology, alternative energy and service industry. In addition, there is a potentiality of carbon market in which carbon emission reduction activities appear as a big service trading in the world. The present carbon market is more than $ 59 billion including CDM carbon trade. The carbon market will extend after the implementation of REDD in the post 2012. This will change life style and consumption pattern if low carbon economy appears effective. In some developed countries, we can see it solar vehicle transportation of household and solar energy cooking stoves and household energy. Besides it, we can find shifting into energy efficient household electric appliances (Refrigerator, Television, Electric heaters, Rice cookers and Bulb). Thus, the low carbon economy is seen as lower carbon intensity production and consumption behavior and also as the market pattern for sustainable economy and climate change mitigation. Developing countries such as Asia, Africa and South America have entertained that economy as new prospective for economic development. Simultaneously, these countries are curious about its relevancy, situation and prospects. Carbon intensity of consumption and production in these countries are higher than developed countries. In rural areas, still large rural population depends on primitive energy means i.e. agricultural residual and fuel wood. African and Asian countries consume more than 70 percent fuel wood energy. This energy dependency and consumption behavior leads to deforestation and then to carbon emission. Stern (2006) considers deforestation as major driver of carbon emission growth and then climate change. In this context, REDD as climate change mitigation is relevant to developing countries. Some developing countries (Brazil, Bolivia, Indonesia etc) have already implemented it. Nepal is in the readiness. This chapter addresses what will its implication in Nepal.

Low Carbon Economy and Developing Countries

Broad objective of this chapter is to study empirically low carbon potential in Nepal. Its specific objectives are to estimate carbon emission reduction potential, to estimate household expectation on low carbon activities in forest and to suggest policy implication.

LOW CARBON ACTIVITIES IN NEPAL REDD is to increase trees density and coverage particularly for environmental service role of forest. This carbon is a trans-boundary tradable service having huge voluntary and non voluntary markets. This role supplements carbon incentive to the forest dependent population in developing countries. The stakeholders of forest expect carbon incentive and forest products (fuel wood, grass, litter, fodder, timber etc.) for addressing their livelihood issues and socio economic improvement. Thus, forest can provide commodity plus carbon service. Studies claim giant market prospects of REDD carbon relative to the carbon of CDM carbon. Estimation of preliminary survey in developing countries found huge potentials of REDD carbon. Inclusion of this carbon may flood carbon in the global carbon market. Economically, its contribution will be significant in terms of volume, scale and coverage. However, some literatures contradict these arguments by saying issue of opportunity cost. Studies argue a huge opportunity cost of REDD. In addition, there are issues of financial mechanism, organization, function and technical sides such as leakages, reference line and permanence. Despite some extent of uncertainty, developing countries accept REDD for meeting livelihood demand of the forest dependent population. In the context of REDD, Nepal initiated to internalize it in the forest governance of community forest and leasehold by signing Readiness Grant Agreement and committed internationally its implementation. Now, Nepal has become 14th

RPN country. Nationally, institutional and policy mechanism required for REDD preparation have been developed, considering different livelihood issues such as carbon benefit sharing, right of livelihood, land right of indigenous people, women and disadvantaged people, market modality etc. Addressing these issues, REDD+ approach has been discussed. Further, some literatures have focused benefit sharing of the indigenous people, women and disadvantaged people. Other literatures emphasizes on their land right. There is a question why Nepal desire to implement REDD. Nepal claims REDD just like as community forest management and governance to avoid deforestation, if environmental service is included. There may be the inflow of huge carbon incentive from community forest management in accordance with national deforestation reference line. The forest dependent including indigenous people, women and disadvantageous people will be livelihood beneficial from carbon incentive. Nepal wants huge resources, knowledge and technical support to maintain sustainable forest management in long run because of growing forest dependent population and livestock’s population. REDD incentive and technical support can contribute in this regard. In Himalaya regions, where both human and livestock populations are on the rise, the study concluded that payments for conserving the carbon in existing community forest are an important incentive to prevent conversion of forest for agriculture use offsetting the opportunity costs associated with practicing SFM in the face of other optimal (www.REDD-net.com). It is supported by Nepal’s position documents in which Nepal has focused on • • •

•

Enhanced forest protection using participatory approaches Better zoning of protected areas Expansion of participatory forest arrangement (including community forest and collaborative forest management) Additional resources for law enforcement

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(www.REDD-net.com) In addition to carbon incentive, Nepal expects alternative income generating activities to the stakeholders, alternative energy service and social and physical infrastructure development program and livelihood activities (REDD+) for addressing livelihood and poverty issues of the forest dependent population. These expectations are theoretically argued by International Lobby groups including CBOS, INGO and GO. However, these theoretical arguments do not have strong empirical evidence. If we refer empirical studies in developing countries like Brazil, Indonesia, Bolivia etc, we can find some empirical studies using econometric models. Some studies have found positive impact of avoided deforestation activities. For example: Brazil, Indonesia and Bolivia. However, some studies have found higher opportunity cost of long term avoided deforestation. From weak empirical reference of Nepal, the implication of REDD cannot be concretely concluded. Regarding recent studies in Nepalese avoided deforestation; there are two recent papers on avoided deforestation and climate change. They are Adhikari (2009) and Staddon (2009). Both studies are review papers. They are different perspectives and areas although they are related to forest and climate change issues. Adhikari (2009) focuses on forest and climate change issues but Staddon (2009) deals on carbon financing and community forest. Adhikari (2009) argues multiple outcomes of forest commons in any international negotiations. The author emphasizes on the evaluation of the contributions of forest commons not only forest products but also ecological services such as biodiversity conservation, climate change mitigation and poverty alleviation. Staddon (2009) argues carbon offsetting as a solution of three issues: climate change, biodiversity conservation and socio-economic development. The author argues the issues of equity, control and power in community forest under REDD.

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This study differs with above these studies from different respects. The analysis of avoided deforestation is used at the unit level of districts including some districts and types of tree level. A data source of the study is different in terms of area, coverage, size and method. The method of analysis is also different including allometric method and econometric method.

METHODOLOGY Models Carbon Accounting from Biomass There are pre and post REDD initiation: deforestation and forestation. Change of biomass gives carbon stock of the forest between pre and post REDD. Pre REDD gives baseline carbon stock and post REDD get the committed period carbon stock. The difference between two carbon stocks is REDD tradable carbon credit. Carbon stock depends on tree biomass (dry weight of above ground biomass), “Y” which is determined diameter “X” and height of tree “h”. It can be expressed into non-linear equation as follows: Y = a Xb

(1)

Taking log in both sides LnY= ln a + b ln X

(2)

where, “a” and “b” are parameters. This Equation is called allometric equation developed by Satoo (1955) in the given dbh (at 1.3m). This equation applied by Ovington and Madgwick, 1959; Nomoto, 1964; Ogino, Sabhasri and Shidei, 1964 in their studies on several types of forests found reliable and applicable. The expression of Equation (2) is modified by Jenkin(2003) to calculate aboveground biomass

Low Carbon Economy and Developing Countries

(in kg) and Carbon content (in Kg) by using the formula Y= Exp (a + b In (X))

(1)

This study will use Jenkin’s model Equation (1) to calculate Above Ground Biomass (Tree, Litter and Shrub) and Below Ground Biomass and its carbon stock. Based on carbon stock outcome, carbon credit will be measured by time period carbon stock change method and carbon benefits of the carbon producers will be accounted by using discounting method.

Binary Choices of Households about REDD Consider there are heterogeneous socio economic characters (xi) of nth households in terms of income level, awareness level, occupation, age, food sufficiency, literacy and organization. These characters are determinants of nth household’s responses on dichotomous choices. Different preferences and choices of the households influence in policy decision making. Such type of issue can be trapped by using Sequential Model (Greene, 2005; and Maddala & Lahiri, 2009) for determining the probability of REDD for alternative livelihood for the poor in Nepal. Probit Regression Model will be as follows: Probit(Yi) = β Xi + ui

(2)

if Y *>0 where Y = ∫ 10=otherwise Where, β= vector of regression coefficient (0<β<1) xi= vector of predictor variables (e.g. avoided deforestation, livelihood, REDD etc) ui= vector of Random variable(error term) π= probability of an outcome From probit model, we will get probability of better alternative livelihood of REDD which will be dependent variable. The relationship between

dependent variable and independent variables will be captured by using multiple regression models. P (better alternative livelihood of REDD=1) = β0+β1 income+β2 landholding+β3education+β4 caste+β5 household size+β6 occupation+β7 area +β8organization + ε Where, β0= intercept ,β1,β2, β3,β4,β5, β6, β7, β8=regressors, 0<β0,β1,β2, β3,β4,β5, β6, β7, β8<1 ε=error term

Data Sources Carbon data set is based on secondary and carbon inventory method. The Kafle community forest was selected for the carbon inventory on the basis of the relevancy to REDD, history and representative CF and easy accessibility. The carbon inventory method was used in the sample plot in the Kafle community forest for collecting carbon data. The sample plot was made circle by making a central point from that point there was made area of 10 meter radius into four directions east-west-south-north (see Figure 1). In the sample plot, carbon inventory was used to measure dbh of trees. In the carbon inventory, approximately 30 tree species were measured (see in appendix). Average dbh in KCF was 13.56. Household Data is based on household questionnaire survey conducted in Lamatar Village in 2010(April-May-June). Out of 63 households, the 48 sample households are selected randomly. It Figure 1. Sample Plots of KCF

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represents approximately 70 percent of household of KCF. The pre questionnaire test was conducted. The questionnaire which was used in the household survey is divided into three sections: section 1: basic information about household socio-economic, section 2: household’s participation and dependency in KCF and section 3: familiarity about REDD.

Characteristics of Study Area Characteristics of Kafle Community Forest (KCF) Location and Geo-set up: Nepal is the study country lying between China and India has three ecological belts: himal, hill and terai. In these ecological belts, there are divergent forest species all over the country. Lalitpur District is District Unit Study Area in which Kafle Commuity Forest is the case study. Lalitpur district locates in Kathmandu Valley. The district has approximately 15,253 ha (FD, 2006). Out of total forest, there was 65 percent (9,923ha) community forest managed by 162 CFUGs (FD, 2006). One of CF is Kafle Community forest. The Kafle Community Forest, areas of 96 ha managed by 63 households is located in Mathilo Khoriya Dada in the east, Gumati khola in the north, Chisapani Peepal Tree to way to Bhihawar in South and main road to Khatri Bhajho in the west. Altitude of KCF ranges from 1540 meter to 1970 meter. For forest management and utilization, KCF was managed into five blocks such as A, B, C, D, and E with area of 20, 31,27,6 and 10 hectares respectively. The forest is dominated mixed type regenerated trees (DFO, 2002)

Characteristics of KCF Institutional Characters: Collectivism concept came out in the community level for collective action for forest conservation, when Kafle forest

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had over extraction and free riding under open access and public regime in 1980’s. Its consequences were scarcity of livelihood forest products (firewood, leaf litter, grass, water resources etc). This forest dependent community was suffering from livelihood issues. In 1993, the community collectively decided to set up Kafle community forest user group (KCFUG) in accordance with Forest Act 1993. In this common property right regime (CPRR), the community became the owner of the Kafle forest for conservation, management and utilization. The institution functions democratically through General Assembly and Executive Body. In General Assembly, all general members of KCF are included to be members of this Assembly. Major work is to reach collective decision on policy, budget and election of executive body (KCFWP, 2007). Executive body is governing body having 11 members from the General Assembly. It executes the decision of the General Assembly. Its meeting is held per month. Major work is to protect the forest, proper utilization of forest products and other functional activities. Households were homogeneity of upper caste Brahmin in caste wise but were heterogeneity in socio economic level and status, despite upper caste Brahmin. There were majority households having less than 12 months food sufficiency. Kafle Community Forest is used for livelihood objectives (KCF, 2007). Self and Collective Governance: KCFUG has the self governance system. Policy decision and execution process is collectively done within the institution for transparency and effective community participation. Its result is Operating Plan prepared in 2005 and executed for five years. Collective action is ruled into forest management, protection and patrolling from illegal extraction and proper distribution of livelihood forest products. In forest protection, there is prohibition of grazing, poaching of wild animals and plants, illegal cutting, mining and encroachment. Violation of this prohibition will attract fines and punishments. In distribution of NTFP, there is rule of extracted

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about 1000 kg of green fuel wood, 500 kg of dry fuel wood, 500 kg of grass fodder, and 1000 kg of leaf litter and 500 kg of nigalo every year. On special occasions such a marriage, religious ceremony or funeral, any member was allowed to extract 350 kg of fuel wood for the same price. It is only for 96 hectares of KCF. Forest Management: Forest management including cutting, cleaning, thinning, pruning and plating is a part of collective action. The KCF land was categorized into five blocks for these activities in the support of NGO, CBO and District Office of Forest. KCF using modern scientific techniques of forest management had established Demonstration Plot of 0.08625 hectares in 2002 and extended to 1.64 hectares. In the plot, there were planted with 787 seedlings and 46 plot size NTFPs such as Chialune, Jingaine, Hinguwa, Angari, Bakle, Laligurans, Lakuri, Saru, etc. KCF had further extended the size of model plot by planting different medicinal and other NTFPS. In addition, KCF has planned to develop the whole Kafle Community Forest as the Model Community Forest.

Household Characteristics of Stakeholders Household Resource Endowments: There are two major resource endowments: land and livestock presented in Table 1. Each Household holds 0.2 hectare in average irrigated land and 0.17 hectare in average marginal land. Livestock resource endowments are just conventional. It indicates poor resource endowments of households. HH size and Composition: The poor households have generally large family size. However, family size (4.85) is less than national average (5.4) (CBS, 2010). Further, the rich family has less than the poor and medium income group. Outlier is 9 family members size. So, labor endowments may be less than of large family size. Family composition by sex is similar (see Table 2).

Table 1. Household resource endowments Land Holding

Mean

Standard deviation

Min

Max

irrigated land

2.7

2.0

0.1

10.0

marginal land

2.3

1.6

0.1

8.0

Cow/buffalo

1.57

0.5

1

2

Goat/sheep

2.73

1.5

1

6

Livestock

Source: Field Survey, 2010

Table 2. Household composition and demography HH

Mean

HH Size

Standard deviation

Min

Max

4.85

1.42

2

9

Male

2.48

0.88

1

6

Female

2.46

1.009

1

5

Literate

4.45

1.54

1

9

Illiterate

1.04

0.21

1

2

Education

Source: Field Survey, 2010

Household economic condition: In accordance with World Bank’s per day earning poverty reference line, 67.38 percent households are poor, despite higher literacy level. This is also supplemented by food sufficiency measurement. This absolute poverty needs alternative resources for livelihood (see Tables 3 and 4). Household Participation: Household’s participation in forest protection is 85.3 percent, followed by forest management at 84 percent, development activities at 82 percent, resource Table 3. Poverty scenario Poverty

Relative poor

Absolute poor

Mean

5.06

14.17

Standard Error

0.419

1.31

Standard Deviation

1.6

4.18

Population

76

157

%

32.62

67.38

Source: Field Survey, 2010

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Low Carbon Economy and Developing Countries

Table 4. Household socio economic condition HH categories

No of HH

Average

Average Food Sufficiency

Size of HH

12 month

Less than 12 month

4.9

4

8

Economic Poor

12

Medium

25

4.9

8

16

Rich

11

4.58

4

8

Literate

45

24.35

15

29

Illiterate

3

0.5

Education 3

Sex Male Female

45

2.37

12

26

3

2.45

3

6

details in Table 6 and 7). At nominal charges, member can extract additional forest product. Firewood extraction is higher than leaf litter, grass etc. However, there is not required additional time allocation for it. Members claim 70 percent less energy expenditure from firewood. Similarly, availability of water resources is positive externality to the community. It is supplied in all member households at free of cost. KCF earns annually Rs 182,797.9 revenue from sale of timber and NTFPs. Average share KCF income is higher than average share income from service and agriculture sectors (see its details in Table 8). Thus, KCF is supporting livelihood of households.

Source: Field Survey, 2010

utilization at 76.6 percent, decision making 73.0 percent and training at 55.99. These measure values indicate effective participation of households in terms of labor contribution and attendance (see Table 5). Household Livelihood Dependency: In Nepal, community forest is perceived as alternative livelihood local resources for the poor (Ninth Plan, 1997). Each member annually extract in average 16.4 bhari (656 kg) firewood, 4.4 (176 kg) bhari grass and 7.6 bhari (304 kg) leaf litter. However, there are extreme extractions: 100 bhari (4000kg) firewood followed by 40 bhari (1600kg) grass and 50 bhari (2000kg) leaf litter(see its Table 5. Household participation in percentage Participation

Higher

Medium

Lower

None

Decision Making

29.5

43.2

25

2.2

Development Activities

28.8

53.3

17.7

Forest management

27.2

56.8

15.9

Forest Protection

29.2

56.1

14.6

Resource Utilization

16.2

60.46

16.29

Training

15.9

40.09

34.09

Source: Field Survey, 2010

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Table 6. Statistical descriptive summary of NTFP extraction Forest Product

Minimum

Maximum

Mean

Standard Deviation

Firewood

0

100

16.4

18.0

Grass

0

40

4.4

5.6

Leaflitter

0

50

7.6

12.9

Source: Field Survey, 2010

Table 7. Annual income of sample households from different sources (Rs) Income Source

Min

Max

Mean

Sta Dev

Service

0

726000

179958.3

133483.1

Agriculture

-1000

268800

41122.55

46675.5

CF

73000

328500

182797.9

52003.4

Total

72000

1323300

403878.8

232161.9

Source: Field Survey, 2010

Table 8. Statistical character of biomass and carbon service Indicators

Min

Max

6.9

Biomass/ha

45.23

90.46

95.95

5.26

9.09

Cton/ha

52.22

104.44

47.91

2.67

Source: Field Survey, 2010

Mean

Standard Deviation

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ESTIMATION AND DISCUSSION Estimation of REDD Potentials UNFCCC, IPCC and Stern have projected huge REDD potentials in developing countries. CIFOR estimates US$17-33 billion per annum in future. There is still room for query in developing country like in Nepal about its potentials and implications. In Nepal, World Bank and UNDP have been engaging in preparation for REDD. In KCF, ICIMOD has supported for three areas such as sample plotting development and capacity development for carbon inventory and carbon inventory for biomass and carbon data. There were developed eight samples plotting out of KCF. District Ranger Office had given carbon inventory training to three members of KCF in 2005. Since then, there were annual carbon inventory for biomass and carbon data. On the basis of these references, this study focus on assessment of REDD potentials.

Biomass and Carbon Service Biomass of Tree is storage of carbon. Growth and size of Biomass are major determinants of carbon sequestration and stock. Measurement of Biomass is entry point of carbon accounting. There are different methods in which carbon inventory method is one of popular and well practiced. In carbon inventory, dbh measurement is generally used. In KCF biomass measurement, carbon inventory method with dbh measurement was applied in the selected sample plotting of approximately 1 hectare in which 10 meter radius was made for selecting trees. Allometric method was used to estimate biomass stock for estimation of carbon stock. Biomass and carbon stock were found as follows in Table 9.

Income Potentials of KCF It is claimed that REDD entry depends on carbon income potentials. Graph 2 reflects huge carbon

Table 9. Model-1 (Familiarity about REDD) Variable

Mean

HH Size

Std. Dev

Min

Max

5.42

2.07

3

9

12>

0.35

0.52

0

2

9>

0.14

0.35

0

1

Food Sufficiency

6>

0.33

0.47

0

1

3>

0.2

0.41

0

1

Land Holding 10>

0.1

0.3

0

1

5>

0.2

0.41

0

1

1>

0.62

0.48

0

1

0.041

0.2

0

1

Earning per day 1>

0.22

0.42

0

1

2>

0.52

0.5

0

1

>2

0.25

0.43

0

1

Rsex

0.79

0.41

0

1

Rlivestock

1.8

1.39

0

1

>SLC

0.41

0.49

0

1

SLC>

0.22

0.42

0

1

Literacy

Literacy

0.27

0.44

0

1

Per day earning

1.68

1.07

0.1

5.2

54.68

90.32

18

66

Seminar

0.29

0.45

0

1

Training

0.14

0.35

0

1

0.083

0.279

0

1

Age Source of Information

Newpaper

Source: Field Survey, 2010

potentials over years, when Rs 12 per ton (i.e. carbon per ton rate of CDM) is used. Income from forest product is Rs 24549.55 shown by blue trend line meanwhile income from carbon service is Rs. 39, 81,196 shown by red trend line. If carbon potential is included in KCF income, mean income from KCF will increase at Rs. 1013711.33. The income from carbon credit is 41 times more from than income from NTFP.

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Low Carbon Economy and Developing Countries

Estimation and Analysis of Expectation about REDD Binary Discrete Choice Questionnaire about REDD was set up into two levels. They are level 1: familiarity about REDD and level 2: if yes, better alternative of REDD to CF. The questionnaire was surveyed 48 household stakeholders of Kafle Community Forest. In the household survey, there was a major concern on awareness level, opinion and expectation of stakeholders about REDD. These stakeholders’ character, capacity and decision might show future direction of REDD in Nepal at stakeholder level in community forest management.

Table 10. Model-2 (if yes, better alternative of CF) Variable HH Size

Model-1: Familiarity about REDD In Model-1: familiarity about REDD, there were binary choices: Yes and No. These choices reflect effects of household socio economic characters and household awareness level. There were used independent variables: HH size, food sufficiency, land holding, earning per day, Rsex, Rlivestock,

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Std. Dev

Min

Max

5.125

1.39

2

9

12>

0.33

0.48

0

1

9>

0.25

0.44

0

1

6>

0.29

0.46

0

1

3>

0.12

0.33

0

1

10>

0.208

0.41

0

1

5>

0.208

0.41

0

1

1>

0.58

0.503

0

1

1>

0.208

0.41

0

1

2>

0.54

0.51

0

1

>2

0.25

0.44

0

1

Rsex

0.916

0.28

0

1

Rlivestock

1.86

1.57

0

1

Food Sufficiency

Land Holding

Earning per day

Descriptive Statistics of Independent Variables for REDD Discrete choice of households on REDD is assumed to be influenced from heterogeneous socio economic household characters when households response on these choices. These characters including such as literacy, poverty level, food sufficiency level, sex, land holding, family size and income level are assumed independent variables in the selected models. Their statistical characters of Model-1 and 2 are presented in Tables 9 and 10. In summary, HH size within age group is measured in terms of number unit. Food sufficiency of households is measured into months. Landholding of HH is in local unit that is Ropani(0.07 hectare). In earning per day, there is used per person per day in terms of dollar. Earning is considered as exogenous variable. Livestock of HH is in number unit.

Mean

Literacy >SLC

0.5

0.51

0

1

SLC>

0.25

0.44

0.4

5.2

Literacy

0.25

0.44

0.4

5.2

1.8

1.11

0.33

2.5

44.62

15.24

19

73

Per day earning Age Source of Information Seminar

0.58

0.5

0

1

Training

0.25

0.44

0

1

0.125

0.337

0

1

Newpaper

Source: Field Survey, 2010

literacy, per day earning, age and source of information surveyed in the 48 households. Statistical characters of these variables are estimated and presented in Table 9. Model-2: Better Alternative of REDD to CF If the respondents were familiarity with REDD in Model-1, the question about better alternative of REDD to CF would be asked. There were only 25 respondents familiar with REDD. With similar

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independent variables, the 25 respondents were surveyed for the model-2: better alternative of REDD to CF. Statistical characters of these independents are estimated and presented in Table 10.

Estimation and Result of Probit Model In the study, probit model was used for the estimation of parameters. The estimation was extended from first level to two levels. There were two levels: level-1: familiarity about REDD and level-2: better alternative of REDD to CF. Model 1: Familiarity about REDD In Model-1, the probability of familiarity about REDD is estimated by using HHsize, food sufficiency, land holding per households, different earning per day, Rsex, Rlivestock, literacy level, per day earning, age and training as independent variables. Familiarity about REDD is the binary dependent variable having two choices: yes and no. In the model, yes is coded as one and no is coded as zero. Positive coefficient of independent variables implies for increase in the probability of familiarity about REDD. The estimation of probit model for level 1: familiarity about REDD is presented in Table 11. The higher LRχ2 (16) test of better alternative of REDD to CF shows that the model has good explanatory power. The estimated parameters show that hh size, food sufficiency (12>, 9>, 6>, 3>), land holding (5>) and Rlivestock are significant and negative at 95 percent confidence level. It implies that the probability of familiarity about REDD decreases, if the households have large hh size, increasing food sufficiency, greater land holding than 5 ropani and large number of Rlivestock. Similarly, the positive parameters show that age, literacy, per day earning, Rsex and Training are significant and positive at 95 percent confidence level. It implies that older people, increasing literacy, per day earning and male participation will increase the probability of familiarity about REDD.

Table 11. Model-1(Familiarity about REDD) Variable

Probit

constant

Coeff

St. Err

-14.8

5.72

-0.24

0.4

12>

-5.01

2.37

9>

-3.47

1.85

6>

-5.73

1.76

3>

-4.41

HH size Food Sufficiency

Land Holding 10>

–

–

5>

-4.54

–

1>

5.38

1.66

1>

4.14

2.59

Earning per day 2>

1.73

1.5

>2

–

–

Rsex

2.09

1.55

Rlivestock

-0.176

0.358

>SLC

9.27

0.91

SLC>

9.01

–

Literacy

8.61

1

Per day earning

1.35

0.89

0.044

0.028

Training

0.14

1.16

Psedo R2

0.48

LR(x2)(16)

27.5

Literacy

Age

Prob>x2 No of observation

0.0512 48

Source: based on Field Survey, 2010

Model-2: Alternative of REDD to CF The higher LRχ2 (13) test of better alternative of REDD to CF shows that the model has good explanatory power in Table 12. The estimated parameters show that land holding (10>) and Rlivestock are significant determinant with negative sign in 95 percent confidence level. It implies

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Low Carbon Economy and Developing Countries

Table 12. Model-2 (Alternative of REDD to CF) Variable

Probit Coeff

St. Err

-93.15

564737

5.02

6022.2

12>

39.14

3962.4

9>

55.37

constant HH size Food Sufficiency

of better alternative of REDD to CF increases if households have higher literacy, higher age, per day earning, higher and less than 2 dollar earning per day. In Rsex, it implies probability of better alternative of REDD to CF for more male participation in the respondent. Concerning less than 1 Ropani land holding, it implies positive on better alternative of REDD to CF.

6> 3>

CONCLUSION

Land Holding 10>

-12

125906

5> 1>

37.75

Earning per day 1> 2>

6.57

>2

1.57

144569

Rsex

13.53

131869

Rlivestock

-11.8

2513.07

SLC>

10.51

6980.3

Literacy

10.98

7580.8

Literacy >SLC

Per day earning Age Psedo R2 LR(x2)(16) Prob>x2 No of observation

14.06

8875.52

0.13

211.07

1 20.02 0.09 20

Source: based on Field Survey, 2010

that the probability of better alternative of REDD to CF decreases, if households have large size of land holding and of livestock. Similarly, positive value of parameters show that food sufficiency, land holding (1>), different earning per day, Rsex, literacy, per day earning and age are significant determinant with positive sign in 95 percent confidence level. It implies that the probability

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Collective Action and Decision is used as policy instrument of avoided deforestation for livelihood objectives. The poor households are more dependent on the community forest for NTFP. Share of forest products is approximately 45 percent. They contribute more labor endowments in forest management and conservation. Estimation of biomass and carbon per hectare provides REDD potentials, if KCF enters in REDD. In case of KCF, it has earned Rs 24, 549.55 but potential income is Rs. 39, 81,196, if KCF enters in REDD. It shows 41 times more carbon incentive benefit potentials, despite cost of avoided deforestation. Thus, community forest has huge carbon service and carbon incentive potential. The study estimates expectation of households reflected from familiarity about REDD and better alternative of REDD to CF. There are found 52 percent household stakeholders having familiarity about REDD but 48 percent not having familiarity about REDD. Out of 25 household stakeholders, 44 percent household stakeholders who are familiar about REDD expects that REDD is a better alternative livelihood to the poor. Further, 63 percent households expect livelihood from REDD. Thus, the result is mixed between familiarity and non familiarity about REDD. Large household respondents don’t believe that REDD will be a better alternative livelihood to the poor. Almost households expect REDD for livelihood objectives.

Low Carbon Economy and Developing Countries

In conclusion, household stakeholders who have good asset holdings (land and Rlivestock) think that REDD will not be a better livelihood alternative to the poor. However, the household stakeholders who have literacy, different food sufficiency level, land holding (1>), different earning per day, Rsex, per day earning and age think that REDD will be a better alternative. In other words, rich household characteristics by asset holding don’t support that REDD will be a better alternative. However, poor household characteristics by asset holding support that REDD will be a better alternative.

REFERENCE Adhikari, B. (2009). Reduced Emissions from Deforestation and Degradation: Some Issues and Considerations. Journal of Forest and livelihood, 8(1). Forest Department (FD). (2006). Report of CFUG 2006. Kathmandu: Forest Department. Greene, W. H. (2004). Econometric Analysis. 5th ed. India: Peterson Education.

Kafle Community Forest. (2007). Kafle Community Forest Working Plan. Lalitpur, India: Kafle Community Forest User Group. Maddala, G. S., & Lahiri, K. (2009). Introduction to Econometrics (3rd ed.). India: Neekunj Print Process. Satoo, T. (1955). Materials for the study of growth in stands. [in Japanese, English summary]. Tok. Univ. For. Bull., 48, 69–123. Staddon, S(2009). Carbon Financing and Community Forestry: A Review of the Questions, Challenges and the Case of Nepal. Journal of Forest and livelihood, 8(1). Stern, N. (2006). The Economics of Climate Change: the stern review. New York: Cambridge University Press. UNFCCC. (2007) Reducing Emissions from Deforestation in Developing Countries: Approaches to Stimulate Actions. Decision-/CP.13. Available at: http://unfccc.int/files/ meetings/cop_13/ application/pdf/ cp_redd.pdf (Accessed on 1 July 2008) www.REDD-net.com

IPCC. (2001). Guidelines for national greenhouse gas inventories – volume 4: Agriculture, land use and forestry (GL-AFOLU). Retrieved from http://www.ipcc-nggipiges.or.jp/ public/2006gl/ vol4.html

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Chapter 7

Transition to Low-Carbon Hydrogen Economy in America: The Role of Transition Management Jacqueline C.K. Lam The University of Hong Kong, China Peter Hills The University of Hong Kong, China

ABSTRACT This chapter describes the process of transitioning to a low-carbon hydrogen economy in the United States and the role of transition management (TM) in this process. Focusing on the transition process for hydrogen-based energy and transport systems in the United States, especially California, this study outlines the key characteristics of TM that have been employed in managing the transition. Several characteristics of TM have been noted in the United States’ hydrogen transition, including: (a) the complementarity of the long-term vision with incremental targets, (b) the integration of top-down and bottom-up planning, (c) system innovations and gradualism, (d) multi-level approaches and interconnectedness, and (e) reflexivity by learning and experimenting. These characteristics are instrumental in bringing about the development and initial commercialization of HFCVs and related energy infrastructure in the United States.

INTRODUCTION In response to the challenge of climate change many countries such as Denmark have already started shifting towards a low-carbon pathway, with huge efforts directed towards the develDOI: 10.4018/978-1-61350-156-6.ch007

opment or adoption of low-carbon energy and transport technologies (Energi Styrelsen, 2009). Technological Environmental Innovation (TEI) is an essential strategy to bring about the low-carbon transition (Murphy & Gouldson, 2000). TEIs, such as hydrogen energy, and cleaner transport technologies, offer the potential to significantly

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Transition to Low-Carbon Hydrogen Economy in America

reduce greenhouse gas (GHG) emissions, enhance resource use efficiency, and increase productivity and long-term competitiveness. However, due to the high complexity and the low compatibility of these technologies with existing technological, institutional, social and political institutions, the transition to low-carbon energy and transport technologies has proved to be a major challenge to these countries, developed or developing alike (Gouldson & Murphy, 1998). In European countries, such as the Netherlands (Kemp & Loorbach, 2005) and the United Kingdom (Foxon et al., 2009), TM has been taken as an approach in managing socio-technological transitions towards the goal of sustainability. This approach has been applied to harness sustainable low-carbon mobility and energy. Transition management assumes that a systems approach is needed for technological innovation. Hence, in managing transitions to a low-carbon technological system, co-evolutions in socio- and political- systems are needed. Transition management utilizes both top-down and bottom-up approaches, and employs both forecasting and back-casting techniques in identifying short-term, intermediate and long-term sustainability goals, and possible pathways to realizing these goals. Different groups of stakeholders, including political stakeholders, are engaged and deliberate in each small step of socio-technological transitions so that the barriers associated with system incompatibility and complexity are addressed incrementally and plausible pathways to reaching sub-goals are explored before any of the pathways is fixed and selected. The involvement of political stakeholders in parallel with the engagement process ensures that the policy and political plans developed synchronize with the workable plans developed by relevant technological and societal stakeholders (Kemp & Loorbach, 2005). To understand how TM is used to steer lowcarbon energy and transport technologies, the case of the United States’ transition to a low-carbon

hydrogen economy is selected. It is a vivid demonstration of how, by means of TM, political, public and private stakeholders coordinate their efforts and jointly push forward the development and commercialization of hydrogen fuel cell vehicles (HFCVs) and hydrogen fuel in the United States, through stakeholder participation, cooperation in technology development, financing, and technology demonstration in incremental steps. Given the technological immaturity of hydrogen technologies, and the institutional and systemic complexity associated with the development and commercialization of the technology, co-evolutions at the niche, regime and landscape levels; interactions among social, institutional and technological systems; and collaboration among a wide array of public and private stakeholders, have been conducted resulting in the emergence of alternative zero-emission hydrogen energy and FC transport systems in the United States, especially in California. The complex zero-emission energy and transport systems also requires that multi-dimensionality of knowledge, expertise and resources have to be sought from the stakeholders involved. As observed in the United States and California’s transition to a hydrogen economy, stakeholder participation and learning serve as the essential elements to ensure that the wide range of perspectives can be attended to carefully, that consensus can be reached, and that the knowledge and expertise in support of the changes can be drawn from a wide ranging group of stakeholders at the technological, social and institutional levels, including investors, energy companies, car manufacturers, transit operators, consultant companies, public citizens and political leaders (USDOE, 2002a, 2002b; CaFCP, 2008, 2009; ITS-UC Davis, 2009). In the next section, we will describe the model of TM, discussing the co-evolution in various systems and the low-carbon transition, and its key defining features. Next, we will briefly introduce the current status of the hydrogen economy

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transition in the United States. Assessment of the future prospects for the hydrogen transition will also be briefly covered. This is followed by an examination of the typical features of TM that have been displayed in the United States especially in California for attaining the vision of a lowcarbon hydrogen economy. Lastly, a conclusion concerning the role of TM in the United States and its strength in steering a low-carbon hydrogen economy will be presented.-

TRANSITION MANAGEMENT: ROLE, DEFINING FEATURES AND EXPERIENCES Why is Transition Management Necessary for Low-Carbon Energy and Transport Development? The transition to low-carbon energy and transport depends on a new set of technological configurations and involves systemic change at many different levels, including: technological, societal, institutional and political. To bring about this structural change, many challenges have to be tackled, including: high system complexity, risks and uncertainties concerning the long-term impacts of systemic change; technological lock-in; the unstructured, and the highly complex nature of decision-making; and the problem of political discontinuity. In the face of these challenges, the transition to low-carbon energy and transport systems needs to adopt a gradual approach instead of traditional top-down approach of plan-andcontrol. By using an adaptive and incremental approach and carefully exploring viable solutions through transition experiments and research, the complexity and uncertainty associated with systemic innovation can be reduced. Further, the top-down approach is not suitable for dealing with systemic change which involves a high degree of complexity. Simple solutions to complex problems

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formulated and implemented by policy-makers unilaterally will likely result in policy failure. A new governance approach, known as TM, which deals with the complexity of system innovation and yet reduces the negative consequence of topdown management is proposed (Rotmans, 2005). It is a co-evolutionary approach which provides a platform for developing long-term visions and agendas, experimenting and exploring feasible pathways for fulfilling the visions, steers change in socio-technological-political systems to bring about system compatibility, and creates opportunities for stakeholders to engage in learning, debates and consensus-building.

What is the Role of Transition Management? Transition management takes on the role of modulating instead of dictating or planning-and-controlling. Traditionally, policy objectives are achieved through the top-down and plan-and-implement approach. However, this top-down approach may attract resistance from the affected stakeholders especially when the policy objectives are predetermined and unsustainable, or when systems change and structural transformation are involved. More importantly, the traditional approach is not suitable for societal challenges which deal with a high degree of complexity (Rotmans, 2005:36). Transition management breaks away from the top-down plan-and-control model and brings in modulation. TM aims at steering processes of variation and selection and exploring alternative options and solutions, instead of setting a predetermined pathway to any sustainability vision. Kemp and Loorbach (2005:11) define TM as “a form of process management against a set of goals set by society whose problem-solving capabilities are mobilized and translated into a transition programme, which is legitimized through the political process.”

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What are the Defining Characteristics of Transition Management? Long-Term Focus with Incremental Targets A key defining feature of the TM model is its longterm focus which is combined with short-term, incremental targets. TM advocates achieving the long-term goal of sustainability through short-term policies, targeted experiments, incremental system improvements and learning-by-doing (Heiskanen et al., 2009:412). Transition arenas are set up to explore problems and pathways. Policy goals are translated into visions through backcasting transition pathways. The transition paths are further explored through practical experiments with alternative socio-technical configurations (Heiskanen et al., 2009). In the TM model, a range of options is supposed to remain open within the defined direction for a relatively long time (Rotmans, 2005)

Combination of Top-Down and Bottom-Up Processes Transition management utilizes both top-down and bottom-up approaches to derive sustainability visions, and to identify various options and pathways in reaching sustainability goals. The top-down plan-and-implement approach defines common visions and interim objectives, as well as close monitoring, evaluation and revision of objectives. The bottom-up approach explores different options and pathways to sustainability using planning techniques such as backcasting and forecasting, and engaging stakeholders in planning and implementation, especially fore-runners who hold the innovative solutions (Kemp & Loorbach, 2005; Heiskanen et al., 2009). Policies are developed in parallel with the viable option based on transition experiments through stakeholder participation.

System Innovations and Gradualism TM embodies “the ambitions to design policies for long-term sustainability through systems-level innovations”. The transformation of the entire sector is being targeted (Heiskanen et al., 2009: 412). Apart from systems innovation, TM also focuses on systems improvement, though these two are not mutually exclusive (Kemp & Loorbach, 2005). Gradualism is emphasized under TM to reduce social resistance to structural change associated with systems innovation. “The rationale behind the gradual approach is that a transition can be brought about by the gradual transformation of an existing system (systems improvement), instead of planned creation of a new system (systems innovation) (Rotmans et al., 2001:11). Kemp et al. (2007) argue that the long-term system effects of a transition needs to be explored. Hence, given that such effects are highly unpredictable and uncertain, there is a need for transition paths that focus on exploration, instead of implementation. “[T]he crux for dealing uncertainties is that, rather than making definite choices, small-scale experiments are set up and executed from which much can be learned, so that better information is available later on the “un”sustainable aspects of pathways and the related experiments. In this respect better defended choices can be made by better-informed actors, such as decision-makers. Some paths will obtain extra support (from public and private sources) than others” (Kemp et al. 2007: 323).

Multi-Level Approach and Interconnectedness TM is rooted in the philosophy that systems innovation/change involves dynamic interplays between different levels, domains and actors (Heikanen et al., 2009). First, TM assumes that multi-level coordination is needed for systemic innovation or technological transitions: that in order to foster shifts in technological systems,

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interactions and co-ordination at various levels —landscape (macro), regime (meso) and niche (micro) levels -- are needed (Geels, 2002; Rotmans, 2005). This is based on the assumption that for major technological change/transition to occur, modulation of dynamics at different levels, including the identification of new protected spaces for the development and application of promising technologies (niches, such as new initiatives, techniques, cultures and management), transformations in relevant rules and practices that are embedded in institutions and infrastructure (regime, such as user practices, industry structure, policy and knowledge), as well as other background variables (landscape, such as political culture, social values and world paradigms), are needed. Transition dynamics does not start in one place but at different locations and at different scales. (Berkout, Smith & Stirling, 2004: 52-53; Rotmans, 2005: 25, see Figure 1). Second, TM emphasizes a multi-domain approach. Social, technological and institutional systems must be compatible in order that systemic change can occur. It is based on the view that technologies are seen as being formed by, and embedded within, particular economic, social, cultural and institutional structures and systems of beliefs. Hence, technological transition does not only involve technological shifts, but involve dynamic processes of “structuration

of technologies” and their social context (Berkout, Smith & Stirling, 2004: 49-50). Third, TM calls for a multi-actor approach. Various players, including the community, industry, and political stakeholders, should play critical roles in the process. Different visions, ideas, experiments, and transition pathways are explored through stakeholder learning and participation (Kemp & Loorbach, 2005; Rotmans, 2005).

Reflexivity by Learning and Experimenting TM encourages the reflexive process of searching, learning and experimenting to steer system innovation. In the TM model, new perspectives, knowledge and ideas are needed to bring about systemic innovation. The processes involve a range of actors and cover a series of reflexive approaches ranging from collective thinking, communicating, interacting, negotiating, consensusbuilding, to conducting experiments, in order to structure problems, set transition agendas, develop transition pathways, conduct transition experiments, and identify effective pathways to realizing short-term, intermediate and long-term goals (Rotmans, 2005).

Figure 1. Different scale levels of a transition (source: Adapted from Geels (2002) and Rotmans (2005))

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TRANSITION MANAGEMENT OF THE LOW-CARBON HYDROGEN ECONOMY: THE CASE OF THE UNITED STATES The section describes and assesses the United States’ current status of transitioning to the hydrogen economy, followed by an introduction of the characteristics exhibited during the United States’ transition to hydrogen economy, particularly in California.

Current Status of the United States’ Transition to the Hydrogen Economy The concept of the low-carbon hydrogen economy originated in the early 1970s. Transitioning towards the hydrogen economy requires the development, coordination and integration of the hydrogen energy system, including production, storage, distribution and applications. The progress has been slow since then, though increasing momentum has been achieved by stakeholders pushing forward the hydrogen transition in the last decade. In terms of production, the total volume of hydrogen demand reached 11 million tons per year in 2006, equivalent to 48 GWt(e) and accounting for 5% of the natural gas consumed in the United States. Centrally produced hydrogen sold for industrial and chemical uses amounts to 1.5 million tons and the demand for hydrogen worldwide is expected to double by 2010. The bulk of hydrogen produced worldwide is from fossil fuels: 50% from natural gas, 30% from oil, and about 20% from coal. Steam methane reforming (SMR) is the most common and least expensive method of production. This production route does not reduce the use of fossil fuels and carries a negative impact on the environment. The storage volume of hydrogen is 3,000 times larger than the equivalent amount of gasoline. Hydrogen is stored on-board primarily as compressed gas. Other methods of on-board storage are more costly and heavier, and are still in the early stages of research

and development. In terms of distribution, most hydrogen is produced on-site. Hydrogen pipelines exist only in selected geographical areas of the United States, primarily Texas, Louisiana, California and Indiana. Other than on-board or pipeline storage, hydrogen is stored as tube trailers and cylinders and transported by rail, truck or barge. Although liquid hydrogen is easier to transport and handle for long mileages, the cost of distribution of liquid hydrogen is about 15 times higher than for an equivalent amount of liquid hydrocarbon fuel. In terms of applications, hydrogen can be applied in agriculture, mainly in the production of nitrogen fertilizers. The main area of application is transportation. In 2003, there were less than two dozen FCVs on the road in the United States. As of 2006, 23 fueling stations were available in California. In 1998, Chicago became the first city in the United States to use hydrogen-fuelled buses in its transit system. A high proportion of hydrogen-fuelled transit buses are running on the streets of California for demonstration only. Commercialization of hydrogen powered vehicles in the United States was not yet ready as of 2006 (Lattina et al., 2007). Despite these limited achievements, the transition to a hydrogen economy has encountered several major obstacles in various stages of system development and applications. In terms of production, the primary energy sources for current hydrogen production are natural gas and fossil fuel. Hydrogen production based on hydrocarbon fails to avoid emission of GHGs. The environmental benefits of hydrogen cannot be fully realized as a result. Nuclear energy is potentially the best alternative as a primary energy source that does not contribute significantly to global warming, and has been predicted to play a significant role in the energy system. However, in the absence of presumed growth of nuclear power this vision cannot be realized. The economics of electrolytic hydrogen are even more unfavourable than hydrocarbon-based hydrogen. In terms of storage and distribution, the on-board storage and perfor-

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mance of fuel cells under all conditions present technological challenges in the development of hydrogen-fuelled transportation. In terms of its major application in transportation, the costs of FCVs remain an order-of-magnitude more expensive and cannot compete economically with the conventional internal combustion engine. The scarcity of hydrogen cars also means that there is little market demand for infrastructure to support hydrogen fuelled transportation. The market for hydrogen cars is immature and there is little demand for the infrastructure to support hydrogenfuelled transportation (Lattina et al., 2007). A similar assessment of the United States’ hydrogen economy has been made by Dougherty et al. (2009). They conclude that current hydrogen production based on hydrocarbons could have resulted in far greater negative impacts than conventional energy systems. Under the current situation, on environmental terms, hydrogen derived from hydrocarbons will face stiff competition from other energy options such as bio-fuels and electricity generated from renewable energy sources, which are strong contenders for secure, clean and low-GHG energy. Other technology options such as battery technologies for electric vehicles will also outcompete hydrogen as clean transport fuel alternatives. Further, hydrogen will not be an environmental option in the near term because the current reliance on on-site production is based on fossil fuels, though in the long-term, hydrogen supplies that rely on centralized distribution options can yield more GHG benefits. At the system-wide scale conversion to the hydrogen system still requires major R&D advancements in achieving cost-effectiveness and performance targets. Preferences for existing energy and vehicle infrastructure (or technology lock-in) create a considerable amount of inertia for the transition to the low-carbon energy option. This presents a typical chicken-and-egg problem with simultaneously building up a hydrogen fuelling infrastructure while expanding hydrogen demand leads to a considerable “first-mover” problem.

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We will now examine in detail what transition characteristics have been exhibited in the United States’ transition to the low-carbon hydrogen economy with particular reference to the hydrogen energy and transport transitions in the United States, especially the transitions that take place within California.

Transition Characteristics in the United States’ Transition to the Low-Carbon Hydrogen Economy Long-Term Focus with Incremental Targets In 2001, the United States Government outlined a plan to explore alternative energy technologies under the National Energy Policy with the objective of improving the nation’s energy security and reducing reliance on fossil fuels, combating the impacts of climate change and improving economic competitiveness (NEPD, 2001). The possibility of hydrogen as an alternative energy source and the transition to hydrogen economy was subsequently explored. In 2002, a shared national vision to help the United States transit to a hydrogen economy to 2030 and beyond was subsequently established based on the consensus reached during a stakeholder workshop held in 2001. The vision set forth both the long-term goal of establishing a hydrogen economy, and short-, intermediate- and long-term targets to achieve the goal. The vision carried a strong future orientation. It outlined expectations concerning future hydrogen production processes, infrastructure, storage devices, conversion technologies, and end-use energy markets for the hydrogen economy transition (USDOE, 2002a: 18-19). A phased approach was employed for realizing the goals set out within the vision spreading through the short-, intermediate- and long-term time frame in the period from 2000 to 2040 (USDOE, 2002a: iv). The vision outlined potential options for production, delivery, storage, conversion methods/

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technologies, and end-use application alternatives for attaining the hydrogen economy. Images for achieving a hydrogen-based energy and transport system were developed through scenario forecasting (USDOE, 2002a: 21-23). The long-term vision was further supplemented by a roadmap where different pathways to realizing the vision of the hydrogen economy were proposed. The important role of stakeholder collaboration in the transition process was recognized. The National Hydrogen Energy Roadmap was developed from a stakeholder meeting to identify the current status, and challenges of achieving the vision and the transition pathways (USDOE, 2002b: 3). The roadmap outlined major issues/challenges to the emergence of a hydrogen future and the pathways to address the issues and overcome the challenges (USDOE, 2002b). These pathways were proposed by multiple key stakeholders and experts of the field, and thereby offered realistic pathways to achieve the long-term vision (USDOE 2002b: 39). In California, a vision for the rollout of FCVs and hydrogen fuel stations was developed by the California Fuel Cell Partnership (CaFCP) in 2008, supplemented by a concrete action plan with incremental targets laid out by key industrial and business stakeholders for realizing the long-term vision across a 40 year period. The vision laid out the goal and the timeline for commercializing fuel cell vehicles (FCVs) through 2017 (CaFCP, 2008:4, see Figure 2). Possible pathways for achieving the commercialization of FCVs, for establishing hydrogen fuel stations, and relevant actions to be taken by CaFCP from 2008 through 2012 (CaFCP, 2008) were proposed. An action plan was developed by CaFCP stakeholders, including the automakers and energy companies, to provide a blueprint for FCVs market development and commercialization. The plan put forward the details regarding investments and relevant actions that are needed for pushing forward self-sustaining FCV markets. The plan was charted against the background of the Executive Order S-3-05 set by Governor Schwarzenegger, with the goal to

reduce GHG emissions from transportation and to reduce California’s GHG emissions to 80% below 1990 levels by 2050 and to achieve commercial success of FCVs (CaFCP, 2009:2, see Figure 3). Incremental targets were proposed, including: market introduction of commercially viable technology by 2020, acceleration of FCVs manufacturing for wide availability by 2030, and large scale commercialization by 2040. It was estimated that by 2017, automakers will have placed almost 50,000 FCVs in customer hands with 80% of those in Southern California. To introduce FCVs to the market, investments and actions are needed through 2014. A strategy had also been put forward by CaFCP to implement immediate actions for meeting the needs of current FCVs customers and for building fuel stations through 2014. The strategy includes the development of specific “hydrogen communities” for passenger vehicle rollout where customers are expected to be found in four potential areas in California; investment in transit stations in the San Francisco Bay Area; and investment in regulation development in the Sacramento area. Estimated costs and timelines for setting up hydrogen stations in California needed through 2012 have been established to guide the transition to hydrogen FCVs.

Combination of Top-Down with BottomUp Approaches Top-down and bottom-up approaches have been evident during the early stages of transition to hydrogen economy in the United States. First of all, a broad-based top down objective to pursue alternative and renewable energy technologies including hydrogen was laid down by the government using a top-down approach. In May 2001, the first proposal to transit to the hydrogen economy, the National Energy Policy, was produced. A multi-departmental group, the NEGP, which consists of the Secretaries of Energy, Transportation, Commerce, the Interior, and Emergency Management was established and directed by the

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Figure 2. Fuel cell vehicle commercialization overview (source: Adapted from CaFCP (2008))

Figure 3. Path to commercial success to meet 2050 goals (source: Adapted from CaFCP (2009))

President George Bush. The group “develop[ed] a national energy policy designed to help the private sector, and, as necessary and appropriate, State and local governments, promote dependable, affordable, and environmentally sound production and distribution of energy for the future” (NEGP, 2001:1). Subsequently, the NEGP recommended to the President the need to explore alternative energy sources to provide reliable, affordable

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and environmentally sound energy for the United States’ future (NEGP, 2001:7). The Policy recommended increasing the United States’ use of alternative and renewable energy, and to raise the potential of hydrogen as an alternative to traditional fossil-based transportation fuel. The need for public-private partnerships to spearhead commercialization of renewable energy technologies was also identified.

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The government-led top-down approach was complemented by a more bottom-up, or more precisely speaking, a participatory and collaborative approach to decision-making. The vision and roadmap to transit towards the hydrogen economy was developed through stakeholder meetings, in which consensus among stakeholders was achieved. In late 2001, a stakeholder meeting, consisting of 53 public and private stakeholders involving business executives, Federal and State energy policy officials, leaders of universities, environmental organizations, and representatives of the national laboratories, was held to develop a long-term vision of the hydrogen economy for the United States. The subsequent roadmap was a product of another stakeholder workshop which “help[ed] identify the strategic goals, barriers, and key activities required to evaluate costs and benefits of a hydrogen economy and to lay a foundation for the public-private partnerships needed to implement the plan” (USDOE, 2002b: 3). Further, the roadmap was developed in conjunction with the parallel effort by the U.S. Department of Energy and was reported to the congress, which aimed to identify “… the technical and economic barriers to the commercial use of FC in transportation, portable power, and stationary and distributed power applications by 2012” (USDOE 2002b: 3). The consensual/collaborative approach ensures that key systemic barriers and priority challenges encountered can be identified by stakeholders and solutions can be developed to effectively address barriers. In California’s hydrogen transition, a similar approach can be identified. The State governor’s climate change policies and emission targets, and vehicular emission regulations such as the Zeroemission Bus Regulation, and the Zero-emission Vehicle Mandate, and fuel standards that promote higher energy efficiency and low-carbon intensity, were in place to push the development of cleaner fuels and vehicles, including hydrogen fuel and FCVs. A vision for the rollout of FCVs and hydrogen stations and strategic action plan to spearhead

the transition to a FCV and hydrogen future was established by the CaFCP, the government-industry partnership (CaFCP, 2008). Of importance was the co-creation of a vision and roadmap for transition to hydrogen by key representatives from a comprehensive range of public and private institutions, including the California Air Resources Board, California Energy Commission, United States Department of Energy, CaFCP, UC Davis, automobile manufacturers and energy companies, such as Chevron, Daimler AG, General Motors etc. The collaborative efforts between different stakeholders throughout the workshop played a key role in consensus-building, information gathering, and identification of relevant public policy mechanisms for bringing forth near and short-term investment decisions for the transition towards a hydrogen economy (ITS-UC Davis, 2009:4-5)

Systems Innovations and Gradualism The transition to the hydrogen economy in the United States took the whole-system approach. Since the pursuit of the hydrogen economy requires a fundamental shift from fossil-based to a hydrogen-based system, it is not possible for the transformation to occur overnight. Various system components were identified to bring about the transition. Covering production, delivery, storage, conversion to end-use energy applications. To facilitate and accelerate technological change to a hydrogen system, various drivers to spearhead transformations in technological, social, institutional and economic domains had to be identified. They included the establishment of codes and standards to insure hydrogen safety, building government/industry partnerships for technology demonstration and commercialization, coordinating activities by diverse stakeholders, maintaining a strong R&D programme in fundamental science and technology development, and implementing effective public policies (USDOE, 2002b: iv). The vision of the United States’ transition to a hydrogen economy adopted a four-phase

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transition. It covered the first phase of making progress in technologies, policies and markets, transition to marketplace, expansion of markets and infrastructure, to the final phase of realizing the hydrogen vision (USDOE, 2002a: 21-23). The 40-year planning horizon adopted by the vision was intended to bring the new hydrogen and FC technologies, infrastructure and markets to maturity (see Figure 4). A whole system approach has also been adopted by the CaFCP in TM of hydrogen economy since 1999. It included systems validation (19992003), demonstration of FCVs (2004-2007), commercialization of FCVs and hydrogen fuel stations and hydrogen communities development (CaFCP, 2008). The whole system approach in California was clearly reflected in the establishment of CaFCP, which provided the platform for close partnership between different types of stakeholders (including automakers, energy companies, government, academic institutions and the community) to spearhead a systemic change in a wide array of areas, including the existing energy infrastructure and distribution pattern,

vehicle and fuel types, and to harness the integration of social-political and technical systems for transitioning to hydrogen energy and transport systems (CaFCP, 2008; ITS-UC Davis, 2009).

Multi-Level Approach and Interconnectedness A multi-level approach was adopted for the lowcarbon hydrogen economy transition in the United States. At the niche level, the National Vision and Roadmap fixed the hydrogen-based energy system as the key priority for TM. Protected spaces were developed accordingly by fixing hydrogen as the priority for energy development and applications. Priorities were set for new technologies development, technology validation for production, storage, delivery and application of hydrogen energy supply and end-use technologies in large-scale, and on introduction of demonstration programs for hydrogen supply and end-use technologies, education and outreach programs for raising nation-wide awareness and acceptance of hydrogen. Attention was also placed on the development

Figure 4. Overview of transition to the hydrogen economy (source: Adapted from USDOE (2002a))

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of niche domestic and international markets for hydrogen energy and FC transport. At the regime level, new institutions, rules and practices was established to facilitate niche development of hydrogen-based energy and transport systems. For instance, a new industry partnership model, in the form of public-private partnership, was established to spearhead the development of new infrastructure and demonstration program for FCVs. Specifically, the CaFCP was established to create a platform for public-private stakeholder discussion on California’s transition to a hydrogen economy and industry partnership on hydrogen infrastructure development and FCVs demonstration. New knowledge and information regarding the capability, costs, and market maturity of hydrogen energy infrastructure and FC technologies have been gathered through CaFCP stakeholder workshops and demonstration programs, transition action plans, experiments and roadmaps were developed subsequently to guide California’s transition to the hydrogen economy. A scenario forecast of hydrogen infrastructure growth for the LA Basin was developed in parallel with the CaFCP Action Plan which defined the vehicle and station needs through 2014 by UC Davis. The transition experiment provided a longer-term perspective through the next decade on hydrogen station requirements, and enhanced investment cash-flow analysis and quantitative metrics for station network layout, and also provided policy recommendations. The information obtained through transition experiments facilitated fuller understanding of market demands for FCVs and hydrogen stations across different time-span and enabled CaFCP stakeholders to evaluate progress towards the commercialization of hydrogen stations and FCVs (ITS-UC Davis, 2009: 7-8). Meanwhile, knowledge and acceptance of hydrogen gradually developed within Californian community through education and outreach programs initiated by CaFCP. New regulatory and policy proposals also emerged gradually over the last 20 years to spearhead the hydrogen transition.

Funds had been dispensed in support of hydrogen energy research, development and demonstration since 1990 by the Congress; market-based environmental policies were developed over the years to provide industries with financial incentives to invest in low-carbon or carbon-free energy systems that could accelerate hydrogen energy development. Moreover, momentum was gathering at the national level to push forward a comprehensive carbon policy with the aim to push forward a deep cut in carbon reductions of 80% by 2050, and to accelerate new markets for ZEV alternatives with hydrogen serving as one of the viable options. In California, stringent requirements of the Zero Emission Vehicle Mandate under the Clean Air Act had been in place since the late 1990s to drive the sale of zero-emission vehicles and the purchase of hydrogen-using commercial bus fleets by the transit agencies in California starting from 2000. Further, various national-level government policy documents, including the National Energy Policy (May, 2001); the Office of Energy Efficiency and Renewable Energy Strategic Plan (October, 2002); the Department of Energy Strategic Plan (September, 2003), Energy Policy Act of 2005, and the Hydrogen Posture Plan (December, 2006), were developed to spearhead R&D and demonstration of hydrogen and commercialization of hydrogen technologies. At the landscape level, a number of forces are at work to push the United States to pursue the lowcarbon alternative fuel and transport pathway. Four major forces shaped the political-social context for the United States’ transition to the hydrogen economy at the macro-level. First, there was a need for the United States need to strengthen its national security by reducing dependence on fossil fuels and oil imports; second, there was a need to address global climate change and reduce GHG emissions and pollution through investment in alternative and renewable energy and transport technologies; third, there was an increasing trend for global population and economic growth and a need for new clean energy supplies at afford-

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able prices; finally, there was a need to address the increasing deterioration in air quality and the need to reduce emissions from vehicles and power plants (USDOE, 2002a: 11). TM of hydrogen economy has taken on a multi-domain approach. A system view has been adopted with the assumption that technological transitions cannot be disassociated from the wider social, economic and institutional context under which technological transformation takes place. The TM of the hydrogen economy project is characterized by a close interlink between technological/technical change and societal/economic/ political/institutional transformations. In response to the various non-technological barriers being addressed in both the Vision and the Roadmap in association with the hydrogen technology development, proposed solutions cover various domains, including: parallel development of technological configurations (e.g. production processes, delivery, storage technologies, conversion technologies) with economic transformation (e.g. the development of end-use energy and transport markets for hydrogen), development of public policy framework that emphasizes community outreach and promoting public acceptance towards hydrogen technologies (USDOE, 2002a, 2002b). Apart from adopting a multi-level and multidomain approach, a multi-actor approach was also adopted for transitioning to the low-carbon hydrogen economy. Multiple stakeholders ranging from business executives, federal and state energy policy officials, and leaders from universities, environmental organizations, and national laboratories who have the experience and the requisite capabilities and experience to provide the direction and chart the possible hydrogen energy development pathways, were invited to participate in the vision-setting and road-mapping process. For smooth transition to the hydrogen economy, systems management from resource to end-use and interfacing are critical. For example, it may not be best to have refueling stations at one and pressure and onboard storage tanks at another. The

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multi-actor approach, which calls for the working together of industries, universities and the government can strengthen systemic planning, increase cooperation between public and private entities to overcome hurdles and to develop technologies and policies that provide a better interface of various systemic components of a hydrogen economy, and facilitate better coordination and cooperation between different phases of the transition (USDOE, 2002a). A multi-actor approach was also adopted for transitioning to the low-carbon hydrogen economy in the United States. Multiple stakeholders ranging from business executives, federal and state energy policy officials, and leaders from universities, environmental organizations, and national laboratories, who have the experience and the requisite capabilities to provide the direction and chart the possible hydrogen energy development pathways, were invited to participate in the vision-setting and road-mapping process. For smooth transition to the hydrogen economy, systems management from resource to end-use and interfacing are critical. For example, it may not be best to have refueling stations at one and pressure and onboard storage tanks at another. The multi-actor approach which calls for the working together or industries, universities and the government can strengthen systemic planning, increase cooperation between public and private entities to overcome hurdles and to develop technologies and policies that provide a better interface of various systemic components of a hydrogen economy, and facilitate better coordination and cooperation between different phases of the transition (USDOE, 2002a). The multi-actor approach is exemplified in the development of the transition arena. The transition arena or platform was established both nationally and state-wide. A wide range of stakeholders with diverse backgrounds and comprehensive range of expertise, including non-federal organizations, such as energy companies, automobile companies, FC manufacturers, hydrogen equipment manufacturers, state agencies and environmental

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organizations, and representatives from the federal government organizations such as the Department of Energy, the US Navy, the National Aeronautics and Space Administration participated closely in the transition process. These stakeholders played a key role in developing the national and state-wide hydrogen visions and roadmaps (USDOE, 2002a: 1). In California, CaFCP provides a critical transition arena for managing the hydrogen economy transition. Based in Sacramento, CaFCP promotes FC vehicle commercialization as a means of moving towards a sustainable energy future (CaFCP, 2010). Since 1999, the CaFCP has been involved in demonstrating vehicle technology by operating and testing vehicles in real-world situations, and exploring commercialization of hydrogen fuel and vehicle technologies. Various stakeholders who are frontrunners in the field of energy and transport development and operation have been involved in the transition process. Auto manufacturers, energy providers, FC companies, government agencies, and associate partners, who are engaged on a day-to-day basis to move the FCVs closer to the market, were involved and collaborated closely in the planning, development and implementation of FC projects. Energy members worked to build hydrogen stations within an infrastructure that is safe, convenient and fits into the community. Fuel cell technology members provided FCs for passenger vehicles and transit buses. Government members laid the groundwork for demonstration programs by facilitating steps to create a hydrogen fueling infrastructure (CaFCP, 2010). A strong group dynamic was developed and enhanced the competence among individual stakeholders in hydrogen transition, resource sharing, and strengthened the integration of different components of a system for hydrogen transition. For instance, energy members collaborated closely with automotive partners to provide demonstrations of hydrogen stations and to experiment with new station technologies (ITS-UC Davis, 2009), which as a result strengthened the connectivity between hydrogen-based energy and transport

system planning and development, and provided better working relationships between government and other stakeholders. As of June 2009, more than 300 FC passenger vehicles and transit buses, and 26 hydrogen stations, have been developed in California communities because of the collaborative effort of CaFCP (Dunwoody, 2009: 12). Interconnectedness at various levels created the necessary dynamics to steer the United States’ transition to the hydrogen economy. For instance, the niche development of hydrogen technologies is greatly facilitated by standards and regulations (e.g. Zero-emission Vehicle Mandate), and incentive funding and programs that support low-carbon and zero-emission fuels and vehicles (e.g. hydrogen fuel and HFCVs) at both federal and state levels, and the presence of public-private partnership institutional mechanisms (e.g. CaFCP). The regime development has created protected spaces for new hydrogen technology experimentation and demonstration (niche development). Furthermore, the socio-political background and stakeholder views in support of hydrogen energy and HFCVs development and commercialization have gathered momentum for the development of a vision, action plan and roadmap for the transition, and promotes the creation of closer stakeholder collaboration, stronger policies and regulations, and funding support; and further R&D, experimentation and demonstration of hydrogen fuel and vehicle technologies.

Reflexivity by Learning and Experimenting In order to transit to the hydrogen-based energy and transport systems, a new set of technological configurations and a compatible societal system are needed. At the broader level, activities of learning and experimentation were held at the national level to set up visions and agendas, in order to develop roadmaps and transition pathways, and to identify effective pathways to realize shortand long-term goals through R&D and scenario

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analysis. Specifically, stakeholder workshops and meetings were held by both private and public stakeholders to collectively identify issues (drivers and barriers) related to the introduction of a new vehicle infrastructure and distributed generation systems. This collective effort led to the delivery of a common national vision for hydrogen to become a prime energy carrier for the United States. The vision was the first of a series of learning exercises that provided a coordinating foundation to help various stakeholders, including industry, policy makers, and researchers formulate future actions leading to a hydrogen economy (USDOE, 2002a). The development of the hydrogen energy roadmap was a joint effort of 220 technical experts and industry practitioners from public and private organizations who participated in a workshop, with seven leaders from industry and academic with expertise in hydrogen systems helped guide the subsequent roadmap develop process. As a result of the brainstorming exercise, a common set of objectives and activities agreed upon by government, industry and research institutions were established (USDOE, 2002a: 3). The collective thinking process allowed the stakeholders to develop comprehensive strategies to tackle the challenges for developing the hydrogen-based energy and transport systems and to design multifaceted solutions to overcome these challenges. Suggestions of possible pathways for reaching specific targets for system development of a hydrogen energy system were raised during the road-mapping process. Many different analytical exercises were conducted in addition to vision-setting and roadmapping to guide the United States’ transition to the low-carbon hydrogen economy. These involved workshops and analyses, through which information to guide the planning, development and policy formulation of hydrogen transition was assembled. R&D experiments and exercises to plan and evaluate the transition phase were conducted by the national academies through the National Research Council (NRC) (NRC, 2004).

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Four DOE transition to hydrogen and scenario analysis workshops, and one fuel cell seminar for public dissemination of information were conducted in 2006 and 2007 respectively (Gronich, 2006, 2007). A wide range of participants from energy and automotive industries, federal and state governments, national laboratories and academia participated in these planning and evaluation exercises. Scenario analysis was conducted by the government to analyze the implications of alternative market interventions for achieving major policy objectives. One analysis predicted that the expected market penetration of HFCVs could reach 10 million by 2025 with various supporting initiatives from the government by 2012 and 2021 respectively and that HFCV consumers are located in the major metropolitan areas which gave rise to significant regional differences in market opportunities. Another scenario analysis provided understanding of the costs associated with different market penetration scenarios of hydrogen infrastructures. With the scenario analysis of market trends for FCVs, transition strategies were proposed on what priorities and approaches should be adopted in the distribution of hydrogen stations. Infrastructure feasibility surveys were also conducted to examine how many hydrogen gas stations are feasible in key demand areas and the predicted number of gas stations across the United States during 2012-2025. The analysis led to conclusions on locations for stations so that they can be placed strategically to maximize coverage and the minimum penetration rate of hydrogen gas station that would be needed for transiting to the hydrogen economy. The market analysis also led to the generation of new policy proposals. New policy measures to push forward early transition (2010-2025) and measures for market transition (2025+) of FC technology were put forward. A total of $10B to $50B over 14 years would be needed to push forward the FC technology and would require appropriate government policy support. Policy measures were also needed to keep track of technology status, and to synchronize

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infrastructure deployment. Meanwhile, future social policies such as carbon taxes and Corporate Average Fuel Economy (CAFE) standards to enhance the competitive advantage of different types of hydrogen production were recommended. In California, stakeholders relied on learning and experiment to obtain the information and knowledge that were needed to spearhead transition to non-fossil based hydrogen infrastructures and FC transport technologies. A public-private stakeholder discussion forum was held in December 2009 to discuss challenges associated with a transition to a hydrogen economy. The forum aimed to come up with a consensus on technical and cost issues and align perspectives among the stakeholders to enable constructive action and to provide a platform for information gathering, such that industry stakeholders can coordinate vehicles and station placements. Furthermore, it also aimed to identify policy mechanisms that could constructively align stakeholder actions for near term action, through policies that change longterm investment decisions. During the workshops, different factors that can motivate the automotive and energy industries around hydrogen and FCVs were identified. Scenario analysis was conducted subsequently by UC Davis researchers to formulate a ten-year hydrogen growth scenario for the LA Basin, including an analysis of network conve-

nience, station cost analysis, and an infrastructure cash flow analysis. The results helped stakeholders estimate the required investment in hydrogen over a ten-year time span, and how many hydrogen stations, clusters, and the ratio of portable and stationary hydrogen stations are expected with the given amount of investment (ITS-UC Davis, 2009; see Figure 5). The results of scenario and transition analysis helped to determine the current status of FCV and hydrogen station technologies, and the likely timeline of their future development. Furthermore, these workshops also provided an opportunity for stakeholders to reach consensus on defining and agreeing upon progress metrics (e.g. vehicle component costs, FC durability, vehicle range and hydrogen station capacity and costs), and timelines toward commercialization. Such common understanding is critical in building consensus on the challenges and actions that are required to transit to the hydrogen economy. A common consensus was developed that the hydrogen FCV alternative would be an important part of the future. There was clear acknowledgement that existing public policies need to change in order to incentivize long-term energy infrastructure investments.

Figure 5. Transition pathway for building an early H2 infrastructure in southern California (source: Adapted from ITS-UC Davis (2009))

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CONCLUSION The United States faces unprecedented challenges in energy security, economic competitiveness, environmental pollution, and environmental consequence in relation to climate change challenge. These factors have provided a strong impetus to search for alternative low-carbon energy and transport technologies in the near and the long term. TM guides the United States’ transition to low-carbon hydrogen economy at the federal and state levels. Although the transition to hydrogen in the United States is still in the early stages, TM has played a critical role in the transition process. First, it provides long-term vision and incremental targets. It utilizes both top-down and bottom-up approaches in vision-setting, road-mapping, agenda-setting and implementation of the transition programs. It focuses on systems innovation and steers system changes using a gradualist approach at multiple levels and with various stakeholder groups. It relies on learning and experimenting to search for the optimal solutions. By developing a 40-year long term vision, the charting of a transition roadmap with timelines, incremental targets, and possible pathways enables stakeholders to move forward with clear guidelines on when and how various components can be achieved. The reliance on both top-down and bottom-up approaches in planning and implementing the transition helps the government garner support from industry and public stakeholders, and enhances the credibility and legitimacy of the transition. The emphasis on systems innovation and gradualism provides the essential ingredient to drive system innovations and accelerate the transition to hydrogen. Given the complexity of the issues, the compatibility between different stages of development and commercialization of hydrogen technologies, between technology and other domains, and coordination among stakeholders are needed to bring about system changes. As long-term system effects of the transition are highly uncertain, the use of transition experiments,

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demonstration programs, through stakeholder meetings and workshops, to explore the most viable pathways and solutions throughout the course of TM minimizes risks. The United States’ TM of transition to hydrogen is characterized by a multi-level approach that created the necessary protected spaces for new hydrogen technologies development and the necessary group dynamics for carrying out transition experiments. The multilevel approach is reinforced by an institutional network and policies that promote and accelerate social learning and generation of new knowledge and information. Furthermore, coordination among stakeholders and sharing of resources and expertise reduce the barriers to the transition to hydrogen. Finally, TM is characterized by reflexivity gained through learning and experimenting. Through various meetings, workshops, and demonstration programs, new knowledge and information with regard to HFCVs and hydrogen energy infrastructures emerged and different options were evaluated. Transition action plans and roadmaps that have gone through the process of collective reflexivity through stakeholder learning, consensus building, and are carefully guided by transition analysis, debate and discussions will ultimately receive a higher level of acceptance and support by these stakeholders and have a higher chance of success than those which are simply established by the government and which involve non-evidence based approaches. Despite having made some progress in FCVs demonstrations and the roll out of hydrogen stations and FCVs in the United States, full commercialization of FCVs and hydrogen infrastructure in the United States has yet to be achieved. The transition to a hydrogen economy has encountered several major obstacles in various stages of system development and applications. In terms of production, the primary energy sources for current hydrogen production are natural gas and fossil fuel. Hydrogen production based on hydrocarbon fails to avoid emission of GHGs. As a result, the environmental benefits of hydrogen

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cannot be fully realized. Nuclear energy is potentially the best alternative as a primary energy source that does not contribute significantly to global warming, and has been predicted to play a significant role in the energy system. However, in the absence of presumed growth of nuclear power, this vision cannot be realized. The economics of electrolytic hydrogen are even more unfavorable than hydrocarbon-based hydrogen. In terms of storage and distribution, the onboard storage and performance of fuel cells under all conditions present technological challenges in the development of hydrogen-fuelled transportation. In terms of its major application in transportation, the costs of FCVs remain an order-of-magnitude more expensive and cannot compete economically with the conventional internal combustion engine. The scarcity of hydrogen cars also means that there is little market demand for infrastructure to support hydrogen fuelled transportation. The market for hydrogen cars is immature and there is little demand for the infrastructure to support hydrogen-fuelled transportation (Lattina et al., 2007). A similar assessment of the United States’ hydrogen economy has been made by Dougherty et al. (2009). They conclude that current hydrogen production based on hydrocarbons could have resulted in far greater negative impacts than conventional energy systems. Under the current situation, on environmental terms, hydrogen derived from hydrocarbons will face stiff competition from other energy options, such as bio-fuels and electricity generated from renewable energy source which are strong contenders for secure, clean and low-GHG energy. Other technology options, such as battery technologies for electric vehicles, will also outcompete hydrogen as clean transport fuel alternatives. Furthermore, hydrogen will not be an environmental option in the near term because the current reliance on on-site production is based on fossil fuels, although in the long-term hydrogen supplies that rely on cen-

tralized distribution options can yield more GHG benefits. At the system-wide scale, conversion to the hydrogen system still requires major R&D advancements in achieving cost-effectiveness and performance targets. Preferences for existing energy and vehicle infrastructure (or technology lock-in) create a considerable amount of inertia for the transition to the low-carbon energy option. This presents a typical chicken-and-egg problem with simultaneously building up a hydrogen fuelling infrastructure while also expanding hydrogen demand, leading to a considerable “first-mover” problem. The current slow progress in developing various system components implies that policy intervention is needed to mobilize hydrogen energy system development and applications. Dougherty et al. (2009) proposes that a coherent national effort is needed to steer the low-carbon hydrogen transition and to overcome the technology lockin associated with existing energy and transport infrastructure. This will require concerted policy interventions on the grounds of environmental and energy security objectives, and will not emerge from market-driven incentives alone. In other words, TM of low-carbon hydrogen economy in the United States should be facilitated by national policy that directly addresses the issues of energy security and climate change and steers environmental sustainability through GHG reduction. Until this happens, the United States’ transition towards a low-carbon hydrogen economy will remain an uphill task.

ACKNOWLEDGMENT The assistance of Miss Esther C.T. Wong, a research assistant of the Kadoorie Institute, in editing this article and the help of Mr. Samuel Wang, a graduate student of the Kadoorie Institute, in creating the figures and tables, is gratefully acknowledged.

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REFERENCES Berkhout, F., Smith, A., & Stirling, A. (2004). Socio-technological Regimes and Transition Contexts. In Elzen, B., Geels, F. W., & Green, K. (Eds.), System Innovation and the Transition to Sustainability. Theory, Evidence and Policy (pp. 48–75). Cheltenham, UK: Edward Elgar. CaFCP (CaFCP). (2008). Vision for Rollout of Fuel Cell Vehicles and Hydrogen Fuel Stations. Retrieved 4 October 2010 from http://www1.eere. energy.gov/ hydrogenandfuelcells/ education/ pdfs/ 200707_complete_vision_deployment.pdf CaFCP (CaFCP). (2009). Hydrogen Fuel Cell Vehicle and Station Deployment Plan: A Strategy for Meeting the Challenge Ahead. Retrieved October 4 2010, from http://CaFCP.org/ sites/files/ Action%20Plan%20FINAL.pdf CaFCP (CaFCP). (2010). Retrieved December 29, 2009, from http://www.fuelcellpartnership.org/ Davis, I. T. S.-U. C. (2009). Roadmap for Hydrogen and Fuel Cell Vehicles in California: A Transition Strategy through 2017. Retrieved October 4, 2010, from http://h2fcvworkshop.its. ucdavis.edu/ vehicleroadmap2009.pdf Dougherty, W., Kartha, S., Rajan, C., Lazarus, M., Bailie, A., Runkle, B., & Fencl, A. (2009). Greenhouse gas reduction benefits and costs of a large-scale transition to hydrogen in the USA. Energy Policy, 37, 56–67. doi:10.1016/j. enpol.2008.06.039 Dunwoody, C. (2009). Transition to a commercial fuel cell vehicle market in California. Fuel Cells Bulletin, (November): 12–14. doi:10.1016/S14642859(09)70375-X Energi Styrelsen. (2009). The Danish Example – the Way to an Energy Efficient and Energy Friendly Economy. Energy Styrelsen, February 2009. Retrieved December 29, 2009, from http:// www.denmark.dk/ en/menu/ Climate-Energy/ COP15-Copenhagen-2009/cop15.htm 110

Foxon, T. J., Hammond, G. P., & Pearson, P. J. (2009). Transition pathways for a low-carbon energy system in the UK: co-evolution of governance processes and technologies. Paper presented at 8th International Conference of the European Society for Ecological Economics (ESEE), University of Ljubljana, Slovenia. Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: a multilevel perspective and a case-study. Research Policy, 31(8-9), 1257–1274. doi:10.1016/S00487333(02)00062-8 Gouldson, A. P., & Murphy, J. (1998). Regulatory Realities: The implementation and impact of industrial environmental regulation. London: Earthscan. Gronich, S. (2006). 2010-2025 Scenario Analysis. Paper presented at DOE 2010-2025 Scenario Analysis Meeting. Retrieved October 4, 2010, from http://www1.eere.energy.gov/ hydrogenandfuelcells/analysis/ scenario_analysis_mtg.html Gronich, S. (2007). Market Transition Analyses. Paper presented at the International Partnership for the Hydrogen Economy. Retrieved 4 October 2010 from http://www.iea.org/ work/2007/ hydrogen/Gronich.pdf Heiskanen, E. (2009). m Kivisaari, S., Lovio, R., & Mickwitz, P. (2009). Designed to travel? Transition management encounters environmental and innovation policy histories in Finland. Policy Sciences, (42): 409–427. doi:10.1007/s11077009-9094-2 Kemp, R., & Loorbach, D. (2005).Dutch Policies to Manage the Transition to Sustainable Energy. In Jahrbuch Ökologische Ökonomik 4 Innovationen und Nachhaltigkeit (pp. 123-150). Marburg, Germany: Metropolis Verlag.

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Kemp, R., Rotmans, J., & Loorbach, D. (2007). Assessing the Dutch energy transition policy: how does it deal with dilemmas of managing transitions? Journal of Environmental Policy and Planning, 9(3-4), 315–331. doi:10.1080/15239080701622816

United States Department of Energy (USDOE). (2002a). A National Vision Of the United States’ transition to a hydrogen Economy-To 2030 And Beyond. Retrieved October 4, 2010 from http:// www1.eere.energy.gov/ hydrogenandfuelcells/ pdfs/ vision_doc.pdf

Lattina, W. C., & Utgikarb, V. P. (2007). Transition to hydrogen economy in the United States: A2006 status report. International Journal of Hydrogen Energy, 32, 3230–3237. doi:10.1016/j. ijhydene.2007.02.004

United States Department of Energy (USDOE). (2002b). National Hydrogen Energy Roadmap. Retrieved October 4, 2010 from http://www1. eere.energy.gov/ hydrogenandfuelcells/pdfs/ national_h2_roadmap.pdf

Murphy, J., & Gouldson, A. (2000). Environmental policy and industrial innovation: Integrating environment and economy through ecological modernization. Geoforum, 31, 33–44. doi:10.1016/ S0016-7185(99)00042-1

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National Energy Policy Development (NEPD). (2001). National Energy Policy. Retrieved October 5, 2010, from http://www.pppl.gov/common_pics/ national_energy_policy/ national_energy_policy. pdf National Research Council (NRC). (2004). The Hydrogen Economy: Opportunities, Costs, Barriers, and R&D Needs. Washington, D.C.: National Academies Press. Rotmans, J. (2005). Societal Innovation: between Dream and Reality Lies Complexity. Netherland: Erasmus University Rotterdam. Rotmans, J., Kemp, R., & van Asselt, M. (2001). More evolution than revolution: Transition management in public policy. Foresight, 03(01), 15–29. doi:10.1108/14636680110803003

United States Department of Energy (USDOE). (2003). The Department of Energy Strategic Plan - Protecting National, Energy, and Economic Security with Advanced Science and Technology and Ensuring Environmental Cleanup. Retrieved October 5, 2010 from http://www.er.doe.gov/ bes/ archives/ plans/ DOE_Strategic_Plan_2003.pdf United States Department of Energy (USDOE) & United States Department of Transport. (USDOT). (2006). Hydrogen Posture Plan: An Integrated Research, Development and Demonstration Plan. Retrieved October 4, 2010, from http://www.hydrogen.energy.gov/ pdfs/ hydrogen_posture_plan _dec06.pdf

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Chapter 8

Operational Hedging Strategies to Overcome Financial Constraints during Clean Technology Start-Up and Growth S. Sinan Erzurumlu Babson College, USA Fehmi Tanrisever Eindhoven University of Technology, The Netherlands Nitin Joglekar Boston University, USA

ABSTRACT Clean technology startups face multiple sources of uncertainty, and require specialized knowhow and longer periods for revenue growth than their counterparts in other industries. These startups require large investments and have been hit hard during the current credit squeeze. On the other hand, clean technologies create important positive externalities for the economy. Hence, loan guarantees and other incentive schemes are being developed that are conditioned upon operational benchmarks. The authors offer a framework to establish the extent wherein operational hedging can reduce risk and increase the probability of obtaining financing. They examine a variety of evidence, ranging from production outsourcing to creation of joint ventures, to posit that operational hedging may affect both the marginal cost of capital and the marginal return on investment through mitigating the informational problems in the market. However, operational hedging may not be an effective strategy in all settings: the decision for creation of such hedges ought to weigh the benefits of reduced marginal cost of capital and the opportunity cost of reduced future growth potential against a status quo.

DOI: 10.4018/978-1-61350-156-6.ch008

Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Operational Hedging Strategies to Overcome Financial Constraints

INTRODUCTION Clean technology, including the clean energy (wind, solar, water, biomass, biofuels, hydrogen, geothermal, fuel cells), green transportation, green chemistry, information technology and energy efficient appliances, has been a fast growing area in startup financing (Noci & Verganti, 1999; Stern, 2006). Although the market for clean technology has recently witnessed growth and gains, it is still highly unpredictable and susceptible to the vagaries of the business cycle. During the recent financial crisis, a significant amount of capital has been lost in the global capital markets, leading to a milieu wherein a bulk of private equity and venture financing seems to have dried up (Lawsky, 2009). The clean technology startup firms are no exception to these challenges and are not protected from the current credit crisis. The term startup in this paper refers to newly founded firms, small and medium size enterprises, and even project financing situations in established firms, wherein the size of the financing is at a scale that requires access to the capital markets in order to fund the endeavor. The clean technology industry has been hit particularly hard by the current credit squeeze because the complexity of technological and market challenges, such as production scale-up and supply chain integration, in this industry require large capital outlays. An example of such scale up is the US biofuel firm Verenium’s staged growth: Verenium started out with a “laboratory” scale, 10,000 liters per year, as a demonstration of its technology in 2005, and aims to scale up its business model in a stage wise manner. In 2007 it brought on-line its “pilot” scale plant in Jennings, LA, to demonstrate a process capability to produce 50,000 gallons per year of ethanol from bagasse and energy cane. Finally, in early 2009, it brought on-line its “demonstration” scale plant in Jennings, LA to demonstrate a process capability to produce 1.4 million gallons per year (MGY) of ethanol using multiple varieties of feed stocks.

These plants are all research and development tools to optimize the production process prior to building a “commercial” scale plant with 36 MGY of capacity (Joglekar & Graber-Lopez, 2009). The scale of these investments forces many, if not most, clean technology firms to rely on project financing from banks or other institutions to fund their capital outlays (Cheung, 2009). Startup teams are exploring alternative management strategies to overcome this squeeze, while institutions are rushing in with guarantees and other incentives to make credit available (LaMonica, 2008). However, startup firms, endowed with unique characteristics, bear significant operational and financial uncertainties, which make it very hard to assess and verify the prospects of such firms. These uncertainties exacerbate the informational gaps and asymmetries between the owners of startup companies and the resource providers (i.e., creditors) about the firm’s prospects (Shane, 2003). Hence, startups face severe financial limitations and problems in accessing capital markets and have to be creative in their search for funds from various sources at different stages of their development and growth. Verenium has been able to line up a series of grants from the Department of Energy (DoE) to support its development. For instance, in 2007 Verenium is allocated $4.6M from DoE for enzyme development. In 2008, it was awarded a grant from a $40 million program to support the development of small-scale cellulosic ethanol biorefinery plants. In 2008, it was awarded a three-year, $5.4 million grant from the New Zealand Foundation for Research, Science and Technology. Most importantly for the Verenium/BP joint venture (Vercipia Biofuels), it has applied for a loan guarantee for the Highlands County, FL, commercial project from DoE. Obviously, the capital markets for clean technology startup are not perfect. The uncertainties related to early start-up stage and growth significantly influence the availability of financing to clean technology by mitigating or amplifying the informational problems between the entrepreneurs

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and the resource providers. In this regard, within the context of clean technology startup firms, both operating and investment decisions can be employed as hedges to mitigate such uncertainties and secure future financial resources to successfully survive and grow. Although some types of risks like commodity price and exchange rate exposure can be hedged in the financial markets, other various types of uncertainties like adjustments needed by compliance towards government regulations and commercialization (e.g. production scale up and allied R&D outcomes) cannot be fully hedged by the financial markets and may call for operational hedging. The role of operational hedging has been studied within the context of established firms that are not encumbered by financial problems to tackle circumstances like demand drops, disruptions and supply shortages (Huchzermeier & Cohen, 1996; Boyabatli & Toktay, 2004; Weiss & Maher, 2009; Chod et al., 2009). The operations management literature has interpreted operational hedging as investing in real options to reduce mismatch between supply and demand. In the clean technology industry some of the uncertainties can be mitigated by non-real option type operational hedging strategies like outsourcing, geographical diversification and implicit risk sharing. Therefore, similar to Van Mieghem (2003), we define operational hedging in a general context as a firm employing operational, rather than financial, activities to mitigate a firm’s risk exposure and reduce downside risk. For example, many renewable energy startups like Dutch wind energy firm, Emergya, and US solar startup, Evergreen Solar, have been struggling with financing problems since they moved on to production and commercialization phase of their technology. These problems influenced Emergya to establish a joint-venture with a global manufacturing firm and Evergreen Solar to outsource its panel production to China. Both these operational policies have not only helped firms reduce their fixed costs and created hedges against production and demand risks, but they have also served as an

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instrument to secure financing for future growth by making the balance sheets more robust. The central questions explored in this chapter are as follows: What are the drivers of the capital market imperfections for clean technology startups? How do these imperfections lead to short-term and long-term financial constraints? And, how could the operational activities, and allied policy incentives, help these startups in enhancing the probability of finding adequate financing at an economic cost? We develop an initial framework that links short term and long term financial constraints with the relevant risks, and then incorporate hedging into this framework. For each step of our argument we examine the cost and benefits of the hedging decision, while accounting for market imperfections and uncertainties. We use the framework to conjecture that to the extent these imperfections and uncertainties are successfully managed by the startups, there will be more financing options available at an economic cost. However, the use of the operational hedging strategies will shift the marginal cost of capital and the marginal return on investment, and the managerial decision to deploy such hedges is conditioned upon whether the shift of these curves offers an improvement over the status quo. This approach could also aid policy makers by providing a rationale for examining the justification of the economic viability of a project, while assessing the suitability of various claims for financial aid or infrastructural support. Since the optimal set of public policies seeks instruments designed explicitly to increase innovation and diffusion, our argument about the impact of operational hedging could be a step to overcome issues in policy making and speed up the clean technology developments. To summarize: we posit that operational hedging can create conditions to overcome financial constrains during clean technology start-up and growth stages, particularly in situations where startup firms need large investments to overcome commercialization and manufacturing scale up

Operational Hedging Strategies to Overcome Financial Constraints

challenges. However, such hedging is not a panacea for all firms. The efficacy of such strategies must be judged in terms of their economic viability by financial institutions and governmental policies. The remainder of the chapter is organized as follows. In Section 2 we elaborate on the clean technology startup challenges during early startup stage and growth stages respectively. Section 3 develops the initial step of our framework by examining the informational asymmetries and allied financial constraints, and explains the suitability of different types of institutional financing for each stage. Section 4 develops the framework further and illustrates the tradeoffs that the startup will have to examine during the assessment of operational hedging in the presence of financial constraints. Then, we offer a conjecture for the implications of our arguments on the marginal costs and returns, and finally lay out anecdotal evidence on clean technology startups which have used operational hedging and improved their financing options. Section 5 highlights future research opportunities for exploiting operational hedging as a lever to overcome financial constraints during clean technology startup. Finally, Section 6 concludes the chapter with managerial and policy implications.

BACKGROUND We divide the clean technology life cycle effects into two epochs (Sehgal, 2009): early start-up stage and growth. We exclude the maturity phase of the industry lifecycle since the clean technology industry, particularly clean energy, is constantly evolving with disruptive technological developments and market formation (Pernick et al., 2010). We ignore the ramp down or decline in demand phase for reasons of brevity. We define the early start-up stage as founding of a firm through the first 5-7 years, which is fairly a short term, compared to the lifecycle of the technologies which might be in the range of 20-30 years. Moreover,

we do not include large firms (with deep pockets and access to capital) in the discussion of startups. Growth describes a situation when the firm is trying to scale up its product or process technology to meet an anticipated market demand. In the clean technology context, long time scales, large financial outlays and technological uncertainty associated with scale up often lead to failures and hence, this phase of life cycle has been described as a “valley of death” (LaMonica, 2008). In addition, similar to high tech startups (Carpenter & Petersen, 2002), clean technology startups are open to capital market frictions and susceptible to shocks in the capital markets. This is because of multiple reasons. First, clean technology firms heavily invest in innovations with highly skewed and uncertain returns. Second, clean technology investments involve high levels of technical complexity which is hard to communicate with the outside investors, social communities and governments. Hence, informational asymmetries and their financial consequences are more likely to be severe in this market. Accordingly, clean technology firms possess high levels of intangible assets (R&D investments and know-how) and very specialized tangible assets. Such assets usually have little liquidation value and hence, cannot be effectively used as collateral when borrowing (Berger & Udell, 2002). Figure 1 captures an initial step for a framework that links the short term (early start-up stage) and long term (growth) financing issues with the external business environment and salient characteristic of a business segment. The entrepreneurial firm must understand the business environment to fully assess business opportunities as well as risks and uncertainties when overcoming any shortcomings for financing and support over time. The external environment is characterized by regulations, supply and demand uncertainties which impede or accelerate the early stage and growth of a productive technological innovation. The salient characteristic of a business segment, such a bio-fuel processing or solar panel

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production,` are characterized by idiosyncratic features which facilitate the firm’s involvement and commitment, external factors stemming from social, institutional and market actors, and characteristics of the environmental technologies (Gonzalez, 2009). Following, we elaborate on the uncertainties and risks regarding the early start-up and growth of the entrepreneurial firm. At the early start-up stage, the firm is mostly concerned with company formation, technology development, acquisition of initial set of customers, cash flow and survival. Startups should make the least possible investment to first prove its capability (technological feasibility and economic viability) before it commits to making further investment into the growth phase. Such startups typically deal with new technology development risk affected by the capital and R&D intensity of the industry (Norberg-Bohm, 2000); and these startups must also account for the impact of the technological improvements on the existing technologies (Rosenberg, 1972). On the demand side, the early stage startups are concerned with delivering credible information to the consumers about the benefits of this new technology, its adoption costs and

positive network externalities (Hall, 2003). Further, although the regulatory framework such as price setting, financial practices, labor laws, marketing and distribution laws, and accounting laws play a critical role in the decision (Miles & Snow, 1978), the early start-up stage is more concerned with how the creation of regulative institutions lowers the sector risks and contributes to the spread of knowledge about the sector (Sine et al., 2005). Upon entering the growth stage, the firm focuses on building a large scale (often termed as utility scale, if services customers though existing utility firms) commercialization effort by anticipating a high diffusion rate for its technology. Therefore, it needs to recognize a broad array of stakeholders, including investors, customers, government, social communities, etc. and examine the market for growth. First, the large scale commercialization requires the development of operational infrastructure and complementary technologies (Struben & Sterman, 2008). Second, the firm is more concerned with long term market uncertainties caused by the size and structure of the market, industry concentration and social structures in the long run (Sine et al., 2005). Finally, while simi-

Figure 1. Antecedents of operations decisions subject to financing constrains

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lar regulatory drivers of the early start-up stage will also affect the growth stage, the timing and stability of the regulations become more crucial because the firm has a higher investment scale and longer planning horizon in the growth stage. Tariff adjustments and regulatory compliance are examples of regulatory risks that can jeopardize the economical viability of the clean technology projects (Bond & Carter, 1995). In addition, short and long term issues are faced by both early stage startups and growth stage firms that are trying to scale up their technology. However, short term financing issues tend to dominate during the early start-up phase and long term project financing plays a much larger role during the growth phase of clean technology firms. Next, we discuss some of the early start-up and growth related financing issues that have come up in the entrepreneurship, finance and operations management literature.

CLEAN TECHNOLOGY FINANCING Startups are endowed with unique characteristics regarding their asset structure, organization type, growth orientation (Gifford, 2005), and their operational decisions are often restricted by debt and other financial considerations (Bhide, 2000; Berger & Udell, 2005). Conventional finance literature (Modigliani & Miller, 1958) suggests that financial and operational decisions are separable when there are no capital market frictions (e.g., taxes, transaction costs and informational asymmetries). The existence of informational asymmetries, which is natural to startup businesses, excludes the possibility of such separation in the startup context. Thus, operational and financial decisions of a startup firm, as well as the availability of financing options, are closely interrelated. Further, financial constraints due to these capital market imperfections hold back innovation and growth (Rajan a&nd Zingales, 1998; Hyytinen & Toivanen, 2005). Under full information, all posi-

tive net present value (NPV) projects are funded regardless of whether the firm has sufficient resources of its own, so lack of internal funds would not be a barrier to invest in good projects (Meza & Webb, 2000). However, informational asymmetries create a wedge between the costs of internal and external finance for the startup firms. As this wedge gets larger, the startup firms are more likely to underinvest and forgo positive NPV innovation and growth opportunities (Hubbard, 1998). The premium for external finance is driven by informational asymmetries between the firms and possible resource providers, since such informational problems can lead to adverse selection and moral hazard problems (Stiglitz & Weiss, 1981). Many clean technology projects proposed by well established firms, when faced with financial and operational risks, are delayed or derailed because it has been an arduous undertaking for their promoters to explain how they intend to overcome the challenging market conditions. For example, the discussion around carbon pricing and global carbon constraints has an opportunity to provide a cleaner energy mix, but the debates and uncertainty surrounding the pricing of carbon has also created uncertainty and challenges for the nascent clean technology development projects. Besides the uncertainties due to the nature of the startup, the uncertainty about how carbon credits would be calculated and awarded, and longer-term concerns about the availability of those sources have fueled the extent of informational problems that needs to be resolved for funding (Pernick et al., 2010). In the clean technology context, these informational problems and allied impact of financial constraints are more likely to be severe for several reasons. First, clean technology projects involve diverse uncertainties and risks regarding market, R&D, operations and regulations. Second, clean technology firms are usually characterized with high intangible firm specific assets with little collateral value and have to incur large investments and costs. Finally, to become commercially viable

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in the long run clean technology, firms have to overcome uncertainties and financial constraints, and obtain a sufficient rate of technology diffusion. Such uncertainties and characteristics create financial constraints and limit the startup’s access to capital markets by exacerbating potential informational problems between the clean technology startup and the creditors. In the remainder of this section we examine these constraints individually for early stage and growth financing.

Financing the Early Start-Up Stage In the early start-up stage the development of clean technologies requires large investments with variability of input costs, a high degree of development uncertainty and limited near-term profit. Moreover, generating sufficient cash flows to keep up with the current liabilities (including accounts payables, short-term debt, bank overdrafts, and other short-term cash outflows) is a key concern during this phase. Failing to secure sufficient cash flows in the short-term (i.e., a default on financial obligations) may lead to severe financial distress costs (FDC), bankruptcy and even an inefficient liquidation of the firm due to severe informational problems. Therefore, the uncertainties that increase informational problems and the cost of capital determine the financial constraints, which influence the price of a technology and associated costs of the innovation and commercialization. Figure 2 illustrates the effect of financial constraints during the early stage of a startup (Hyytinen & Toivanen, 2005). The marginal cost of capital (MCC) reflects the opportunity cost required by each additional unit of new investment. In perfect capital markets, MCC is constant since there will be no wedge between the cost of internal and external capital. With imperfect markets, once the internal funds are depleted, the MCC curve quickly becomes upward sloping with the level of investment and risk. The marginal rate of return (MRR) is downward sloping as it ranks the investment opportunities of the firm in decreasing

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order of expected return. The vertical difference between MRR and MCC curves indicates the net marginal return on investment. The total return of an investment, say I, is given by integrating the net marginal return from zero to I. The startup will continue to invest as long as the net marginal return is positive, i.e., until it reaches to point A. In Figure 2, point A denotes the optimal investment level during the early start-up stage. Compared to the Modigliani and Miller solution, denoted by point C, the firm forgoes some positive NPV growth opportunities due to informational problems that create the imperfect MCC. As discussed in the previous section, informational problems and allied financial constraints are particularly salient for the clean technology startups. The availability and cost of financing during the early start-up phase are largely driven by the short-term risks and uncertainties perceived by the creditors in the market. Further, future growth potential which accounts for a significant portion of the value of early stage high-tech startups may quickly erode in case of a financial distress (Cornell & Shapiro, 1988). Thus, the management of short-term cash is vital during the early phase of the clean technology firms to avoid costly financial distress and bankruptcy. For example, if a bank financed firm fails to deliver a short-term debt obligation, this is usually perceived as a negative signal by the bank and the firm may be forced into bankruptcy. Since the clean technology startups usually have little tangible assets that can be used as collateral and their assets are highly specialized, managing internal cash is vital to deliver the debt obligations and secure future debt financing from the bank. Further, given the high levels of informational problems, Venture Capital (VC) intermediation can be quite valuable in the clean technology businesses. Indeed, VCs are becoming active with financing the clean technology market and more willing to invest in novel developments (Stack, 2007). For instance, during the last quarter of 2009, 23% percent of all VC investments

Operational Hedging Strategies to Overcome Financial Constraints

Figure 2. Impact of financial constraints on the early start-up stage

went to clean energy businesses, surpassing biotechnology and software industries (Deloitte & Cleantech Group LLC, 2009). To overcome the informational problems during the startup financing process, VCs usually adopt a staged financing scheme when providing funds to entrepreneurs. They intensively monitor the performance of the investment during each financing stage before they release new capital. Further, VCs face a higher cost of capital because of the liquidity constraints imposed by their own investors. Failing to meet the performance targets of the VCs may also lead to the liquidation of the project (depending on the on-going concern value perceived by the VC). Or, even if the VC decides to infuse new capital, the entrepreneur’s stake at the company can be significantly diluted. In general, the financing benchmark of the VC serves as a soft debt limit in the short-term.

Financing the Growth During the growth phase, startups are more concerned with scale up of operations and diffusion of technology into the market by establishing manufacturing capabilities and setting up a distribution network. The availability and cost of long-term growth financing are determined by

the long-term economic viability of the firm and the perceived long-term risks by the creditors in the market. On one hand, the firm is less prone to short-term financial shocks in this phase since some portion of informational problems are resolved after the early start-up phase. For instance, at the end of the early stage, the firm can make an IPO or it might have accumulated significant amount of tangible assets which can be used as collateral. Consequently, the MCC curve (see Figure 3) during the growth phase is less steep compared to the early start-up phase. On the other hand, major clean technology projects have been historically avoided by investors due to high risks and uncertainties, scale of upfront investments, length of the project and time of future returns (Nemet & Kammen, 2007). Availability of internal funds or VC funding, are usually well below the range of required funding to finance the large scale commercialization and growth of the firm. Therefore, the startup firms seek different group of funding resources like private equity, hedge funds, government funds, public financing and other tools like renewable energy certificates. According to New Energy Finance, total investments in clean energy startups have reached $155.4 billion in 2008 across a wide range of investment categories including private

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Figure 3. Impact of financial constraints during the early start-up and growth phases of clean technology development

equity investors, public market activity (IPO, etc.), asset financing, public funding and government funding. Informational problems and allied long-term financing depend on specific characteristics of clean technology business environment. The degree of informational problems is likely to be less severe in markets with less innovation and diffusion uncertainties. Accordingly, banks are more willing to provide financing for the growth of such clean technology businesses due to relatively low risks and high recovery value of the assets. For instance, wind energy firms employ a relatively mature production technology and spend little on product and process R&D (Personal Interview with Emergya, 2010). Indeed, wind turbines also have high collateral value due to low asset specialization and high market liquidity. Consequently, it is easier for such businesses to finance their operations with bank loans. Dutch startup firms Emergya and Scholt Energy heavily rely on long term bank loans to finance their investments in wind power (Personal Interview with Scholt Energy, 2010). On the other hand, Verenium, a biofuel startup mentioned in the introductory section, is develop-

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ing a special technology for biofuel production, and characterized by high intangible assets, high R&D intensity and highly special tangible assets with low liquidation value. Also, R&D competition in the biofuel market is stiff, which amplifies the uncertainties regarding the future prospects of the businesses. Consequently, it is hard for Verenium to obtain bank financing. This lack of liquidity might explain Verenium’s willingness to set up a series of pilot and demonstration plants, backed by DoE support, and to seek loan guarantees. In general, the informational problems between the clean technology startups and the resource providers are exacerbated by high levels of innovation and diffusion uncertainties as well as lack of collaterals due to low asset tangibility and high asset specificity. Figure 4 illustrates the impact of the uncertainties and perceived risks on the long-term availability of financing and growth. Basically, as the uncertainties associated with the future prospect of the firm increases, the MCC curves get steeper and the growth potential is limited. As the scale of the clean technology project grows, governmental support could play a key role. However, the role should be less to support R&D than to provide more regulatory guidance

Operational Hedging Strategies to Overcome Financial Constraints

Figure 4. Effect of uncertainty on the MCC curve

and improve the legitimacy for the technology, with which the development and deployment of new technology can more effectively nourish (Walker, 1986). Market development through active involvement of governments as a customer, information provider, and policy maker is required to promote clean technology companies during the growth stage. For example, development of specialized energy efficiency financing windows, development of skills for energy efficiency project appraisal and design of specialized financial products are counted as measures to accelerate the diffusion of energy efficiency (Painuly et al., 2003). Solar Cells Hellas, a Greek startup, is a good example of the extent and result of government regulations. The company has benefited from local feed-in tariffs placed timely by Greek government and grown to a commercial scale. It is now building production capacity through acquisitions and new funding to become a major player in the European market (Makower et al., 2009). Thus, government financing could remove financial barriers for renewable energy and bring a number of social, economic, political and environmental benefits (Huacuz, 2005) and lead

renewable energy technologies to utility-scale adoption (Schroeder, 2009).

OPERATIONAL HEDGING The investment and operating decisions affect the startup’s early stage and growth risks perceived by the creditors and other resource providers, and in the presence of capital market imperfections, they shape the availability of future financial resources. In other words, operations and financing create a systematic cycle because operations induce financing which further affects future growth opportunities and strategic options. We begin by developing a framework for the assessment of operational hedging in the presence of financial constraints. As an example to this interaction between operations and financing, consider the deployment of gasifier-based energy system in India. This system has been perceived as high risk by financial institutions due to high technological uncertainties and low financial credits of the users, and this has slowed down the investment and business growth. Therefore, the scale-up approach has been to prove the economic feasibility of specific

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applications in the short term and determine the large scale deployment by earning the confidence of financial institutions with the outcome of these applications (Ghosh et al., 2006). Hence, the operations policy could serve as a hedge for startups facing uncertainty, risk and financial constraints. The operational hedges in our context take two forms: (1) short-term operational hedges aimed to secure early stage financing and (2) long-term operational hedges targeting funds to achieve a sustainable growth. Figure 5 provides a schematic representation of this framework with loops for the early start-up and growth stages. Since the startup firm must demonstrate its short and long term capital risks and the funding institution must monitor the firm and exercise some control over its decisions, the pioneering efforts to create the clean technology industry may find it very beneficial to examine the role of operational hedging on managing the risks of clean technology financing.

We offer a conjecture that explains the impact of operational hedging on the investment and financing decisions for clean technology startups. Although operational hedging may reduce informational problems and consequently improve the borrowing capacity of the firm, it, however, excludes or distorts some high-risk high-return projects in the investment set. Figure 6 illustrates this conjecture during the early start-up phase. This is displayed in Figure 6 with the right shift of the MCC curve and the (possible) left shift of the MRR curve. The overall impact of these two changes is denoted by the shift of the optimal investment point A to Ah. Managerially, if the area between MCC and MRR (to the left of optimal investment point) after operational hedging is larger than the area before hedging, the net benefit of the hedging is positive. In other words, the startup’s hedging decision generates a positive surplus. The net effect of hedging may be negative as well, due to

Figure 5. Operational hedging can impact investment and operating decisions

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Figure 6. Conjectures on the impact of operational hedging

the shift of the MRR curve. In particular, the firstbest policy (point C) is never achievable, because operational hedging distorts the set of possible investment opportunities to induce financing for these projects. The impact is analogous for the growth phase, and the details have been excluded from the discussion for brevity. In the remainder of this section we discuss these findings with explicit operational hedging strategies, and lay out anecdotal evidence on clean technology startups which have successfully used operational hedging and improved their financing. In the early stages of the startup, underinvestment could serve as an operational hedge helping the firm secure valuable working capital, which is vital for the survival of the company. This type of short-term operational hedging is readily observed within the context of debt-financed startup firms, where the availability of future bank loans strictly depends on the firm’s ability to deliver its current debt services. This creates a bias to focus more on exploiting short-term business opportunities and adopting more conservative operating plans to secure the delivery of the debt service and avoid an immature liquidation of the project. Indeed, when external financing is more expensive than internally generated funds, firms

may underinvest giving up some positive NPV investment opportunities (Myers, 1977; Froot et al., 1993; Tanrisever & Gutierrez, 2010). Further, startups may also tailor their operating decisions with their investment plans creating short-term hedges in the form of under-production or overproduction to secure the delivery of the short-term debt (Tanrisever et al., 2011). During the early stages of the clean technology businesses demand fluctuations coupled with high upfront fixed investment costs, may deprive the short-term cash reserves of the firm and lead to severe financial distress or even bankruptcy. In the short-term, outsourcing may help the firms by creating an operational hedge. For example, Evergreen Solar is a clean tech startup designing new and more efficient solar panels for industrial applications. At the moment, the company designs and develops the processes, but outsources the production of the panels (Wang, 2009). Regarding the unit cost, the firm pays a premium to the manufacturer; however, he does not hold the entire risk of the fixed cost of investment. This way, if the market for the novel product develops late, the firm will not face the entire fixed cost of operations and hence, will not run out of cash in the short-term. In this regard, outsourcing serves as

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an operational hedge for Evergreen Solar improving the availability of internal cash in case of an economic down turn. Since it reduces the risk by avoiding investment in highly specialized assets with low collateral value, the firm may also have more excess to bank financing in the short-term. Similar early stage hedging practices are also observed within the context of stage financing of startups by VCs. Although stage financing is an effective method of reducing informational asymmetries, it cannot completely eliminate informational problems since it is not possible for the entrepreneurial firm to reveal all its private information about the business when looking for external financing (Casson, 2003). Consequently, VC may only impose control over the startup operations through imperfect monitoring and setting heuristic performance benchmarks. When VC retains the option to abandon the project, entrepreneurs may adopt myopic operating plans that are more aligned with meeting the VCs re-financing benchmark. While this would help to secure future financing it may hurt the firm’s future prospects. This is similar to “window dressing” within the context of fund management (Lakonishok, 1991; Morey and O’Neal, 2006) and it is also a consequence of informational asymmetries between the two parties (Cornelli & Yosha, 2003). In the realm of growth the startup has to demonstrate long term operational capability as a proof of its economical viability to secure long-term financing. The entrepreneur’s desire to secure future financing could lead to aggressive and flexible investment in its R&D operations. Since no dominant clean technologies are yet present in some sectors, many technologies still require further basic and applied R&D, which depends on adequate and accessible funding. Startup companies which are doing relatively unguided research with flexible R&D portfolio could improve their risk to reward ratio and enhance the likelihood of finding funding (Foxon et al., 2005). Startup firms can create a long-term hedge to finance the business by designing its operational

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infrastructure, which constitutes material and energy resources, production capacity, physical networks of procurement and distribution, and servicing. In the growth stage the operational infrastructure that emphasizes the capability of operational flexibility can be preferred by the startup since it mitigates the long-term business risks. For instance, ITC Holdings Ltd. built a network of transmission lines to establish flexible distribution of the clean energy. This bold move would bring renewable power to scale for the US market and make ITC a likely candidate for US federal stimulus dollars (Makower et al., 2009). Decentralized energy plans that allow certain production flexibility meet energy needs and develop alternate energy sources at least-cost to the economy and environment (Hiremath et al., 2007). Public financing options like public ventures and government could also create a bias for these operational decisions since the government agencies are inclined to finance projects that are more likely to create certain public benefits in the long term (Miller & Hope, 2000). A distributed production network that underlines geographical diversification is more likely to receive long-term public financing relative to a single scaled up manufacturing facility due to increased employment opportunities (Lerner, 1999) and creating growth options (Kogut & Kulatilaka, 1994). Coordination across the supply chain could diminish risk for startup firms. For example, integrated efforts in the production of feedstocks for the biofuel industry remove the supply uncertainty (Caesar et al., 2007). Firms could also form strategic alliances for resources and competences and reduce the development risks. This coordination between a startup and an established firm improves efficiency and future performance through equity alliances, corporate venture capital and the organization of research activities. Startups could also cooperate with universities, industry and research organizations so long as the type of alliance is suited to certain objectives and well defined by rules (Onida & Malerba, 2008).

Operational Hedging Strategies to Overcome Financial Constraints

Joint-venturing, another common form of collaboration employed by the startups, may also serve as a long-term operational hedge. Forming a joint-venture with an established firm provides the startup with the opportunity to secure funds in the long-term while sharing the benefits of its own intellectual property with the other company. For instance, Emergya Inc., a Dutch wind energy startup company, employs a unique wind mill manufacturing technology which significantly reduces the maintenance cost of wind mills. In addition to technological complexities, the manufacturing of wind mills also requires very high initial cash outlays as well as operating costs (e.g., an average wind mill costs around half million Euros.) Emergya established a joint venture with a Chinese firm to setup a manufacturing plant in China. This operating policy enables the firm to finance a significant portion of the initial capital outlay and the long-run operating expenses through the joint venture at a more economic level. On the down side, the firm runs the risk of quality and reliability problems in the manufacturing process.

To illustrate different operational activities as hedges, Table 1 presents a summary of examples of strategies and startups that have faced financial challenges and proactively managed technological, market and regulatory uncertainties through operations. It shows the type of operational hedging, the startup’s life cycle phase, the effect of the hedge on the financing, and whether the technological innovation and adoption is product or process related.

RESEARCH OPPORTUNITIES Corporate risk management programs have long been concerned with managing risk exposure of firms to increase firm value. In this context, operational hedging has been used as a mechanism to manage risks stemming from operations (Van Mieghem, 2003). Boyabatli and Toktay (2004) provide a review of the research on operational hedging in a variety of fields, operations management, finance, strategy and international business. More recently, Chod et al. (2009) have studied

Table 1. Evidence on operational hedges Company

Operational Hedge Type

Early Start-up Stage/Growth

Effect on the Financing Options

Product/ Process

Accuwater

Underproduction

Early Start-up

Increased availability of internal funds in case of an economic down turn.

Product

XYZ Solar

Window dressing

Early Start-up

Reduced financial distress and increased chances for second round VC funding.

Product

Evergreen Solar

Outsourcing

Early Start-up

Increased availability of internal funds in case of an economic down turn, and increased excess to debt financing.

Process

Scholt Energy

Insourcing

Growth

Reduced need for a margin account to hold forward contracts and increased availability of internal cash to invest in growth.

Product

ITC Holdings

Distribution flexibility

Growth

Increased chances of getting long-term government funding.

Process

Verenium

Decentralized production

Growth

Increased chances of getting long-term government funding.

Process

SolarReserve

Strategic alliance

Growth

Increased private financing from various fund investment groups.

Product/ Process

Emergya

Joint venturing

Growth

Reduced marginal cost of production. Secured long-term financing by the joint venture.

Process

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the relationship between financial hedging and operational hedging in the corporate settings. They consider a situation in which operational flexibility and financial hedging are used simultaneously to manage firm’s exposure to uncertain demand and show that operational hedging can increase profit variability. Weiss and Maher (2009) investigate operational hedging against severe disruptions to normal operations. Hence, operational hedging addresses critical managerial issues and offers methods to evaluate the extent that operational activities serve as a hedge against various risks and uncertainties. In spite of its relevance and growing interest, the study of operational hedging, particularly for startup firms, has not yet received enough attention in the operations, finance and entrepreneurship literatures. The study of startups differs from corporate settings mentioned above because the startups do not have the deep pocket necessary to finance development. Especially, with the growing need to finance large scale clean technology investments, operational hedging could provide valuable methods and metrics to create unique advantages for emerging clean technology businesses and sustainable infrastructures. As a step in this direction, this chapter identifies a framework for assessing the hedging strategies that might help with the financing problem for clean technology startups. In particular, our conjecture on the impact of operational hedging suggests that such hedging alters the set of possible investment and operating plans, reduces capital market frictions, and thereby enhances the probability of securing financing. However, the benefits of operational hedging stemming from such increased financing options must be balanced against the costs of operational hedging due to its endogenous effects in terms of distorting the investment and operating plans. This proposed conjecture is a first step and the consequences of operational hedging require further study. Further, a combination of qualitative and quantitative methods should be

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applied to examine the economic feasibility of various operational hedging strategies. Another logical follow up lies in the realm of empirical studies, to test allied operational decisions within the startup framework. An additional fruitful area of inquiry is to establish a taxonomy of different operational activities that can be used as a hedge, for financial and non-financial reasons, and their impact on the value creation process during the startup of clean technology firms.

CONCLUSION The recent economic crisis proves that the clean technology industry is particularly susceptible to the market volatility. However, the climate-change debate and economic recovery strategies in many countries provide clean technology startups with the opportunity to gather momentum. Thus, the current business environment is fertile for startups that could find creative, quick, affordable and scalable clean technology solutions.

Managerial Implications Clean technology projects are usually characterized by large initial capital outlays with highly uncertain future cash flows. Such projects also involve highly intangible and illiquid assets which will quickly depreciate in case of a financial distress. Accordingly, private financial institutions (such as the banks, VCs, angel investors, etc.) are usually very reluctant to finance clean technology projects, especially during a credit squeeze. To assist managers, we first created a framework that conceptualizes the uncertainties and risks regarding the clean technology lifecycle for early start-up and growth stages. We then described the relevance and use of operational hedging to mitigate the firm’s risk exposure. Specifically, managers acknowledge the need for non-financial tools. Operational hedging provides the startup managers with a mechanism to reduce

Operational Hedging Strategies to Overcome Financial Constraints

the perceived risk exposure of their investment plans and improve their financing portfolio. This is indeed a verification mechanism, since only the “good” type of firms will be willing to implement such operational hedges to reveal their true type to the market. The conjecture presented in this chapter has the potential to address the economic implications of the operational hedging for clean technology startups. We observe in our study that operational hedging enhances the financing options of startup firms by mitigating informational problems in the clean technology industry. This way, more funding becomes available to economically viable startup firms, and the economy as a whole could expedite the clean technology development. Consider the clean technology firms in Table 1, which have successfully improved their financing by reducing their risk exposure through hedging practices. However, improved financing options come at the cost of reduced future growth opportunities. Therefore, the startup managers should consider this tradeoff between financing and growth opportunities when taking on the operational activities.

Policy Implications Despite a bruised stock market and considerable challenges facing the clean technology industry, clean technology is at the center of many governmental recovery efforts and offers great opportunities to create new jobs and rebuild economies. Jaffe et al. (2004) argue that the rate and direction of technological advance is influenced by incentives from the market and regulation. They discuss that environmental policy based on incentive-based approaches is more likely to foster technology development and diffusion than policy based on command and control approaches. They suggest a strategy of experimenting with different policy approaches. In this vein, providing incentives to the clean technology startups to manage their risks through operational hedging is important to increase the development rate of clean technol-

ogy industry and facilitate economic growth and employment. For example, during the recent financial crisis many clean technology firms such as the Dutch wind mill manufacturer Emergya Inc. or the US solar energy company Evergreen Solar are enforced to move their operations abroad (particularly to China) to get better financing deals and incentives. If appropriate incentive schemes can be developed to encourage these firms to better manage their internal risks through operational hedging, it is possible to keep these firms inside their country of origin. Consequently, considering operational hedging during the financing of the clean technology projects can serve as an effective policy tool. In particular, operational hedging could increase the long-run cost-effectiveness of the industry and foster policy design by displaying the mitigation of risks. In return, public and private agencies could mandate the economic factors by operational hedging in their policy designs. For instance, public financing policies can create supply chain infrastructure or provide investment support, e.g. loan guarantees for manufacturing technology scale up. Given this possibility, it would be useful if the policy guidelines could explicitly call for assessment of operational hedging options when private and public entities seek support for such scale up. That is, policy guidelines for loan guarantees provided for manufacturing scale up/ supply chain infrastructure growth should be reviewed both from an operational and financial risk management perspective and the projects involving operational hedges to reduce the risk should receive priority in financing. Further, similar to the staged financing schemes of VCs, public financing policies can be staged based on the operational hedges to be employed by the firm. This way, only good projects receive government financing and if a project proves to be a failure during later stages of development, it can be liquidated and the funds can be allocated to other projects with higher potential. Staged

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capital infusion based on operational hedging should enable better allocation of scarce capital in the market place. However, these suggestions come with a caveat. The uncertainties regarding various stakeholders (governments, companies, public, etc.) are large and their interactions can be complicated when it comes to policy design for R&D credits, manufacturing scale up, infrastructure deployment and supply chain integration. A key problem in this area is lack of systematic industry studies which might furnish data sets and analytical insights for further action in a sector where a considerable amount of resources will be expended over the next 25-50 years. Commissioning of such studies is also deemed to be an exercise that would yield considerable dividends and move the debates from speculation towards carefully crafted manufacturing science in an area that has been deemed critical by many governments while the global economies looks into options for sustainable growth.

ACKNOWLEDGMENT We thank managers from Verenium, Emergya, Accuwater, Scholt Energy and Evergreen Solar for participating in our conversations and sharing their experiences. We also thank one anonymous reviewer for his/her valuable insights and feedback. We are grateful to Jiri Chod and Onur Boyabatli for reviewing earlier drafts of this chapter. We also thank the participants at the POMS 2010 conference for their feedback. This work was supported in part by funds from Babson Faculty Research Fund.

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KEY TERMS AND DEFINITIONS Clean Technology: The technological developments, with a more sustainable outcome, which are economically viable, environmentally benign and socially equitable, including the clean energy

(wind, solar, water, biomass, biofuels, hydrogen, geothermal, fuel cells), green transportation, green chemistry, information technology and energy efficient appliances. Early Start-Up Stage: The life cycle phase of a startup, typically the first five years, in which the startup firm is concerned with company formation, technology development, acquisition of initial set of customers, cash flow and survival. Financial Hedging: Trading of financial instruments such as options, futures or other financial derivatives to counterbalance firm actions. Growth: The life cycle phase of a startup in which the startup firm is concerned with building a large scale commercialization effort by anticipating a high diffusion rate for its technology. Loan Guarantee: The commitment of a guaranteeing agency or enterprise to pay off a loan if the borrower defaults. Marginal Cost of Capital: The opportunity cost of increase in investment and expansion. Marginal Rate of Return: The expected return of an investment. Operational Hedging: The operational activities of a firm to mitigate the firm’s risk exposure and reduce downside risk by counterbalancing actions without using financial instruments. Scale Up: The operational decisions by a firm to improve its product or process to meet the large scale market demand. Such scale up requires major investments and is subject to technological, organizational and regulatory uncertainties. Startup: A company, generally recently formed, with limited funding and operating history that displays unique characteristics regarding their asset structure, organization type, growth orientation.

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Chapter 9

Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading Ying Yin The University of Hong Kong, China Zongwei Luo The University of Hong Kong, China

ABSTRACT Warehouse financing has been emerged as one of the most effective financing approaches for small and medium-sized enterprises (SME). Its basic working mechanism is to transfer the company’s assets to collaterals which are more acceptable by the bank. As a logistics service provider, the 3rd Party Logistics (3PL) coordinates and controls the whole financing process. With the professional 3PL’s help, it is easier for SMEs to get loan from the bank. In the meantime, the 3PL’s profit margin has also been increased by providing financing service in addition to their traditional logistics based functions. This chapter explains the basic working mechanism of warehouse financing, applies SCOR reference model to identify financing activities and the risks caused by them. Then this chapter synthesizes four relevant risk analysis / management frameworks from previous literatures, and proposes a new risk framework and evaluation measures aimed specifically for warehouse financing. Finally, a case of carbon trading in China is studied using the previous framework.

INTRODUCTION In China, small and medium sized enterprises (SME) have always been facing the difficulty of financing. This difficulty becomes more severe in current economic downturn. Many of these DOI: 10.4018/978-1-61350-156-6.ch009

enterprises sell their products to other larger and wealthier companies. In order to get loans from the bank for their manufacturing or corporate expansion, these SMEs with low credit liability and weak historical records either have to face prohibitive interest rate when borrowing from the bank, or do not borrow at all. When they do bor-

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Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

row, the bank also face the risk of SME’s failing to pay back the loan. Warehouse financing, however, could both solve the difficulty of SME financing and lower the bank’s risk at the same time. Since warehouse financing may involve transaction and management of assets or commodities, 3rd party logistics (3PL) is employed to provide such services. This chapter will examine the risks and the risk measurements of this model of financing. To clarify some terms, SMEs play different roles under different circumstances. From the supply chain’s point of view, SME is the supplier. As for international/domestic trade, SME is the seller/exporter. When applying for loan, SME is the money borrower. Under all three circumstances, 3PL acts as the service provider and coordinator of all other participants.

Literature Review It is more and more recognized that SMEs are playing an important role in the socio-economic infrastructure, such as providing more jobs for the community, contributing to national tax incomes, and improving the overall economy of the country. In 1979 China, private-owned enterprises only accounted for 1% of the country’s total GDP. (Zhang W., 2008) And in the end of 2001, 99% or 2.4 million of the registered enterprises in China are small and medium sized ones. (Gazette on Second National Census of Basic Units, 2003) However, lack of funds is almost always the common inhibitor for all SMEs’ development. Since financing from the stock market is too expensive and thus impossible for SMEs in China, they have to turn to banks for direct loans. The banks have to charge them higher interest rate than that of big or state-owned enterprises with good credits or reliable owner companies. This either leads to the unwillingness of SMEs to borrow, or leads to the possibility of default. As a result of this financing environment in China, SMEs depend less on professional financing institutes for financing. Javed Hussain, Cindy Millan and

Harry Matley (Jave H., 2006) did a survey and found that owners/managers of SMEs running up to 5 years business in China still depend mainly on financial support from their immediate family and to a lesser extent of professional financial institutions; while most of their counterparts in UK rely on borrowing from financial institutions and less on direct family support or savings. To settle the financing difficulty that SMEs, especially newly start-ups, warehouse financing has emerged as a new approach. Warehouse financing is a kind of asset-based financing, in which SMEs with low cash flows mortgaged their assets (usually their manufactured goods or in-process materials) in a warehouse to the bank. A professional logistics company (3rd Party Logistics) is usually involved for inventory management, transaction and transportation of the assets. HSBC defines warehouse financing as a structured method of financing, wherein funds are extended to manufacturers and processors based primarily on the underlying asset - commodities as identified by a warehouse receipt issued by an independent collateral manager appointed by the Bank (HSBC). Feng Gengzhong, Yu Yang, Zhao Wenyuan, Wang Simiao analyzed the 2 business models of warehouse financing in China, which will be discussed later, and their respective business processes in detail (Feng G., 2004). They also gave a list of common risk for all businesses in the market and a list of specific risk regarding to warehouse financing. There are also many articles discussing financing approaches from the supply chain perspective other than warehouse financing. Nevertheless these other approaches can still give us great insights into what we are going to discuss about warehouse financing. John A. Buzacott and Rachel Q. Zhang tried to incorporate asset-based financing into production decisions. Using a simple deterministic model, they proved that with the SME’s assets mortgaged in the warehouse as a security base, both the bank and the SME borrower can expect less risk and bigger gain with lower interest

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Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

borrowing rate than unsecured financing (John A., 2004). Usually the Third Party Logistics (3PL) takes charge of all the operation of the warehouse on behalf of the bank, and also help bill, manage account receivables and inventory, and insurance.

also has increased the profit margin, which is usually low in traditional 3PL. Warehouse financing works especially for small and medium sized enterprises, since it acts as a warrant for them to borrow money from the banks.

Motivation of this Chapter

Available Financing Technologies

There are many articles already having discussed SME financing risks and supply chain risk management respectively, but there is no discussion of the risk exposures and their measurement standard about the particular financing model. This article will focus on the risk management of the process of warehouse financing, and come up with a list of key measures to monitoring risks in those processes. Using these measurements, one can have a brief picture of view about the underlying risks, their impacts, and whose responsibility they are of the financing process. The organization of this article is as follows. Section 2 introduces what is warehouse financing, and the detailed processes and steps of financing. Section 3 applies the SCOR model to the warehouse financing. Section 4 synthesizes available frameworks and come up with a new framework. Section 5 uses the new framework to identify the risks from each step and the associated SCOR process (if any) which may cause the risk, and made a summary. Section 6 is a case study of China’s carbon trading market, and the risk framework is used and in fact simplified to suit for the carbon trading. Section 7 summarizes the paper.

Before discussing warehouse financing, we will first take a glance of current available financing technologies. Allen N. Berger and Gregory F. Udell propose a conceptual framework for SME financing, and discussed all the financing technologies under different lending infrastructures and environments (Allen N., 2006). Every financing technology has its own pros and cons. According to Allen N. Berger and Gregory F. Udell, choice of methods depends on financial institution structure and lending infrastructure. SMEs in China, especially newly startups usually neither have reliable owners nor strong relationships with banks. Therefore financing purely on company credit or relationship is not practical. SMEs also have very limited value of fix assets. Some even have to rent houses as factories and rent equipments for manufacturing in the beginning stage. In this case fixed-asset financing and leasing financing is not available. What’s more, some SMEs are not informationally opaque, or do not have strong enough financial data to be financed by the bank. All these restrictions leave us only 2 choices, asset-based financing (or warehouse financing) and factoring. In fact these two are very similar to each other in that both of them are collateral-based, and involve asset transaction (selling and buying), asset valuation and management. The only difference is that factoring is only concerned with selling the account receivables to a factor (usually the bank) at a discount. The SMEs transfer their risk to the bank and receive immediate cash, but sometimes the discounted selling price might be so low that they actually lose money. Therefore

BACKGROUND The warehouse financing is the result of the integration of 3PL and financial services. It is a service innovation for SMEs to borrow from financial institutions with a reasonable interest rate, and ensure the profits of every participant involved in the borrowing. Warehouse financing

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Table 1. Financing technologies Financing Tech

Financing on

Advantages

Financing statement

Financial statements reflecting financial data and conditions

May reflect a variety of different contracting elements

Reserved for informationally transparent firms

Credit scoring

Hard information about SME’s owner company as well as SME itself

Applies to very opaque SMEs

Dependent on their owners

Asset-based (warehouse)

Assets(account receivables + inventory)

Secures the financing by accounts receivable and inventory

Often associated with other financing techs (e.g., financing statements, relationship, credit scoring), thus does not distinguish itself from others

Factoring

Only account receivables

Secures the financing by accounts receivable

Asset monitoring needed, big selling discount may counter SME’s profit

Fixed-asset

Long-lived and not-sold assets (e.g. real estate)

No problem of monitoring the assets

High default risk of no timely repayment according to the schedule

Leasing

Purchase of fixed assets

Flexible, addressed opacity problem

Restrictions of leasing equipments, loss of residual value, understatement of lessee’s asset, double sale

Relationship

Soft information of SME, its owner and the local community

Especially in favor of small banks

Proprietary information not easily obtained or verified

SMEs have to balance between the return earned by the firm’s investment on production and the cost of utilizing a factor, and carefully decide whether to use a factor. After comparison with other financing methods, warehouse financing definitely stands out for start-up small enterprises with small amount of initial cash and weak relationship with banks and communities.

Working Mechanism The general working principle of warehouse financing is like this. First, the SME puts part of their manufactured goods as mortgage in the 3rd Party Logistics’ warehouse authorized by a bank. The 3PL is responsible for operating the warehouse by monitoring goods, managing inventory and account receivables, arranging goods transportation from the suppliers and to the buyers, and ensuring the amount of goods as valid collaterals not to fall below the safety line. When the above condition is satisfied, the 3PL issues a receipt for that mortgage. The receipt reflecting the value of the account receivables or the inventory can be

Disadvantages

sold to buyers, who can also sell it to still other buyers. Then the buyer holding that receipt owns inventory covered by the receipt, and pays the receipt to the SME. The essence of warehouse financing is to transfer the unfavorable assets (such as raw materials which cannot be directly turned into value) by the bank to acceptable collaterals under the 3PL’s guarantee. There are two common working models of warehouse financing. Their working steps are shown in Figures 1 and 2. (LIU X., 2007) Model 1. In this model, 3PL acts as the bridge between the SME supplier and the bank, and performs the logistics function. Step 4 and 5 could happen in the same time. In this case the buyer pays the loan to the bank on behalf of the supplier. Model 2. In this model, authorized by the bank, the 3PL’s role includes regular bank’s service as well as its usual logistics service, such as lending loans and receiving debts. Model 2 is a special case of Model 1. The purpose of this model is to simplify the financing process and operation. Participants share the same risk in Model 1 as in Model 2. Our discussion about risk management will primarily focus on Model 1.

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Figure 1. Warehouse financing model 1

Figure 2. Warehouse financing model 2

ACTIVITIES IDENTIFICATION One key participant in warehouse financing is the Third Party Logistics (3PL), to whom the SMEs’ logistics functions are outsourced. Before identifying any risks, we will first look into the SCOR reference model, and map the activities in warehouse financing to those in SCOR. Then, we will examine the performance metrics in SCOR to see whether they apply to warehouse financing.

Brief Overview of SCOR The Supply-Chain Operations Reference-model (SCOR) is the product of the Supply-Chain Council (SCC). SCOR model provides a unique framework that links business process, metrics, best practices and technology features into a unified structure to support communication among supply chain partners and to improve the effec-

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tiveness of supply chain management and related supply chain improvement activities. (Counsil) SCOR is based on five distinct 1st level management processes: Plan, Source, Make, Deliver and Return. SCOR spans include all customer interactions (from order entry through paid invoice), all product (physical material and service) transactions, and all market interactions. Each process can be described by three types: Planning, Execution, and Enable. Combination of level 2 process type and level 1 SCOR process leads to “SCOR Configuration Toolkit”. Later analysis will select appropriate process categories from SCOR configuration toolkit to represent the supply chain aspect in the warehouse financing. Similarly, each level 2 process category can be further divided into level 3 detailed process elements, and each element can then be comprised of level 4 implementations and business procedures customized by different companies.

Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

Modeling with SCOR SCOR recognizes different types of models, such as business scope diagram, geographic map, thread diagram, and workflow or process model. Each serves a different purpose. Our analysis will only use business scope diagram and workflow diagram. We will first draw a business scope diagram of warehouse financing, and then map establish a more detailed SCOR thread diagram which contains level 2 processes.

Business Scope Diagram Business scope diagram sets the scope for a project or organization. This diagram for warehouse financing is based on Model 1, with material/ cash/information flows highlighted. In this diagram, 3PL is the core part that connects every other participant. It has information access to all the other participants, thus facilitates the whole financing process.

Thread Diagram We will first identify level 2 processes for each participant, and link them using the material flow in the business scope diagram. Since the thread diagram focuses on material flow, with information flow as assistance, we temporarily ignore the

Enable process in the thread diagram. We will include the Enable process in the next section. •

•

• •

Supplier: Plan Delivery (P4), Deliver Stocked Product (D1), Source Stocked Product (S1) 3PL: Plan Supply Chain (P1), Plan Source (P2), Source Stocked Product (S1), Plan Return (P5), Source Return Defective Product (SR1), Source Return Excessive Product (SR2) Warehouse: Source Stocked Product (S1), Deliver Stocked Product (D1) Buyer: Plan Source (P2), Source Stocked Product (S1)

Risky Activities We will examine each step in the business scope diagram, and find the corresponding SCOR level 3 process elements impacted by those steps. Subject is the one who initiates the process. Key issues are the potential risks that deserve observation in each level 3 SCOR process (see Tables 2-7).

ANALYZING RISK There has already been a variety of risk analysis frameworks proposed respectively from supply

Figure 3. Business scope diagram

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Figure 4. Thread diagram

Table 2. SCOR step 1: The SME supplier mortgage collaterals to the warehouse SCOR Process

Subject

Key issues

P1.1 Identify, prioritize & aggregate supply chain requirements

3PL

Uncertainty of demand in the market

P2.1 Identify, prioritize & aggregate product requirement

3PL

The product may be not appropriate as collaterals

EP.4 Manage integrated supply chain inventory

3PL

Inventory management on collateral quantities and cost

Table 3. SCOR step 2: The warehouse checks and returns defective/excessive products to the supplier for remake if any, and issues a receipt reflecting the value of the collaterals to the bank SCOR Process

Subject

S1.3 Verify product

Warehouse

Quality of product as collaterals

ES.4 Manage product inventory

Warehouse

Collaterals may be damaged due to theft or corruption; total value of collaterals may fall below the safety level; collateral value fluctuation during a relatively long period of time

SR1.1 Identify Defective product

Warehouse

Some of the collaterals may be of low quality

SR3.1 Identify Excessive product

Warehouse

Product may be more than needed for collaterals and safety stock for immediate customer orders

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Key issues

Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

Table 4. SCOR step 3: The bank sanctions a loan to the supplier SCOR Process D1.15 Invoice

Subject Supplier

Key issues The amount of the loan is based on the value of the invoice and credit history of the supplier. The loan may be either overrated or underrated

Table 5. SCOR step 4: The supplier sells goods to its customer. 3PL helps transport goods either from warehouse or supplier’s factory. In our model we only consider transportation from the warehouse. SCOR Process

Subject

Key issues

D1.7 Select carrier and rate shipments

Warehouse

Transportation risk, e.g. transit time, service/cost tradeoff

D1.12 Ship product

Warehouse

Deliver on time

Table 6. SCOR step 5: The buyer pays the goods SCOR Process S1.5 Authorize supplier payment

Subject Buyer

Key issues Late or failure of payment

Table 7. SCOR step 6: The supplier pays back the loan SCOR Process None

Subject

Key issues

Supplier

Failure to pay the loan

Since this step only involves cash transaction, so we do not use SCOR processes.

chain perspective or finance perspective. We will first examine several relevant frameworks, which, however, might not serve a direct a purpose regarding warehouse financing. Then we will synthesize those frameworks together to get a new risk framework suitable for our discussion. Finally we will feed the SCOR activities into the new framework, and draw a complete risk analysis of warehouse financing.

Risk Framework Review There are numerous risks accompanying the whole process of warehouse financing, and equally numerous literatures that propose different risk classification methods and risk management approaches. This section will only review four risk

analysis frameworks extracted from three articles and one book.(Feng G., 2004; Grath, 2008c; Ila M., 2008; Tang, 2006) The combination of these four reflects most, if not all, angles of warehouse financing risk management, though overlap is unavoidable. The first one focuses on risks that the SME itself and the bank may suffer. It is the most relevant to our discussion. The second framework proposes four basic approaches for managing supply chain risk. The third provides a more straightforward approach to classify and manage risk. The fourth framework which discusses more on trade policies and financial issues than operations only applies when international trade is involved. At the end of each framework, a brief summary is given to pick the appropriate risks applicable and relevant to warehouse financing.

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Warehouse Financing Risk Control Feng Gengzhong, Yu Yang, Zhao Wenyuan, and Wang Simiao proposed a conceptual risk control of warehouse financing in China. (Feng G., 2004) They classify the risks into 2 types: common risk for all financing activities and specific risk for warehouse financing. Each type can be further divided. 1. Common risk: a. Credit risk (validity of mortgage). The SME’s credit and past records affects the bank’s assessment and decision on whether to lend or not, and how much to lend. b. Documentary risk. Improper delivery of stock may result in failure or inaccurate documents or guarantees. c. Inside cheating risk. Insider of the supply chain commits crime (e.g. fraud) against regulations. d. Mortgaged stock selection risk. Liquidation, price stability and steady demand are crucial in selecting collaterals. e. Market fluctuation risk. Market fluctuation may cause devalue of the collaterals. f. Operation risk. The risk deals mainly with operational issues or inefficiency within the supply chain. 2. Specific risk: a. Repository document risk. In China there is still no specific law to define or regulate the formal legislation of format, signing/issuing, segmentation and transferring of the repository documents. b. Inventory risk. Minimum safety quantity of inventories must be maintained in the warehouse.

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Four Approaches to Managing Supply Chain Risk The framework of four approaches for managing supply chain risk in Christopher S. Tang’s review (Tang, 2006) was originally aimed at classifying supply chain risk management (SCRM) articles. But the four approaches summarized four aspects of risks in supply chain, so we can treat it as a practical guide of constructing the risk analysis framework as well. Supply management. A firm can coordinate or collaborate with upstream partners to ensure efficient supply among the supply chain. Demand management. A firm can coordinate or collaborate with downstream partners to influence demand in a beneficial manner. It deals with the pricing mechanism in terms of shifting demand across time, across market, and across products. Product management. Product variety is an effective strategy to increase the market share. But product variety can at the same time increase the manufacturing cost, manufacturing complexity, and inventory cost. Information management. Information management strategy differs between short lifecycle products (fashion products) and long lifecycle products (functional products). For fashion products, short replenishment lead time makes it possible for more orders in a given season. For functional products, market information is critical in generating accurate demand forecasting information. For the supplier, planning according to the orders leads to the “bullwhip effect”. Demand forecasting, batch ordering, supply shortage, and price variation are the root cause of the bullwhip effect. Collaboration and information sharing among wholesalers, distributors, manufacturers and retailers can enable them to access information crucial to make decision and mitigate the bullwhip effect.

Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

Four Types of Risk in Global Supply Chain In Ila Manuj and John T. Mentzer’s Global Supply Chain Risk Management Strategies (Ila M., 2008), risks are categorized into: supply, demand, operational, and security. Supply risk is the distribution of outcomes related to adverse events in inbound supply that affect the ability of the focal firm to meet customer demand (in terms of both quantity and quality) within anticipated costs and time, or causes threats to customer life and safety. Demand risk is the distribution of outcomes related to adverse events in the outbound flows that affect the likelihood of customers placing orders with the focal firm, and/or variance in the volume and assortment desired by the customer. Operational risk is the distribution of outcomes related to adverse events within the firm that affect a firm’s internal ability to produce goods and services, quality and timeliness of production, and/or profitability. Security risk is the distribution of outcomes related to adverse events that threaten human resources, operations integrity, and information systems; and may lead to outcomes such as freight breaches, stolen data or proprietary knowledge, vandalism, crime, and sabotage.

Trade Risks and Risk Assessment Unlike previous sections which mainly focus on the logistics, this section discusses risks from the international trade and financial perspective. However, companies that only deal with domestic trade (like most SMEs) still share most of the risks. The information is an excerpt from Chapter 1 of The Handbook of International Trade and Finance by Anders Grath. (Grath, 2008c) Product risks. Product risks are the risks that the seller automatically has to accept as an integral part of their commitment. Factors con-

tributing to product risks include manufacturing and transportation risk. Commercial risks. Also called purchaser risk, often defined as the risk of buyer going into bankruptcy or being in any other way incapable of fulfilling the contractual obligations. Credit information helps the supplier understand more about their customers and build, nurture and maximize lasting customer relationships. Adverse business risks. Adverse business risks refer to all sorts of corrupt practices that flourish in many countries, particularly in connection with larger contracts or projects: bribery, money laundering and a variety of facilitation payments. A strong policy of anti-corruption law is needed to combat this corruption. Political risks. Political, social and economic instability causes the political risk of buyers fulfilling their obligations and paying in local currency. Currency risks. When international trade is involved and payment is made in a currency other than that of the supplier, this risk arises. Domestic trade does not have this kind of risk. Financial risks. The need for financing its purchasing, production or shipment processes before payment plays a financial burden on the supplier. This burden of financial risk might be heavily affected by political and / or commercial risk.

Framework Refinement and Synthesis With the 4 framework presented and reviewed, it is time to refine them to meet our need in this chapter. The goal of this chapter is SMEs, so they are given the highest priority while the frameworks are refined. Supply and demand are always the biggest concern that affects all aspects of business. Two frameworks have mentioned supply and demand risks and corresponding management approaches. In the warehouse model there is no upstream of SME, so this chapter is not going to discuss the

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management of SME’s suppliers and manufacturing process. The focus is given to how SMEs manage to meet their downstream customers’ demand enhancing their demand forecasting accuracy. However, shifting demand does affect some of the supplier’s decision making such as how much they will be able to expand their business and to borrow. So we will discuss the demand’s impact on suppliers. Supply chain operation affects the efficiency, product quality and safety, and overall performance of the suppliers. It consists of many other risks. Inventory management is the key issues of cost management for the suppliers. Though supplier may have their own factory and warehouse, this chapter will only study the warehouse controlled by 3PL in which collaterals are placed. Transportation and delivery reflects the supplier’s reliability, so that will also be discussed. Operational risk appeared in framework 1 and 3, but in fact all 4 frameworks have more or less mentioned it, though not explicitly. Likewise, all 4 frameworks have mentioned product risk or product management. Framework 1 specifically states the risk of collaterals selection and the likelihood of product devalue. Product variety and quality are also crucial for deciding how much a SME can borrow. Credit risk is also what the bank has to consider when sanctioning a loan. Currency risk impacts both the supplier and the buyer when doing international trade. We will discuss the above 2 risks in detail. The other risks, such as documentary risk, information risk will be discussed briefly. To conclude, the risks to be discussed in the following chapter are: 1. 2. 3. 4. 5. 6.

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Product risk Demand risk Operational risk Credit risk Currency risk Information risk

RISK MANAGEMENT AND MITIGATION This section will continue Section 4 and discuss every risk in detail. A risk transfer diagram (Luo, 2009), if there is any, will be followed with each discussion.

Product Risk Used as collaterals, the product selection is the very first step. Inappropriate product selection may lead to devalue or overstock in the warehouse. The principle of selecting collaterals is: easy to sell, easy to store, low speculation, and under the background that the stocks must be movable. Banks obviously favor collaterals of high liquidity and high price stability. Other market factors the banks have also to consider includes consumption efficiency. Consumption efficiency is the choice of a high efficient product—product with lower price and / or higher quality than others (Lee, J. 2005). Product differentiation is defined as the process of distinguishing of a product of a product from others, to make it more attractive to a particular market. Examples of differentiation are difference in quality, function features or design. (Wikipedia) Quantitative definitions are created and mathematical models are built for the collaterals selection decision making. Damian Robert Ward gave a list of quantitative characteristics of product differentiation, and proved by using Data Envelope Analysis (DEA) that high efficiency product has a strong correlation with the variety of product. (Ward, 2009) While product differentiation, or in a more common term, product variety increases a firm’s market share and leads to high efficiency of customer choice, it increases manufacturing cost and inventory cost due to demand uncertainty. To balance the variety and cost, maintaining a product portfolio with controllable cost is also needed.

Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

To manage and mitigate the product risk, the postponement strategy has been studied and widely used. Consider a manufacturing system has N stages. Postponement is to postpone the stage of differentiating the customized end products at stage k. It is shown that inventory level and cost can be reduced by delaying the differentiation point, but inventory holding cost is increasing at later stage. By postponing the differentiation, the production quantity decision for a final product can be made in a later period of time. Product selection is crucial for the bank’s evaluation of the collaterals in the warehouse. Good collaterals may even increase the collateral’s value. But the product management is mainly dependent on the SME itself. So this risk cannot be transferred to others.

Demand Risk In an ordinary context of supply chain management, demand risk is more of a responsibility that the retailers have to take. But in SME financing, it is equally important for SMEs to have an accurate sense of the market demand of their collaterals. Although the final decision is made by the bank, it is still beneficial for SMEs to have the knowledge of demand forecasting and their manufacturing capacity, so that they can meet their customer’s order better and make decision on how much to borrow and to expand their business. Christopher S. Tang reviewed managing demand risk in 3 categories: shifting demand across time, shifting demand across market, and shifting demand across product. (Tang, 2006) Demand may vary heavily between peak seasons and off-peak seasons. In the context of supply chain management, advance-commitment discount program offers a price discount to entice customers to pre-commit their orders prior to the beginning of the selling season. Many literatures have studied the optimal discount and timing of this program. Advance-commitment discount program not only offers the buyer a lower price,

but also reduce the risk of demand uncertainty by securing orders in advance. Likewise, demand postponement strategy entices the customers to accept late shipments. For shifting demand across markets, Billington et al proposes a “solo rollover” strategy that is selling the new product in different market with non-overlapping selling seasons. (Billington, C., 1998) But as manufacturers, SMEs usually will not involve in direct selling their products to the end users in the market. For the shifting demand across products, there are possibilities that customers will shift from one product or brand to another. Shifting customers’ demand from one product to another enables a firm to keep its supply/demand balance. This can be achieved by product substitution and product bundling sales. But since SME’s warehouse financing is usually short-term financing, the cost is too high to manage this kind of demand risk during the collateral period. As for the risk transfer, similar to the product risk, demand risk is the intrinsic risk of all manufacturing enterprises. It is independent of the enterprise’s financing approach. So there is no transfer of this type of risk.

Operational Risk The term operational risk is so abstract and conceptual that it consists of many other risks. In fact those risk management approaches discussed in 5.1 and 5.2 help improve the supply chain operation. This section will only discuss 2 risks: (1) delivery and transportation that reflect the supplier’s timeliness and reliability, and (2) inventory management that plays the key role in warehouse financing.

Delivery and Transportation This task is the traditional logistics function outsourced to 3PL, who provides services such as transportation, warehousing, distribution,

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financing service, and information system. 3PL makes the optimal decision of what, when, how, and how much to deliver. To monitor the service, we can look at product differentiation, service/cost trade-off, and transit time (John J., 2003b) of the transportation company owned or hired by the 3PL. Product differentiation - Differentiating a company’s products, and delivering them on a consistent, timely and undamaged basis can reflect the transportation service quality. Sometimes differentiation will increase the transportation complexity and cost. • •

Transit time – The time from the request of shipping to the time of the arrival Service/cost trade-off – Quicker delivery and shorter responsive time always accompany higher cost. This is another effective measurement of risks when making decisions.

Inventory Management The very first step of managing inventory as collaterals is to assess the inventory value to determine the loan by the bank. First let’s see how the bank should make decision based on the SME’s inventory and receivables. Buzacott and Zhang assumes the maximum asset-based loan at a given time is determined as the sum of the cash, inventory value, and account receivables and then minus account payables.[15](John A. Buzacott, 2004) ψt =γC x t + γRyt +γFG $I tFG

+γWIP $I tWIP +γRM $I tRM −γP z t 

•

• • • • •

$It stands for inventory value, which includes finished goods, work-in-progress goods, and raw materials FG=finished goods WIP=work-in-progress RM=raw materials yt=account receivable zt=account payable

γC , γ R , γ FG , γ WIP , γ RM , γ P are the respective weights reflecting the bank’s assessment of risk associated with each asset categories. Initial cash x0 is usually crucial for the bank to make decisions on loans in traditional lending technique, with no loan limit based on the borrower’s asset. To secure their own return, banks tend to set higher interest rate for SMEs whose initial wealth is often low. However prohibitive high interest rate will prevent SME borrowers from being able to pay the loan, thus causing them bankrupt. Buzacott and Zhang later proves that given the same initial wealth of borrowers, the bank’s return is increasing by setting a loan limit this way than without a loan limit. In addition, the probability of retailer’s bankruptcy is independent of their initial wealth. Collaterals in the warehouse secure both the bank and the SME’s profit at a low interest rate. After evaluating the inventory value, we must maintain the lowest limit amount of collateral goods, and an extra safety stocks to be delivered to buyers in time. However, uncertainty of demand and lead time length affect the decision of the safety stock in the warehouse. If we assume the demand and lead time length are normally distributed, we can use the following formula (John, J., 2003a) to determine the safety stock

where

X =R(X LT )

• •

and

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ψhere A. Buzacott, 2004) xt=cash at time t

Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

2

σ = X LT (σR ) +R (σLT ) 2

2

•

e = expected excess

x = mean (average) demand during lead

TAC represents the total annual cost of a certain amount of goods stored in the warehouse. It’s essential the supplier control this cost well. Obviously the buyer demand, lead time, and cost of storage directly contribute the total warehouse cost. The operational risk transfer diagram is as the following:

• •

X LT = mean (average) lead time length ÃLT = standard deviation of lead time length

Credit Risk

• •

R = mean (average) daily demand ÃR = standard deviation of daily demand

where • •

time σ = standard deviation of demand during lead time

To ensure the safety stock is more than the demand at a fairly high probability, we set the safety stock or reorder point at x + 1σ, at which level 84.13% probability that demand during the lead time will not exceed the inventory available. The loan borrowed from the bank must also cover the inventory management cost as well as inventory value. The following formula is a rough estimate of the annual cost of inventory. Let Q be the total quantity of goods in the warehouse, covering both for the buyer’s demand and the bank’s funding. It directly affects the warehouse cost, reflected in this formula: TAC =

1 R R QVW + A +eVW  +G 2 Q Q

where • • • • • •

TAC = total annual cost Q = total quantity A = cost of placing an order or setup cost VW = storage cost per year R = annual rate of demand R = expected stock cost G Q

Credit risk is defined as the risk of going into bankruptcy or being in any other way incapable of fulfilling the contractual obligations (Grath, 2008c). Both the supplier and the buyer bear this kind of risk. The bank and the supplier bear the risk of the buyer’s failure to pay the account payable. The buyer also faces the possibility of their demand not being met, or of unqualified goods. To manage credit risk, both the supplier and the buyer have to obtain detailed knowledge about their customers. In the warehouse financing supply chain, the professional 3PL together with financial institutes such can provide them valuable information such as historical records, ratings, and help them use the records to discover and determine potentially reliable and profitable customers. Many documents must be involved to support the supplier’s or buyer’s credit. The bank or 3PL authorized by the bank, issue the guarantee documents for SMEs, especially those who are not able to provide convincing credit related documents themselves. The most common forms of those documents are bonds, guarantees, and standby letters of credit. One kind of guarantee is contract guarantee, often associated with the trading contract together. There are all sorts of contract guarantees both for sellers and buyers. Such as tender guarantee (bid bond), repayment guarantee (advanced payment guarantee) and performance guarantee are issued

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Figure 5. Operation risk transfer

on behalf of the sellers to ensure the undertaking of the contract, the repayment for the buyers if some contractual undertaking is not fulfilled, and the delivery of the goods. On the other hand, payment guarantee and guaranteed acceptance cover the buyer’s ability and obligation to pay. (Grath, 2008a) However, such documents are far from enough for the supplier to reduce commercial risk the supplier is facing, especially in international trade. So it is common in international trade, the supplier has to insure some parts of unsafe transaction with some professional international insurance companies. Export credit refers to the credit that the seller offers the buyer in the contract for sale of goods and services or credits given to finance such a sale. Export credit insurance is normally divisible into commercial risk of the buyer and political risk in the buyer’s country. The insurer and the insured enter into a contract. The SME exporter pays the premium. The insurer provides insurance covering some special conditions and terms that are outside the range of regular guarantees or letters. Standard export credit insurance covers against non-payment of debts owing to the buyer’s commercial risk or political risk. The insurer assesses the risks, and may compensate up to 90 percent of 146

the outstanding receivables. In the mean time, the insurers also provide the SME credit management system, enabling more efficient and responsive maintenance of the credit portfolio and limits. Tailor-made credit risk insurance is also available with more sophisticate insurance packages. There is private sector insurance and government supported insurance in the export credit insurance market. For SMEs, the export and financing period is usually urgent and short, ranging from several months to less than 2 years. Goods under this kind of trade can only be covered by the private sector according to established OECD rules, while government-supported insurance is only allowed for longer periods. Within 2 years, the commercial risk is the main risk for the exporter. Many countries have established their own Export Credit Agencies (ECA) to facilitate the exports in their countries. Example companies include China’s SINOSURE (China Export & Credit Insurance Corporation) and Hong Kong’s HKEC (Hong Kong Export Credit Insurance Corporation). Apart from the core service of export credit insurance, SINOSURE provides a variety of other services such as domestic credit insurance and investment insurance, while HKEC provides tailor-made insurance and collateral discounting export bills for SMEs.

Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

Advantages of credit insurance, especially of private export credit insurance most applicable to SMEs are obvious. In addition to covering more transaction or deals that regular bank’s guarantees cannot cover, the export / domestic credit insurance increases levels of export finance, since the insured part of transaction may add to the bank’s willingness to lend. The credit risk transfer diagram can be seen in Figure 6.

Currency Risk Currency risk is a common to all international trade activities. Most international trade participants peg to a base currency (often USD). However, this pegging may be a risky business in the long run if the underlying economic development is different between the countries, and could trigger sudden and often violent currency disturbance. (Grath, 2008b) Currency risk arises because of international trade and volatile exchange rates, thus influencing the company’s account balance. In warehouse financing, the supplier stores collaterals in the warehouse in order to get loan of a certain currency from the bank. The buyer may buy the supplier’s goods in the warehouse and thus pay the supplier’s loan, however, in a different currency over sometime. As the exchange rate fluctuates during this period, risk occurs for the suppliers that their goods depreciate with the trading currency, and the initial payables cannot cover their

loans. In the same way the buyer also faces the similar risk that they have to pay more when the goods appreciate in the warehouse. SME’s warehouse financing is usually short term, usually 6 months to 2 years. To correctly estimate the short term exchange rate variation is crucial. Some people consider Purchasing Power Parity (PPP) rates a fairly reliable predictor of exchange rate over time. (Wikipedia) St St −1

P * / Pt *−1 = t Pt / Pt −1

where • •

St is the spot exchange rate Pt is the price level in period t (foreign values are marked by an asterisk).

However, the use of PPP rate is based on the assumption that it costs the same to buy the same basket of goods in one country and another. The estimation of the purchasing prices of the same goods in different countries is not always an easy task, sometimes even misleading. Monitoring and evaluating the currency risk can be transferred to evaluating company’s account balance and account payment. (Grath, 2008b) In warehouse financing, we care only supplier’s collateral, loan and the trade associated with them. Therefore, account balance can be reflected by assets of collaterals in the warehouse. Account

Figure 6. Credit risk transfer

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Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

payment is the flow of payments in foreign currencies. When analyzing the account balance and the payment, the company should also look into their respective currency volumes, composition of currencies over time as well as the currencies themselves. The most common methods of hedging currency includes but not limit to choice of invoicing currency, currency steering, payment brought forward contracts, currency derivatives, shortterm currency loans, currency clauses, and tender exchange rate insurance. A combination of these alternatives is most often used. (Grath, 2008b) The principle of hedging currency risk for SMEs is to transfer the risk to the buyer, the bank, or insurance companies. But improper manipulation of the currency may just result in the opposite that the SMEs have to receive other parties’ currency risk. The currency risk transfer diagram can be seen in Figure 7.

Information Risk With more and more rapidly improved technologies both in manufacturing and transportation, the Figure 7. Currency risk transfer

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product lifecycle is becoming shorter and shorter. Nowadays customers’ demand may come in any time; payment procedure is more flexible; and information more accessible and communication easier. This improvement is a two edge blade. Uncertainty in demand is increasing in accordance with technological advance. Lacking clear knowledge about the market, the manufacturers/ suppliers have to plan totally depending on their downstream customers’ order. This vague and unsynchronized information leads to the “bullwhip effect”. The essence of managing information risk is to increase supply chain visibility such that the upstream suppliers have knowledge of downstream status and generate more accurate forecasting and plans. Basically there are 3 approaches to managing information risk--information sharing, vendor managed inventory (VMI), and collaborate forecasting. Information sharing is the retailers share their knowledge about the market demand with the suppliers. But retailers may be reluctant to share their knowledge with their suppliers due to corporate privacy or information leakage. Vendor managed inventory is to delegate the supplier to

Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

order and replenish the retailer’s inventory, which enables the supplier’s accessibility of downstream information. Collaborative Planning and Forecasting and Replenishment (CPFR) could be adopted to forecast the demand. Then the supplier takes responsibility to replenish the supplies based on the actual rate of sale or invoice. (Huo Yan-Fang, 2007) The prediction is linear regression based on historical data. Y = a + bx where, • • • •

x = quarter Y = demand computed using regression equation a = Y intercept b = slope of the line

After determining the value of a, b, and seasonal factors, we project the line, and thus are able to calculate the demand Y given a quarter x. Then we have to adjust Y by multiplying the corresponding seasonal factor. Step 2 in CPFR is to replenish the inventory quickly and responsively. Just in Time (JIT) supply is the most effective mode of fulfilling the customer’s demand, which gives the minimum inventory level in the warehouse and thus significantly saves warehouse and inventory management costs. In our warehouse financing model, CPFR can also be used to determine in advance the amount of financing and collaterals, given future prediction of sales and demand.

3PL and 4PL’s services enable information flow and improve supply chain visibility. 3PL has the right to access the supplier’s inventory controls and to make warehouse decisions. The service of 3PL has different categories of focus: transportation based, distribution based, forwarder based, financial based information based. The 3PL in our model is a combination of financial based and traditional logistics company. 4PL service goes even further. Unlike 3PL, 4PL does not own physical equipments such as warehouse, transportation tools, or shipments. Instead, 4PL provides customers with true business management, enhances the collaboration of participants, and integrated different resources including 3PL providers, IT service providers, and possibly financial institutions. The information risk transfer diagram can be seen in Figure 8.

Summary of Risks The following table sums up all risks identified in this article, including the top level risks, sublevel risks (if any), management approaches, performance measures, and SCOR process during which the risk may occur. The risk owner, its impacted participants are also summarized in Table 8. The severity of each sublevel risk is quoted in the brackets, 1 being the severest, 3 the least important. The risk transfer diagram (Luo, 2009) shows how we can the risks are transferred within warehouse financing model. The rectangular indicates the parties that participate in the warehouse financing. The ellipse means the assets that generate uncertainty and risk. The diagram shows that

Figure 8. Information risk transfer

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Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

Table 8. Risk summary Risk

Description

Management Approaches

Evaluation measures

SCOR process

Product

Collateral selection Product differentiation

Postponement of differentiation

Liquidity, stability, consumption efficiency

P1.1, P2.1

Demand

Demand uncertainty

Advance-commitment discount, product substitution

Responsiveness to customer’s orders

P3

Operation

Inventory management

Loan limit evaluation, safety stock determination

Value of collaterals (RM, WIP, FG), SME’s financial state, warehouse storage cost

EP.4, ES.4, S1.3

Transportation

Carrier selection of different products

Transit time, service/ cost ratio

D1.7, D1.12

Credit

SME’s credit ability

Export credit insurance

Guarantee documents, historical data

D1.15

Currency

Collateral devalue

Choose correct invoicing currency, hedge currency risk using derivatives

Exchange rate, PPP rate

None

Information

Understanding of market, collaboration of participants

Information sharing, vendor managed inventory, collaborating forecast

Supply chain visibility

All processes

SMEs transfer their risk of commodity price to the bank by receiving the bank’s assurance and loan. SMEs also transfer the commodity price to 3PL in the form as collateral assets. Coordinated by 3PL Buyers buy finished products from SMEs, thus share the risk of collaterals in the 3PL’s warehouse. The buyers’ risk is also reduced by the assurance documents or guarantees issued by Figure 9. Risk transfer

150

the bank after the bank and 3PL investigate the credit record of SMEs and issue those documents. And finally, all the credits given out by the bank are rested on the collaterals. It is clear that in this mode and financing and lending technology, the collaterals or SMEs’ assets play the core part, upon which other risks are transferred.

Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

COST OF IMPLEMENTING THE MEASURES This section analyzes the validity of obtaining and implementing those measures. Evaluation measures with regard to the production and operational process is relatively easy to obtain, since all the relevant information is shared with the 3PL, who owns the warehouse and coordinates every process. Logistics data, such as product evaluation, transportation information (transit time, ratio of service/cost, lead time), market demand, and inventory cost control are easy to obtain. But it is difficult to quantify the responsiveness of the whole supply chain and the supplier. A new tool needs to be developed to measure the efficiency and preciseness supply chain’s performance. More historical data and advanced information management system could assist evaluating the responsiveness, which means more cost. Documents in order to address the credit risk of the supplier or buyer’s reliability are accessible. Usually 3PL keeps various records of their customers as references when evaluating their credit. And documents and guarantee letters issued by banks are also open to other parties. So once accessing the database containing all companies’ records, we can immediately get this kind of measurements. Exchange rate is open to all public, so the changing value of the inventory is also transparent over time. But as the exchange rate fluctuates constantly, we have to calculate the inventory value from time to time, usually once a week or once a month, depending on the nature of the product as collaterals. Too often calculation may also be costly. Finally, measuring the information risk and information visibility is not an easy task, since first of all building a complete and highly efficient information management system used and by all parties in the warehouse financing is costly. Especially when the structure or business processes of the supply chain change drastically, which seldom

happens though, part of the system or the whole needs new development. Companies need to buy or develop new software to evaluate the information system and the supply chain visibility.

CASE STUDY: CARBON TRADING Carbon Trading and Financing The reason I select carbon trading as the case study is that I treat carbon trading as ordinary domestic or international trade. The only difference is the trading “commodity” here in carbon trading is emission permit. Carbon trading and ordinary commodity trading has a lot in common. In current stage of China, the forest landowner or any organization that supply the carbon emission permits can ask the bank for financing their forestry project much the same way as ordinary warehouse financing. Here the “collateral” is the permit. The risk analysis framework can still be applied to carbon trading, though with some alterations. The next section will analyze the risks of carbon trading using the former risk analysis framework in Section 5.

Introduction Emission trading is an approach to administer and control pollution by putting a limit or a cap on the amount of greenhouse gases (GHG) (e.g. CO2, SO2, NOx) that can be emitted. The cap is set by a controlling agency or government. Emission permits are issued to companies. The total emission levels cannot exceed the cap. Companies who wish to emit more than their permits must buy credit from those who pollute less or forest landowners. Forests absorb and fix CO2 in the atmosphere through the photosynthesis. Pollution credit trade essentially encourages emission reduction, and thus achieve environmental goal. Carbon trading is a kind of emission trading specifically for carbon dioxide, and currently

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makes the bulk of emission trading. With the ultimate goal of reducing carbon dioxide emission in a global level, carbon trading has definite advantages over traditional carbon tax (Zhang, 1998): (1) it reduces the difficulty of carbon tax that initial energy prices are different between fuels across countries; (2) it offers a built-in feature of resource transfer by emission sources to developing countries, and gets them engaged in controlling GHG emissions; (3) it gives incentives to companies to pollute less such that they sell their excess permits to boost revenue. It is estimated by World Bank that the demand of carbon trading will be 7-13 tons during the year 2008 to 2012. There are 2 markets of carbon trading—involuntary carbon trading and voluntary carbon trading. The trading subjects in the involuntary trading markets are mainly countries in Annex I of Kyoto Protocol who have committed themselves to a reduction of greenhouse gases. These countries must meet their emission limitation by either reducing their emission or purchasing carbon credits. Some countries have invested forest resources in other developing countries to meet their reduction requirement. For example, according to the clean development mechanism (CDM) in the Kyoto Protocol, Italy and Spain invested $300 million of forest in Guangxi province in China, to purchase credit. As a non-Annex I country, China does not have greenhouse gas (GHG) emission restrictions, but still have financial incentives to develop GHG emission reduction projects to receive carbon credits that can be sold to Annex I countries. There are also many parties other than countries signed in the Kyoto Protocol who voluntarily trade carbon credit in the market. Their trade is not limited by the Kyoto Protocol. Such market is called voluntary market. The trading parties include private companies, investment companies, charities and the public. Since the voluntary market is not limited by the Kyoto Protocol, the price is negotiated between the trading parties, and usually is lower than that in the involuntary market.

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Examples of voluntary carbon trading markets include Chicago Climate Exchange (CCX) and Costa Rica forest carbon trading. China has voluntary forest projects going in Yunnan, Liaoning, and Shanxi province, but the trading is based on the whole project level. There is no market for carbon credit level trading in China yet.

Risk Analysis on Carbon Finance in China Carbon credit trade differs from commodity trade discussed in previous sections, since there is no material flow in credit trade. In fact the voluntary carbon market is more like a typical stock market, where the trading price depends on supply and demand and is negotiated between suppliers and buyers. This section applies the risk framework in Section 5 to carbon trade finance, but the framework is somewhat altered for the credit trade. As there is no commodity transaction, the logistics operation risk in previous framework is neglected. The trading object is carbon credit or allowance, so there is no risk regarding product selection and production process. Other risks will also be viewed from different perspectives.

Demand Risk According to the framework, the demand varies across time, across market, and across product. Here shifting demand of product can be ignored, so only demand across time and across market counts. Demand across market. Involuntary market and voluntary market are the only 2 markets for carbon trading. According to Lauterbach, S, current exiting carbon demand is driven primarily by the regulated involuntary market, and to a lesser extent, voluntary market. (Lauterbach, 2007) Regulated by Kyoto Protocol, the price in the involuntary market is higher than that in the voluntary market. However, China surpassed the United States as the biggest emitter of CO2 from

Warehouse Financing Risk Analysis and Measurement with Case Study in Carbon Trading

power station on August 27, 2008 (Development, 2008). So although China does not have mandatory responsibility of controlling GHG emission under a certain cap, the carbon credit demand in the voluntary market may increase, as enterprises purchase credits in preparation for the possible mandatory requirement of emission reduction in the near future. Other incentives for buying carbon credits include returning society and improving the enterprise’s image. Demand across time. It is believed that the demand of carbon credit will keep increasing beyond 2012, since in the first stage (2005-2007) of Kyoto Protocol there are too many credits and government allowances issued to the developed countries. The World Bank estimated demand from EU nations for carbon credits, as well as from other Kyoto signatories, would reach the equivalent of 2 billion metric tonnes of carbon dioxide emissions over the five years to 2012, while there is likely to be about 1.7 billion metric tonnes of supply (Prosser, 2007). The price of carbon credit is expected to increase. China will surely benefit from both the international and domestic increasing market.

Credit Risk Previously credit risk is defined as the risk of going into bankruptcy or failure of fulfilling the contractual obligation. Legal systems are crucial for the transparent and fair trade between supplier and buyer of credit. In warehouse financing, under necessary guarantee and mortgage laws, the credit record and bank’s guarantees or standby letters can be used for monitoring the enterprises’ fame and reliability. Let us first look into involuntary market in China. The finished and on-going forestry emission reduction projects for the international involuntary market are mainly invested by local bank, government, or foreign governmental organizations. Further, China signed United Nations Framework Convention on Climate Change in

1992 Kyoto Protocol in 1998. These two documents validate China’s implementation of carbon forestry projects and regulate the trading system. With reliable investors and assurers and regulation/ legal system, the default risk of these projects is under control. Voluntary carbon market, however, is far from mature. China has no secondary carbon market yet, not to mention the parties participating carbon trade. To establish a complete carbon trading market, not only do the trading parties need training of regular credit trading expertise, the legislation also needs improving. The present Forestry Act does not cover carbon trading. Good legislation is the assurance of a complete and regulative carbon market.

Currency Risk Currency risk is common in all international trade. Capital flow causes exchange rate of countries receiving financial capital to appreciate, and depreciates the currency of countries that give out their capital (Warwick J., 2000). China is a big supplier and exporter of carbon credit to foreign developed countries. Therefore China will expect more capital flow inward, and RMB may still further appreciate.

Information Risk The basic knowledge of carbon market and information about carbon credit demand impacts the development of China’s carbon market. Most of the subjects of carbon trading do not have expertise, and some forestry owners are ordinary farmers who do not possess the least trading market knowledge. Collaboration, joint demand forecasting or information sharing among carbon credit suppliers, buyers, banks and possible agencies are even beyond question. At the initial developing stage, the Chinese government must take the responsibility of educating the potential traders (both organizations and individuals) in the

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future distributing relevant market information and forecast of carbon credit to the public.

CONCLUSION To analyze the risk of adopting warehouse financing for the SMEs, this chapter first compares different available financing approaches with warehouse financing. And then this chapter applies SCOR model to identify the SCOR processes involved in warehouse financing. After that, four risk analysis frameworks of supply chain or financing perspective are comprehended and synthesized to a new one, and detailed risk analysis and their respective measurement are presented. I used carbon trading as a case study, and applied the new risk framework to address the risks and potentials of carbon market and trading in current China. It shows that the demand of international carbon credit is expected to grow, but the probable increasing trend of RMB value may give a negative impact on the China’s export of carbon. Furthermore, the legal system supporting carbon market and monitoring the successfulness of fulfilling either party’s obligation is far from mature. In China, except for a few experts, there are not many people who are familiar with carbon market, even those potential future traders such as forest landowners. Information sharing and collaboration among trading parties and government still need improving. To conclude, there is still a long way to go in China to build a complete and mature carbon trading market and environment, though a few forest projects for international involuntary markets are on-going now. Although this chapter has proposed a risk analysis framework and measures, the risks and associated measures are only qualitative. Quantitative analysis of risk evaluation and risk impacts is left for further study.

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REFERENCES Allen, N., & Berger, G. F. U. (2006). A More Complete Conceptual Framework for SME Financing. Journal of Banking & Finance, 30(11), 2945–2966..doi:10.1016/j.jbankfin.2006.05.008 Billington, C., H. L., CS Tang. (1998). Product Rollover: Process, Strategies, and Opportunities. Sloan Management Review. Counsil, S. C. (n.d.). SCOR 9.0 Overview Booklet. Retrieved from https://www.supply-chain.org/ filemanager/ active?fid=454 Development, C. f. G. (2008, 8 Sep. 2008). China Passes U.S., Leads World in Power Sector Carbon Emissions. Retrieved 4 Dec. 2009, from http:// www.cgdev.org/ content/article/ detail/16618/ Feng, G. Y. Y., Zhao W., Wang S. (2004). The Business Model and Risk Control of Warehouse Financing in China. Paper presented at the IEEE International Conference on E-Commerce Technology for Dynamic E-Business, Beijing, China. Gazette on Second National Census of Basic Units. (2003). Grath, A. (2008a). Bonds, Guarantees and Standby Letters of Credit The Handbook of International Trade and Finance: The Complete Guide to Risk Management, International Payments and Currency Management, Bonds and Guarantees. Credit Insurance and Trade Finance. Philadelphia: Kogan Page. Grath, A. (2008b). Currency Risk The Handbook of International Trade and Finance: The Complete Guide to Risk Management, International Payments and Currency Management, Bonds and Guarantees, Credit Insurance and Trade Finance. Philadelphia: Kogan Page.

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Grath, A. (2008c). The Handbook of International Trade and Finance: The Complete Guide to Risk Management, International Payments and Currency Management, Bonds and Guarantees. Credit Insurance and Trade Finance. Philadelphia: Kogan Page. HSBC. (n.d.). Warehouse Financing. Retrieved from http://www.hsbc.co.in/1/2/ business/globaltrade-solutions/ warehouse-financing Huo Y.,& J. X.-y. (2007). Research on CPFR and Warehouse Management - A Method of Enhance Supply Chain Visibility. Paper presented at the International Conference on Wireless Communication, Networking and Mobile Computing, Shanghai. Ila, M., J. T. M. (2008). Global Supply Chain Risk Management Strategies. International Journal of Physical Distribution & Logistics Management, 38(3), 192–223. doi:10.1108/09600030810866986 Jave, H., C. M., Harry M. (2006). SME Financing in the UK and in China: A Comparative Perspective. Journal of Small Business and Enterprise Development, 13(4), 584–599.. doi:10.1108/14626000610705769

Lee, J., S. H., Kim, T. (2005). The Measurement of Consumption Efficiency Considering the Discrete Choice of Consumers. Journal of Productivity Analysis, 23(1), 65–83. doi:10.1007/ s11123-004-8548-y LIU X., L. J.-p., Li Y. (2007). The Analysis of China Finance Logistics Model. Logistics SciTech, 30(1). Luo, Z. (2009). Service Innovation in Cross Border Financing Logistics Management with a Case Study on Bonded Logistics Parks. Computer Systems & Applications, 18(6). doi: CNKI:SUN:XTYY.0.2009-06-035 Prosser, D. (2007, 3 May 2007). Demand for Carbon Emission Credits to Exceed Supply. Retrieved 4 Dec. 2009, from http://www.independent.co.uk/ news/business/news/ demandfor-carbon-emission-credits-to-exceed -supplyby-2012-447234.html Tang, C. S. (2006). Perspective in Supply Chain Risk Management. International Journal of Production Economics, 103(2), 451–488.. doi:10.1016/j.ijpe.2005.12.006

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Zhang, W. (2008). Financing Model and Technology Research for China’s SMB. MBA, Chongqing University, Chongqing. China Academic Journal Electronic Publishing House database.

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Chapter 10

Modeling Closed Loop Supply Chain Systems Roberto Poles University of Melbourne, Australia

ABSTRACT In the past, many companies were concerned with managing activities primarily along the traditional supply chain to optimize operational processes and thereby economic benefits, without considering new economic or environmental opportunities in relation to the reverse supply chain and the use of used or reclaimed products. In contrast, companies are now showing increased interest in reverse logistics and closed loop supply chains (CLSCs) and their economic benefits and environmental impacts. In this chapter, our focus is the study of remanufacturing activity, which is one of the main recovery methods applied to closed loop supply chains. Specifically, the authors investigate and evaluate strategies for effective management of inventory control and production planning of a remanufacturing system. To pursue this objective, they model a production and inventory system for remanufacturing using the System Dynamics (SD) simulation modeling approach. The authors primary interest is in the returns process of such a system. Case studies will be referred to in this chapter to support some of the findings and to further validate the developed model.

INTRODUCTION Industry in general, and society more broadly, have come to recognize the limited availability of natural resources and are moving towards the manufacture of more environmentally friendly DOI: 10.4018/978-1-61350-156-6.ch010

products and the recovery of resources. For this reason, the modern trend, particularly in developed countries, is to use fewer environmental resources such as water, air and raw materials to manufacture products. Moreover, interest in strategic sustainability is growing among multinational companies, some of whom are developing sustainability

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Modeling Closed Loop Supply Chain Systems

reports to demonstrate both their concern for the environment and their commitment to conducting socio-ecological activities in business. In addition, sustainability can be used as a competitive strategy to create company branding, comply with government regulations on the environment and optimize the cost of operational processes. This emerging economic and environmental consciousness within business has increased the focus on reverse logistics activity (company processes that recapture value from product returns) over the last decade (Blumberg, 2005). Indeed, this reverse logistics activity, particularly remanufacturing (the process of reusing returned products in production), can play an important role in sustainability, as well as in developing competitive strategies aimed at reducing the use of natural resources and recovering value from used products. However, several factors make the development of reverse logistics processes difficult. In particular, the complex integration between the forward (from the producer to the consumer) and the reverse (from the consumer to the producer) supply chains can negatively affect operations and logistics management activities such as production planning, inventory control and distribution planning. According to the Reverse Logistics Executive Council, reverse logistics is the process of moving goods from the point of consumption to the point of origin for the purpose of either recapturing value or proper disposal. Stock (2001) has defined reverse logistics as “the term most often used to refer to the role of logistics in product returns, source reduction, recycling, material substitution, reuse of material, waste disposal, and refurbishing, repair and remanufacturing” (p. 5). Companies can pursue several methods of recapturing value from returns, and carry out a range of recovery methods (Kulwiec, 2006). For example, products can be reused directly after cleaning or reconstruction. This is a common practice for items such as used pallets, bottles/ glass or containers. However, products whose parts

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or materials need to be repaired or replaced can be reused after repair as rebuilt or used products. Another method of recovery is remanufacturing, a process in which parts and materials from returned products are reused for production. Remanufacturing requires more extensive work, since the returned product must be completely disassembled, its parts and modules examined and either repaired or replaced, and then reassembled into a new product. Remanufacturing is practiced in many industries, including for photocopiers, computers, telecommunications equipment, automotive parts, office furniture and tires. A final recovery option is recycling. In this case, some or all of the parts and materials from returned products can be processed to make different products (Kulwiec, 2006). The concept of reverse logistics has changed in recent years (Dekker, Fleischmann, Inderfurth, & Wassenhove, 2004). More specifically, the concept of a closed loop supply chain (CLSC) has been developed to refer to the complete loop from the customer, back to the plant, through a reprocessing operation, and then back to the customer (French & LaForge, 2006). Closed loops consist of two integrated supply chains—a forward and a reverse chain—through which a recovered product re-enters the original forward chain (Wells & Seitz, 2005). CLSC is sometimes treated as an extension of the traditional concept of supply chain management. In this case, reverse logistics is not managed independently of forward logistics, but rather both processes form part of a complete supply chain process whereby products start with the manufacturer to reach the customer, then come back to the plant and return to the customer once more. This process has been defined by this new concept of the closed loop supply chain (Eoksu, Sungwon, Haejoong, & Jinwoo, 2004). The integration of reverse logistics activities within the structure of the original production and distribution systems leads to additional complexity in the closed loop supply chain system. This complexity, which can hinder the integration process,

Modeling Closed Loop Supply Chain Systems

comes from the significant differences between the forward and reverse supply chains. The latter are characterized by operational and business factors that disrupt the traditional approach to dealing with supply chain management, such as: (1) the complexity of forecasting returns; (2) a lack of uniformity in returns quality; (3) reverse logistics costs being less directly visible; and (4) production and inventory management being affected by the flow of returns. These factors make it necessary to rethink and re-plan the original supply chain. In particular, in the context of the remanufacturing process, production and inventory management entail the added complexity of coordination between the remanufacturing and production activity in terms of size of orders and lead times. Moreover, such coordination must account for the new kind of inventory generated by the additional flow of collected returns to be integrated into the remanufacturing process. However, this additional flow is not directly available to the manufacturer because of the unpredictability of the quantity, timing and quality of the products returned by customers. The uncertainty around the quantity and timing of returns is one of the main factors that make the implementation of closed loop supply chain processes difficult, particularly in the case of integration between the forward and reverse supply chains. For example, the difficulty of determining the quantity of used products that will be returned by customers negatively affects remanufacturing and traditional production planning. Moreover, the lack of tools and guidelines on planning, controlling and managing remanufacturing operations has limited the growth of the remanufacturing sector (Guide, 2000). If not well designed, closed loop supply chain activities such as remanufacturing and disposal can increase company costs (Inderfurth, 2005). For this reason, a company objective is to integrate the reverse and forward supply chains so as to minimize the total cost and consequently obtain economic benefits.

Mindful that these problems add complexity to this field of research, in this chapter we aim primarily to model the factors affecting a production and inventory system that combines returns and remanufacturing, and to evaluate effective control strategies that address dynamic production and inventory management issues in order to improve the performance of the system. For this purpose, the System Dynamics (SD) simulation modeling approach (Forrester, 1958, 1961)—a methodology used for studying and managing complex feedback systems, particularly business and social systems—is adopted to model a production and inventory system for remanufacturing within the context of closed loop supply chains in order to understand the complex and dynamic interaction of factors that affect the behavior of the system. Our primary interest is the returns process in the context of closed loop supply chains. Moreover, this model is used to evaluate strategies for the effective management of remanufacturing and returns processes. In order to assess some of the findings and to further validate the developed model, the methodological approach taken in this chapter is based on case study research. The selection of companies employed as case studies was based on their engagement in remanufacturing and returns processes, which made them useful for our study. The companies studied are: (1) the Australian Mobile Telecommunications Association (AMTA), which has commenced a national recycling program for mobile phones; and (2) Fuji Xerox Australia, which is involved in the remanufacturing of assemblies and sub-assemblies of printers and copiers.

BACKGROUND The introduction of new environmental legislation in recent years has strengthened the need to focus on reverse logistics and CLSC among logistics operators. While legislation introduced in

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Europe, North America and Japan encourages this awareness, many corporations have proactively taken measures in anticipation of evolving environmental performance requirements (Savaskan, Bhattacharya, & Wassenhove, 2004). Several examples of government regulations can be considered. In Japan any products purchased by the government must contain recycled materials and the European Union (EU) has issued a directive for producer responsibility to collect, process and recycle waste from both “white goods” (e.g. refrigerators, washing machines and freezers) and “brown goods” (e.g. TVs and speakers) (Kulwiec, 2006). In Europe, many countries have forced industry to develop collection and recycling systems in order to reduce waste. This has led to the issuing of several environmental regulations such as the EU Directive 2000/53/EC (Blanc, Fleuren, & Krikke, 2004) for responsibility in taking care of used products, the EU Directive 2002/96/EC and 2003/108/EC for electrical and electronic equipment, and the EU Directive 2002/525/EC for end-of-life vehicles (Tang, Grubbström, & Zanoni, 2007). According to the US Environmental Protection Agency (EPA), in the United States (US) the amount of waste generated has increased from 88 million tons in the 1960s to 196 million tons in 1990 alone. Consequently, companies need to develop techniques for product recovery and waste management (Gungor & Grupta, 1999). Furthermore, hundreds of environmental laws and regulations for recycling operations and responsibility for packaging recovery have been developed (Kulwiec, 2006). Local governments in North America promote the reduction of landfill use, which is the main driver of non-toxic solid waste, pushing companies towards improving and innovating in their manufacturing activities (Biehl, Prater, & Realff, 2007). This places an onus on manufacturing firms to use reverse logistics activities as a form of extended producer responsibility (EPR), which makes them responsible for their products throughout their life cycles (Klausner & Hendrickson, 2000).

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In the US, the precise extent of reverse logistics activities is difficult to determine because most companies have not quantified them (Reverse Logistics Executive Council, 2007). However, reverse logistics activities account for a significant portion of logistics costs in the US. These are estimated to account for approximately 10.7% of the US economy and approximately 4% of the total logistics costs (Reverse Logistics Executive Council, 2007). Reverse logistics costs are estimated to be approximately 0.5% of total US gross domestic product (GDP), which equated to around US$58.34 billion in 2004. In 1999 the total value of returned merchandise in the US, with an estimated handling cost of $40 billion, was $62 billion (ReturnBuy, 2000). The US is not the only country where reverse logistics activities are increasing. In Europe, the annual production of remanufactured automotive parts was approximately 20,000,000 units in 2005, and is expected to increase to 30,000,000 units in 2015 (Automotive Parts Rebuilders Association, 2007). In Australia, the Eco Manufacturing Centre of Fuji Xerox, a company that continually adopts new remanufacturing programs, has aimed for zero waste discharge and has achieved 90% reuse and recycling (Environment Protection Authority, 2002). Based on these data, it is evident that many companies have realized that reverse logistics/ CLSC, particularly remanufacturing, is an important competitive and strategic component of their business mission. Indeed, the use of reverse logistics in the business sector is increasing not only because of the implementation of more stringent environmental regulations, but also for reasons related to competition. The results of a survey involving 1,200 logistics managers and more than 150 managers with reverse logistics responsibilities in the US found that 65% of companies believe that returns management is an important strategic tool for their business (Rogers & Tibben-Lembke, 2001). Indeed, through reverse logistics and closed loop supply chain activities companies can fulfill

Modeling Closed Loop Supply Chain Systems

the environmental responsibilities stipulated by government regulations and at the same time optimize operational processes. Such optimization can be achieved both by reducing operating expenses, and by improving company image through a reduced use of resources. The cost of remanufacturing, for example, is typically 40–60% of the cost of manufacturing, and remanufactured products are of the same quality as new products, and sold with the same warranties (Mitra, 2007). Moreover, customers have become more environmentally conscious, which in recent years has led to an increase to over US$2,000 billion worth of environmentally friendly products on the global market (Mitra, 2007). This growing pressure to improve the market competitiveness of companies through the reverse logistics process has pushed researchers to analyze, model and explain why and how reverse logistics can lead to economic and environmental benefits. A number of studies describe the role of reverse logistics in economic and environmental activities during the product development process. In particular, several models have been developed to support managerial decision-making and to optimize processes in different reverse logistics areas. The scope of these models is mostly to minimize costs and optimize profits through analysis of the parameters and variables as defined in the modeling method. However, the characteristic of variability in the quality, quantity and timing of returned products and the integration of the returns flow within the original forward supply chain make reverse logistics activities, particularly remanufacturing, difficult to plan, control and manage (Guide & Wassenhove, 2001). For this reason, systems that are not appropriate for dealing with returns could increase operating expenses. In a study by Guide (2000), 61.5% of the firms under study were found to have no control over the timing or quantity of returns. He discusses several characteristics of recoverable manufacturing systems that complicate production planning and points out that there is a significant lack of specific

technologies and techniques for remanufacturing logistics. These include: uncertainty around the timing and quantity of returns; balancing returns with demands; disassembly of returned products; and materials recovery uncertainty, which are all factors that require considerable research. These characteristics could be addressed by focusing on several issues, such as the methods (e.g. leasing, deposits) used to reduce uncertainty around timing and quantity of returns; forecasting models; aggregate production planning models that consider returns; and models that support material recovery planning and prediction based on the age and usage rate of products. Production and inventory management requires appropriate control mechanisms to integrate the return flow of used products within the material planning for the forward flow (Fleischmann et al., 1997). This can vary for different reverse logistics situations. For example, for companies whose business is recycling, returns are the only inventory resources for the forward production process and used products or materials are the only raw materials. Traditional inventory control methods might be satisfactory in this context. However, the mechanism is different for remanufacturing or reuse where used products are returned for introduction into the main production stream. In this case, returned goods consist of an additional inventory source to the usual inventory procured from outside. Moreover, this additional flow is not directly available to the manufacturer because of the unpredictable factors of quantity, time and quality of the products returned. Hence, inventory management can be made particularly complicated by remanufacturing activities since key information such as that related to on-hand inventory, lead time and yield is not clearly known (Toktay, Wein, & Zenios, 2000). For these reasons, in this study we model a production and inventory system in which production is integrated with remanufacturing activity. Several authors have conducted research into such a system. They have focused mainly on: production

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and remanufacturing lead times (Inderfurth & van der Laan, 2001; Kiesmuller, 2003; Kiesmuller & Minner, 2003; van der Laan, Salomon, & Dekker, 1999); optimization procedures for inventory levels and economic order quantity (Kiesmuller & van der Laan, 2001; Koh, Hwang, Sohn, & Ko, 2002; Teunter, 2001; van der Laan, Dekker, & Salomon, 1996; van der Laan, Dekker, Salomon, & Ridder, 1996); comparisons between pull and push strategies (van der Laan & Salomon, 1997; van der Laan, Salomon, Dekker, & Wassenhove, 1999; van der Laan & Teunter, 2006); and capacity planning (Georgiadis, Vlachos, & Tagaras, 2006; Kleber, 2006; Vlachos, Georgiadis, & Iakovou, 2007). However, to our knowledge no previous study has examined a system in which the returns process is modeled and analyzed in relation to the particular system variables involved in our research. Several inventory models for the remanufacturing process have been developed where returns are included as exogenous variables, without any or with only simple correlation between demand and returns. Many of these have adopted simple assumptions regarding the returns process such as the homogeneous Poisson Process for demand and/or return flow, or that returns are independent of the demand (de Brito & Dekker, 2003).

MODEL BUILDING The modeling process undertaken in this research was characterized by a sequence of iterative activities and stages that involved continuous revisions and changes. Indeed, this modeling can be defined as a continual process of iteration among problem articulation, the generation of hypotheses, data collection, model formulation, testing and analysis (Sterman, 2000). In this section, the final results of this process of iteration, which led to the model building for the production and inventory system for remanufacturing, are presented.

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Description of the System under Study The purpose of this chapter is to model the factors affecting a production and inventory system that combines returns and remanufacturing within the context of closed loop supply chains, and to evaluate effective control strategies aimed at improving the performance of the system, focusing particularly on the returns process. For this purpose, we model and simulate a single-product production and inventory system for remanufacturing within the context of closed loop supply chains. The system involves several operations including: production of new products; collection and inspection of used products; remanufacturing; and disposal. Our focus in this study is on the return of products by customers/product users at the end of the products’ useful life; other returns such as product recalls and B2B commercial returns are excluded from examination. The system under consideration is depicted in Figure 1. The forward supply chain involves the production of new products to meet customer demand. After product use, returns are collected, inspected and either stored as remanufacturable/ recoverable inventory or disposed of depending on whether the quality of returns is deemed suitable for remanufacturing according to the company’s policy on quality standards. The serviceable inventory, used to fulfill external demands, is fed by the production of new or remanufactured products which are as good as new. Production and remanufacturing activities are important components of any production and inventory system for remanufacturing. Equally important to the process is analysis and decision-making regarding inventory, operational and marketing activities. The following assumptions are adopted throughout this analysis in order to simplify the system and facilitate the modeling process by focusing on the most important factors:

Modeling Closed Loop Supply Chain Systems

Figure 1. Production and inventory system for remanufacturing

•

• •

•

Uncontrollable disposal is not considered, whereby instead of the product returning to the remanufacturer, it is disposed of in an uncontrolled manner, sometimes in opposition to the manufacturer’s instructions or environmental regulations. The planned disposal of recoverable inventory is not considered. The capacity of several activities such as collection, inspection, remanufacturing and production are considered infinite. Backordering and lead times are not considered.

In our model, the returns rate incorporates the uncertainty around the quantity and timing of returns and a pull inventory control policy is applied. This policy is implemented through reorder point inventory replenishment policies, which are basic features of several industries in the context of supply chain/inventory planning. The returns rate, which is used to calculate the number of returns after the time of use, is represented as the ratio between the probable returns flow of sold products and the demand. The probable returns flow and the time of use are calculated on the basis of the relationship between two factors: the return index and the residence time. The latter is

the factor defined in the study of Georgiadis et al. (2006), and represents the average time for which a product stays with its customer before it is returned. The ways in which companies manage the returns process for products sold to customers, through service agreements and sales contracts with retailers or the customers themselves, can influence the returns rate and particularly the quantity of returns. For example, leasing contracts ensure that almost all products are returned after the residence time. In our model, the service agreement with customer factor is used to relate the quantity of returns with the demand for different products in different industries. Customer behavior is another factor that can influence this relationship. The attitude of the customer in terms of their return activity and their response to a company’s returns process incentives can affect the returns rate and in particular the likelihood of a particular product being returned. Hence, the return index is obtained by considering the relationship between company incentives/service agreements aimed at recovering used products and actual customer behavior in returning products. Put simply, the return index is the tendency of the product to be returned by the customer during its lifetime, which varies across different products

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and industries. This is explained in more detail in the next section.

Quantitative Modeling A System Dynamics (SD) simulation modeling approach is adopted in this research. SD was introduced in the early 1960s by Jay Forrester (1958, 1961) as a modeling and simulation methodology aimed at dealing with the dynamics and controllability of management systems (Coyle, 1996). The SD method is commonly used to analyze how the dynamic behavior patterns of system variables change in response to dynamic inputs. Controllability refers to the “control systems” (Coyle, 1996) by which the policies employed and applied in the system structure control system behavior over time. The objective of using SD is to identify strategies to improve system performance (Sterman, 2000). For these reasons, SD has become a computer-aided method of analyzing and solving complex problems, particularly in the area of policy analysis and design. It is applied in a number of fields, including: corporate planning and policy design; economic behavior; public management; biological and medical modeling; energy and environmental studies; social science; dynamic decision-making; complex nonlinear dynamics; software engineering; and supply chain management (Angerhofer & Angelides, 2000). The choice of a simulation approach, in particular the SD approach, rather than other methods such as analytical approaches was based on the recognition that SD can model an entire system in which several policies and factors can be used to evaluate strategies aimed at improving system performance. Moreover, SD can handle the issues arising from models in which dynamic forces and nonlinear relationships play a significant role. All business and social systems contain a host of different asset stocks or accumulation of resources which change according to their physical inflows and outflows (Morecroft, 2007). According to Morecroft, this stock and flow structure

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of systems reveals the operations behind the causal influences or relationships among system variables. In SD, the stock and flow structure of systems is represented by the Stock and Flow Diagram (SFD). Such a diagram is obtained by identifying the stock, flow and auxiliary variables among the variables used to model a system. The stock variables determine the state of the system and are represented by an accumulation of the difference between the inflow to a process and its outflow (Sterman, 2000), while the flow variables determine the physical flows in the system and generate change in the stocks, which is then used to make decisions. The auxiliary variables can be useful for clarifying the structure and process of the model. They represent constants or exogenous inputs into the model, as well as converters or intermediate variables used in the mathematical equations of the model (Kirkwood, 1998; Sterman, 2000). The objective in developing an SFD is to analyze and define the dynamic relationships among stock, flow and auxiliary variables through mathematical equations in order to run simulations of the model. The SFD for the production and inventory system for remanufacturing is presented in Figure 2. The rectangles represent stock variables which equate with accumulations of items, while the valves represent flow variables which correspond to the physical flow of items feeding or depleting the stocks. The physical flow of items is represented by a double line with arrows, while the flow of information (i.e. connections among variables and their relationships used for mathematical formulations) is represented by a single line with arrows. The auxiliary variables shown in upper case letters represent constants or exogenous inputs, while those in lower case letters represent the converters used in calculations. In the diagram above, an increase/decrease in returns increases/decreases the rate of collection, which in turn increases/decreases the level of Collected Returns. At this stage of the process, returned products are inspected in order to check

Modeling Closed Loop Supply Chain Systems

Figure 2. Stock and flow diagram

their quality and remanufacturability. Failed items decrease the level of Collected Returns, through a failed returns flow, and at the same time increase the level of Disposal which represents the quantity of non-reusable items that are disposed of. The flow rate of failed items depends on the value of percentage disposED and inspection time. The former affects the flow rate as any change in the percentage leads to a change in the quantity of failures. For example, an increase in the percentage value of disposal leads to an increase in the flow of failed items for a given time period. Percentage disposED also represents the quality standards policy of the company and is affected by several parameters and techniques used to check returned items. It is defined as an average percentage of collected returns that are disposed of, and differs for different products and different quality standard policies adopted (Vlachos et al., 2007). Inspection time represents the period of

time required to inspect collected items. This affects the inspection flow, as a faster/slower inspection time leads to an increase/decrease in the flow. Accepted items increase the level of Recoverable Inventory that is ready to be remanufactured through the accepted returns flow. The flow rate of accepted items depends inversely on the value of percentage disposED, as a lower percentage of disposed items leads to a higher number of remanufacturable items. Remanufacturable items are stored as Recoverable Inventory from which items are used for remanufacturing purposes when necessary and stored as Serviceable Inventory in order to fulfill customer demand. In this system, remanufacturing, which is preferred to the more expensive production activity, occurs when necessary as a pull inventory policy is applied. Several studies have previously modeled push and pull inventory policies in a production and inventory system

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for remanufacturing (Kiesmuller, 2003; van der Laan & Salomon, 1997; van der Laan, Salomon, & Dekker, 1999). A more detailed picture of the inventory pull policy was provided in a study by van der Laan, Salomon and Dekker (1999), who similarly did not consider the disposal of recoverable inventory in their model. According to these authors, REMANUFACTURE UP TO LEVEL (Sr) and LOW LEVEL OF SERVICEABLE FOR REMANUFACTURING (sr) are two variables that affect the remanufacturing rate and are used to implement a pull policy in the system. Sr represents the upper value limit for remanufactured batches, while sr represents the lower value for remanufactured batches as well as the level of a Serviceable Inventory at which a remanufacturing batch is required. Sr - sr represents the level of Recoverable Inventory for which it is possible to produce a remanufacturing batch. The pull policy is represented by the Recoverable Inventory level. Only when this level reaches the difference (Sr sr), and the Serviceable Inventory level is lower than sr, is it possible to produce a remanufacturing batch. Two additional variables that affect production flow are incorporated to implement the pull inventory policy: PRODUCTION UP TO LEVEL (Qm), which is the upper value for production batches; and LOW LEVEL OF SERVICEABLE FOR PRODUCTION (sm). Production activity is only used to increase the Serviceable Inventory level when the Recoverable Inventory level is lower than (Sr - sr) and the Serviceable Inventory level is lower than sm (the Serviceable Inventory level at which a production batch is required). However, production flow is mainly affected by Sr and sr, because it is only when the Recoverable Inventory level is lower than Sr - sr and the Serviceable Inventory level reaches sm that a production batch is manufactured and stored in Serviceable Inventory. This strategy increases the cost of the Recoverable Inventory but reduces the cost of the Serviceable Inventory which is usually more expensive. Moreover, remanufacturing is preferred to production activity, as sm is lower

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than sr. Remanufacturing flow is also affected by the replenishment frequency of the inventory. Replenishment frequency represents the time taken to replenish remanufacturing orders and an increase/decrease in its value generally leads to a decrease/increase in the order size. The returns process starts with customer demand, which depletes the Serviceable Inventory level. Product demand is considered equal to sales. After a period of time or RESIDENCE TIME, products in use can be considered to be used products. This is represented by the flow between the rate of the variable demand inflow and the level of Used Products. The variability of RESIDENCE TIME represents the uncertainty that affects the timing of returns in a closed loop supply chain. For this reason, in this model not all used products are considered to be returns but rather as possible returns after an average period of use that is dependent on the type of product. A portion of these used products become returns, which are consequently collected. This is represented by the physical flow in which returns deplete the Used Products level and the information flow between returns and collection. Uncertainty around the quantity of used products returned by customers negatively affects collection, remanufacturing, production planning and inventory control. For this reason, several variables are used to reduce the effect of this uncertainty and set the quantity of returns. The return index is used to set the number of returns based on customer demand. The number of returns is represented by the returns inflow, which is influenced by both returns index and demand. Two parameters influence the return index: SERVICE AGREEMENT WITH CUSTOMER and CUSTOMER BEHAVIOR. The former defines the level of the service agreement or incentives that the company offers to the customer at the end or during the use of the product in order to stimulate the returns process. However, it also represents the level of responsibility the company has towards the recovery of its own products. The latter parameter,

Modeling Closed Loop Supply Chain Systems

CUSTOMER BEHAVIOR, defines the attitude of the customers towards returning used products and their response to company incentives aimed at increasing the number of returns. The relationships among these three factors are shown in Figure 3. The difficulty in obtaining and documenting real data has led to the use of a distributional form constructed on intuitive grounds. However, a similar approach to the analysis of influences was adopted by Georgiadis and Vlachos (2004), who analyzed different parameters such as market behavior and the “green image” factor in relation to products in different industries. In Figure 3return index for a particular product is obtained based on the level of service that a company offers to its customers to encourage the return of the product after its use. In this regard, the values given to SERVICE AGREEMENT WITH CUSTOMER lie between 0 and 100%, which correspond to 0 and 1 of the return index, respectively. High values of service agreement are obtained for companies that offer incentives for the full return of sold products, such as through leasing contracts (e.g. cars and photocopiers) or service at the end of the useful life of a product/ component (e.g. single-use cameras and toner

cartridges). Also included are companies that maintain full responsibility for product recovery to abide by government environmental regulations. High values of service agreement correspond to high values of the return index, for which it is assumed that almost all sold products are returned by customers. The minimum value of service agreement corresponds to the kinds of products not involved in reverse logistics activity, particularly remanufacturing, resulting in a zero return index and no efforts by companies to generate product recovery. For all values in between, the relation between the index and service agreement depends on CUSTOMER BEHAVIOR (CB). A range of company incentives are used in practice to stimulate a desired customer behavior in the area of product recovery (de Brito, Dekker, & Flapper, 2004), including: a deposit that must be paid when purchasing the product; free collection or repurchase of used products; a monetary incentive paid upon return of used products; and a trade-in that involves the possibility of obtaining a newer version of a product when the original product is returned. Currently, products designed for easier disassembly and clear information/ advertising about reverse logistics activities and

Figure 3. Relationship between SERVICE AGREEMENT WITH CUSTOMER and return index for various CUSTOMER BEHAVIORS

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environmental responsibilities are being developed by companies to encourage the return of used products. However, the reaction to such incentives depends on individual customer behavior. To incorporate various possible customer behaviors into this study, three alternative relationships are assumed, which are represented in Figure 3. CB2 corresponds to a proportional relationship between service agreement with customer and return index. In this case, it is assumed that customers respond proportionally to incentives and services offered by companies attempting to recover used products. The symmetric curves CB3 and CB1 correspond to a quicker and a slower response from customers, respectively. Particularly in relation to CB3, it is assumed that the response of customers and consequently the associated return index changes quickly for low values of service agreement with customer while it is almost the same for higher values. This is different for CB1 which becomes more acute for higher values of service agreement with customer. This influence analysis is used to account for the relationship between customer behavior and quantity of returns, as much as is possible.

Mathematical Formulation The dynamic relationships among the variables of the developed SFD for the production and inventory system for remanufacturing are defined by a set of mathematical equations. The symbology and the form used for the equations follow the conventions of the simulation software used to build the model: Vensim PLE v5.6d (Ventana Systems, Inc.). This provides a simple and flexible means of building simulation models from Stock and Flow Diagrams (Ventana Systems, Inc., 1999). The mathematical equations include several constant parameters or inputs. The latter correspond to the exogenous inputs of the SFD, which were represented in upper case letters, as well as to the initial values of the stock variables. Usually, when modeling a specific real system the

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values of these parameters are finetuned in order to reproduce the behavior of the system. However, because in this study we are developing a generic SD model of a production and inventory system for remanufacturing, assumptions are adopted for the value of the parameters. Indeed, given the objective of running a simulation of this generic model to evaluate strategies with a particular focus on the returns process, to improve the performance of the system we believe that an intuitive understanding regarding the impacts of the structure of the model on its dynamic behavior is more important than determining the exact value of the parameters. However, the assumptions used for the values of these parameters, which will be provided for the simulation analysis of the model, are set to correspond as far as possible to a meaningful concept based on real-world situations. The mathematical meaning of an SFD results from the conventions used by Forrester (1961) to define this particular diagram based on the hydraulic metaphor of the flow of water into and out of reservoirs (Sterman, 2000). Specifically, stocks (reservoirs) accumulate material (water), which changes dynamically in response to the flow variables (inflow and outflow of water). For this reason, stocks can be represented mathematically through a differential equation (Sterman, 2000) or an equivalent integral equation. Thus, the stock variable Collected Returns is defined by a time integral of the net inflow (collection) minus the net outflows (accepted returns and failed returns): t

Collected Returns (t)=

∫

(collection(t)

t0

– accepted returns(t) – failed returns(t))dt + Collected Returns (t0). The collection flow is equal to the returns flow. This means that at time t, all returns follow a collection process: collection (t) = returns (t). Infinite collection capacity is assumed as all possible returns are collected. Failed returns at

Modeling Closed Loop Supply Chain Systems

time t are equal to total Collected Returns times the PERCENTAGE DISPOSED divided by the INSPECTION TIME (Vlachos et al., 2007). The percentage of disposed returns and the inspection time are assumed to be constant due to the difficulty of representing and modeling the real dynamic variance for this factor which depends on product characteristics, company quality policies and inspection strategies; and this particular issue is not within the scope of this study. Accepted returns at time t are the Collected Returns that pass the inspection process. For this reason, the percentage of returns accepted for remanufacturing is 1 – disposal percentage. Equations can thus be formulated as: accepted returns(t) = Collected Returns(t) * (1- PERCENTAGE DISPOSED) INSPECTION TIME

failed returns(t) = Collected Returns(t) * PERCENTAGE DISPOSED INSPECTION TIME

An IF THEN ELSE function and the logical operator AND are used to define the production quantity in the system. In particular, they provide the number of production orders during the simulation period. The logical expression defines the condition when the Serviceable Inventory level is less than or equal to the low level of serviceable for production and also when the Recoverable Inventory level is less than the remanufacture up to level minus low level of serviceable for remanufacturing. If the condition is true, the expression returns a production order value equal to the ratio between production up to level minus the serviceable inventory on hand and the replenishment frequency; otherwise the returned value is zero. A similar equation defines the remanufacturing quantity and the number of remanufacturing orders in the model. In this case, the condition requires that the Serviceable Inventory level is less than or equal to low level of serviceable for remanufacturing and that Recoverable Inventory is greater than

or equal to remanufacture up to level minus low level of serviceable for remanufacturing. The possible returned values are a remanufacturing order that is equal to the ratio between remanufacture up to level minus low level of serviceable for remanufacturing and replenishment frequency if the condition is true, or zero otherwise. Recoverable and Serviceable Inventory levels are defined in the equations below: t

Recoverable Inventory(t) =

∫ t0

(acceptedreturns(t) – remanufacturing(t))d + Recoverable Inventory(t0) The assumed values for the product demand are set in order to generalize the model for different kinds of products. Specifically, demand is set to a uniformly distributed random number. The random values are set at between 300 and 2,000 items with a fixed noise seed, in order to have the same sequence of random values for every simulation, which equals 2. The formulation of demand is thus represented as: demand = RANDOM UNIFORM (300, 2,000, 2). Demand inflow represents the flow of previously sold products currently in use which are now used products and possible returns after the residence time has elapsed. In order to model this process the function DELAY FIXED is used. This function returns the value of the input demand delayed by the delay time, which in this case is the residence time. Zero is the initial value of demand inflow at the start of the delay process: demand inflow = DELAY FIXED (demand, residence time, 0). The flow of actual returned items that are collected is represented as a dynamic ratio between the proportion of Used Products through the use of a returns rate and the time required to return and collect the items: returns(t) =

(Used Products(t) * returns rate(t)) RETURN TIME

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The variable Used Products is defined as: t

Used Products(t) =

∫

(demandinflow(t)

t0

– returns(t))dt + Used Products(t0) Returns rate represents the proportion or percentage of used products that are returned during the time period under consideration. Several authors, such as Kiesmuller (2003), Kiesmuller and Minner (2003) and Inderfurth (2005), use the returns rate variable in their models. In order to define the quantity of returns, they consider a returns rate to be the ratio between the average returns and the average demands. Consequently, the returns rate in this model is represented as a dynamic ratio between returns inflow and demand: Returns rate (t)

returns inflow(t) demand(t)

The function DELAY FIXED is used to identify the value of the input given by the previous ratio delayed by the residence time plus one time period. The reason for this delay is due to the necessary time equivalence between the variables returns and returns rate, as the accumulation of used products and the actual returns flow start one time period after the residence time. Returns inflow represents the expected returns of demand. A forecast of returns is obtained using the return index: returns inflow (t) = demand (t) * return index (t). A functional relationship between two variables is used for the formulation of the return index at time t. This is obtained by using a lookup function which allows the definition of a customized relationship between a variable and its causes. Specifically, an equation gives the value of return index at any value of SERVICE AGREEMENT WITH CUSTOMER through a linear interpolation between the values specified in return index lookup (Figure 2) as: return index = return index

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lookup (SERVICE AGREEMENT WITH CUSTOMER). Return index lookup defines the lookup function that expresses the relationship between return index (dependent variable) and SERVICE AGREEMENT WITH CUSTOMER (independent variable), as shown in Figure 3. For this reason, the variable return index is formulated through a combination of IF THEN ELSE and lookup functions: return index (t) = IF THEN ELSE (CUSTOMER BEHAVIOR = 3, return index lookup 3 (SERVICE AGREEMENT WITH CUSTOMER), IF THEN ELSE (CUSTOMER BEHAVIOR = 2, return index lookup 2 (SERVICE AGREEMENT WITH CUSTOMER), return index lookup 1 (SERVICE AGREEMENT WITH CUSTOMER))). This equation represents the tendency of a particular product to be returned by customers, considering individual customer behaviors and differing levels of service agreement or company incentives. The constant CUSTOMER BEHAVIOR can assume three values, 1, 2 or 3, represented by the three different curves in Figure 3. In the function of value assumed for CUSTOMER BEHAVIOR, return index is calculated through one of the lookup functions. Finally, the accumulation of returns from returns inflow is defined through the time integral of the flow: t

Returns Accumulation(t) =

∫ t0

(returnsinflow(t))dt + Returns Accumulation (t0)

Validation Analysis Before we can run a simulation of the model for evaluating and investigating strategies to improve the performance of the system, a validation analysis must be performed to determine whether the model is suitable for this objective. In SD theory, model validation primarily involves the assessment of the structure and behavior of the model

Modeling Closed Loop Supply Chain Systems

in terms of its consistency with the available facts and descriptive knowledge of a real-world system (Morecroft, 2007; Sterman, 2000). Such assessment, as Sterman states, is useful to build confidence that a model is appropriate for its purpose. Although the theoretical basis of the quantitative modeling in this study was obtained from the existing literature as well as from some of the information and data collected from the companies employed as case studies, in order to develop a model that corresponds to a meaningful concept representing the real world (Sterman, 2000), we deemed it necessary to perform validation tests to define its capacity to reflect the structure and behavior of a real process model. The validation tests were the direct structure tests and structure-oriented behavior tests (Barlas, 1996). Specifically, for the direct structure tests, in which simulation was not involved, we engaged extreme condition tests in order to check whether each mathematical equation of the model made sense and was reasonable given the available knowledge of the real system. For the structure-oriented behavior tests, which involved simulation of the entire model, we undertook behavior sensitivity tests. These involved sensitivity analysis on particular parameters of the model aimed at comparing the high sensitivity of these parameters between the model and a real system. Based on the outcome of the validation analysis we determined that the developed model of the production and inventory system for remanufacturing is suitable for evaluating and investigating strategies aimed at improving the performance of the system through a simulation of scenarios. Specifically, through the direct structure validation we confirmed the capacity of the model equations to be logically and dimensionally consistent with the available knowledge of real-world scenarios. Through the sensitivity analysis, we also identified that the changes in the behavior patterns of the model, due to particular assumptions regarding the changes in value for particular parameters, are consistent with a real system.

Another outcome of the sensitivity analysis was the identification of those parameters to which the model showed sensitivity. In particular, in line with the purpose of the model, it was found that the model showed sensitivity to the changes or joint changes in the values of the main parameters involved in the returns process (RESIDENCE TIME, SERVICE AGREEMENT WITH CUSTOMER and CUSTOMER BEHAVIOR). Thus, these parameters can be used for the simulation analysis of scenarios to achieve the aims of the model.

SIMULATION OF SCENARIOS The development of the model, and its validation, led to the final stage of SD simulation modeling, which involved simulation of scenarios focusing on the main parameters of the returns process in order to reach conclusions, specifically to identify and evaluate the best policy and strategy to adopt, and what occurs in the system if factors change or events intervene. In particular, the main purpose of the simulation is not to create predictions or forecasts of a future event, but rather to evaluate scenarios or alternative futures that could occur given certain assumptions or conditions (Morecroft, 2007). Before the various scenarios can be designed and simulated, we must first identify and present the measure of performance and the base scenario used for the simulation analysis. We then discuss the various scenarios employed in the analysis and present the results of the simulations from which an evaluation can be undertaken of the best strategies to adopt to improve system performance.

Performance Measure The measure of performance for this simulation analysis represents the output variable the value changes of which under different scenarios and given certain values of the parameters enhance our understanding of the conditions that might arise

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in the system. These conditions which represent the simulation results lead to an evaluation of strategies, which in turn provide guidelines as to how to improve the performance of the system. As was found in the existing literature, for most production and inventory systems for remanufacturing the objective is to minimize the total inventory cost; therefore, the latter will be considered as a measure of performance for the simulation analysis. However, the analysis is not aimed at determining the optimal order quantity or reorder inventory level. Rather, the objective is to identify the effects on the system of the main factors involved in the returns process, in particular residence time, service agreements offered by companies and customer behavior. Changes in these factors are considered to be events that intervene in a production and inventory system for remanufacturing where the returns process is characterized by uncertainty around the quantity and timing of returns. The value of the total inventory cost for the analysis is obtained by adding a number of operational costs. These are the set-up costs for each production order and remanufacturing order, the cost of stockout for each out-of-stock sale, and the holding costs for recoverable and serviceable inventory. The choice of this particular sum of total cost in which the remanufacturing and production costs are excluded is based on several observations. First, one of the assumptions previously mentioned in the description of the system under study determines that the model posits a disposal activity resulting from the inspection process rather than a planned disposal in recoverable inventory. This means that all accepted returns from the inspection are stored as recoverable inventory and used for the remanufacturing activity. Therefore, there are no inventory decisions that affect the remanufacturing and production activity in terms of quantity of items to be remanufactured and produced (van der Laan & Teunter, 2006). Moreover, the mathematical formulation of the model considers the number of remanufacturing orders and production orders

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rather than production and remanufacturing rates. These observations render the system independent of the remanufacturing and production activity, and consequently it excludes the production and remanufacturing costs. However, in order to potentially consider cheaper remanufacturing in place of production activity (remembering that the cost of remanufacturing is typically 40–60% of the cost of production), different set-up costs are assumed for production orders and remanufacturing orders. Moreover, this analysis focuses on the total inventory costs which exclude the collection and inspection/disposal costs.

Base Scenario The base scenario represents and determines the values of the parameters that we initially set in order to run the simulation of scenarios. Specifically, these scenarios are obtained by changing the value of the main parameters under study while at the same time keeping the other parameters at the value of the base scenario. The base scenario used for this simulation analysis involved the use of several assumptions aimed at ensuring the model corresponds as much as possible to a meaningful representation of the real world. Such assumptions reflect the theoretical basis drawn from the existing literature and to a degree information obtained from the companies employed as case studies. Indeed, the generic models based on remanufacturing and closed loop supply chains presented in the literature use several assumptions in relation to the values of the model parameters. For this reason, we deemed it appropriate to adopt similar assumptions for the purposes of the simulation analysis. In setting the values of the model parameters, INSPECTION TIME, REPLENISHMENT FREQUENCY and RETURN TIME were set to one month. It was assumed that all returns collected in a given month are inspected within that same month, that the remanufacturing and production orders are replenished monthly, and that there is

Modeling Closed Loop Supply Chain Systems

a monthly collection of returns. The initial values at the beginning of the simulation horizon for the stock variables were set to zero (Georgiadis & Vlachos, 2004) for Collected Returns, Recoverable Inventory and Used Products, while 2,000 items were set for the Serviceable Inventory. PERCENTAGE DISPOSED was set at 0.1 (10%), 500 items for low level of serviceable for remanufacturing (sr), 4,000 items for REMANUFACTURE UP TO LEVEL (Sr), 4,000 items for PRODUCTION UP TO LEVEL (Qm), 300 items for low level of serviceable for production (sm) and 12 months for the RESIDENCE TIME. CUSTOMER BEHAVIOR was set at 2, with a proportional relationship between return index and SERVICE AGREEMENT WITH CUSTOMER, which in turn assumed a value equal to 50%. In defining the values of the parameters related to the measure of performance, set-up costs were set at $20 per remanufacturing order and $50 per production order. The cost of stockout was set at $10 per out-of-stock sale, with $0.5 and $0.8 per unit recoverable and serviceable inventory holding cost, respectively. However, the generality of the model provides the opportunity to finetune and customize the values chosen for the basic scenario for different kinds of products and different industries.

Scenarios Derived from the Returns Process The simulation of scenarios focused on the main parameters considered for the returns process. Thus, the scenarios were obtained through the combination of a range of values for those parameters related to the system policies defined for the returns process. Moreover, the model was sensitive to these parameters during the sensitivity analysis. Specifically, these parameters are RESIDENCE TIME (RT), SERVICE AGREEMENT WITH CUSTOMER (SAWC) and CUSTOMER BEHAVIOR (CB). The analysis focused on the effect of the three parameters on the total inventory cost during the

planning horizon, which was set at 60 months. Specifically, through this measure of performance changes in the behavior of the modeled production and inventory system for remanufacturing were examined using 6 levels of residence time, 5 levels of SERVICE AGREEMENT WITH CUSTOMER and 3 levels of CUSTOMER BEHAVIOR. Figure 4 lists the values of the parameters used for the analysis, which involved a total of 90 scenarios. The selection of the assumed values for the parameters was intended to correspond as much as possible to a meaningful reflection of the real world for a broad range of products. However, following the purpose of this simulation analysis to evaluate strategies aimed at improving the performance of the system, we believe that the exact value of the parameters is not as important as an understanding of the changes in the behavior of the system under different scenarios. The various scenarios are characterized by low and high residence times, which correspond with fast-used products and slow-used products, respectively. The choice of this table structure was based on the relationship between residence time and type of product. service agreement with customer is considered to be the policies/incentives that companies use to retrieve used products and customer behavior the customer tendency to return them, respectively. The parameter values used to set the residence time are realistic as they can be associated with several remanufacturable products (Georgiadis et al., 2006). The assumed values for service agreement with customer represent a broad range of company policies and incentive types that develop a relationship between companies and their customers in the returns process. In the same way, the different levels of customer behavior are representative of a broad range of responsive aptitudes. Figure 5 represents the evolution of the total inventory cost for various simultaneously simulated levels of residence time and service agreement with customer, given a customer behavior level equal to 3.

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Figure 4. Parameter values used for returns process scenarios

The first observation shows a decrease in the total inventory cost for a high level of residence time. This is due to the reduced cyclic nature of returns for slow-used products, which remain with customers for longer. In the model the reduced cyclic nature of the return of a product is represented by the long time period between the product sale and its possible return. Specifically, a portion of products sold at time 0 of the planning horizon become remanufacturable returns only after a long residence time, and during this residence time they do not affect the recoverable inventory and its associated cost. Conversely, products with a short residence time engender a fast cyclic nature of return and are quickly involved

in recoverable inventory and remanufacturing activity. Therefore, in the short time period, slowused products lead to a reduced use of recoverable inventory and remanufacturing activity with a consequent reduction in the total inventory cost. This reduction is also due to the serviceable holding costs, which, according to our analysis, resulted indifferent to the remanufacturing or production of the product and consequently of the residence time, and stockout costs were often special events. Our aim is not to reveal that slow-used products are promising candidates for profitable remanufacturing systems. On the contrary, in the context of a closed loop supply chain, examples

Figure 5. Evolution of total inventory cost changing RT and SAWC

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of profitable remanufacturing processes include fast-used products such as single-use cameras and assemblies or sub-assemblies of copiers/printers. These kinds of products generate high levels of return and recoverable inventory with a subsequent increase in the total inventory costs. At the same time, cheaper remanufacturing activity can then be adopted as a substitute for more expensive production activity, as was shown by the reduction of the total set-up cost during the simulation analysis. As the structure of the model does not involve remanufacturing and production costs then it is not possible to draw any conclusions regarding the profitability of remanufacturing. However, several examples have been provided in the literature of products with low residence times or short lifecycles and high returns rates for which high stock levels increase inventory costs. At the same time, in the long run these products generate a more profitable remanufacturing activity due to reduced production costs such as purchasing and service costs (Flapper, Van Nunen, & Van Wassenhove, 2005). The second observation obtained through the scenarios simulation reinforces the possibility that a high returns rate can generate a more profitable remanufacturability in closed loop supply chains. From Figure 5 it is possible to observe a decrease in the total inventory cost for each level of residence time except for the highest one, by increasing the service agreement with customer from 20% to 100%. The cost variation is not as significant as that which occurs when increasing the residence time. However, it can improve efficiency in managing inventory in the remanufacturing process. High incentives for product recovery and consequently a high returns rate and quantity of remanufacturable returns can increase the level of recoverable inventory, which can be used to generate remanufacturing orders faster as a substitute for production. This reduces the average level of recoverable inventory and consequently the total inventory cost, as was shown by the reduction of the recoverable holding

costs during the simulation analysis. Moreover, increasing remanufacturing activity does not negatively impact the effectiveness of the system, as stockout quantity and costs showed a reduction for a higher returns rate. The simulation analysis showed a higher reduction of the total inventory cost, increasing the service agreement with customer from 20% to 100%, for fast-used products than for slow-used products for which an increase in cost characterizes the highest residence time (42 months). This difference in cost trend is due to the lesser influence of slow-used products on recoverable holding costs and recoverable inventory, as noticed in the first observation. In this case, an increase in the service agreement or incentives increases the quantity of recoverable inventory but only after a long residence time. This surplus of recoverable inventory does not affect remanufacturing as a substitute for production activity in the short term. Therefore, an increase in service agreement for product recovery could have a reduced or negative effect on total inventory cost for slow-used products over a short time period. This is different for fast-used products, where an increase in incentives affects the quantity of recoverable inventory in a shorter time period, which can then be used sooner in remanufacturing activity with subsequent benefits in recoverable inventory and production activity reduction. However, in the long term the return of slow-used products can be improved by a percentage increase in the service agreement. Increasing the planning horizon from 60 to 120 months, the total inventory cost for slow-used products with a residence time equal to 42 months decreases from $3,834 for 20% of service agreement with customer to $3,601 for 100% of service agreement with customer. Examples of closed loop supply chains for slow-used products such as white goods are presented in the literature (Flapper et al., 2005). The main driver of the reverse logistics process for such products is government legislation, which stipulates the responsibilities of producers to recover their end-of-life products.

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However, incentives to the customer and several other factors involved in the process are given in order to increase the returns rate and improve economic and environmental benefits. Simulations, using the same values for the parameters RESIDENCE TIME and SERVICE AGREEMENT WITH CUSTOMER presented in Figure 4, were undertaken by setting CUSTOMER BEHAVIOR at equal to 2 and 1. Figure 6 shows the evolution of the total inventory cost for changes in customer behavior and percentage of service agreement with customer, with a residence time equal to the base value of 12 months. A reduction in the value of customer behavior from 3 to 1 decreases the returns rate and consequently the average number of returns. This applies to every percentage value of service agreement with customer except for 100%, for which the return index and consequently the returns rate is found to be independent of customer behavior. These simulation results correspond with and are thus representative of real-world scenarios, as a lower response from customers to company incentives for the recovery of used products in a closed loop supply chain system can reduce the quantity of returns. However, this does not tend to occur for products for which companies maintain ownership such as products under leasing con-

tracts, or have recovery responsibilities as determined by government legislation. This reduction in returns quantity resulting from changes in customer behavior has several consequences on the system and its total inventory cost. From Figure 6 it is possible to observe that for higher percentages of service agreement with customer, such as 60% and 80%, the total inventory cost increases if the customer response (and returns rate) is lower. This is due to the lower level of recoverable returns in the short term. Higher levels of service agreement or incentives coupled with higher response levels from customers to these incentives increase the level of remanufacturable returns. Therefore, in a shorter timeframe it is possible to use remanufacturing as a substitute for production activity with subsequent economic benefits obtained from the reduction of recoverable inventory and holding costs and the cheaper manufacturing processes. This observation was confirmed by lower recoverable holding costs obtained in a shorter period within the simulation analysis. Specifically, it was observed that when the remanufacturable returns start to accumulate, the recoverable holding costs are higher for the higher value of customer behavior due to the greater quantity of recoverable inventory. However, the latter allows for a prompt

Figure 6. Evolution of total inventory cost changing SAWC and CB

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remanufacturing activity, which in turn reduces the recoverable inventory level and the costs. The same observation has been previously noted for an increase in service agreement with customer. From Figure 6 it is evident that for lower percentages of service agreement with customer, such as 20% and 40%, the total inventory cost decreases if the customer behavior value decreases. In this case, as the analysis revealed, the low quantity of returns due to low incentives and low customer response leads to a low level of recoverable inventory, which is almost always lower than the level of recoverable inventory for higher values of customer behavior. The latter involves more remanufacturing activity but not enough to reduce the recoverable inventory level. Therefore, in the case of low values for customer behavior the level of recoverable inventory involves lower holding costs but at the same time lower remanufacturing activity. This scenario could negatively impact companies involved in remanufacturing activity for closed loop supply chain systems which require a sufficient quantity of returns to increase remanufacturing as a substitute for production activity. However, the analysis showed that in the case of low customer behavior the increased percentage in the service agreement might not generate an expected reduction in cost. As already mentioned, this is due to the insufficient level of remanufacturing activity needed to reduce and lower recoverable holding costs.

Evaluation and Recommendations Remanufacturing activity in closed loop supply chain systems requires an adequate quantity of remanufacturable returns in order to establish a manufacturing process in which cheaper remanufacturing can be used as a substitute for production activity. This process leads to economic and environmental benefits in reducing the reliance on the more costly production activity. Remanufacturing uses 85% less energy than production, and reduces landfill, pollution and raw material usage

(Gray & Charter, 2007). Moreover, the simulation analysis revealed that an increase in remanufacturing activity can optimize the inventory system and its costs through enhanced efficiency in the management of recoverable inventory. Slow-used products with a longer residence time in the short term present a reduced use of recoverable inventory due to their slower cyclic nature of return and consequently lower inventory cost compared to fast-used products. However, in the short run this could negatively affect remanufacturing as a substitute for production activity due to a shortage of remanufacturable returns. On the other hand, fast-used products can be used within a shorter time period and therefore prompt remanufacturing activity, which reduces the inventory cost through greater efficiency in recoverable inventory management. A prompt remanufacturing activity depends on the recoverable inventory on hand which, as the simulation analysis showed, is also influenced by SERVICE AGREEMENT WITH CUSTOMER and CUSTOMER BEHAVIOR. An increase in both these parameters leads to a higher returns rate, which in turn generates a higher level of recoverable inventory on hand and increases the possibility of prompt remanufacturing activity. For these reasons, the evaluation suggests that, for companies involved in remanufacturing activity as a substitute for the more expensive production activity, a shorter residence time combined with an increased level of service agreement or incentives for product recovery and a higher response from customers to these incentives can generate economic benefits through an increase in the quantity of remanufacturable returns. However, uncertainty around the returns flow, particularly related to the timing and quantity of returns, could influence the results of the previous analysis of the inventory system. Some companies manage this uncertainty around the quantity of remanufacturable returns stored in the recoverable inventory without attempting to balance returns with demands, preferring instead to dispose of excess inventories on a periodic basis (Guide,

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2000). In the same way, several authors incorporate a planned disposal of recoverable inventory in their models and posit a simple probabilistic returns quantity or define all demands as returns (van der Laan, Dekker, & Salomon, 1996; van der Laan & Salomon, 1997; Vlachos et al., 2007). In our model, planned disposal of recoverable inventory is not considered, which could add a new inventory cost and be more profitable, following the reverse logistics preponement concept (Blackburn, Guide, Souza, & Wassenhove, 2004), during or prior to the inspection stage. However, uncertainty around the timing and quantity of returns is specifically tackled in this study through the use of parameters such as Residence time, SERVICE AGREEMENT WITH CUSTOMER and CUSTOMER BEHAVIOR. Analysis of their interrelationships in the returns process can provide a forecasted returns rate and a possible time of return for used products with different product characteristics and in different industries. Knowledge of a product’s residence time coupled with combinations of incentives in product recovery such as trade-in and leasing contracts can also assist in estimating the time and quantity of returns. Moreover, incentives, particularly leasing contracts and changes in product design for easier disassembly and recovery of product/components, can result in a reduction of the residence time, and subsequent benefits as those previously mentioned in connection to fast-used products. For example, the introduction of leasing contracts, or changes in product design that enable customers to easily disassemble and return used products/components, can fix or reduce the residence time. Through such incentives, companies can influence customer behavior in returning used products. By adopting policies such as the introduction of deposit fees, free collection or repurchase of used products, fees paid upon the return of used products, and improving product design, alongside providing clear information/advertising about reverse logistics activities and environmental responsibilities, can assist in

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enhancing customer behavior in relation to the returns process.

CASE STUDIES In this section we assess the robustness of the findings obtained through the simulation analysis, which leads to the evaluation of the strategies aimed at improving the performance of the system. For this purpose, we use the data and information collected from the two companies employed as case studies: the Australian Mobile Telecommunications Association (AMTA) and Fuji Xerox Australia. These companies are involved respectively in reverse logistics and remanufacturing activity, and in particular are engaged in returns process activities, which meant they were appropriate cases for our study. The findings were obtained from a generic model of the production and inventory system for remanufacturing. For this reason, the assessment involves a comparison between the findings and the data and information collected from these companies in order to find similarities and to verify the former by referring to the actual real-world practices of these companies. Specifically, the quantitative data and information collected from AMTA are employed first. Then the similarities between the AMTA data and both the qualitative data and the information collected from Fuji Xerox Australia are identified and discussed.

Australian Mobile Telecommunications Association (AMTA) The first Australian company case is examined to assess the findings that revealed that an increase in the service agreement or incentives for product recovery, which in turn increases the likelihood of customers to engage in the returns process, leads to a higher returns rate.

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AMTA is the national body of the mobile telecommunications industry in Australia and is currently supporting the official national recycling program of the mobile phone industry (KPMG, 2008). This program is a free recycling project for mobile phone users, which avoids landfill activity and recovers material from used mobile phones, bringing environmental and economical benefits. Project managers were contacted in order to obtain data and information, not about the recycling program (which is not directly relevant to this study) but about the influence of incentives on customers to return used products, and on customer behavior and returns rates in general. Data and information were obtained from the organization’s 2007–2008 annual report (KPMG, 2008). According to the report, the company has developed numerous incentives and service agreements with customers, retailers and other reverse logistics actors such as local councils and recyclers in order to increase returns/collection rates and improve customer behavior in relation to the returns process. In particular, the focus of the program is on free used product collection from customers. This has been achieved by distributing reply paid recycling satchels available in selected mobile phone packs and by setting up public collection points nationwide in retailers and Australia Post outlets. Other incentives include customer communications and environmental campaigns about the program published in catalogues, on websites, through direct marketing and television advertising presented by mobile manufacturers, service centers and retailers. These activities have achieved varying results. In particular, it was interesting to notice the evolution of particular key performance indicators for the program for the years 2005 to 2008. Since 2005, awareness of the recycling program, which represents consumer attitudes towards the reverse logistics program, has increased from 46% to 75% and at the same time collection and collection rates have increased from 42 tons to 97 tons and from 15% to 19%, respectively. The latter two factors are representa-

tive of the quantity of returns and the returns rate of used mobile phones. Moreover, the disposal to landfill rate has decreased from 9% to 4%. Therefore, similar to our findings, these company incentives have enhanced customer behavior in relation to the returns process, which in turn has led to an increased returns rate.

Fuji Xerox Australia The case study of Fuji Xerox Australia is presented in this section in order to assess two of the findings obtained from the simulation analysis. The first regards our belief that benefits can be obtained from remanufacturing activity through the combination of a shorter residence time and an increased level of service agreement or incentives for product recovery. The second regards the significant influence of service agreement or incentives for product recovery on the uncertainty around the quantity and timing of returns. This case study employs qualitative data and information on this world leader in remanufacturing processes, obtained from the company management and drawn from the existing literature. Specifically, the qualitative data and information refer mainly to the Eco Manufacturing Centre located in Sydney. The Eco Manufacturing Centre is the distribution center for remanufactured printer and copier assemblies or sub-assemblies, which would otherwise be landfill for the Asia-Pacific Region (Fuji Xerox Australia, 2007). Assemblies and sub-assemblies, removed from equipment during maintenance service calls, are remanufactured at this center. According to the managers, the Eco Manufacturing Centre focuses on and deals only with remanufacturing activity. For this reason, returns are an essential element in its remanufacturing activity, and predicting the quantity and timing of returns is essential to ensuring an efficient inventory and planning/scheduling for remanufacturing.

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The information obtained from the managers is used to assess the first research finding. Specifically, the introduction of changes in product design as an incentive for product recovery through the easier disassembly and recovery of components rather than the whole machine, which in turn leads to faster recovery of returns or shorter residence times, has been strongly supported by the Fuji Xerox managers. Indeed, new machines are built for easier and faster disassembly, recovery and remanufacturing processes. In this way, remanufacturing activity can focus on modules that have shorter residence times and easier remanufacturability. The company’s aim is to increase the number of remanufacturable returns and maximize engagement in the more profitable remanufacturing activity as a substitute for production activity. The work of the Eco Manufacturing Centre, which deals only with remanufacturing activity, supports this aim. In terms of the second research finding, Fuji Xerox managers believe that a full service agreement with customer is an important strategy in the remanufacturing process. Fuji Xerox draws up a full service and maintenance agreement with its customers, whose preference is usually to lease the products. The full service agreement has a number of targets, including a marketing strategy aimed at increasing service levels for the customer. However, from the remanufacturing activity point of view the full service agreement is also a returns process strategy which functions to increase control of the quantity and timing of returns and to improve remanufacturing activity. The service is conducted by engineering teams, who provide service and repairs for breakdowns or when customers find the product is not working satisfactorily, as well as preventive maintenance of products. Using diagnostic tools, this maintenance service identifies the reasons for failure and opportunities to extend the product life (Fuji Xerox, 2007). This process involves two main analyses:

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Failure Mode Analysis: to identify the reasons for the failure of assemblies and sub-assemblies. Signature Analysis: to determine the remaining life of the assemblies and sub-assemblies through an examination of their critical performance parameters.

Data collection on these processes has led to continuous improvement of the basic product design and has resulted in a number of improvements. The durability of main assemblies and sub-assemblies, which is usually measured on the basis of the possible maximum number of copies to be completed, is designed so as to enable replacement of all of the components at the same time. In this way, during preventive maintenance it is possible to predict when it will be necessary to replace the assemblies to improve product performance, and when it will be necessary to replace them altogether. This process is guided by computer systems that identify whether or not there is a remanufacturing program for an assembly. During the breakdown and preventive maintenance, assemblies and sub-assemblies are replaced, and information on problems, solutions and forecasts for future replacement is provided. If the replacement is not possible at the maintenance stage because there is no ready availability of new assemblies, an order is submitted to the local warehouse. Used assemblies and sub-assemblies are collected by the service engineers, valet service staff or dealers to be returned and stored at the local warehouse. From the local warehouses, they are transported to the Eco Manufacturing Centre to be re-engineered and remanufactured as products that are good as new. Finally, they are packaged and stored in the surrounding warehouse as new products, awaiting transportation to the central distribution center of Fuji Xerox Australia. From the information collected we can conclude that one of the reasons why the Fuji Xerox managers at the Eco Manufacturing Centre strongly support the introduction of a full service

Modeling Closed Loop Supply Chain Systems

and maintenance agreement with customers is that it will increase control of the quantity and timing of returns. Indeed, the use of leasing contracts for products, combined with a service agreement that can generate a forecast regarding the quantity and timing of returns, allows the company to keep track of the quantity and location of equipment and maintain a degree of control over the returns rate.

FUTURE RESEARCH DIRECTIONS The study described in this chapter explored a series of issues relevant to the field of reverse supply chains and in particular closed loop supply chains. However, we believe there is scope for further lines of enquiry in this area, to be considered by future researchers. The findings of this study were obtained through the modeling and simulation of a generic production and inventory system for remanufacturing which is generally in line with those used in previous studies. In order to simplify the analysis and interpretation of such a system, a number of assumptions were made. However, an opportunity for further research lies in the evaluation of system performance by relaxing the remaining assumptions. This could be achieved by remodeling the structure of the system and incorporating the factors and their influence relationships that affect the system activities/processes. Improving the model structure and its applicability to the real world might also be achieved through an extension of the meaning of the existing factors. Specifically, several exogenous factors such as service agreement with customers, customer behavior and residence time were defined in a generic way without considering their social, economic or management backgrounds. An exploration of the origins and roles of these factors could expand the models to cover new areas of research focusing on different fields of study. For example, in the returns process the factors residence time and service agreement with customers could be

connected with perspectives from the social, legislative and marketing disciplines. Therefore, this study could be the starting point for further research aimed at analyzing the impact that such perspectives and considerations may have on a reverse supply chain system.

CONCLUSION In this research, we developed an SD simulation model of the production and inventory system for remanufacturing within the context of closed loop supply chains. Our focus, particularly for the simulation analysis, was on the returns process within the system in which the returns rate was modeled by manipulating the relationships between particular factors that affect the system. The selected factors identify the time period for which products stay with customers, or residence time, and the quantity of possible returns based on customer demand or the return index. In particular, the return index was obtained by considering the relationship between the company incentives or service agreement with the customer aimed at encouraging used products recovery, and the customer behavior in terms of product return. The remanufacturing process was also modeled and a pull inventory control policy was applied to the process. By analyzing the total inventory cost as a measure of the performance of the system, several findings were obtained regarding the effects of residence time and changes in the level of company incentives and the resulting customer behavior. The main finding is that companies engaged in remanufacturing activity can enhance their efficiency in managing inventory through shorter residence times and an increased level of company incentives, which results in improved customer behavior. This leads to a higher level of recoverable inventory on hand and consequently the possibility of prompt remanufacturing as a substitute for the more costly production activity,

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which in turn reduces the recoverable inventory level and its related holding cost. Moreover, company incentives for the recovery of used products have significant influences on the uncertainty around the quantity and timing of returns and ultimately on total inventory costs. Increasing company incentives or enhancing service agreements with customers, which in turn improves customer behavior in returning used products, can improve the control of returns. Two company case studies (the Australian Mobile Telecommunications Association and Fuji Xerox Australia) were employed to assess the findings. It was found that Fuji Xerox Australia in particular offers incentives such as changes in product characteristics, full service and maintenance agreements with customers, and leasing contracts that function to reduce the product residence time and improve the company’s control over returns. Following our purpose to model a production and inventory system for remanufacturing within the context of closed loop supply chains and to evaluate effective control strategies aimed at improving system performance, this chapter made several contributions in different research areas. Specifically, the modeled returns process can be linked to specific product categories using the knowledge of the distinctive elements of particular products, which in this case was the average residence time. The latter characterizes different types of products depending on their variable time of use and recovery time, which in turn affects the system through the timing of returns. Service agreements with customers can influence customer behavior in relation to returning used products, and consequently affect the quantity of returns within the system. The variability of customer behavior, which generates uncertainty around the quantity of returns within the system, has been modeled and analyzed through three different customer behavior patterns. Our contribution in this area is to extend the current research with an approach to product recovery in which the correlation between

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demand and returns is identified through the use of these particular factors. On the basis of the findings obtained from the simulation analysis, our contribution is to offer observations regarding efficiency in managing inventory activities for this particular system, and to provide guidelines for determining the quantity and timing of the return of used products by customers in order to reduce the uncertainty surrounding the timing and quantity of returns.

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KEY TERMS AND DEFINITIONS Closed Loop Supply Chain: Integration between a forward and reverse supply chain in reverse logistics processes. Customer Relationship Management: Company strategies/policies for managing relationships with customers and organizing business processes.

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Remanufacturing: A sustainable method applied particularly in closed loop supply chains to recapture value from product returns. Reverse Logistics: A sustainable process applied in returns management in order to reuse, remanufacture, and recycle or properly dispose of product returns. Sustainable Production: A production process that creates goods and services and is aimed at minimizing the usage of energy and natural resources.

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System Dynamics: A modeling and simulation approach aimed at understanding the dynamic behavior of complex systems in order to analyze and solve complex problems with a focus on policy analysis and design. Systems Modeling: Representation and construction of business systems through the use of models which correspond as much as possible to a meaningful concept based on real-world situations.

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Chapter 11

Bike Transportation System Design Avninder Gill Thompson Rivers University, Canada

ABSTRACT The main objective of this chapter is to address the facility design and location issues in a public bike transportation system. The major decisions in introducing a public bike transportation system include determining the number of bike facilities and their locations. The present chapter considers a case study from city of Vancouver bike transportation system to demonstrate the importance of these decisions through a real world application. The city intends to decide the number and location of bike terminals. Addressing these two decisions is the main focus of the present chapter and the chapter employs linear programming and center of gravity approaches to arrive at the solutions. The chapter also provides a basic introduction to bike facilities and discusses the sustainability benefits of bike transportation mode.

BACKGROUND Public transport represents an essential component of urban transportation system development. Public transportation sets the development theme of a city and reflects a city’s preferences and priorities towards environmental sustainability. Therefore, sustainability is a major objective in the design of public transportation systems. Most public DOI: 10.4018/978-1-61350-156-6.ch011

transportation systems face certain challenges in terms of accessibility, social acceptability, convenience, availability and affordability. Despite these challenges, a rapid population growth and commercialization has led to a sharp increase in the demand for urban transport services. Major cities around the world depend on public transportation to alleviate their day to day problems. The main problems encountered in these cities include higher traffic congestion, pollution, poor

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Bike Transportation System Design

accessibility and others. In some congested cities (e.g. Bangkok and New Delhi), the weekday peak-hour traffic speeds average about 10 km per hour or less and the average one-way work commute is more than 60 minutes for reasonable commuting distances (Iyer, 2001; Habitat, 2001). A study (OCMLT, 1998) estimated that the direct annual economic costs of congestion in Bangkok at 163 billion baht. This included 27 billion for the costs of extra vehicle operations, 20 billion for additional labor, and 116 billion for lost time of passengers. This cost does not include the cost of damage to the environment and health of mankind (OCMLT, 1998). In some cities, the level of traffic congestion is so high that even small improvements result in significant health and financial benefits. A World Bank study estimated that a 10 per cent reduction in peak-hour trips in Bangkok would provide benefits of about US$ 400 million annually (ESCAP & ADB, 2000). Most countries address these challenges by increasing road-lengths, building bridges and city by-pass perimeter roads and developing complementary public transport systems. Towards this end, bicycle transportation has emerged as a viable transportation mode. Recreational purposes remain the main use of bicycling, but people are becoming increasingly more aware of the usefulness of this energy efficient, cost effective, healthy and environmental friendly mode for other purposes. In recent times, we have witnessed a significant rise in the number of people using bicycle mode for work commute and other travel. In order to further promote this trend, different tiers of Governments must encourage more bicycle-related programs and considering bicycle mode during all phases of transportation planning, roadway construction and infrastructure capacity increments. Expanding opportunities for cyclists has numerous benefits not only for individuals but for the society as a whole. The lack of bicycle facilities and the integration of these facilities with existing transportation infrastructure remains a serious concern.

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Some of the negative aspects of bike transportation include the safety of bike rider especially on shared-tracks, lack of integration with other modes and inability to travel over longer distances and under severe weather conditions. Despite these concerns, bicycling offers significant benefits to both individual users as well as the society. Bicycle mode is well-suited to urban situations as it opens up numerous possibilities for commuters and recreational bikers by providing access to remote areas that otherwise could not be served by other modes. Moreover, there is no denying the fact that increased use of bicycling will substitute auto trips yielding benefits to society in terms of decreased congestion, improved air quality and less dependency on non-renewable sources of energy. This mode of transportation emits no harmful emissions into the environment and is cheaper to own and maintain (NCHRP, 2006). The cost of providing bicycle facilities is small when compared to the money spent on developing other modes of transportation (Marlin, 2008; Bowman & Vecellio, 1994). Public health and obesity issues remain a major concern to many governments. Biking provides a healthier transportation option. The relationships between community design, transportation facilities, and physical activity levels have been well documented in various studies (Handy, 2002; Sallis, 2004). Bike transportation also provides benefits in terms of efficient land development. There has been a mad rush for sprawling land development which increases the travel distance for work, shopping and recreational activities and forces people to use autos (Frank, 2000; Wilkinson, 2002; Pucher & Dijksra, 2003). Incentives for bike ridership may mitigate the negative effects of urban sprawl by efficient land development. Therefore, bicycling may very well emerge as a viable mode of transportation for everyday commute provided the right infrastructure is developed. Public and private development agencies prefer to acquire the land for various projects well in advance.

Bike Transportation System Design

Land acquisition at later stages is an expensive proposition due to real estate appreciation. It can also create many potential conflicts when the temporary developments need to be demolished. It becomes prudent to acquire and preserve the land sooner and temporarily use it in ways which are cheaper to maintain, demolish and refit. Bicycle facilities provide an ideal alternative for the temporary use of such land. These facilities are not only cheaper to demolish, but in most cases, they can co-exist with the newer development and continue to provide benefits to the communities.

BICYCLE INFRASTRUCTURE AND INTEGRATION The main purpose of bicycle lanes is to increase the safety and convenience of bicycle riders. The bicycle lanes could be on-road bicycle lanes, separated or off-road shared use pathways but a major bicycle project will include several elements from one or more of these categories (Marlin, 2008). The on-road bicycle lanes include widecurb lanes, shared streets and signed routes for bicycles. These lanes normally have a width of 4 to 5 feet, one-way direction and marked as solid white line for cyclists. Contrary to common belief that on-road bike lanes are unsafe for bike riders, research has provided evidence that the separation of bicycle and auto traffic is more unsafe (Ochia, 1993; Pucher et al., 1999). The objective of a bicycle lane is to delineate the right of way of cyclists and motorists, and to predict their sudden movements. A separate facility for cyclists and motorists may sometimes place them out of view of each other during turns. The off-road bicycle lanes are separated from the main motorized traffic through some kind of barrier. These lanes could be paths-ways or trails which run parallel to the motorized traffic, alongside abandoned rail-yards

and lanes passing through parks, beaches etc. Off-road lanes can be dedicated for bicycle use or shared with pedestrians. These off-road lanes are popular with young and inexperienced riders and are often used for recreational purpose. Connecting the bicycle lanes is quite critical to develop a truly integrated bicycle network. A truly integrated network not only connects various bike lanes, but it also integrates bike lanes with roadways, railways and waterways. Such integration and connectivity can be promoted in various ways. For example, busses and metro rail systems could be equipped with bicycle racks that allow cyclists to carry their bikes onboard. It can also be promoted by maintaining and developing bike lanes that lead to transit stations and ensuring that enough bicycle parking exist at these transit stations. Several benefits accrue from increased connectivity and integration between various transportation modes (Marlin, 2008). Increased connectivity serves a greater geographic area through inter-modal choices which results in increased efficiency for both bicycles and public transportation. The use of a bicycle to a transit stop significantly increases the catchment area for that public transit stop as cyclists from far away areas can travel to these stops as compared with the scenario where they had to walk (Litman et al., 2006). This ability to draw a larger group of people to each stop increases the ridership of public transportation. On the other hand, bicyclists also benefit from this connectivity. This enables them to travel greater distance than by bicycle alone, and also help them to overcome potential topographical barriers on the bikeways. After providing the necessary background and introduction to bicycle infrastructure, in the remaining sections, we focus on our case problem, its literature review and associated models.

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Bike Transportation System Design

BICYCLE FACILITY DESIGN AND LITERATURE REVIEW There are several considerations that the planners have to keep in mind while deciding the type of public bicycle facilities to be installed and their location. The bicycle rental facilities which are the focus of this chapter operate mostly on financial considerations. Despite the fact that bike rental ridership use the same public bike lane system, these rental facilities operate to achieve their business goal of maximizing the revenue. Therefore, the location models for these facilities need to take into account additional factors such as existing bike counts, traffic and population density, percentage commute to work and proximity to bus and train stops etc. For the remainder of this chapter, we discuss the location of such rental facilities by discussing a real life case study at city of Vancouver which is aggressively promoting the bicycle culture through privately owned as well rental bike systems. However, bike sharing rental system (rather than privately-owned bikes) will be the focus of this chapter. Bike sharing systems have gone through three generations over the past 35 years (DeMaio, 2003; DeMaio, 2004). The first generation programs began in 1968 in Amsterdam where ordinary white colored bikes were provided for public use. However, bike theft was a major problem and the program collapsed within days (Associated Press, 1998). In 1995, in Copenhagen, a second generation of bike-sharing programs was launched with improvements. These bikes were specially manufactured and could be picked up and returned at specific locations throughout the central city with a coin deposit. However, theft of bikes in second generation programs continued to be a problem, which gave rise to the smart bike or third generation bike-sharing programs equipped with customer tracking GPS systems, anti-theft devices, electronic locking racks, bike locks, telecommunication systems, smartcards, mobile phone access and sometimes on-board

190

computers. Paris launched its bike sharing program Vélib’ with about 7,000 bikes in July 2007 and has expanded to 20,600 bikes with 1,450 terminals or one every 300 meters, making it the largest initiative of its kind in the world (Vélib’, 2008). Vélib’ has made an improvement in this area with the launch of its “V+” concept. As it requires more physical effort and time for customers to reach uphill stations, V+ gave additional 15 minutes to access 100 designated uphill stations. These extra 15 minute credits may be saved up and used for other trips (Vélib’, 2008). According to Matsuura (2003), two models of bike-sharing exist—one for community use and the other for residential use. In the community bike-sharing model, an individual checks out a bike from one of many locations and returns it to another location. The residential bike-sharing model requires bikes to be returned at the same location from where they were checked out. Pucher and Buehler (2006) mention that in recent years, cycling levels, public transport and bike-and-ride trips have risen sharply in the U.S. and Canada. Marten (2007) discusses the challenges faced in promoting bike and ride transportation in Netherland. Pucher and Buehler (2009) provide an overview of bike-transit integration in large American and Canadian cities. Montreal launched its fully operational BIXI (i.e. bi-cycle taxi) system in 2009 with 3,000 bicycles available for self-service at 300 stations (Bixi, 2009). Midgley (2009) reviews the state of the art of bike-sharing systems based on experiences in selected European cities and suggests that the basic premise of the smart bike-sharing concept is sustainable transportation providing fast and easy access, using diverse business models and making frequent use of applied technology available for bikes sharing systems. Besides these technological aspects, business models as well as consumer trends, the literature on mathematical models dealing with the bike system design and its costeffective operations, are almost non-existent. The present chapter intends to fill that gap and therein lies the contribution of this work.

Bike Transportation System Design

CASE CONTEXT The City of Vancouver is trying to promote its image as a greener city by introducing new green initiatives. One of these initiatives is encouraging residents to use emission free bike transportation. Metro Vancouver has a unique advantage for bike transit integration in terms of TransLink, which is a fully integrated, multi-modal regional transportation authority for the city. Public transport, major roadways and bicycling in Vancouver are all under the control of same agency. Therefore, the integration of cycling and other public transport is easier to implement. Over the past 10 years, TransLink has shown its commitment to this initiative by spending more than $12 million on bike-transit integration. All buses in Vancouver are equipped with bike racks. Bikes are allowed on SkyTrain except during peak hours and in peak directions. Sea-Buses also allow bikes onboard at all times without extra charges. All the train and ferry services offered by TransLink are accessible through elevators, ramps and level boarding, thus facilitating bike-and-ride. Most of the skyTrain stations, express rail stations as well as park-andride lots are equipped with bike racks. In 2008, the city boasted of total 1,060 bike parking spaces at transit stops: 660 spaces in bike racks and 400 secured bike lockers. TransLink is committed to increase these capacities in the coming years. The construction of new SkyTrain lines provided for protected adjacent bike lanes to further foster bicyclist access to rail transportation. These facts clearly indicate the bike friendly attitude of the city and its commitment towards sustainability. The City of Vancouver is not only increasing bike lanes to make bike transportation convenient, but is also looking at the possibility of introducing a public bike transportation system. The system will include bike terminals where bikes can be rented or dropped off. Under the proposed system, a customer can go to a terminal and use a credit card or membership card that will be charged for $1.50 rental and a $200 deposit which is refundable

when the customer returns the bike. The membership card is a monthly payment option. After the payment, one of the bikes will be unlocked for the customer and then the customer can drop it off at another terminal or back to the original terminal. The customer will be charged an additional $1 for every half hour after the first hour. The public bike transportation is better suited for short commutes. This model was first introduced in Paris and now several cities are using this model. Vancouver is the latest city to consider this option. Currently Paris brings in revenues of over $20 million a year from bike rentals. While revenue is a secondary consideration, the main thrust of this project is the green initiative to lower carbon emissions, lessen traffic density and free up parking space. Vancouver has remarkable resemblance to Paris (city with a successful bike system) in terms of average summer temperatures of 20C and winters at 3C; total length of bike lanes are 371 Km and 300 Km for Paris and Vancouver respectively; and both cities have a flat topography. According to the Vancouver Cycling Statistics (2009 a, b), in 2006 approximately 4% of the commuting trips in Vancouver were made using bikes. Currently, there are over 60,000 trips made by bike per day in Vancouver, but mostly by people who own bikes. More than 3,500 cyclists commute to work downtown every morning which is the equivalent to 65-75 full transit buses. Overall, 15.9% Vancouver residents cycle or walk to work whereas 41.4 percent in the Downtown and West End cycle or walk to work. Furthermore, almost half of all Vancouver residents commute less than five km to work and more than 80 percent commute less than 10 km – making these relatively short distances ideal for cycling. Currently, there are over 180,000 daily sky train users in Vancouver who can potentially benefit from this initiative. Based on the statistics, the city has every reason to believe that the public bike transportation would be successful, but would like to conduct a pilot run in downtown Vancouver.

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Bike Transportation System Design

While the public bike program in Paris is highly successful, the system does have its issues. For example, some terminals are always short of bikes because of their popular locations and trucks are utilized to re-distribute the bikes to even out the allocations amongst terminals. Occasionally, bikes are not returned in proper condition, or the bike return is not acknowledged by the system and it continues to charge the customer with the possibility that someone else can take that bike. While most of these problems are operational issues that can be avoided using technology or manual solutions, a good bike distribution network with adequate number of bikes at ideal terminal locations is certainly the most desirable objective.

CASE DESCRIPTION Before the pilot run can be undertaken, the City of Vancouver has to consider many factors such as the number of terminals needed, the location

of the bike terminals and how many bikes will be made available at each terminal. Addressing the first two issues is the main focus of the present paper and the paper employs linear programming and center of gravity approach to decide the minimum number of terminals and their locations. The third issue of bike stock levels or terminal capacity would require a comprehensive simulation model and is the subject of authors’ ongoing study. The long term goal is to cover all of Vancouver, but for the first phase (i.e. pilot stage) the area of focus is Downtown, West end, Coal Harbour, Yaletown, Gastown, Mount Pleasant, Kitsilano, and some of East Vancouver. These locations are heavily populated and have a high density of daily bike riders. As the project moves further, other areas and sky train locations can be added. The following map shows the percentage of trips to work using a bicycle. The highest percentages fall within the area of concentration as shown on the map (Figure 1).

Figure 1. Concentrations using bike trips to work commute

192

Bike Transportation System Design

The City of Vancouver had to establish the maximum distances between the terminals as people will only use the system if the terminal locations are closer and convenient. For the first phase of the public bike transportation system, bike terminals ware decided to be no more than 1km apart. Demand and ideal locations have been determined using the following data (Figure 2) taken from summer of 2008 at peak bike transport hours. The numbers in Figure 2 represent how many bikes passed going each direction. The black lines illustrates bike paths and the thickness of the lines show the demand; the thicker the line the higher the demand. Before being able to design a mathematical model, the points of interest had to be determined. After much deliberations and discounting some points due to their infeasibility, fifty three points were selected based on the peak hour bike counts in summer 2008. This fared well since those locations did contain high density and

sky train locations. Next task was to find the distances between all pairs of these 53 points. The following Table 1 lists the chosen locations showing how many bikes passed that location and the percentage of work commutes from that location using bikes. Due to confidentiality reasons, we assign letter names to locations.

MATHEMATICAL MODELLING APPROACH To meet the specific objectives of our case study, we used three mathematical optimization models in a sequential manner. A set covering linear program model has been used to determine the minimum number of bike terminals required. Once the minimum number of terminals is determined, we formed clusters of neighboring terminals around those locations using an allocation procedure. Clusters are groups of locations that can be

Figure 2. Peak-time bike counts in Summer 2008

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Bike Transportation System Design

Table 1. Points of interest for bike terminals Point

A

Bike count 53

Percent commute to work 11.8%

Point

T

Bike count 150

Percent commute to work 6.5%

Point

L1

Bike count 69

Percent commute to work 4.0%

B

88

11.8%

U

281

8.0%

M1

399

9.2%

C

86

9.6%

V

320

7.2%

N1

373

9.2%

D

27

9.6%

W

114

7.7%

O1

370

9.0%

E

96

6.3%

X

566

9.8%

P1

259

9.0%

F

240

3.9%

Y

157

5.4%

R1

150

4.2%

G

160

3.9%

Z

708

6.4%

S1

117

2.2%

H

288

3.9%

A1

265

5.4%

T1

31

2.2%

I

318

4.0%

B1

155

5.4%

U1

122

2.2%

J

231

7.4%

C1

133

1.3%

V1

128

2.2%

K

77

8.3%

D1

255

3.9%

W1

112

0.7%

L

24

9.6%

E1

222

3.0%

X1

206

11.2%

M

50

6.8%

F1

198

2.8%

Y1

190

5.6%

N

185

8.7%

G1

274

3.5%

Z1

34

7.2%

O

414

8.7%

H1

15

5.0%

A2

52

1.9%

P

355

5.8%

I1

80

5.0%

B2

166

3.6%

R

148

5.4%

J1

35

3.0%

C2

71

1.9%

S

265

9.8%

K1

142

1.6%

serviced with one terminal because the distance between them falls below the 1 km requirement. Once we find the clusters, they are used in the center of gravity model using the peak hour bike counts as the weights. The center of gravity method essentially determines the exact location of the bike terminal for a cluster. Since the peak hour bike count is used as a weight for a demand point, a higher bike count has a stronger gravitational pull on the bike terminal and the terminal tends be relatively closer to this point. Alternative methods for determining terminal locations instead of the mathematical models given in this report may include primary research such as surveying residents on preferred locations; assess the interests among local businesses to sponsor the bike system which will establish a bike terminal next to their businesses. Let,

194

y j = 1 if location j is selected for terminal, 0 otherwise. d ij = the distance between points of interest i and j; for i, j=1,2,3,....,53. αij =1 if dij ≤1Km or 0 otherwise; ∀ i, j=1,2,3,…,53. Based on the maximum distance of 1 Km, αij essentially develops a binary coefficient matrix to be used as an input to the mathematically programming model.

Model 1. Identify Minimum Terminals Using the binary coefficient matrix as an input to the following set covering model, the best terminal locations to cover the area of focus has been identified as follows: Find vector y so as to

Bike Transportation System Design

53

Minimize ∑ j =1

yj

(1)

Model 2. Allocation for Points-ofInterest

subject to: 53

∑ j =1

bike terminal at the listed locations to cover each of the other 53 locations.

α ij . y j ≥1 ∀ i=1,2,3,…,53.

(2)

yj∈ {0,1} ∀ j=1,2,3,…,53. The objective function (1) expresses the minimization of the number of bike terminal locations selected. This objective indirectly minimizes the investment needed. The constraint set (2) ensures that each bike density point is covered by at least one terminal. This set of constraints is necessary to give coverage to all communities. The actual size of the problem is smaller than above as some constraints will be redundant. Furthermore, for several constraints αij will be zero if the distance is more than 1 km. For the bike terminal problem, the constraint set essentially models the situation depicted in Table 2. Table 2 is a coverage table that gives the requirement of establishing at-least

While the above formulation identifies minimum number the terminals, it does not allocate all points of interests to these terminals. Furthermore, it may be possible that a point of interest is covered by more than one terminal. Such a scenario is always desirable due to the additional service and options available to the commuters and our model does not discount such possibilities. For modeling purposes, we need to ensure that a point of interest is allocated to one terminal to avoid any double counting of trips. We use the following procedure for this allocation based on the proximity of a point of interest to a terminal. Step 1. Identify a set θ={j} such that yj =1. We use the locations suggested by mathematical model as the centers of clusters.

Table 2. Bike terminal requirements At least one terminal at locations A

At least one terminal at locations O, I

At least one terminal at locations D1

At least one terminal at locations S1, R1, W1, U1, X1

B

P, R

E1, F1

T1

C, D

R, P, S

F1, E1, G1

U1, S1, W1

D, J, C, E, N

S, R, X, Y

G1, F1, H1, I1

V1

E, D, F

T, U

H1, G1

W1, S1, U1

F, E, G

U, T, V, W

I1, G1, J1

X1, S1

G, F, H

V, U, W, Z1

J1, I1

Y1, W

H, G, I

W, U, V, Y1, Z1

K1

Z1, V, W, B2

I, H, O, R

X, S, Y

L1, M1, N1

A2, B2

J, D, K, N

Y, S, X, Z

M1, L1, N1

B2, A2

K, J, L

Z, Y, A1, B1

N1, L1, M1, O1

C2

L, K

A1, Z, B1

O1, N1, P1

M

B1, Z, A1, M1

P1, O1, R1

N, D, J

C1

R1, P1, S1

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Bike Transportation System Design

Step 2. Consider the distance matrix [d ij] for j є θ, i=1, 2,...53. Here, the columns represent the terminals and rows represent points of interest. Step 3. Set i = 1. This is the initiation step. Step 4. Find dij* = min(vector dij) for j є θ, i.e. the minimum entry in ith row. The column index for this minimum entry is j*, assign ith point of interest to j* terminal. This step assigns points of interest to terminal locations to which it is closest. Step 5. While i < 53, set i=i+1 and repeat step 4. This step ensures that all points of interests have been allocated a terminal location. The application of set covering mathematical programming (model 1) identifies 23 terminal locations to cover the entire range of points of interests and the allocation procedure (model 2) makes the clusters as given below in Table 3.

Model 3. Identify Centers of Gravity for Clusters The preliminary terminal locations and clusters in Table 3 are purely based on distance considerations. These terminal locations treat all the

cluster members equally so long the distance from terminal is less than 1 km. For example, whether a demand point is 100 or 900 meters away from terminal, it has the same membership in a cluster. Furthermore, these terminal locations do not take into account the fact that certain points of interests have a higher demand or bike count as compared with others and therefore the terminal should be located closer to these points. In this section, we further refine our search for terminals by considering the x and y coordinates of cluster members and treating the bike-counts as the weights that exert gravitational pull on the terminals. In the center of gravity method (Ballou, 2004), we perform the following steps. Step 1. We lay a grid with x-y axes, over the focus area of the map. Using the grid, we find the x and y coordinates of the points of interest on the map. The x-y coordinates of all the points along with their bike count weights are given in Table 4 which is used as an input to next step. Step 2. For each cluster in Table 3, we use the following equations to determine the ideal location of the service terminal using Table 4 as input.

Table 3. Bike terminals and associated clusters Terminal

Cluster

Terminal

Cluster

A

A

E1

E1, F1, G1

B

B

I1

I1, G1, J1

D

C, D, J, E, N, F

K1

K1

F

F, E, G, H

L1

L1, M1, N1

I

H, G, I, O

O1

N1, M1, O1, P1, R1

L

L, M, K

S1

S1, R1, W1, U1, X1

R

R, P, S, X, Y

T1

T1

S

S, P, R, X, Y, Z

V1

V1

U

U, T, V, W, Z1

Y1

Y1, W, Z1, V, P1

Z

Z, Y, S, X, A1, B1, M1

B2

B2, A2, Z1, W

C1

C1

C2

C2

D1

D1

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Bike Transportation System Design

Let, t = The cluster index; k = The location index within the cluster; lt = Number of locations within cluster t; Xtk= X-coordinate of location k within cluster t; Ytk= Y-coordinate of location k within cluster t; Ctk= Bike-count weight (from Table 4), for location k within cluster t; The x and y coordinates of the center of gravity of cluster ‘t’ are given in Equations 3 and 4 as follows:

X

t

=

k =1

tk

lt

∑C k =1

Y

t

=

k =1

tk

tk

lt

∑C k =1

, t=1,2,......23.

(4)

tk

The (Xt,Yt) coordinates given by Equations 3 and 4, provide the ideal locations for the bike transportation system as given below in Table 5.

DISCUSSION ON APPROACH AND RESULTS

lt

∑ C .X

lt

∑ C .Y

tk

, t=1,2,......23.

(3)

tk

The approach followed in the present chapter evaluates fifty three candidate locations for establishing bike-terminals and suggests twenty three ideal terminal locations for the bike transportation system as given in Table 5. The approach also allocates the remaining communities to these

Table 4. Input to center of gravity model Point

Bike count

Location (X,Y)

Point

Bike count

Location (X,Y)

Point

Bike count

Location (X,Y)

A

53

(0.1,12)

T

150

(9.8,10.6)

L1

69

(8.9,13.2)

B

88

(2,12.5)

U

281

(10.9,10.6)

M1

399

(9.2,13.2)

C

86

(3,12.3)

V

320

(11.5,10.6)

N1

373

(10,13.2)

D

27

(3.4,12.4)

W

114

(11,11)

O1

370

(10.5,13.2)

E

96

(3.7,12.5)

X

566

(8.6,11.2)

P1

259

(11.5,13.2)

F

240

(4,13)

Y

157

(8.6,12)

R1

150

(12.1,13.2)

G

160

(4.4,13.2)

Z

708

(8.9,12)

S1

117

(13,13.2)

H

288

(4.5,12.4)

A1

265

(7.9,12.1)

T1

31

(13.2,15)

I

318

(4.5,11.6)

B1

155

(8,12.7)

U1

122

(13.5,13.2)

J

231

(3.4,11.5)

C1

133

(6.8,12.9)

V1

128

(14.5,13.2)

K

77

(2.5,11.4)

D1

255

(5.6,12.2)

W1

112

(13.2,13.2)

L

24

(2.5,12)

E1

222

(5.8,13.4)

X1

206

(12.5,13.2)

M

50

(2,11.4)

F1

198

(5.4,14)

Y1

190

(11.5,12)

N

185

(3.4,10.7)

G1

274

(5.2,14.9)

Z1

34

(11.7,11.3)

O

414

(4.7,10.7)

H1

15

(5,15.5)

A2

52

(12.3,11.5)

P

355

(6.5,10.7)

I1

80

(5.5,15.7)

B2

166

(12.5,11)

C2

71

(13.3,10.6)

R

148

(7.6,11.5)

J1

35

(6.1,15.4)

S

265

(8.6,10.6)

K1

142

(7.5,13.2)

197

Bike Transportation System Design

Table 5. Ideal bike terminal locations Cluster

Terminal (x, y)

Cluster

Terminal (x, y)

A

(0.1,12.0)

E1, F1, G1

(5.4,14.2)

B

(2.0,12.5)

I1, G1, J1

(5.3,15.1)

C, D, J, E, N, F

(3.6,12.0)

K1

(6.1,13.2)

F, D, E, G, H

(4.2,12.7)

L1, M1, N1

(9.5,13.2)

H, G, I, O

(4.6,11.7)

N1, M1, O1, P1, R1

(10.4,13.2)

L, M, K

(2.3,11.5)

S1, R1, W1, U1, X1

(12.8,13.2)

R, P, S, X, Y

(8.0,11.1)

T1

(13.2,15.0)

S, P, R, X, Y, Z

(8.3,11.4)

V1

(14.5,13.2)

U, T, V, W, Z1

(11.0,10.7)

Y1, W, Z1, V, P1

(11.4,11.7)

Z, Y, S, X, A1, B1, M1

(8.7,11.9)

B2, A2, Z1, W

(11.9,11.1)

C1

(6.8,12.9)

C2

(13.3,10.6)

D1

(5.6,12.2)

twenty three terminal locations. However, these locations are ideal in terms of center of gravity model and certain adjustments will have to be made to arrive at more realistic and practical locations. For example, an ideal center of gravity location may fall on a building or water. In such cases, the location will need to be adjusted to the nearest available space or to a location where the nearby businesses are willing to sponsor them. Furthermore, it may be noted that results may also be sensitive to political agendas in terms of locating terminals in certain areas or in front of certain businesses. Therefore, an effort was made in this chapter to disguise location names with letters and numbers. These terminal locations are optimal as well as sufficient to cover the implementation area for phase 1 of the project and meet the criteria of maximum 1km between the terminal and the neighboring communities. However, if a community is isolated at the edge of the map, a distance slightly larger than 1 km may be acceptable to the city. In those cases, a possibility exists to further reduce the number of terminal locations using a visual inspection of the map.

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The centre of gravity method takes into account group satisfaction within a cluster. It considers bike counts as importance weights for terminal locations. At the same time, the approach does not loose sight of the objective of minimizing the distance for all the communities within that cluster. An alternative approach would be to use another linear program which picks the highest bike-count locations but such an approach ignores communities except the ones with highest bike-counts and hence does not ensure group satisfaction for the whole cluster.

CONCLUSION This chapter provides the essential background in designing a public bike transportation system. The benefits of public bike transportation system and the issues related to its integration with other modes have been discussed. Next, the chapter focuses on two major issues in designing a public bike sharing program. These issues involve determining the number as well as location of the bike terminals. The decisions regarding number and location of bike terminals have been explained

Bike Transportation System Design

with the help of a case study at city of Vancouver. The City of Vancouver plans to implement a public bike transportation system in order to promote its image of a green city with lower greenhouse gas emissions, less traffic density and not-so-congested parking. The city is looking to provide an affordable and sustainable mode of transportation through a public bike rental system. The project will be implemented in phases with the first phase (i.e. pilot phase) to focus on Downtown and surrounding areas. The main issues faced by the city are to determine the minimum number of bike terminals needed, identify the best locations for those terminals and assess the number of bikes to be made available at those terminals in order to meet the requirements. The present chapter uses the past commute data to identify the high density points to serve, determines the minimum number of bike terminals needed and their ideal locations using a set covering technique. These locations are further refined using an allocation procedure and center of gravity approach for single facility location among a cluster of demand points. The chapter contributes towards providing a unified approach to public bike system design using operational research optimization techniques when the literature survey suggests a scarcity of such applied models.

REFERENCES AASHTO. (1999). Guide for the development of bicycle facilities: american association of state highway and transportation officials (AASHTO). Washington, DC: Task Force on Geometric Design. Associated Press. (1998). White bikes return to Amsterdam. Retrieved January 11, 2011 from http://www.communitybike.org/ cache/ ap_white_bikes_return.

Ballou, R. H. (2004). Business logistics & supply chain management (5th ed.). New York: Prentice Hall. Bixi (2000). Retrieved June 22, 2010 from www. bixi.com. Bowman, B. L., Vecellio, R. L., & Haynes, D. W. (1994). Strategies for increasing bicycle and pedestrian safety and use. Journal of Urban Planning and Development, 120(3), 105–114. doi:10.1061/ (ASCE)0733-9488(1994)120:3(105) DeMaio, P. (2003). Smart bikes: public transportation for the 21st century. Transportation Quarterly, 57(1), 9–11. DeMaio, P. (2004). Will smart bikes succeed as public transportation in the United States? Journal of Public Transportation, 7(2), 1–15. ESCAP & ADB. (2000). Economic and social commission for asia and the pacific (ESCAP) and asian development bank (ADB) report. United Nations, 1-4. Frank, L. D. (2000). Land use and transportation interaction: implications on public health and quality of life. Journal of Planning Education and Research, 20, 6–22. doi:10.1177/073945600128992564 Habitat (2001). The state of the world’s cities. United Nations Centre for Human Settlements (Habitat), Nairobi. Handy, S. L. (2002). How the built environment affects physical activity: views from urban planning. American Journal of Preventive Medicine, 23, 64–73. doi:10.1016/S0749-3797(02)00475-0 Iyer, N. V. (2001, February 12-14th). Measures to control vehicle population: The Delhi experience. Paper presented at the Workshop on Fighting Urban Air Pollution: From Plan to Action, Bangkok.

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Litman, T., Blair, R., Demopoulos, B., Eddy, E., Fritzel, A., & Laidlaw, D. (2006). Pedestrian and bicycle planning: a guide to best practices. Victoria: Victoria Transportation Policy Institute. Marlin, J. W. (2008). Bicycle transportation issues: Describing the attitudes and opinions of cyclists in Austin, Texas. Unpublished Masters of Public Administration Thesis Project, Texas State University, Austin, Texas. Martens, K. (2007). Promoting bike and ride: the dutch experience. Transportation Research Part A, Policy and Practice, 41, 326–338. doi:10.1016/j. tra.2006.09.010 Matsuura, M. (2003). Rental cycle system (RCS) and community cycle system (CCS). Retrieved June 10, 2010 from http://web. mit.edu/ masam/ www/ e/bicycle/rcsccs.html. Midgley, P. (2009). The role of smart bike-sharing systems in urban mobility. Journeys, (May Issue), 23–31. NCHRP. (2006). Guidelines for analysis of investments in bicycle facilities. National Cooperative Highway Research Program (NCHRP), Report #552. Washington, D.C.: Transport Research Board. Ochia, K. (1993). Bicycle programs and provision of bikeway facilities in the U.S. Transportation Quarterly, 47(3), 445–456. OCMLT. (1998). Office of the commission for the management of land traffic (OCMLT), Working paper on Land use Control. Operations City, 2-4. Pucher, J., & Buehler, R. (2006). Why canadians cycle more than americans: a comparative analysis of bicycling trends and policies. Transport Policy, 13, 265–279. doi:10.1016/j.tranpol.2005.11.001 Pucher, J., & Buehler, R. (2009). Integrating bicycling and public transport in North America. Journal of Public Transportation, 12(3), 79–104.

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Pucher, J., & Dijkstra, L. (2003). Promoting safe walking and cycling to improve public health: lessons from the Netherlands and Germany. American Journal of Public Health, 93(9), 1509–1516. doi:10.2105/AJPH.93.9.1509 Pucher, J., Komanoff, C., & Schimek, P. (1999). Bicycling renaissance in north america? recent trends and alternative policies to promote bicycling. Transportation Research Part A, Policy and Practice, 33(7/8), 625–654. doi:10.1016/ S0965-8564(99)00010-5 Sallis, J. (2004). Active transportation and physical activity: opportunities for collaboration on transportation and public health research. Transportation Research Part A, Policy and Practice, 38, 249–268. doi:10.1016/j.tra.2003.11.003 Vancouver Cycling Statistics. (2009a). Vancouver cycling statistics. Retrieved May 15, 2009 from http://vancouver.ca/ engsvcs/transport/ cycling/ stats.htm. Vancouver Cycling Statistics. (2009b). Vancouver cycling statistics update. Retrieved May 15, 2009 from http://vancouver.ca/ ctyclerk/ cclerk/20090217/ documents/tt1.pdf. Velib (2008). Retrieved May 15, 2009 from www. velib.paris.fr. Wilkinson, W. C. (2002). Increasing physical activity through community design: a guide for public health practitioners. Washington, D.C.: National Center for Bicycling and Walking.

ADDITIONAL READING AASHTO. (1999). Guide for the development of bicycle facilities: american association of state highway and transportation officials (AASHTO). Washington, DC: Task Force on Geometric Design.

Bike Transportation System Design

Austin Bicycle Plan. (1996). Austin bicycle plan part 1. City of Austin – Bicycle Pedestrian Program., Retrieved January 11, 2011 from http:// www.ci.austin.tx.us/ bicycle/ plan1.htm#text1 Pinsof, S. A., & Musser, T. (1995), Bicycle facility planning. Planning Advisory Service Report Number 459. Chicago: American Planning Association. Sharples, R. (1999). The use of main roads by utility cyclists in urban areas. Traffic Engineering + Control, January Issue, 18-22. Stinson, M. A., & Bhat, C. R. (2003). Commuter bicyclist route choice: analysis using a stated preference surve. Transportation Research Record. Record, 1828, 107–115.

KEY TERMS AND DEFINITIONS Bike Facility Location & Design: Bike facility location and design is a term used for a group of decisions taken to determine the physical location, number and sizing of bike terminals. Green Bike Transportation: The term refers to the environmental friendly sustainable mode of bicycle transportation used for daily commute as well as recreational purposes. Mathematical Optimization: Mathematical optimization is a set of applied mathematical tools that could be used to optimize some objective(s) subject to certain restrictions that are inherent in the decision making situation. Public Transportation: Public transportation refers to the mass transportation mode and facilities provided by the authorities for the benefit of common people.

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Chapter 12

Data Center Technology Roadmap Tugrul Daim Portland State University, USA

Nitin Katarya Portland State University, USA

Timothy R. Anderson Portland State University, USA

Dhanabal Krishnaswamy Portland State University, USA

Mukundan Thirumalai Portland State University, USA

Neelu Singh Portland State University, USA

Ganesh Subramanian Portland State University, USA

ABSTRACT Datacenters have been in existence all over the world for the past several decades. In today’s dynamic world, especially with most of the businesses being heavily dependent on Information Technology, interconnecting various systems within the organization and the outside world is a mandatory requirement for the success of any business. Datacenters all around the world perform this role to some level of satisfaction. Since datacenters started to play a significant factor in any organization’s success, companies realize the value of having a datacenter oriented strategy as one of the strategic initiatives for the success of their organization. Despite the agreement that the value of having such an initiative for datacenters is important, there is a lack of clarity in terms of the technical know-how involved in datacenters. The author’s objective here in this study is to fill that gap in the Industry. They wanted to portray the different facets of datacenters in terms of how can they be classified, what are the underlying technologies, are the current challenges faced by the industry and where the industry is headed in the next 10years. They illustrate the evolution of the datacenter industry in the last decade and how it is going to continue in the next 10 years graphically in the form of a Technology Roadmap. They based their research on going through existing industry literature, analyze challenges and develop a technology roadmap for data center industry with emphasis on energy efficiency and cost reduction. The wide audience for this roadmap would include IT professionals, datacenter managers, company strategists, the Government as well as environmentalists. They intention is to present the audience with a singleDOI: 10.4018/978-1-61350-156-6.ch012

Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Data Center Technology Roadmap

stop snap shot of the data center industry on how the industry has evolved over the time and where it is heading in the future. The authors present their findings based on analyzing the data obtained from literature research and expert knowledge. The key research areas of our study were challenges, market trends, technological what innovation, energy efficiency, cost reduction and government involvement. In this chapter, they take you through the general roadmap architecture starting with market drivers, products, technology and its components followed by their recommendations and inference from the study.

INTRODUCTION The 1980’s were the years that the computer industry saw the boom of the microcomputers, as an article by Amy Nutt on the ‘History of the datacenter’ suggests. Computers were installed everywhere and little or no thought was given to the environmental or the operating requirements. Lack of knowledge regarding organization of information and lost data were major stumbling blocks. The 1990’s saw an increase in the complexity of information and a demand for a more controlled environment for the IT systems. Client-Server Computing became the buzz word and the servers for these started replacing the old computers. Hierarchical design with much easier access to inexpensive networking equipment and better industrial standards for network cabling gave rise to the new look at data center concepts. The latter half of the 1990’s and the early 2000’s saw the dot com bubble’s growth and the companies realized the need for their presence on the internet. Fast and reliable internet connectivity, coupled with highly available applications and infrastructure, became the need of the hour at that time. This morphed into construction of extremely large data facilities – internet data centers that revolutionized technologies and operating practices within the industry. The physical space, equipment requirements, and highly-trained staff made these large data centers extremely expensive and sometimes impractical. The evolution continued and private data centers were born that allow small businesses to

have access to the benefits of the large Internet data centers without the expense of upkeep and the sacrifice of valuable physical space. Currently, the field of construction and the operation of the data centers have evolved to be a widely recognized industry in itself. New standards and metrics for documentation, evaluation and control, have added a layer of consistency in the data center design. Security including disaster recovery and business continuity frameworks has tried ensuring reliability and availability of the data centers. Managed hosting providers, a fast emerging sect in the data center industry, have started delivering “higher level” of managed IT services for deploying and hosting e-business, security, disaster recovery, and business continuity solutions for the mission-critical applications. The future is probably that the data center industry and the consumers of the data center services are more likely to place more emphasis on Green IT infrastructure and applications that would be hosted and support Green IT. This would go a long way to reduce the IT costs, but also ensure sustainability and green IT practices including the hardware roots like the on demand and energy efficient chips.

LITERATURE REVIEW The literature review spanned a wide variety of documents - white papers from the industry partners including the Government to determine what are the market drivers, what are the categories

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of data centers, technologies that have a bearing on these categories, the technology components, the trends that the infrastructure as well as the underlying applications are going through, the challenges that the data centers are facing and can face in the future, the frameworks that affect the infrastructure and the expectations of the business users who ultimately are the decision makers. Based on this research, the forthcoming sections would report on the items above that the reader of the roadmap can utilize to navigate and make an informed progress in the decision making process. A recent survey conducted by the Symantec Corporation for the State of the Data Center Regional Data confirmed the observation that the data center managers reported both an increase in what they needed to accomplish as well as a strong drive to reduce costs. It had become imperative that there needed to be quite a few initiatives at the data center targeted at increasing the IT efficiency. The survey centered on data center staff in 1600 companies worldwide that ranged in size from 5,000 to more than 50,000 employees, with the median company having between 10,000 and 19,999 employees. The median company in those 1600 reported having 500 people working in IT, 30 to 49 data centers, and about 20 percent of their IT staff dedicated to data center responsibilities. According to Charles King from the Pund-IT Research[25], an IT industry analysis firm, the following are the issues to be considered behind the cost issues that need to be looked at while determining the need or the continuance of a data center: Facilities: Where do you plan to house your datacenter? How will rising power and other costs affect your ROI over time? Will you use an existing building or engage in new construction? If the former, will it require notable electrical and cooling or other upgrades? If the latter, how will the cost of land and construction impact overall TCO (Total Cost of Ownership) and affect your

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organization? In both cases, what sorts of local regulations and permits are involved? In addition, what sort of timetable do you need to meet for your project, and is it realistic? Equipment: Who are your IT or channel vendors of choice? What is their experience in datacenter projects? Do they offer any incentives for projects such as the one you are considering? Do you plan to purchase all new servers, storage, and networking equipment? If you plan to incorporate existing equipment, how will that affect overall costs and ROI (Return on Investment)? Staffing: What sort of datacenter experience does your current operations staff have? Will your new facility require additional hires? How many? How about support staff (maintenance, cleaning, security, etc.)? How will staffing requirements affect TCO? Datacenter alternatives: Have you considered alternatives such as hosted datacenter services? What sorts of hosted services do your preferred vendors offer? How about competing vendors? How do those offerings compare economically and technologically to running your own facility? An IBM survey, that the Infotech Research Group has published in 2009, on the Green Initiatives that the companies have resorted to, the efforts etc., has revealed the following: Controlling cost is the strongest factor driving all 11 initiatives. Under the cost-savings umbrella, four main benefits rise to the top: decreased electricity use, decreased consumables use, decreased future operational expenses or investments and realizing credits or rebates from local utilities and governments. Two additional benefits were also cited as key considerations by many businesses: the ability to better meet customers’ demands and increased features and functionality for the business.

Data Center Technology Roadmap

The survey included about 1047 IT and business professionals from eight industries and the results showed that there were already some measures like the following below that nearly 80% of the midsize companies adopted and were planning to adopt in the near future. 1. Virtualization (including Storage consolidation, Server virtualization, Desktop Virtualization and thin clients), that go to increased features and functionality as well as decreased expenditure in the data centers 2. Existing Server upgrades, New Server room builds, IT energy measurements, that contribute to a part of the benefits listed above(a). 3. PC Power management, Printer consolidations etc., that reduce the energy consumption 4. Remote teleconferencing, telecommuting etc., that goes towards a decrease in consumables. The data center size should not be the only focus of cost savings attempts. Steve Sams, the VP of sites and facilities for IBM Global Technology Services warns “The physical footprint is not where the cost of the data center is. The physical footprint typically represents, in traditional data center builds, 7 to 10 percent of the cost.” If space-conscious managers focus on systems and the accompanying support infrastructure, a footprint reduction will be the logical outcome, Sams says. About 60% of a data center’s cost is from the power and cooling infrastructure -- the air conditioning systems, the chillers, the generators, the uninterruptible power supplies (UPS), the power distribution units and so on, he says. It would be pertinent at this point to revisit the introduction to see how the data centers evolved in the light of all these aforementioned points. Thus from our research it is imperative that cost is one of the serious factors influencing the upgrade and upkeep of Datacenters. Along with that we also found Computing Demand and En-

ergy Efficiency are the other key factors which are influencing the Datacenter Industry. Of late, the impact of proper data center strategies and optimal utilization of data center resources has come to be looked at from the sustainability perspective as well. It is not surprising that in Chapter 35 (Means of Implementation – Science for Sustainable Development) of Agenda 21 of its Core Publications, the Division for Sustainable Development of the United Nations Department of Economic and Social Affairs, has emphasized on working out long-term strategies for development. It has reinforced the science should be utilized to constantly reassess and promote less intensive trends in resource utilization, including less intensive utilization of energy in the industry. In the paragraphs on the means for implementation, the need for better technologies for data harnessing and management has been stressed on. The activities that should be undertaken for the means also includes the application of systems and technology to constantly monitor and transmit data, expanding the reach of networks and technological data bases to enable sharing of data across systems, global or regional data centers etc.. Onus is placed on technologists as well to search for knowledge and the need to protect the biosphere in the context of sustainable development. It would be good to read UN’s advice on how science and technological advancements in data centers and data provisioning can help towards long term sustainability. On their part, there is a huge mind shift from organizations that until now clamored to have their own data centers towards off-shoring their infrastructure to organizations that specialize in hosting and managing data centers like Google, IBM etc.. It is this kind of specialization that has also resulted in organizations like Google tout environmental conservation as their motto. Google, for example, has been able to save 350 million hours of processing time every year, by making its search engines anticipate the search parameters of users. Far less electrical power, air condition-

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ing etc., would be needed, benefitting not only Google, but the environment as well. IBM on its part has committed $1 Billion to make the data centers of many of its customers energy efficient. This is sought to be accomplished by retrofitting data centers to enable one computer to substitute for different devices, installation of state of the art liquid cooling systems, and re-utilizing the heat generated to produce additional electric power, as a social media (Just means) network points out in its article on Google and IBM. To enable less loss due to anticipated or unanticipated down-time of infrastructure, and to have an unalloyed involvement of the data center personnel (including the management), the Uptime Institute has come up with measures for the data center’s management structure. This is termed as ‘Operational Sustainability’ and encompasses three main elements – Management and Operations (including staffing, maintenance practices, training etc.), Building Characteristics (including infrastructure and operating conditions), and, Site Location (covering the potential of natural or man-made disasters). These three, along with their subcategories, complement the topology aspects of data centers (the tiers). It can thus be seen that data centers contribute heavily to the sustainability agenda within and outside of itself. A meticulously planned approach after consideration of the various parameters will benefit not only the organization that is planning for a data center, but also those that would consume the infrastructure services offered by that data center and in turn result in sustainability in the long run.

METHODOLOGY The project team formulated the research objective which is to research existing industry and academic literature, analyze challenges and develop and technology roadmap for data center

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industry. As a direction to achieve this objective, the team determined the key research questions as stated below: • • •

What are the predominant market drivers? How to classify the products? What are the predominant technologies that alter the dynamics of data center industry?

The project was carried out in two phases – Research and Development.

Research The team started-off the literature research reviewing academic papers, journals, and industrial white papers from leading high-tech companies. The research was focused on the 4 components of the roadmap and was carried out in the same order – Market drivers, Products, Technology components and Technology. Before getting deeper into the literature research, the team had an initial discussion with an industrial expert to validate the overall approach and get inputs and suggestions. Since data center was a broad topic on its own, the team agreed to focus their research more on the physical attributes of the data center. The team conducted a number of team discussions to review, analyze and brain storm the available literature and consolidated the most appropriate technologies and components that had the potential to alter the dynamics of data center industry considering the present industrial scenario and future.

Development After completing the data consolidation, the team started developing the draft roadmap. The team used Microsoft Visio software to develop the roadmap. The team faced a huge challenge at this stage as it attempted to accommodate all the components and technologies in the available space and also distinguish component and

Data Center Technology Roadmap

technology from each other on how it is depicted so that it is easier for the reader to understand the flow of the roadmap in its entirety. The team ended up using three different ways to show the connectivity between the market drivers, products, components and technologies which are explained in the “Roadmap Overview” section of the report. When the development was nearing completion, the team reviewed the roadmap with couple of experts. The roadmap was validated both from academic and industrial perspective by the experts. Their valuable inputs and suggestions were incorporated into the final roadmap. The methodology is also represented diagrammatically in Appendix 1. The components of the roadmap starting with market drivers are explained in detail in the upcoming sections.

MARKET DRIVERS Cost Reduction in costs – both capital as well as the operating serves as an important market driver. Costs can assume the forms that we broadly know – CAPEX (Capital expenditure) and the OPEX (Operations and Maintenance expenditure). It was observed from that in general the capital cost that the energy related components entail is about 60% and that about 75% of the operating costs stem from energy use. Capital costs would see a marked increase with location coming at a premium. Per square meter, data center energy costs are 10 to 30 times those of typical office buildings. Reducing enterprise costs is now the #2 CIO priorities in 2009. The number of volume servers is on the increase due to the afore-mentioned reasons as well as the fact that managed hosting is gaining ground. Reliability as a market driver for the businesses can affect the demand for the infrastructure’s constituents. A report from the Data Center Journal questions as to whether Moore’s law still holds

true for infrastructure requirements since there seems to be a rapid increase in the computing demand and power required for the same than what Moore’s law propounded. What could be the reason for the increase behind such an exponential increase in costs and the energy use? Another survey from IBM (IBM Estimates) detailed the capital costs and the operating energy costs in a data center. The following statistics depict the impending increase in the capital costs as well as costs for power and cooling: 1. Power consumed in powering and cooling a server approx. will be (3*cost of hardware) 2. Data Center Operating Costs would be 3-5 times the capital costs over 20 years. Data centers energy use doubling every 5 years. 3. Per square meter, data center energy costs are 10 to 30 times those of typical office buildings. New data center construction costs are increasing $30 to $50 Million for a 2K square meter data center. 4. Obsolescence of the existing data centers and the move towards consolidation as a way to weather the increasing demand for space, availability etc... a. Eighty Seven percent of the data centers were built before 2001. 78% of the data centers are over 7 years old. 33% of managers expect data centers to last 30 years. b. New, low-cost technologies such as blades consume 20 to 30 kilowatts (kW) of power per rack, when the average data center is designed to support 2 to 3 kW per rack. c. Clients identify power or cooling as the most significant problem they face, according to Johna Johnson from Nemertes Research. d. Fifty-seven percent of organizations surveyed indicated they planned data center consolidations in the next 12

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months; 49 percent planned to build new data centers in the next 18 months At the end of these, according to IBM, the decision makers are faced with the following options: 1. Extend the life of an existing Data Center infrastructure a. 23% average energy savings from audits b. Up to 35% less cost to adopt new technology c. 30-70%TCOsavingsfromvirtualization d. Over 30% savings from energy efficient technology 2. Rationalize the Data Center infrastructure across the company a. Up to 50% reduction in operational costs 3. Design new infrastructure to be responsive to change a. Defer 40-50% capital and operational costs with a modular Data Center approach b. Save up to 50% operational costs from energy efficient design

of GFLOPS (cost for a set of hardware that would theoretically operate at one gigaflop per second) has gone down, but the demand for processing itself has seen an exponential increase.The cost per GFLOPS decreased from $1.1 Trillion during 1961 to $0.42 by 2007, as a Wikipedia on FLOPS article suggests. 5. Technological advancements to support the demand for faster processing like 64-Bit platforms, Multi-core servers, virtualization including systems management, automation and provisioning, server blades etc., would result in high consumption of energy. 6. New trends like dependence on Business Intelligence, on demand computing etc., to list a few, necessitate availability and on demand processing of information for critical decision making purposes. That has come to exert a need on high computing processors, storage etc., which in turn mean a high energy usage. 7. The multiplicity in the number of servers across volume data centers. The charts below from IDC show the increase.

Business

This would result in the following:

Increasing Computing Demand

a. Power consumed in powering and cooling a server approx. will be (3*cost of hardware). b. The power consumption across the different categories of the data centers is detailed below (from an IDC White Paper in 2006). i. From 70000 watts per data center in 2003 to 81000 in 2009 for a very large data center ii. From 4000 watts per data center in 2003 remaining constant until 2009 for a large data center iii. Nearly 3000 watts per data center in 2003 remaining constant until 2009 for a medium data center.

1. Low-cost, scalable technologies drive opportunity for new applications—server installed base is expected to increase. 2. Regulatory actions drive the need for resiliency with the Sarbanes-Oxley Act, the Health Insurance Portability and Accountability Act (HIPAA) and Basel II 3. In the next decade, server shipments will multiply by 6, while storage will multiply by 69, a comparison that IBM’s Maite Frey suggests. 4. The demand for lower cost faster processing like the following trend observed in terms

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iv. Nearly 1000 watts per data center in 2003 remaining constant until 2009 for a small data center. c. Global rate of increase in electricity prices (approx 10 to 25% per year) according to an IDC report quoted in the Wall Street Journal for the period from 1996 through 2010.

The potential for the data center strategy as an industry is tremendous. Considering the opportunities, the perceived monetary figures are as below for the period from 2011 to 2012, as quoted in a white paper from IDC, Infonetics, and Juniper:

Consideration of the power and cooling costs as market drivers occupies predominance as has been re-emphasized in the paragraphs above. The cascading effect that each of the individual technologies in the infrastructure of the data center has on the overall energy has been aptly detailed in Emerson’s white paper. One Watt saved at the server component level saves an additional 0.18 W at the DC-AC, 0.31 W at the AC-DC, 0.04 W at the Power Distribution unit, 0.14 W at the Uninterrupted Power Supply, 1.07 W at Cooling, and lastly, 0.10 W at the Building Switchgear/ Transformer levels. Thus, there are market drivers across each of these technological touch points.

•

Government Initiatives

Market Opportunity

•

For service providers, the market would be nearly $24 Billion USD. This would include Core, Edge, Aggregation and the Security Infrastructure technologies and frameworks. For enterprise data centers, the market would be nearly $29 Billion USD for the data center, network infrastructure, LAN Access and the Security Infrastructure.

Environmental Impact Reduction in the carbon foot print makes it imperative for commissioning new and better data centers or to retrofit the existing. It may not be apparent until we compare how much the data centers contribute to the overall carbon emission. From a white paper published by Sun Microsystems [20], this has been compared with emissions from an automobile as follows: •

•

For the car (a Toyota Camry), at 15000 miles traveled per year, there are 5.3 Tons of CO2 For a 24x7 run server, at 440 watts per server that consumes 3942 kWh/year, there is an emission of 5.3 Tons of CO2 again.

The Federal Government has followed an incentive based as well as a monitoring role in trying to come up with standards as well as regulating the data center sprawl and energy utilization too. The following are some of the initiatives taken by the regulatory agencies of the Federal Government: 1. EPA is working with interested parties to identify ways in which energy efficiency can be measured, documented, and implemented in data centers and the equipment they house, especially servers. a. Energy Star Enterprise Server Specification Development Process i. EPA Finalized Version 1.0 of the Computer Server specification on May 15, 2009. 2. U.S. Environmental Protection Agency (EPA) and the U.S. Department of Energy (DOE) have initiated a joint national data center energy efficiency information program. The workshop convened by DOE and EPA gathered representatives from industry, utilities, associations and NGOs to identify next steps for public and private collabora-

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tion toward advancing toward the goal of improved energy efficiency in data centers. 3. On August 2, 2007 and in response to Public Law 109-431, the U.S. EPA Energy Star Program released to Congress a report assessing opportunities for energy efficiency improvements for government and commercial computer servers and data centers in the United States. The report recommends a mix of programs and incentives, as well as a holistic approach to achieve significant savings. Recommendations include: ◦⊦ Collaborating with industry and other stakeholders on the development of a standardized whole-building performance rating system for data centers. ◦⊦ Federal leadership through implementation of best practices for its own facilities. ◦⊦ Development of Energy Star specifications for servers and related product categories. ◦⊦ Encouraging electric utilities to offer financial incentives to facilitate datacenter energy efficiency improvements. ◦⊦ The Federal government should partner with industry to issue a CEO Challenge, for private-sector CEOs to assess and improve the energy efficiency of their data centers. ◦⊦ Public/private research and development into advanced technology and practices including computing software, IT hardware, power conversion, heat removal, controls and management, and cross-cutting activities. 4. Energy Star Enterprise Storage Specification Development Process: EPA is currently developing a new product specification for enterprise storage. 5. Data Collection Initiative to Develop an Energy Star Rating for Data Centers

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We can thus see how the above market drivers have led to a market pull when data centers have ceased to be treated like a facility only, and have evolved to be a formidable industry with choices that decision makers have to assess and adopt.

PRODUCTS For this project it was a challenge for us to define products. Most of the research what we have done initially have classified Data Center as a Technology which is collective unification of the IT and Facility Management. Finally after several rounds of brainstorming, we decided to use Data Center itself as a Product. We have classified Data Center into 6 different types. It is a combination of our research, work experience and expert review led to this classification into 6 of them.

Legacy Data Center These are Datacenters which were existent for a long period of time. They were initially built for Enterprises to cater to the IT Load. It is fair enough to say that these could have evolved in the late 80s or 90s. While there are tremendous changes with the IT environment, that is the changes at the Server, Application and Network Level, these Data Centers are still not equipped to hold the IT infrastructure in an efficient manner. They often are challenged in extending their capacity, inefficient Power and Cooling Infrastructure and therefore results in high cost to maintain them. Unfortunately Enterprises have to invest heavily to convert these Datacenters into an efficient one and depending on their Budget companies tend to move in the direction of converting them to an efficient one slowly. From our Roadmap perspective these Legacy Data Centers cannot be ignored. They are there in the roadmap to run the IT of organizations so that they can feel Business as usual.

Data Center Technology Roadmap

High End Tier3 DC When we define Data Center as a product, we want to look at what is available as a classification in the Industry. One Classification which is by uptime institute defines a Tier3 Data Center. We went pretty much along with their definition. These Data Centers are robust enough to perform to the potential of the Business need in an organization; however they are not fault tolerant at a Data Center Level. Any downtimes associated with the maintenance of the Data Center with respect to the Power and Cooling Infrastructure can be done independently without causing Outages at these Datacenters. Servers and Network Equipment inside the Data Center would have redundancy by itself however they do not offer any redundancy in a worst event like a Datacenter going down due to a an Earth Quake or a Tsunami.

DC in a Closet Innovation in the Facilities Management and IT infrastructure has collectively led to reducing the size of a Datacenter in to Closet. For Small Businesses and Smaller Hospitals serving the local community, it doesn’t make Business sense for them to invest in the State of Art Datacenter. The Server Virtualization along with Blade Technology is offering the capability to add roughly around 500 Servers in a rack. It can be itself called as a miniaturized Data Center. These Servers will be efficient and use some of the underlying Technological Components used in the other Data Centers. We continue to see a gradual growth in these types of Datacenters where Companies have to reduce the manageability but at the same time for Business reasons cannot outsource their Data Center, and therefore go after DC in a Closet.

Co-Location DC A Co-location Data Center is where multiple customers locate; network, server and storage

equipments and interconnect to a variety of telecommunications and other network service providers with a minimum cost and complexity. Such facilities typically range around 4500-9000 Square Meters, possess Reliable Power and Cooling. Companies find their IT and communications facilities in safe, secure hands. They enjoy less latency and the freedom to focus on their core business. Increasingly, organizations are recognizing the benefits of Collocating their mission-critical equipment within a data centre. Collocation is becoming popular because of the time and cost savings a company can realize as result of using shared data centre infrastructure. Since Collocation Data Centers are built from ground up, they are designed keeping Energy Efficiency and Reliability in mind. Companies who are moving their Servers to Collocated Data Center have to take advantage of the Energy Efficient Infrastructure by optimizing the Rack Space and using Efficient Server Hardware. An example of making the Servers Energy Efficient is moving towards Blade Servers and Virtualization of Server. On the other hand if Companies don’t focus and move their old, inefficient Server Hardware to the Collocated Data Center, then there wouldn’t be much improvement in their Energy Savings. From our research and analysis we find that Companies see Collocation as another alternative. The main selling point for companies to move towards this alternative is on Reliability, Scalability, Energy Efficiency and Redundancy of their Data Centers. There is a good possibility to see this trend grow in future as Collocation Data Centers provide the solution for Companies to meet their Green initiatives as the focal point in decision making.

Tier4 Cloud DC Our next product combines two concepts into one. We had the uptime classification of what a Tier4 Data Center is and we added that along with an emerging trend of Cloud Data Center. To start with, Tier4 Data Center is considered most robust

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and less prone to failures. It is designed to host mission critical applications with full redundancy at Power, Cooling, Network Links, and Storage. They also have compartmentalized Security zone using Biometric method of controlling who gets access to the Data Center. Tier 4 can be build for individual companies where they have to have an uptime of close to 100%. All of the above basic characteristics apply for a Cloud Data Center. Before we talk about that, let us understand about cloud computing. It can be said as a new generation computing which utilize distance Servers for Data Storage and management, allowing the device to use smaller and more efficient chips to consume less energy than Standard Computers. Such Data Centers runs into more than 100000 sqft of floor space. They cater to the need of any small company who doesn’t want to host their Data Center but only require IT in different time periods. They get into any of the cloud providers, and pay as to how much they use the Service of the provider. Companies like Amazon, Google and Microsoft are very much into the Business of building these Cloud Data Center and it is viewed that this Industry is set to grow manifold in the coming years.

POD Our Final Product called Performance Optimized Data Center or POD in its short form is a latest innovation in which a Data Center can be built on a container which in turn can be shipped from one location to another location. What is the significance of POD? Data Centers globally are constrained by Power, Cooling and Space. These limits become especially painful if they cap their amount higher than the actual IT that can be deployed inside the Data Center. One answer to these challenges would be to invest in POD. It comes with a fixed investment and it can be easily scaled depending on the Business requirement. It is also mobile. More than that, these Data Centers can be shipped in a time around 6 weeks where as if

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a Company requires to expand its Data Center as a result of a new Business acquisition it require at least 6 months to scale its Data Center. The beauty using POD here is not only the Data Center inside a Container can be built and shipped in 6 weeks; this capacity can be extended where you want, when you want it anywhere in the world.

TECHNOLOGIES AND ITS COMPONENTS Server One of the Key drivers which enabled the phenomenal growth of Data Centers was the continuous improvements happened around the Server World over the last decade. In our Roadmap we display several Server Technologies. To top on the list are the Mainframe Servers. Mainframes dominated the IT for the longest period of time starting the late 50s. In the early part of 90s, we started to move more towards a distributed computing model and saw a massive influx of smaller Servers. In spite of that, even today Mainframe still exists. There are some high scientific and engineering applications which can run only on Mainframe. Some Industry experts believe that Mainframe is the only platform which can be used to the maximum utilization. Along with the lines of Mainframe, we still see several Mini Systems in Data Centers. HPUX, AIX and SUN-Solaris are some of the typical mini systems which occupy a considerable space in the Datacenter. We continue to see these Servers run proprietary applications. The third key Server Technology which pivoted the growth of DCs is the Wintel Servers. They are combination of running Windows Server Operating System on and Server with Intel Microprocessor in it. These Servers started occupying as low as 4Us of rack space in the beginning of the 2000. Continuous Engineering efforts lead to miniaturize Servers as low as 1U in a rack. A typical rack can occupy 16 1U Servers in it. Today these Servers occupy

Data Center Technology Roadmap

60% of world market share. Finally we also see the emerging trend of running Server Operating Systems on Appliances. These Appliances are highly customized and work specific for the application for which it is serving. Lot of times they are not flexible. However they are very powerful servers, with high Energy Efficiency. We will be seeing some of the Wintel Servers being replaced by these Appliances.

Server Virtualization As the Windows Servers continued to grow, due to application requirements, Data Center managers were often challenged to expand their Facility Infrastructure. On the other hand we also saw Hardware becoming more efficient in terms of Servers capable of handling more Memory, Larger Disk space, Extending to Storage Area Network and Faster Processors. The combination of innovation at the Hardware Level and the Challenge to meet the spurt in growth of Wintel Servers, lead another Technology Push which is the Virtualization of Servers. In a Virtualized Server, a physical host or a Server runs a proprietary Operating System and is capable of holding multiple Guest Operating Systems. A typical example would be a Host running VMware ESX Server and it can host around 25 Windows Servers, depending on its Hardware and the applications running on those 25 Servers. From the user perspective they will not know the difference between whether they are connecting to a physical or virtual Server. Server Virtualization started to appear around 2003. What started to be used in developing and staging environments slowly started moving into production.

Blade Servers Continuing the path of Virtualization, another Technology which is altering the dynamics of Datacenter is the invention of Blade Servers.

Blade servers are self-contained computer servers for high density. Whereas a standard rack-mount server can exist with (at least) a power cord and network cable, blade servers have many components removed for space, power and other considerations while still having all the functional components to be considered a Server. A blade enclosure, which can hold multiple blade servers, provides services such as power, cooling, networking, various interconnects and management—though different blade providers have differing principles around what should and should not be included in the blade itself (and sometimes in the enclosure altogether). Together these form the blade system. They are also capable of holding Virtual Servers. From our expert review we understood a Full Height Blade Chassis, which can hold 16 individual blades and each blade there, is capable of holding at least 25 virtual Servers. Therefore in a rack, we can have a total of 400 Servers. Blade Servers lead the way of redefining the Datacenter. Its reach resulted in the invention of POD that is Data Center in a Container. We will continue to see a tremendous growth of Blade Servers in Datacenters.

Storage SAN A storage area network (SAN) is an architecture to attach remote computer storage devices (such as disk arrays, to servers in such a way that the devices appear as local drives to the Operating System SAN came into existence in the beginning of the century and they are continuously growing. There are new Technologies like Datadeduplication and San Replication which are key contributing factor in the Storage Design of every Datacenter. While Data De-duplication results in less space requirement to store multiple copies of data, SAN replication is helpful for Datacenter redundancy at the Datacenter level. SANs also tend to enable more effective disaster recovery processes. A SAN could span a distant location

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containing a secondary storage array. This enables storage replication either implemented by disk array controllers, by server software, or by specialized SAN devices. Sharing storage usually simplifies storage administration and adds flexibility since cables and storage devices do not have to be physically moved to shift storage from one server to another. Other benefits include the ability to allow servers to boot from the SAN itself. From our road map perspective, we are seeing innovations in SAN are happening on 3 fronts. One is on the increase in size of the Disks. Datacenters with SANs have even grown up to Petabyte and they continuously grow due to application requirements like storing large images and video files. The Second area of growth in SAN is on the speed at which the Server can communicate with the drives. Right now we have a speed of up to 8gigs. This would continue to grow up to 16 and may be 100gig in the future. The third area SAN has advanced is in adding new features to it like SAN replication and Data de-duplication.

NAS Network attached storage (NAS), in contrast to SAN, uses file-based protocols such as NFS or SMB/CIFS where it is clear that the storage is remote, and computers request a portion of an abstract file rather than a disk block.

Back-up Several improvements in the Backup Technology happened from the beginning of the Decade. We saw Backups transitioned from Direct Attached Tape Drives, to a Tape Library backing up on LTOs. Then some Data Centers moved away to backing up on disks. The State of the art Tier4 Datacenters now use SAN replication along with de-duplication to replicate the content of one Datacenter into the Redundant Data Center. This eliminates the need for a Backup solution and

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instead offers 24hrs Backup by replicating the data to the redundant Datacenter.

Network LAN The last 10 years we saw Ethernet holding the center stage connecting Servers, to users, printers and their computers. We did not see any competition to Ethernet. Most of the innovations revolved around the speed of Ethernet. From 100mbps, we went to 1gig and now 10gig Ethernet are not uncommon. We see this trend grow to 100gbps Ethernet in the coming years. Routers and Switches have become more intelligent, efficient and they are integral to the successful operation of every Datacenters.

WAN Various forms of design improvements resulted in WAN speeds of up to 10 gig as of now. In future we see this Trend grow and hit up to 100gbps. The key area of innovation is how effectively and longer the Laser light can be transmitted on the SMF cable would determine the success of 100gig Ethernet.

Thermal Management The heat loads of servers and storage devices are continuing to increase at a rapid rate. These increases are causing data center operators to struggle with how to provide adequate cooling for these high-powered racks. Whether it is a large data center or small server room, the space needs to be designed to keep the electronic components cooled to a certain temperature to ensure proper functionality. Optimizing the temperature and air flow in the room can be a challenging issue whether building a new state of the art data center or retrofitting an existing room to add higher powered more densely packed server racks. As

Data Center Technology Roadmap

companies move to higher density architectures, the heat loads increase significantly requiring higher watts per square foot to operate the center. An optimized server, thermal management and power infrastructure could contribute very much towards a better PUE for the organization.

Cooling Energy consumption and thermal requirements are fast becoming the limiting factors in data center environments. The cooling infrastructure is a significant part of a data center. The complex connection of chillers, compressors and air handlers create the optimal computing environment, ensuring the longevity of the servers installed within and the vitality of the organization they support. Based on a study by PG&E, a Design Guidelines Source Book for High Performance Datacenters suggests that increasing server densities and workloads pose power and cooling demands that could require data center operators to renovate existing facilities or even build new ones. Direct Liquid Cooling refers to a number of different cooling approaches that all share the same characteristic of transferring waste heat to a fluid at or very near the point it is generated, rather than transferring it to room air and then conditioning the room air. Liquid cooling can service higher heat densities and be much more efficient than traditional air cooling. A high efficiency approach to cooling data center equipment takes advantage of liquid cooling’s ability to efficiently move heat by transferring the waste heat from racks to a liquid loop as close to the heat source as possible, at the highest possible temperature. Liquid cooling was main stream in the 1960s and, for example, IBM used liquid cooling in the mid 80’s. Liquid-cooled systems racks are now available commercially as well. But liquid cooling hasn’t yet been broadly accepted by the rack-and-stack Internet server farms due to complexity and service risk. The macro-module containers employ direct liquid cooling to eliminate the space requirements for

CRAC units. Also, no space is required for human service or for high volume airflow. As a result, the system density can be much higher than is possible with conventional air-cooled racks. The only drawback of direct liquid cooling is the risk of spilling liquid and damaging systems, according to the EPA study. Hot Aisle/Cold Aisle is a method of cooling servers in data centers in which every aisle between rows of racks is bounded with exclusively hot-air outlets or exclusively cool-air intakes. Hot-aisle/ cold-aisle containment has gained popularity, as a study by the Lawrence Berkeley National Laboratory and Rumsey Engineers suggests, ever since early 2000 are as a data center design feature, especially in power-dense environments. In this scheme, the cabinets are adjoined into a series of rows, resting on a raised floor. The fronts of the racks face each other and become cold aisles, due to the front-to-back heat dissipation of most IT equipment. Computer Room Air Conditioners (Cracks) or Computer Room Air Handlers (Crash), positioned around the perimeter of the room or at the end of hot-aisles, push cold air under the raised floor and through the cold aisle. Perforated raised floor tiles are placed only in the cold aisles concentrating cool air to the front of racks to get sufficient air to the server intake. As the air moves through the servers, it’s heated and eventually dissipated into the hot aisle. The exhaust air is then routed back to the air handlers. The heat removal capacity of the design is influenced by raised floor height, tile placement and perforation, air handler locations, and room architecture. Sound, integrated designs are necessary, as all of these parts must work in tandem to maintain the data center’s environmental settings. The combination of hot-aisle/cold-aisle containment and variable fan drives (Vedas) can create significant energy savings. The separation of hot and cold air can provide much better uniformity of air temperature from the top to the bottom of the rack. That uniformity of temperature enables data center pros to raise the set point temperature more safely.

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Economization (Free Cooling) A standard data center cooling system can remove heat from the data center it serves only by running compressor(s), a major electrical cost. With an economizer, when the outside air is cooler than the return air, hot return air is exhausted and replaced with cooler, filtered outside air – essentially ‘opening the windows’ for free cooling. Air-Side economizers can bring Mother Nature into the data center whenever the ambient conditions are favorable. It is believed that the use of Air-side Economizers may dramatically reduce HVAC (Heating, Ventilating and Air-conditioning) related energy consumption and costs. The outside air is brought into building and distributed via a series of dampers and fans. The servers ingest the cool air, transfer heat, and expel hot air to the room. Instead of being re-circulated and cooled, the exhaust is simply directed outside. If the outside air is particularly cold, the economizer may mix the inlet and exhaust air, ensuring that the resulting air temperature falls within the desired range for the equipment. The economizer design is typically integrated into a central air handling system with ducting for both intake and exhaust. The equipment includes filters to reduce the amount of particulate matter or contaminants that are brought into the data center space. A water-side economizer eliminates the need for cooling via compressors. When environmental conditions are optimal, the warm return water from the data center is routed to the economizer. There, condenser water accepts this heat and ultimately rejects it to the atmosphere via a dry cooler or evaporative tower. Returned to its desired temperature, the chilled water supply then returns to the data center air handlers. Water-side economizer operation depends on ambient conditions. The outside air must sufficiently cool the condenser water to allow for proper heat exchange between the two loops. Water-side economizers allow users to flick the switch on the compressors for a finite period of time.

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Embedded and Localized Cooling the most efficient thermal management system involves embedding thermal management functionality at the source of the heat to remove excess heat before passing the remaining heat on to the next level. Localized thermal management solutions work deep inside electronic components using thinfilm thermoelectric structures known as thermal bumps. The thermal bump is made from a thin-film thermally active material that is embedded into flip chip interconnects (in particular copper pillar solder bumps) for use in electronics packaging. Thermal bumps act as solid-state heat pumps and pull heat from one side of the device and then transfer it to the other as current is passed through the thermoelectric material. Thermal bumps today are already extremely small — 238µm in diameter by 60µm high — and have the capability to be scaled to different sizes for different applications. They can be introduced into a system at the chip or board level using discrete modules, suggests Nextreme Thermal Solutions.

Air Management Air management in data centers is important both for energy and thermal management. Air management for data centers entails all the design and configuration details that go into minimizing or eliminating mixing between the cooling air supplied to equipment and the hot air rejected from the equipment. When designed correctly, an air management system can reduce operating costs, reduce first cost equipment investment, increase the data center’s density (W/so) capacity, and reduce heat related processing interruptions or failures. A few key design issues include the location of supply and returns, the configuration of equipment’s air intake and heat exhaust ports and the large scale airflow patterns in the room.

Data Center Technology Roadmap

Facility Management and Cabling This section describes about couple of new and upcoming concepts that the data center industry is adapting to in recent times. Modularity The approach to data center construction is moving from an integral approach to a modular approach. The industry in the past has been constructing large multi-component data centers following the integral approach. Authors Chandrakant and Amip, in their Cost Model article on behalf of HP, reiterate that realizing the need to dynamically address the individual sections of the data center to reduce the operation cost, the industry is moving to a modular approach where smaller systems with fewer variables constituting a data center are modeled and built. Such “modules” allow for improved control and greater flexibility. This flexibility facilitates the capability to add additional modules for expanded capacity based on projected growth. This approach helps to optimize the initial capital cost and the operation cost of the data center. Hybrid Technology The data center industry is looking at an alternate approach towards capital and operation cost savings in the form of multi-tiered hybrid data centers. This innovative, integrated approach combines the different tiers of data centers together based on application criticality and other organization requirements, as did HP for its multi-tiered hybrid data center. For example, applications with high criticality could run on the Tier-4 side of the data center which has higher redundancy while less critical applications could run on the Tier-2 side of the data center. This ensures high scalability and optimized cost model. Cabling Cable management becomes important as it simplifies system maintenance and extends the

useful life of the system, as Lisa Huff representing Berk-Tek opines. While structured cabling has been the norm for the data center industry for a long time now, innovative cable designs like overhead cable and patch panels are on the rise due to high-density requirements and space constraints. While the length of cabling required has reduced with the innovation in server technology (pedestal, rack and blade servers), the density of data transmission has grown over the years. Adoption of blades and virtualization has increased the network data rates. Space savings and density are considered to be the top 2 considerations for cabling. To handle the high density requirements, data center industry is venturing into new designs like wire rack-mounted patch panels and high density fiber options. These innovative rack designs also facilitate for optimum air flow.

Humidification Data center design often introduces potential inefficiencies when it comes to humidity. Tight humidity control is a carryover from old mainframe and tape storage eras and generally can be relaxed or eliminated for many locations. Data centers often over-control humidity, which results in no real operational benefits and increases energy use, as humidification consumes large amounts of energy. Humidity controls are frequently not centralized. This can result in adjacent units serving the same space fighting, with one humidifying while the other is dehumidifying. Centralized humidity control can keep all units serving the same space in the same humidification mode, eliminating simultaneous humidification/dehumidification common when using independent Computer Room Air Conditioners (Cracks) controls. When a data center uses a properly controlled economizer, the latent load (dehumidification or humidification) should be minimal. There are few sources in a data center that add or eliminate humidity – a high humidification load is likely due to economizer control or unintended dehumidification from too

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low a chilled water temperature. A dedicated outside air unit should be used to dehumidify or humidify outside air before it is brought into the building. Humidity produced in the space by operators can be easily controlled by supplying outside air at a slightly lower humidity than the set point. [10]

Power PC & D Data center devices are getting smaller and often served by dual or triple power supplies. A single rack of equipment might produce 80 or more power cords to manage. The goal is to minimize the number of expensive power drops to each rack, but power consumption keeps rising. Traditional power facilities are not able to deliver enough power or flexibility for these realities. In high power density environments, rack-mounted e-Plus are quickly being recognized as the optimal solution for rack power distribution. These e-Plus are the perfect complement to the PDR (although e-Plus are certainly compatible with standard remote power panels, and can even be directly connected all the way back to the PDU). There are many advantages of using three-phase, 208V power down to the e-PDU level, compared to using a plethora of single-phase 120V rack power strips: Three-phase distribution can transfer almost twice as much power (1.73 x) as equivalent 120V, single-phase circuits over the same size conductors.By switching from single-phase 208V to three-phase 208V distribution to the rack, 73 percent more power can be transmitted with only a 50 percent increase in copper and losses. Using higher voltage (and power) e-Put’s in the rack reduces the number of cables that need to be brought in and managed A few high-power e-PDU’s replace an unwieldy web of cabling and a mass of low-power plug strips. The result is greatly simplified cable

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management, more space available in the rack, and improved air flow and thermal efficiency. Furthermore, many of our high-density e-PDUs mount vertically at the rear of the rack, saving valuable rack space for other equipment. 415V AC Power Distribution The critical power system represents another opportunity to reduce energy consumption; however, even more than other systems, care must be taken to ensure reductions in energy consumption are not achieved at the cost of reduced equipment availability. Most data centers use a type of UPS called a double-conversion system. These systems convert incoming power to DC and then back to AC within the UPS. This enables the UPS to generate a clean, consistent waveform for IT equipment and effectively isolates IT equipment from the power source.UPS systems that don’t convert the incoming power—line interactive or passive standby systems—can operate at higher efficiencies because of the losses associated with the conversion process. These systems may compromise equipment protection because they do not fully condition incoming power. A bigger opportunity exists downstream from the UPS. In most data centers, the UPS provides power at 480V, which is then stepped down via a transformer, with accompanying losses, to 208V in the power distribution system. These step-down losses can be eliminated by converting UPS output power to 415V.The 415V three-phase input provides 240V single-phase, line-to-neutral input directly to the server. This higher voltage not only eliminates step-down losses but also enables an increase in server power supply efficiency. Servers and other IT equipment can handle 240V AC input without any issues. An incremental two percent reduction in facility energy use is achieved by using 415V AC power distribution, as suggested by Emerson Network Power in its whitepaper.

Data Center Technology Roadmap

Power Supplies Most data center equipment uses internal or rack mounted AC-DC power supplies. Using higher efficiency power supplies will directly lower a data center’s power bills, and indirectly reduce cooling system cost and rack overheating issues. AC power is typically distributed at the mains voltage of 120V, 208V, 230V. For a typical AC power distribution system the power goes through a UPS and a Power distribution unit (PDU) before going into the IT device power supply. There is a considerable savings of about $6000 per year per rack from the use of more efficient power supplies. Efficient power supplies usually will have a minimal incremental cost at the server level. Power supplies that meet the recommended efficiency guidelines of the Server System Infrastructure (SSI) Initiative should be selected for increasing efficiency. The impact of real operating loads should also be considered to select power supplies that offer the best efficiency at the load level at which they are expected to most frequently operate, and Neil Rasmussen attests to this fact in his white paper.

Backup Power There are various types of UPS’s and their attributes often are important for data center industry. It is widely believed that there are only two types of UPS systems, namely standby UPS and online UPS. These two commonly used terms do not correctly describe many of the UPS systems available. Different types of UPS topologies are there and the UPS topology indicates the basic nature of the UPS design. Brief descriptions about how each topology works are given below. Line Interactive UPS The Line Interactive UPS is the most common design used for small business, Web, and departmental servers. In this design, the battery-to-AC power converter (inverter) is always connected

to the output of the UPS. Operating the inverter in reverse during times when the input AC power is normal provides battery charging, according to Neil Rasmussen. When the input power fails the transfer switch opens and the power flows from the battery to the UPS output. With the inverter always on and connected to the output, this design provides additional filtering and yields reduced switching transients when compared with the Standby UPS topology. In addition, the Line Interactive design usually incorporates a tap-changing transformer. This adds voltage regulation by adjusting transformer taps as the input voltage varies. Voltage regulation is an important feature when low voltage conditions exist, otherwise the UPS would transfer to battery and then eventually down the load. This more frequent battery usage can cause premature battery failure. However, the inverter can also be designed such that its failure will still permit power flow from the AC input to the output, which eliminates the potential of single point failure and effectively provides for two independent power paths. Highefficiency, small size, low cost and high reliability coupled with the ability to correct low or high line voltage conditions make this the dominant type of UPS in the 0.5-5kVA power range. Double Conversion On-Line UPS In the Double Conversion On-Line design, failure of the input AC does not cause activation of the transfer switch, because the input AC is charging the backup battery source which provides power to the output inverter. Therefore, during an input AC power failure, on-line operation results in no transfer time. Both the battery charger and the inverter convert the entire load power flow in this design, resulting in reduced efficiency with its associated increased heat generation. This UPS provides nearly ideal electrical output performance. But the constant wear on the power components reduces reliability over other designs and the energy consumed by the

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electrical power inefficiency is a significant part of the life-cycle cost of the UPS. Also, the input power drawn by the large battery charger is often non-linear and can interfere with building power wiring or cause problems with stand-by generators. The Delta Conversion On-Line UPS is a newer, technology introduced to eliminate the drawbacks of the Double Conversion On-Line design and is available in sizes ranging from 5kVA to 1.6MW. Similar to the Double Conversion On-Line design, the Delta Conversion On-Line UPS always has the inverter supplying the load voltage. However, the additional Delta Converter also contributes power to the inverter output. Under conditions of AC failure or disturbances, this design exhibits behavior identical to the Double Conversion On-Line. Rotary UPS Rotary UPS, which utilizes a high speed, very low friction rotating flywheel usually coupled with a backup combustion generator that can start up instantaneously to provide emergency power. When power fails, the stored inertial energy of the flywheel is used to drive a generator until the fast start generator can take over the load. Typically, flywheel systems offer a shorter ride-through time than battery based systems, potentially impacting the selection and redundancy of backup generators. Flywheel systems offer the very high efficiency of line-interactive devices, in excess of 95%. The reliability of these systems compared to inverter systems has not yet been fully proven in the market, however the system is commercially available and rapidly gaining operating hours in a wide variety of critical facility applications. This rapidly maturing technology should be considered when selecting an UPS system.

Alternate Power DC Power In typical data centers, the loss in electrical power through conversions of AC to DC to AC to DC

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occurs for all power flowing to the IT equipment. Efficiency gains have a magnifying effect by reducing need for HVAC or 10% saving at the UPS level for example, could mean 20% saving for the data centre. Eliminating power conversion losses by using DC (direct current) instead of AC (alternating current – from the electricity grid) power to provide electricity throughout the data centre can trim the energy needed to run the centers and improve reliability An area of focus for data centre efficiency gains in the future is research into direct current (DC) power distribution in the data centre, which, researchers like Miya Knight say, can save up to 20% or more on energy consumption and cost. Researchers at the Department of Energy’s Lawrence Berkeley National Laboratory have teamed with Silicon Valley giants including Sun Microsystems, Intel and Cisco, to demonstrate technologies that could save on data centre energy operating costs, as well as improve reliability and lengthen equipment life.

Operations Operations related aspects like metrics in particular, have come to steal at least some of the limelight that the data center industry is currently having. These, along with the other evolving application and infrastructure have assumed a symbiotic existence with the data center technologies.

Metrics One of the foremost among them that continues to continuously be debated about and continuously evolving would be the data center metrics. The first of the main three metrics would be the Data Center IT Utilization (DCIU), meant to represent how much of IT equipments’ potential is currently being utilized. This metric remains academic unless there is some means for measuring or estimating Useful Work in the data center and a means for measuring Total Compute Capacity.

Data Center Technology Roadmap

Data Center Performance per Watt (DCPpW), equaling Total Useful Work divided by Facility Power Consumption, is similar to the Data Center Energy Productivity (DCeP) metric proposed by The Green Grid (The Green Grid, 2008) and CUPS, proposed by Emerson (Emerson Network Power, 2008). As with the DCIU, this metric requires an approach to calculating the Useful Work being performed by a system in the data center. Power Usage Efficiency (PUE) is another metric that has come to occupy a significant place in the data center metrics world. CADE (Corporate Average Data center Efficiency) is a metric used to rate the overall energy efficiency of an organization’s data centers. CADE was introduced in a joint repot from the Uptime Institute and McKinsey that proposed the metric as a single key performance indicator that could be used to compare the energy consumption of one data center against another. CADE = Facility Efficiency (FE) x Asset Efficiency (AE) • •

Facility Efficiency (FE) is equal to (Facility Energy Efficiency) x (Facility Utilization) Asset Efficiency (AE) is equal to (IT Energy Efficiency) x (IT utilization)

A pictorial model for a data center with its critical physical entities is represented in Appendix 2 – (A model for Data center).

DATA CENTER ROADMAP OVERVIEW The strength of this Project report lies in how successfully we have transformed our research into a meaningful mode of displaying a Technological Roadmap for Data Centers. Attached you will find one of the methods of constructing the Technology Roadmap. This Road Map has 4 levels starting from Market Drivers, followed by Products below. The 2 layers below Products are

Technology Components and Technology itself. The methodology adopted here to construct the Roadmap is in the form of Technological Invention and Innovation leading to Technology Components which are actual physical components residing in the Datacenter. These different set of components make up different type of Datacenters. Finally the six different products that is, the 6 different Datacenters, meet their respective Market Drivers. This Roadmap was constructed based on a 20 year time line. The linkages and connections were arrived from detailed research with respect to functionality, facts and future assumptions in the respective areas. At the lower level you see the Technology level coded with appropriate colors with respect to each category and the sub category. We then used the same colors at the Technology Component level to depict the linkages between the Technology and the Technology Components layer. In addition to color coding the Technology Components, we also gave numbers to every sub category derived from main category. These numbers along with the colors were then represented in the products. As an example, our POD datacenter which is one of the products was clearly coded with numbers to represent what are all the Technological Components it consists of. Finally the Products are connected by suitable arrows to every market drivers. We highlight each line going from a particular product and meeting a unique market driver by its own product color. We also see multiple products meeting the same set of Market Drivers. They are highlighted with a thick black backbone line on the left side resulting in the completion of the Roadmap. We followed the conventional method of drawing a Roadmap in connecting the products to its respective market drivers. That is a unidirectional arrow starting from the product reaches its appropriate market driver. The combination of using color coded boxes, numerical coding, combination of lines and arrows should help even a layman to understand this Roadmap without detailed study. The technology roadmap developed by the project ream is shown

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in Section of the report. (The development of this was on the lines of Robert Phaal et al.)

RECOMMENDATIONS, EMERGING TRENDS AND INFERENCES The data center road map shows the options available to an organization before embarking on a new data center venture. This would also be equally applicable to existing data centers in an organization, in the sense; it would list the options to either go in for a hosted or managed data center or to continue with the existing ones. In addition to these, there are other purposes that the data center roadmaps help with. This would however make it imperative for the organizations to consider a few points. •

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As with any other roadmap, there is a constant change, both in the organizational level as well as at the data center industry level. The only thing that is certain about technology is the frequent change. The roadmap user should bear in mind to constantly update the map with due consideration given to these changes. In addition to the point above, it should also be borne in mind that this roadmap is a bird’s eye view level of the data center technologies, the respective components etc.. There should always be a constant endeavor to traverse this roadmap alongside with the individual technology roadmaps. This would help understand how the technology is headed, as well as whether there is any disruptive technology that is going to affect the overall roadmap. As listed below, there are quite a few changes on the applications perspective that can have a bearing on the type of the data centers that could satisfy the roadmap travelers’ organizational objective. These would include Virtualization, Saas

and Cloud Computing in addition to a few others. The current shift is towards making applications available on demand, platforms on demand, infrastructure on demand etc. Though in the stages of infancy, these could heavily reduce the costs for the organization. Hosting Data centers would do well to consider these aspects that are required by their clientele before leaping into capacity planning (which would include servers, storage, type of clouds, availability, demand growth for application data, processing speeds). This lends a different perspective.

Emerging Trends Application Virtualization The infrastructure virtualization has a few dependencies on the architecture itself. It starts with the application. Can this application be deployed in a manner that it can be virtualized? Does it support clustering or are there tools that help it support clustering so that each application instance recognizes state? Can the underlying applications be replicated in real time between redundant sites so that they can resolve requests at any site at any time, ensuring that the data is current? You also have to consider the amount of data and performance during the replication process. In this case, the primary challenge is not about the bandwidth or link capacity. The challenge is how much of that data can be concurrently transferred or put into the pipe while eliminating protocol communication overhead. Penny Crossman, in an article in the Information Week, touches upon three technologies that are emerging, one of those being RDMA Over 10 Gigabit Ethernet, probably one that can address data transfer abilities. Virtualization reduces the redundant sites used for backup as well as enable management of the data replication by turning off the non-performing

Data Center Technology Roadmap

assets and making available on-going assets to function in a distributed scenario. We could do data replication, upgrades, and maintenance on a more-frequent basis, increasing your overall uptime and time-to-market for services, according to the then Sun Microsystems.

Grid, Utility and Cloud Computing Grid computing is a fairly all encompassing concept can be generally defined as: “a system that uses open, general purpose protocols to federate distributed resources and to deliver nontrivial qualities of service.”, according to a discussion from Dell’s site. Utility computing or on-demand computing, is the idea of taking a set of resources (that may be in a grid) and providing them in a way in which they can be metered. Cloud computing, is a subset of grid computing (can include utility computing) with the idea that computing (or storage) is done elsewhere or in the clouds. It refers to the virtualization of the data center, such that server machines are not thought of individually but as just a commodity in a greater collection of server machines. In this model many machines (Grid) are orchestrated to work together on a common problem. Resources are applied and managed by the cloud as needed. (In fact this is a key characteristic of cloud computing. If manual intervention is required for management or operations, then it probably doesn’t qualify as a cloud.) Cloud computing provides access to applications written using Web Services and run on these Cloud Services. Google and IBM as also HP, Yahoo and Intel have been investing in the R&D for Cloud Computing.

• •

The general categorization of SaaS architectures is based on one of four “maturity levels”, whose key attributes are configurability, multitenant efficiency, and scalability, as further discussions on Saas-SOA suggest. •

SaaS An application that is delivered through the Software as a Service (SaaS) model typically is done so: •

Over the internet,

Remotely by a third party, with little/no opportunity to bring that application in-house With a usage-based pricing model

•

On the core technology front, Solid state drives, Ultra multi core servers, RDMA over 10 gigabit Ethernet etc., are changes that should be closely watched and seen as to when these could be commercially utilized. ◦⊦ Solid State Drives are data storage devices that use solid-state memory (meaning they contain no mechanical parts) to store persistent data. They emulate hard disk drives and can seamlessly work with most applications. ◦⊦ Ultra-multicore servers are x86 servers with many processors inside. Nehalem EX servers with four sockets and 64 cores will be available by the end of the year, according to Intel. Advanced Micro Devices plans to release a 12-core server chip in 2010 and is designing an Opteron 6000 chip with up to 16 cores -- which, in a four-socket server, would provide 64 cores -- that will be released in 2011. ◦⊦ RDMA Remote direct memory access, or RDMA, allows data to move directly from the memory of one computer into that of another without involving the operating system, allowing high-throughput, low-latency networking. Highly standardized, commoditized, mostly Intel-based servers:

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◦⊦

•

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•

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Blades - single-board servers that can be densely packed into chassis which share common resources such as cabling and power supplies, SMP (symmetric multiprocessor) computers, with many standard processors sharing one pool of memory ‘bricks’. ◦⊦ Drawback: densely packed processors eat electricity and generate tremendous heat, move towards water cooled data centers again. Dynamic tuning and configuration tools: ◦⊦ Low-level hardware and software interfaces will emerge that enable applications to manage themselves calling on new system resources as required, and responding to change according to pre-defined business policies. Systems to be ‘partitioned’: ◦⊦ Preventing faults in one from impacting another, dynamic re-allocation as different applications make different demands on resources such as memory or processor capacity. Grids: Independent and ‘heterogeneous’ computers may be linked together in socalled ‘grids’ ◦⊦ Promise of almost infinite capacity at peak times and near 100% resource utilization. Outsourcing: Flexible, aligned, value-based: ◦⊦ More flexible, value-added services, access extra capacity on demand and on a metered use basis. Aligned, agile & automated: Businessdriven IT: ◦⊦ One information model that all participating systems can use ◦⊦ Planning tools will increasingly measure and model business demand

It can be seen that there is a lot that is being currently done to the entire concept of data centers and the technologies that support them. It is for sure that very soon, there can be mandates from the Governments on data center greening, data center tiers, as well as data center metrics. Already the consortiums like the Green Grid etc., have been pushing for a standardized set of metrics. It would very much be of benefit to the organizations to be aware of these, adopt and adapt to the ever changing world of data center related technologies.

CONCLUSION The study shows that the data center industry has absorbed its share of both positive and negative impacts from the high technological growth the world has experienced and the high computing demand these days. The old way of just adding hardware and resources based on demand no longer work and there is a need to manage the data centers in a more efficient way. The energy consumption and its associated cost are on its rise over the past decade. The government is expecting the industry to increase their energy and power efficiency. In this situation, the developed technology roadmap would serve as a useful tool to all the mentioned audience to understand how the industry is responding to the challenges and to identify any missing links that are yet to be addressed. Our success lies in how the audience could capture how the technology push and market pull meet each other in a technology roadmap and how one contributes and impacts the evolution of the other. The report along with the Roadmap reflects the current state and a prediction of where the Industry is heading in the next 10 years. Road Mapping process always relies on Current data and a guesstimate of how the future looks from that time. The data is not a constant here and it will keep changing in future due to Technological

Data Center Technology Roadmap

Figure 1. Roadmap

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Innovation, Government Regulations and Operational Improvement with respect to Datacenters. These changes whenever it occurs in future have to be added appropriately so that the new improvements and challenges are taken into account, in order to make the roadmap look closer to real. Redesigning the current Datacenter or rebuilding a new Datacenter to overcome the current challenges is not the common answer for everybody’s problem they face with the Datacenters. Businesses have the option to outsource their Datacenter or Co-locate the services to another facility, provided their Business model allows them to do so. On the other hand, if they wish to re-design or rebuild their Datacenter, there are plenty of options in terms of products to meet their Business Demand and the roadmap in Figure 1 along with the report will guide them with a snapshot of how they could get there.

REFERENCES Crawford, B. (2007). The Data Center Journal. Moore or Less. June 14, 2007 Crossman, P. (2009). Information Week – June 23rd, 2009. Emerging Technologies That Will Change the Data Center. Retrieved from http:// www.networkcomputing.in/ Server-Storage023Jun009-Emerging-Technologies- That-WillChange-the-Data-Center.aspx Datacenter Journal. (2010). What is Uptime Institute’s Operational Sustainability Standard? Retrieved from http://datacenterjournal. com/ index.php?option=com_content&view =article&id=4043:what-is-uptime-institutesoperational-sustainability -standard&catid=28& Itemid=100123 Dell Computers. (n.d.). Retrieved from http:// en.community.dell.com/ blogs/insideit/archive/2008/ 03/09/ cloud-computing-and-saas. aspx

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Ebiz. (2009). Retrieved from http://www. ebizq.net/blogs/ softwareinfrastructure/assets_c/ 2009/02/Saas-SOA.php Emerson Network Power. Energy Logic (2009). Reducing Data center energy consumption by creating savings that cascade across systems. White Paper EPA. (2007). ENERGY STAR Data Center Energy Efficiency Initiatives. Retrieved from http://www. energystar.gov/ index.cfm?c=prod_ development. server_efficiency FLOPS. (2011). Retrieved from http://en.wikipedia. org/wiki/ FLOPS#Hardware_costs Frey, M. (2009). The IBM datacenter family. IBM’s answer on today’s increasing challenges on energy, efficiency, modularity, scalability and cost effectiveness. Retrieved from http://www-05.ibm. com/lt/ ibmforum/pdf/ ibm_data_center_family_maite_frey.pdf Greenberg, S., Mills, E., Tschudi, B., & Rumsey, P., & the Lawrence Berkeley National Laboratory, Rumsey Engineers Bruce Myatt, EYP Mission Critical Facilities. (2006). Best Practices for Data Centers: Lessons Learned from Benchmarking 22 Data Centers. Retrieved from http://evanmills.lbl. gov/pubs/ pdf/aceee-datacenters.pdf HP. (2009). HP Data Center, ‘The Multitiered Hybrid Data Center’Webcast. Retrieved from http://h30423.www3.hp.com/ index.jsp?fr_story=88b3ef5b2eaeb2fe9 eeca25c29d4343645467668&rf=bm Huff, L. (2009). Data Center Cabling Technology Trends and Best Practices. Berk-Tek. IDC (2008). Worldwide Blade Server 2008-2011 Forecast and 2006 Vendor Shares. Doc #210229, Feb 2008

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Infotech Research Group. (2009). Green IT: Why mid-size companies are investing now. Retrieved from http://www-03.ibm.com/press/ attachments/ GreenIT-final-Mar.4.pdf Johnson, J. (2006). Nemertes Research, Architecting and Managing the 21st Century Data Center. Boston conference in 2006. Justmeans (2010). Retrieved from http://www. justmeans.com/ Data-Center-Efficiencies-LeadEnvironmental-Conservation/31877.html King, C. (2009). Search Data Center.A general list of data center cost components. Retrieved from http://searchdatacenter.techtarget.com/ expert/KnowledgebaseAnswer/ 0,289625,sid80_ gci1230813_mem1,00.html Knights, M. (2006/2007). The Power. IET Power engineer Sun Microsystems. Retrieved from https://www.sun.com/ offers/ docs/820-3023.pdf Nextreme Thermal Solutions. (2008). The Thermal Copper Pillar Bump. Enabling improved semiconductor performance without sacrificing efficiency. White Paper Nutt, A. (2007). History of the Data Centre. Retrieved from http://ezinearticles.com/ ?Historyof-the-Data-Centre&id=1478904 - Sep 08, 2008 Patel, C., & Shah, A. (2005). Cost Model for Planning, Development and Operation of a Data Center. Bristol, UK: H P Laboratories. Phaal, R., et al. (2003). Technology Roadmapping- A planning framework for evolution and revolution. Dept. of Engineering, University of Cambridge, CB2 1RX, UK, 26th May, 2003 Greengrid. http://www.greengrid.org Rasmussen, N. (2004).The Different types of UPS systems. An APC White Paper #1 Rasmussen, N. (2006). AC vs DC power distribution for data centers. An APC white paper#63

Rumsey Engineers (2006). Pacific Gas & Electric Company. High performance Data Centers: A Design Guideline Sourcebook. Symantec Corporation. State of the Data Center Regional Data –Global. (2008). Second Annual Report – 2008 – Symantec. Retrieved from http:// www.symantec.com/ content/en/ us/about/media/ DataCenter08_Report_Global.pdf UN Department of economic and Social affairs. (2009). Core publications. Agenda 21. Sec III and IV. Retrieved from http://www.un.org/ esa/dsd/ agenda21/ res_agenda21_31.shtml

KEY TERMS AND DEFINITIONS Chiller: A heat exchanger using air, refrigerant, water and evaporation to transfer heat to produce air conditioning. A chiller is comprised of an evaporator, condenser and compressor system. Computer Room Air Conditioner (CRAC): A modular packaged environmental control unit designed specifically to maintain the ambient air temperature and/or humidity of spaces that typically contain data center equipment. These products can typically perform all (or a subset) of the following functions: cool, reheat, humidify, dehumidify. Cooling Tower: Heat-transfer device, often tower-like, in which atmospheric air cools warm water, generally by direct contact (heat transfer and evaporation). Dehumidifier: A device that removes moisture from air. Economizer, Air: A ducting arrangement and automatic control system that allow a cooling supply fan system to supply outdoor (outside) air to reduce or eliminate the need for mechanical refrigeration during mild or cold weather. Economizer, Water: A system by which the supply air of a cooling system is cooled directly

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or indirectly or both by evaporation of water or by other appropriate fluid (in order to reduce or eliminate the need for mechanical refrigeration). Free Standing Equipment: Equipment that resides outside of data center racks. Generator: A machine, often powered by natural gas or diesel fuel, in which mechanical energy is converted to electrical energy. Hot Aisle/Cold Aisle: A common means to optimize cooling in IT equipment rooms by arranging IT equipment in back-to-back rows. Cold supply air from the cold aisle is pulled through the inlets of the IT equipment, and exhausted to a hot aisle to minimize recirculation. Humidifier: A device which adds moisture to the air. Load: In data centers, load represents the total power requirement of all data center equipment (typically servers and storage devices, and physical infrastructure). Power Distribution Unit (PDU): A floor or rack mounted enclosure for distributing branch circuit electrical power via cables, either overhead or under a raised floor, to multiple racks or enclosures of IT equipment. The main function of a PDU is to house circuit breakers that are used to create multiple branch circuits from a single feeder circuit. A secondary function of some PDUs is to convert voltage. A data center typically has multiple PDUs. Pump: Machine for imparting energy to a fluid, causing it to do work.

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Rack: Structure for housing electronic equipment. Raised Floor: Raised floors are a building system that utilizes pedestals and floor panels to create a cavity between the building floor slab and the finished floor where equipment and furnishings are located. The cavity can be used as an air distribution plenum to provide conditioned air throughout the raised floor area. When used as an access floor, the cavity can also be used to rout power/data cabling infrastructure and/or water or coolant piping. Rack-Mount Power Distribution Unit (rPDU): A device designed to mount in IT equipment racks or cabinets, into which units in the rack are plugged to receive electrical power. Transformer: A device used to transfer an alternating current or voltage from one circuit to another by means of electromagnetic induction. Uninterruptible Power Supply (UPS) Fixed: Typically uses batteries as an emergency power source to provide power to data center facilities until emergency generators come on line. Fixed implies a standalone unit hard wired to the building. UPS (Uninterruptible Power Supply) Modular/Scalable: Typically uses batteries as an emergency power source to provide power to data center facilities until emergency generators come on line. Modular/scalable implies units installed in racks with factory-installed whips allowing for physical mobility and flexibility. (As defined by Greengrid)

Data Center Technology Roadmap

APPENDIX Methodology (Figure 2) Figure 2. Methodology

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Model for Data Center (Figure 3) Figure 3. Model for data center

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About the Contributors

Zongwei Luo is a senior researcher at the E-business Technology Institute, The University of Hong Kong (China). He is the founding Editor-in-Chief of the International Journal of Applied Logistics and serves as an associate editor and/or editorial advisory board member in many international journals. Dr. Luo’s recent interests include applied research and development in the area of service science and computing, innovation management and sustainable development, technology adoption and risk management, and e-business model and practices, especially for logistics and supply chain management. *** Carl Adams’s research interests are with (mobile) information systems, m-commerce/e-commerce and examining adoption and impact of technology within society. Current projects include examining the use and development of mobile and wider ICT technologies and their impact (from mobile phones to autonomic systems); social networking, creativity and innovation and, electronic and mobile payment mechanisms and corresponding (local) exchange mechanisms. This last area explores the wider social impacts of all forms of money and novel exchange mechanisms. Timothy R. Anderson is an Associate Professor of Engineering and Technology Management at Portland State University. He received his MSIE and PhD in Industrial Engineering from the Georgia Institute of Technology after receiving his BS in Electrical Engineering from the University of Minnesota. He has worked for and consulted with a variety of companies including Honeywell, Oki Electric, Menlo Logistics and the US Postal Service. He is currently the Program Chair as well as the Director of Technical Activities for the Portland International Center for Management of Engineering and Technology (PICMET). His current research interests are productivity analysis, operations research, service engineering, technology forecasting and new product development. Recent journal articles have been published in the IEEE Transactions on Engineering Management, Technological Forecasting and Social Change, Technovation and the Journal of Productivity Analysis. He is a fellow in the American Indian Science Engineering Society. Raghu Bir Bista is an Economist. He is a lecturer of economics working in Department of Economics, Patan Multiple Campus of Tribhuvan University, Nepal. He teaches policy economics and development economics. He was an editor of Economic Journal of Development Issues (2005-2008) published by Department of Economics. Foreign Direct Investment in Nepal is his first book published by CIDS in 2005. This was followed by second book, Economics of Nepal in 2008 and third book, Global Role of

About the Contributors

Nepalese forest in 2010. He has been working in the study of climate change, particularly lower carbon behavior and activities in developing countries and REDD since 2008. Julien Chevallier is Assistant Professor of Economics at the Université Paris Dauphine. He is member of the CGEMP/LEDa. Tugrul Daim is an Associate Professor and PhD Program Director in the Department of Engineering and Technology Management at Portland State University (PSU). Prior to joining PSU, he had worked at Intel Corporation for over a decade. His papers appeared in Technological Forecasting and Social Change, Technovation, Technology Analysis and Strategic Management, Computers and Industrial Engineering, Energy, Energy Policy and many others. He is the Editor-in-Chief of the International Journal of Innovation and Technology Management and North American Editor of Technological Forecasting and Social Change. He received his BS in Mechanical Engineering from Bogazici University in Turkey, MS in Mechanical Engineering from Lehigh University in Pennsylvania, MS in Engineering Management from Portland State University, and PhD in Systems Science: Engineering Management from Portland State University in Portland, Oregon. Johanna Etner is Professor of Economics at the Université Paris Descartes. She is member of the LIRAES. Sinan Erzurumlu is an assistant professor of Technology and Operations Management at Babson College. He received a PhD degree in supply chain and operations management from McCombs School of Business and a Master’s degree in industrial engineering and operations research from the University of Texas at Austin. His research interests include Start-up Operations, Clean Technology Commercialization, and Competition and Collaboration in Technology Development and Deployment. He is a member of the Decision Sciences Institute, Institute for Operations Research and Management Sciences, Manufacturing and Service Operations Management Society, and Production and Operations Management Society. He teaches an undergraduate course on business and the environment. Dr. Avninder Gill obtained his Master’s and Ph.D. degrees from University of Manitoba (Canada) and a Bachelor in Engineering from Punjab University (India). Dr. Gill is currently a faculty member at School of Business & Economics at Thompson Rivers University, Canada. Prior to this, Dr. Gill has been a faculty at various Universities in Supply Chain Management, Project Management, Operations Management and Optimization areas. Dr. Gill has worked in the industry as a manufacturing engineer as well as supply chain consultant. He has chaired various divisions and sessions at international conferences. Dr. Gill has published over fifty papers in reputed journals and conferences. His research has appeared in International Journal of Production Research, International Journal of Operations and Production Management, Computers & Industrial Engineering, International Journal of Systems Science, Asian-Pacific Journal of Operational Research and Applied Stochastic Models in Business & Industry. Dr. Gill can be contacted at [email protected]. Peter Hills is the Director and Chair Professor of Kadoorie Institute, the University of Hong Kong. His research interests centre on the relationship between environmental and sustainability issues and the policy-making process. Over the years, his personal research agenda has moved on from an early 247

About the Contributors

interest in transport-environment issues and applications of environmental impact assessment (EIA), to energy-environment problems and more recently to environmental policy processes, environmental governance, sustainable development and ecological modernisation. He has acted as a consultant to various international organizations, including The United Nations Development Programme, The United Nations Economic and Social Commission for Asia and the Pacific, The Asian Development Bank, The International Labour Office, The Asian and Pacific Development Centre, and The European Union. He has recently been working as a consultant for the EU-funded Urban Environmental Planning Programme – Vietnam which has been assisting the Ho Chi Minh City University of Architecture to upgrade its urban planning programmes. Nitin Joglekar is an associate professor of operations and technology management at Boston University’s School of Management. His research addresses Clean Technology Commercialization - Startup and Diffusion, and Complexity and Emergent Outcomes in Distributed Innovation Projects and Portfolios. Professor Joglekar is a Senior Editor at Production and Operations Management, and Associate Editor for Innovation and Entrepreneurship at Management Science. Prior to his academic career, he has worked in the IT and shipbuilding/energy industries. He was a founder of a venture capital backed software firm. He advises entrepreneurial and established firms in their initiatives for achieving business growth and profitability. Pierre-André Jouvet is Professor of Economics at the Université Paris Ouest Nanterre La Défense. He is member of EconomiX. Nitin Katarya works for Intel corporation in Oregon in the areas of Quality as a product Quality engineer. His job includes ensuring and addressing Quality issues faced by Intel’s customers and getting them resolved in a timely manner coordinating with the design team. He holds a Bachelors in Engineering from India and a Masters in electrical engineering from California State University in Sacramento. He is a part-time student of the Masters in Engineering and Technology Tanagement from Portland State University. Dhanabal Krishnaswamy is a part-time student of the Masters in Science from the Portland State University’s Engineering and Technology Department. He is employed as an IT Specialist in a medium scale telecommunications company in Portland. Jacqueline Lam is a post-doctoral fellow at the Kadoorie Institute, the University of Hong Kong. Her current research focusses on the design and implementation of environmental regulation and the effects on technological environmental innovation, especially with reference to electric vehicles and biofuels development and adoption in Hong Kong, China, USA and UK. Recently, she has also applied the theory of transitional management in the transition to low-carbon energy and transport technologies in these countries. Other ongoing research topics of hers include the relationship between open innovation, environmental performance and corporate competitiveness. Simon Mouatt’s research explores the notion of the erosion of state (financial) sovereignty, and general transformation of the capitalist credit system, from a Marxian perspective. He is interested in all

248

About the Contributors

aspects of monetary theory, and the functioning of the capitalist financial system per se. Simon lectures in economics and international financial markets at Southampton Solent University. Dr Roberto Poles is a research fellow in the Faculty of Business and Economics at the University of Melbourne (Australia). He has a diverse background with previous employment as a visiting lecturer at the School of Engineering, Department of Civil & Environmental Engineering. He joined the University of Melbourne upon completion of his PhD research in business information technology and logistics. He also holds an MSc in Industrial Engineering. His research interests lie in strategic supply chain and business process modeling and optimization with a particular interest in reverse logistics/supply chain management and operations management. He is also interested in computer-aided method for analyzing and solving complex problems, such as System Dynamics simulation modeling. He is currently under contract for an inter-disciplinary project in response to the National Water Initiative to develop new methods to efficiently manage surface and groundwater resources for both agriculture and the environment. Neelu Singh is a student of the Masters in Science from the Portland State University’s Engineering and Technology Department with a Masters in Statistics from India. Ganesh Subramanian is a Lead Infrastructure Architect at Providence Health Systems. He had been involved in Design, Engineering and Implementation of several IT Infrastrucure Projects at Providence. Ganesh is a student of the Masters in Engineering and Technology Management at the Portland State University in Oregon. S. Sureshkumar, the author, is a senior scientist and Adviser to Director at National Institute For Interdisciplinary Science And Technology (formerly Rrl,Trivandrum),a CSIR unit located at Trivandrum, India. Incidentally CSIR is a premier public research organization in the country. The author has been associated with environmental technology programs in the institute as a part of the managenet and advisory team of scientists,and of late has been associated with climate change programs,and guiding scholars in this regard. His other areas of research interests include innovation diffusion,science management, and policy studies where he has published widely and participated in conferences. His work on planning and evaluation has helped in developing measures for research performance incorporating basic and application and development aspects. He has over 30 years of experience in the above fields and guided many research scholars and project students in these areas. He is an alumni of the prestigious IITMadras, India. Fehmi Tanrisever is an assistant professor of Operations Management at the Industrial Engineering and Innovation Sciences Department at Eindhoven University of Technology. He received a PhD degree in supply chain and operations management from the University of Texas at Austin in 2009. His research interests include Start-up Operations and Risk Management, Clean Technology Development and Deployment, Commodity Risk Management and Operations-Finance Interface. He teaches graduate and undergraduate level courses in integrated operational and financial risk management, operations management, business finance and financial accounting.

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About the Contributors

Mukundan Thirumalai is a part-time student currently pursuing his Masters in Science from the Portland State University’s Engineering and Technology Department. He is employed as an IT analyst at a reputed Less Than Truckload transportation organization. Prof. J. Zambujal-Oliveira received M.S. degrees in Economics at New University of Lisbon(FE) and in Management Science at Technical University of Lisbon (ISEG). In 2007, he received a Ph.D. degree in Management Science from the Technical University of Lisbon (ISEG). Before coming to the Department of Engineering and Management at Technical University of Lisbon (IST), Prof. J. ZambujalOliveira was employed as a systems engineer at banks, insurance companies and consulting firms, and as an assistant professor of management at the University of Madeira in Funchal. Prof. J. ZambujalOliveira’s research interests include real options analysis and international taxation. In particular, he enjoys working with graduate students and colleagues in developing, analyzing, and testing numerical methods for solving stochastic differential equations, developing and analyzing models in transfer prices systems and developing, analyzing, and testing computational methods in mathematical finance. Sam Wong is a Lecturer in the School of Environmental Sciences at the University of Liverpool, UK. His research interests lie in renewable energy, sustainable technology and water governance in South Asia, West Africa and Ecuador. He has written a book Exploring Unseen Social Capital in Community Participation (Amsterdam University Press, 2007) and co-edited a book Identity in Crossroad Civilizations. His other key publications are in Energy Policy, Urban Studies and Transactions of the Institute of British Geographers. He has received more than £150K from EPSRC, British Council, British Academy and NESTA. Ms. Ying Yin is a graduate from the Msc Program of Computer Science, the University of Hong Kong, Hong Kong, China. She is now working as a business analyst in an investment bank in Hong Kong.

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Index

Symbols 3rd sector 64, 69-70, 75

A above ground biomass 82-83 accepted returns 165, 168-169, 172 air management 216 air-side economizers 216 allometric method 82, 87 alternative energy 80, 82, 98, 100 Australian Mobile Telecommunications Association (AMTA) 159, 178-179, 182, 184

B banking and pooling 34-36, 47 base scenario 171-172 below ground biomass 83 bike system design 190, 199 bike terminals 187, 191-194, 196, 198-199, 201 biofuel 5, 7, 113, 120, 124 biomass 5, 82-83, 86-87, 90, 113, 129, 131 Blade servers 211, 213, 217 business intelligence 184, 208

C California Fuel Cell Partnership (CaFCP) 93, 99103, 105, 110 capital expenditure (CAPEX) 207 carbon credit 16, 82-83, 87, 152-154 carbon data 83, 87 carbon emission compliance 80 carbon incentive 81-82, 90 Carbon intensity 80 carbon markets 3, 9-11, 15 carbon trading 132, 134, 151-154 casino banking 64, 67

Centre for the Study of Financial Innovation 67, 77-78 CER price estimation 25 Certified Emission Reductions (CERs) 6, 16-17, 20-25, 27 Chicago Climate Exchange (CCX) 152 Clean Development Mechanism (CDM) 2, 4-7, 9, 16, 54, 63, 80-81, 87, 130, 152 clean technology 112-115, 117-123, 126-127, 131 Client-Server Computing 203 closed loop supply chains (CLSCs) 157-160, 162, 166, 172, 174-177, 181-183, 185-186 Collaborative Planning and Forecasting and Replenishment (CPFR) 149, 155 complementary currencies 64-65, 70, 75-77 Computer Room Air Conditioner (CRAC) 215, 227 Consultative Group on International Energy Research (CGIER) 7 cooling tower 227 Corporate Average Data Center Efficiency (CADE) 221 cost model 217, 227 credit risk 140, 142, 145-147, 151, 153

D Data Center Energy Productivity (DCeP) 221 data center industry 202-203, 206, 217, 219-220, 222, 224 Data Center IT Utilization (DCIU) 220-221 data center metrics 220-221, 224 Data Center Performance per Watt (DCPpW) 221 datacenters 202-205, 210-215, 221, 226-227 data-deduplication 213 decarbonisation 35 deforestation 9, 52, 79-83, 90-91 demand management 140 Department of Energy (DOE) 27, 101, 103, 105106, 110-111, 113, 120, 209, 220

Index

Desktop Virtualization 205 Development Finance Institutions (DFIs) 3, 8 direct liquid cooling 215 direct structure tests 171 distancing of the customer 66 documentary risk 140, 142 double conversion on-line 219-220 dual currency system 70

E econometric method 82 economic circle 67 EcoSecurities (ECO) 15-27, 29 efficient technology 80, 208 electronic money 69, 71 energy-mix strategies 58 environmental legislation 159 Environmental Protection Agency (EPA) 16-17, 2728, 160, 209-210, 215, 226 EU Emissions Trading Scheme (EU ETS) 34-37, 46-49 European Commission (EC) 35-37, 47, 49, 71, 160, 183 European Union Allowances (EUAs) 45-46 Export Credit Agencies (ECA) 13, 146 extended producer responsibility (EPR) 160

F facility design 187, 190 failed returns 165, 168-169 Failure Mode Analysis 180 fast-used products 173, 175, 177-178 Financial Constraints 112, 114-115, 117-122, 129 financial distress costs (FDC) 118 Financial Services Association (FSA) 71 FOVOC 17, 20-25 Free Cooling 216 fuel cell vehicles (FCVs) 93, 97-103, 105-110

G gasifier-based energy 121 geek credit 71 GHG concentrations 1, 4 Green Bike Transportation 187, 201 green dollars 71 Greenhouse Gasses (GHGs) 1-7, 9, 11-12, 15-16, 80, 93, 97-99, 103, 108-109, 151-153 gross domestic product (GDP) 69-70, 80, 133, 160 Guest Operating Systems 213

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H Health Insurance Portability and Accountability Act (HIPAA) 208 Heating, Ventilating and Air-Conditioning (HVAC) 216, 220 hierarchy of inferiority 57, 60 Hot Aisle/Cold Aisle 215, 228 Household Data 83 Humana People to People (HPTP) 56-57, 60-61 hydrogen fuel cell vehicles (HFCVs) 92-93, 105106, 108, 110

I IBM Dollar 67, 77 ICBA 66 inappropriate exchanges 74 information management 140, 151 Infrastructure Development Company 55-56, 58, 62 Initial Rate of Return (IRR) 4, 20 inside cheating risk 140 inspection time 165, 169, 172 interlocking services 66 International Energy Agency (IEA) 1-4, 13, 27, 54, 62, 110 inventory cost 140, 142, 151, 172-178, 181 inventory risk 140 investment in low-carbon technologies 7 investment timing analysis 25 involuntary market 152-153

J Just in Time (JIT) supply 149

K Kafle Community Forest (KCF) 79, 83-88, 90-91 Kafle community forest user group (KCFUG) 84, 91 Kyoto protocol 2, 6, 15-17, 24, 49, 54, 152-153, 155

L Lighting Africa 51 Local Exchange and Transfer Schemes (LETS) 6465, 70-73, 75 low carbon growth model 1 low-carbon hydrogen economy 92-94, 97-98, 102, 104, 106, 108-109 loyalty vouchers 68

Index

M mainframe servers 212 marginal cost of capital (MCC) 112, 114, 118-122, 131 marginal rate of return (MRR) 118, 122-123, 131 market allocation system 68 market fluctuation risk 140 mathematical optimization 193, 201 mechanisms for mitigation 4 Metro Vancouver 191 Microsoft points 68-69 million gallons per year (MGY) 113 model building 162 mono-system 67 multi-core servers 208 Multilateral Development Banks (MDBs) 7-8 mutual credit system 71

N National Allocation Plan (NAP) 35 Nationally Appropriate Metigation Actions (NAMAs) 3 net present value (NPV) 15, 22-27, 117-118, 123 network attached storage (NAS) 214 New Economics Foundation (NEF) 70 New Energy Finance (NEF) 10, 119 non-banks 65-66, 76-77

O Octopus cards 67 OECD 2, 11, 13, 146 Official Development Assistance (ODA) 1, 4, 6-8 on demand computing 208 operational hedging 112, 114-115, 121-123, 125128, 131 operational sustainability 206, 226 operation risk 140, 146, 152 Operations and Maintenance expenditure (OPEX) 207 Oyster card system 67

P percentage disposed 165, 169, 173 Performance Optimized Data Center (POD) 212213, 221 photovoltaic (PV) technology 7, 55 physical footprint 205 power distribution unit (PDU) 209, 218-219, 228

power usage efficiency (PUE) 215, 221 product management 140, 142-143 profitability index (PI) 24, 27 public bike sharing 198 Public Finance Mechanisms (PFMs) 3, 8, 13-14 public transportation 12, 187, 189, 199-201 Purchasing Power Parity (PPP) 147, 155 purchasing reward schemes 66

R Rack-Mount Power Distribution Unit (rPDU) 228 real economic well-being 70 recoverable inventory 162-163, 165-166, 169, 172178, 181-182 reduction of emission from deforestation and forest degradation (REDD) 9-10, 79-84, 87-91 regional corporation 67 relationship-specific investments 67 remanufacturable items 165 renewable innovations 54 Renewable Portfolio Standard (RPS) 11 renewables obligation (RO) 11 rental bike systems 190 replenishment frequency 166, 169, 172 repository document risk 140 residence time (RT) 163, 166, 169-179, 181-182 retail banking 66-67, 75 retail sector 66-67 return on investment (ROI) 5, 112, 114, 118, 204 Reuters Carbon Market Database 36, 46, 49 reverse logistics 157-161, 167, 175, 178-179, 182186 Rotary UPS 220

S Second Life 69 Server System Infrastructure (SSI) 219 Server Virtualization 205, 211, 213 serviceable inventory 162, 165-166, 169, 172-173 Service Agreement with Customer (SAWC) 163, 166-168, 170-171, 173-178, 180 service credits 71 signature analysis 180 slow-used products 173-175, 177 small and medium-sized enterprises (SME) 132136, 138-148, 150, 154-155 Software as a Service (SaaS) 222-223 solar mirrors 54 Stock and Flow Diagram (SFD) 164-165, 168 storage area network (SAN) 99, 213-214

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Index

Storage consolidation 205 strategic sustainability 157 sunk cost 59 Supply-Chain Council (SCC) 136 Supply-Chain Operations Reference-model (SCOR) 132, 134, 136-139, 149, 154 supply management 140 sustainable cities 11 sustainable development 2, 5, 51-52, 78, 205 system dynamics (SD) 157, 159, 164, 168, 170-171, 181-186

T Technological Environmental Innovation (TEI) 92 technology policy 129 technology roadmap 202, 206, 221, 224 Tecterra 17-18, 20, 22-28 telecommuting 205 terminal locations 192-198 The Energy and Resource Institute (TERI) 55-56 The Other Economic Summit (TOES) 70 thermal management 214-216 Third Party Logistics (3PL) 132-137, 139, 142-145, 149-151 tie-in customers 66 Tier4 Data Center 211 timebank systems 64-65, 70, 73-76 time dollars 71 token-coin systems 68

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token exchange system 68 Total Cost of Ownership (TCO) 204, 208 traditional banking sector 65, 69 transition management (TM) 92-97, 102, 104, 108111 triple bottom line 65, 70

U UNFCCC 5-6, 8, 80, 87, 91 uninterruptible power supplies (UPS) 205, 218-220, 227-228 United Nations Environment Protramme (UNEP) 2-3, 5-6, 10, 13-14

V vendor managed inventory (VMI) 148 Ventilation Air Methane (VAM) 16-18, 20, 22-24, 26-27 venture capital (VC) 5, 118-119, 124, 130 voluntary market 22, 152-153

W water-side economizer 216 Wir 67 World Bank 2-3, 5-6, 8, 14, 27, 50-57, 60-63, 85, 87, 130, 152-153, 188