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CLEAN ENERGY TECHNOLOGY BUYDOWNS: ECONOMIC THEORY, ANALYTIC TOOLS, AND THE PHOTOVOLTAICS CASE Richard D. Duke A DISSERTATION PRESENTED TO THE FACULTY OF PRINCETON UNIVERSITY IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY RECOMMENDED FOR ACCEPTANCE BY THE WOODROW WILSON SCHOOL OF PUBLIC AND INTERNATIONAL AFFAIRS November, 2002 Copyright by Richard D. Duke, All rights reserved. Abstract The conventional responses to the market failures that constrain energy innovation include market tuning (e.g. pollution taxes) as well as supply-push (i.e. public support for research, development, and demonstration). There is no similar consensus favoring demand-pull programs, but this dissertation develops an economic rationale for subsidies to pull emerging clean energy technologies down their respective experience curves. Even with optimal pollution taxes in place, such buydowns can improve welfare primarily by correcting for learning-by-doing spillover that discourages firms from forward pricing (i.e. pricing below the short-term profit maximizing level to reduce costs through production experience). Learning spillover also occurs in other sectors, but the case for clean energy buydowns is unique. Governments wisely seek a broad supply-push portfolio, but only the most promising clean energy options merit demand-pull support because individual buydowns are costly and generate scant spin-offs absent successful commercialization of the targeted technology. Moreover, governments have more information about technologies at the deployment stage and failure to screen out poor prospects can yield entrenched corporate welfare programs (e.g. grain ethanol). The buydown selection criteria proposed herein favor support for photovoltaics (PV), and the recommended implementation strategy optimizes this support. Conventional analyses assume markets fully materialize as soon as the technology reaches financial breakeven, suggesting buydowns should be implemented as quickly as possible. The optimal path method introduced in this dissertation more accurately iii models demand and defines the welfare-maximizing subsidy/output schedule. An optimal PV buydown would triple current demand subsidies and sustain declining perunit support for over four decades. Such a buydown (initially targeting residential markets in industrialized countries) need never raise electricity rates by more than 0.5 percent while delivering roughly $50 billion in long-term net benefits (relative to a nosubsidy scenario) and allowing PV to provide over 5 percent of industrialized country electricity by 2030 (vs. less than 1 percent without subsidies). Finally, implementing buydowns at the regional level bypasses the international collective action problem and reduces the disruption from the failure of any single program. A decentralized approach also facilitates program innovation and reduces free rider subsidy costs a crucial determinant of buydown economics. iv Table of Contents Abstract... iii Table of Contents...v Acknowledgements...x List of Figures... xiii List of Boxes...xv List of Tables...xvi List of Acronyms... xvii Chapter 1: Introduction...1 Motivation...1 Approach...11 Chapter 2: The rationale for clean energy buydowns...13 Market failures attributable to learning-by-doing...14 Imperfect spillover...15 Perfect appropriability...21 The perfect spillover assumption...22 Summary...26 The unique buydown potential of the clean energy sector...27 The buydown advantages of clean energy technologies...27 The buydown disadvantages of technologies from other sectors...31 Summary...38 Appendix...39 No discounting case...39 v Discounting case...42 Chapter 3: Implementing clean energy buydowns...44 Quantifying learning-by-doing...44 Learning curves...44 Experience curves...46 Cost-benefit analysis of buydowns...47 The conventional breakeven method...48 The optimal path method...53 Technology selection criteria...57 Analytic challenges...61 Relation of experience curves to supply curves...62 Experience curves and causality...63 Microstructure in experience curves...66 Implementation challenges...71 Subsidy targeting, regressivity, and the marginal excess burden...71 The politics of buydowns...73 Lessons from the U.S. Grain Ethanol Program...75 Summary...82 Chapter 4: Background on PV technology and markets...83 PV Technology...84 PV market segments...86 Off-grid markets...88 Grid-connected markets...91 vi Summary...94 PV and the buydown technology selection criteria...95 Criterion 1: Competitive market structure...95 Criterion 2: Strong experience curve with a low price floor...96 Criterion 3: Low current sales but strong market acceleration with subsidies...98 Criterion 4: Low market risk from substitutes...98 Criterion 5: Public benefits Synthesis Public sector support for PV technology development and deployment The Japanese PV buydown The German PV buydown The U.S. context Market tuning Buydowns Residential PV market potential Summary Chapter 5: Buydown analysis for distributed grid-connected PV PV buydown literature review Assessing a global PV buydown: the improved breakeven method Base case Sensitivity analysis Assessing a global PV buydown: the optimal path method vii Base case Sensitivity analysis assuming a 33 percent MEB Strategies for effective PV buydown implementation Extending buydowns to maximize social welfare Reducing transfer subsidies using market segmentation by PV application Reducing transfer subsidies through regional market segmentation Global versus regional buydown strategies Quantity mandates versus unit subsidies Designing quantity mandates Quantity mandates reduce information requirements and improve efficiency Subsidy caps further improve quantity mandates Potential pitfalls Disadvantages of targeting support to specific PV sub-technologies Summary Appendix Chapter 6: Conclusion Principal findings Buydown rationales Sector and technology selection criteria Analytic methodologies The PV Case viii Implementation Applicability to clean energy technologies other than PV An agenda for further research Buydown analytics The logistical demand shift assumption Experience curves Net metering Regionally specific issues relating to buydowns Appropriate geographic scope of buydowns Implementation strategies Assessing other clean energy technologies beyond PV Wind electricity Fuel cells Advanced biomass Advanced energy efficiency Institutionalizing clean energy buydowns The challenge ahead References ix Acknowledgements Each of my four committee members has made unique and complementary contributions to this dissertation. Dan Kammen inspired my explorations in this subject and backed me through wide-ranging adventures, including field research in Kenya and a documentary project. These experiences may not have been the shortest path to a degree, but they were instrumental to my development and leave me with some of my best memories and proudest accomplishments. Bob Williams contributions have been equally essential. His tenacity and profound grasp of technology policy have driven me to deeper understanding of the subjects I have tackled, while his sustained commitment and extraordinary intellect have immeasurably enriched my academic experience and this dissertation. David Bradford has guided me towards some of the most fruitful research directions with concise clarity. His unerring intuition has allowed me to weave a coherent story from the many loose threads I had on hand a year ago. Rob Socolow has consistently challenged me to tackle some of the hardest questions raised by my research. This has stimulated my thinking and sharpened my arguments again providing a crucial contribution to the final product. Beyond my committee, the Science, Technology, and Environmental Policy (STEP) Program has been an excellent home for my research. Among the faculty, Frank von Hippel provided the impetus to refocus my dissertation at a point when I had begun to veer off course. Clint Andrews offered useful input on an early term paper that helped to lay the groundwork for this research. More recently, Denise Mauzerall and David Wilcove have been consistently supportive. Among STEP affiliated students, I have x developed an especially rewarding collaboration with Adam Payne. Yesim Tozan, Robert Margolis, Majid Ezzati, Tom Beierle, Dave Hassenzahl, and David Romo Murillo have all been faithful friends and occasional resources, while Junfeng Liu, Xiaoping Wang, Hrijoy Bhattacharjee, and Eun-hee Kim have proven to be excellent colleagues. I thank the U.S. Environmental Protection Agency for generous funding under the Science to Achieve Results (STAR) Fellowship. The Link Foundation, General Motors, and the Tokyo Foundation have also provided funding. The Woodrow Wilson School offered material support and a fine education, while the administration has been unfailingly helpful. I am particularly indebted to Ann Lengyel for her patient assistance in navigating the academic bureaucracy. Beyond Princeton, my research has benefited from contact with far too many people to enumerate, but this dissertation has directly benefited from correspondence with Clas-Otto Wene and Leonard Barreto. Doug Banks was particularly generous in helping me to assemble a conference and film my documentary project in South Africa. Similarly, Mark Hankins and Bernard Osawa were sharp guides and fine colleagues for my efforts in Kenya. I also thank John Stevens and Enersol Associates for giving me the initial opportunity to work with photovoltaics in Honduras. Looking back, I owe an intellectual debt to Harold Ward, Toby Page and the environmental studies community at Brown University not least my partners in countless late night bull sessions, Michael Leuchtenberger, Rob Berridge, and Jeff Fiedler. Since then, my friends Arne Jacobson, Jason Anderson, and Nathanael Greene have proven to be ace partners in our various environmental projects together. xi Ethan Pollock, Rob Gramlich and Mike Hochster have each bolstered me in their own way over the years since Ann Arbor most of all Mike for helping me navigate some of the fiercer math in the economics literature. Helen Kaplan, Mary Lindquist, Ann Morning, Nicole Sackley, Katie Purvis, Tracey Holloway, and Ben Strauss have all enriched my life in Princeton, and my friendship here with Jeff Edelstein has been especially rewarding. I am particularly grateful to Dale Bryk for her sustaining optimism and generosity over the past two years, not to mention her invaluable political reality checks and editorial input on my writing. Finally, I want to thank my parents, siblings, and their spouses for their indispensable support and good counsel. I dedicate this work to my father for the inspiration he is to me. xii List of Figures Figure 1. Optimal forward pricing and market power inefficiency under imperfect spillover...17 Figure 2. Insufficient forward pricing under perfect spillover and competition 23 Figure 3. Buydown cost and benefits using the breakeven method...50 Figure 4. Buydown cost and benefits using the optimal path Method...55 Figure 5. Conditions for a strong buydown NPV...61 Figure 6. Spurious microstructure in PV experience curve...69 Figure 7. Brazilian Ethanol Program...70 Figure 8. Using observed prices to estimate buydown NPV...72 Figure 9. U.S. grain ethanol program...79 Figure 10. PV experience curve...89 Figure 11. Global PV markets...99 Figure 12. Carbon emissions of PV relative to conventional electricity Figure 13. Current PV support allocations by leading IEA countries Figure 14. Trends in PV support among IEA countries Figure 15. PV buydown sales trends in Germany and Japan Figure 16. Japanese residential PV buydown Figure 17. Japanese residential PV system price trends Figure 18. California Emerging Renewables Buydown Program Figure 19. Financial breakeven for PV in new US single-family housing Figure 20. Distributed PV buydown using OECD residential niche markets Figure 21: Buydown costs for all-pv and thin-film PV experience curves xiii Figure 22. Distributed PV buydown (Thin-film Case) Figure 23. PV demand elasticity estimation Figure 24. Logistic demand shift for the optimal path method Figure 25. Base case snapshots for t = 1 and t = Figure 26. Optimal path method base case scenario Figure 27. Slow progress scenario Figure 28. High discount rate Figure 29. Reduced demand Figure 30. Reduced elasticity Figure 31. Thin-film experience curve Figure 32. Fast demand shift (_t = 25 years) Figure 33. Fast demand shift (_t = 10 years) Figure 34. Buydown cut short after ten years Figure 35. Optimal path to maximize NPV net of transfer subsidies Figure 36. The technology triad xiv List of Boxes Box 1. A buydown role for solar home systems?...90 Box 2. Monetizing local pollution costs xv List of Tables Table 1. Capital Costs for Electrical Storage (1997 dollars)...94 Table 2. Summary of PV buydown assessments Table 3. Optimal path method sensitivity analysis using 33 percent MEB throughout xvi List of Acronyms a-si BCG BE BCR CAES CCGT CdTe CIS EIA EPA EPRI ExternE FHWA GAO GWp IPCC kwp kwh MEB MTBE MWp amorphous-silicon photovoltaic modules Boston Consulting Group break even price benefit-cost ratio compressed air energy storage combined-cycle gas turbine cadmium telluride copper indium diselenide Energy Information Agency U.S. Environmental Protection Agency Electric Power Research Institute Externalities of Energy (A Research Project of the European Commission) U.S. Federal Highway Administration U.S. General Accounting Office peak gigwatts Intergovernmental Panel on Climate Change peak kilowatts kilowatt-hour marginal excess burden methyl tertiary butyl ether peak megawatts xvii NPV NSS O&M OECD OTA PCAST PV PVMaT PVUSA RD 2 RD 3 REL RPS SHS TMC twh UNDP USDA WBG Wp x-si net present value no-subsidy scenario operations and maintenance Organization for Economic Cooperation and Development U.S. Office of Technology assessment President s Council of Advisors on Science and Technology photovoltaic Photovoltaic Manufacturing Technology program Photovoltaics for Utility Scale Applications research, development and demonstration research, development, demonstration and deployment German Renewable Energy Law Renewable Portfolio Standard solar home system true marginal cost tera-watt hours United Nations Development Program U.S. Department of Agriculture World Bank Group watts produced by a photovoltaic module at standard test conditions crystalline silicon photovoltaic modules xviii Chapter 1: Introduction Motivation Market economies have an impressive track record of generating growth by efficiently allocating resources and fostering innovation by rewarding risk taking (Rosenberg, 1994). The competitive forces that channel $ billion per year into global capital investments in the energy sector (UNDP, 2000) have successfully contained long-term energy costs despite finite fossil fuel supplies. 1 The energy system has been made more economically efficient by fuel switching as well as technological innovation related to fossil fuel extraction, electricity generation, and end-use appliances. Markets, however, are only as efficient as their price signals, and fossil fuel prices do not accurately reflect the burden of pollution. 2 Partly as a result of this socially sub-optimal pricing, externalities from energy production and use cause more damage to the 1 U.S. energy expenditures as a share of GDP rose from 8 percent in 1970 to 14 percent in 1981, primarily due to an eight-fold increase in the real price of oil driven by OPEC (BP, 2002; EIA, 2001). By the year 2000, exploration and innovation had raised the ratio of oil reserves to production by nearly one-third on a global basis, helping to disrupt OPEC and cut the price of oil by nearly a factor of 2.6 (from $75 to $29/barrel for Brent crude in constant 2000$). Similarly, the real price of internationally traded coal fell by nearly a factor of two from 1987 through In conjunction with the macroeconomic shift toward the service sector, these trends have steadily pulled the energy share of domestic GDP back down, reaching 7 percent by 2001 (EIA, 2001). 2 Industrialized countries, and to a lesser but growing extent some developing countries, have imposed regulations that require pollution control equipment (e.g. SO 2 scrubbers for coal-fired electricity plants and catalytic converters for automobiles) as well as energy taxes (e.g. high gasoline taxes in Europe). Both approaches reduce pollution but not generally to the socially optimal level (i.e. to the point where the marginal cost of further abatement equals the marginal social benefit). Pollution controls, for example, generally fail to cover all pollutants (e.g. small particulates or CO 2 ) while energy taxes often bear little relation to the externality cost of the associated emissions and therefore provide poor incentives for pollution mitigation. For example, Newbery (2001) argues that, considering environmental damages and road usage costs, European nations tax oil and (to a lesser extent) natural gas far too heavily relative to coal (which is untaxed except in Denmark and Finland and was heavily subsidized in many countries until recently). 1 environment and public health than any other sector of the global economy (UNDP, 2000). Direct policy remedies for environmental externalities are well understood but difficult to implement due to political constraints, particularly for transboundary or global pollutants like CO 2. Moreover, even if energy prices fully accounted for pollution costs, other important market failures would still constrain alternative energy notably innovation spillovers that limit the incentive to invent, develop, and commercialize new technologies. There are four components to the innovation chain: research, development, demonstration, and deployment (RD 3 ). The once-dominant linear model gave basic research a privileged position as the prime mover of the entire process and argued for unfettered funding of curiosity-driven basic research (Bush, 1945). Subsequent analysis suggested that use-inspired basic research plays an important role (Stokes, 1997) and underscored the role of learning-by-doing during the development, demonstration, and deployment of new technologies (Arrow, 1962; von Hippel, 1988). 3 Accordingly, revised innovation models incorporate learning-by-doing feedback loops among these stages of the innovation process (Kline and Rosenberg, 1986). These innovation theories broaden the scope for potential government intervention. On the supply-push side, the importance of use-inspired investigation suggests that practical objectives should guide a portion of public funding for basic research, while learning-by-doing bolsters arguments for supporting technologies during the development and demonstration stages of the innovation process. More radically, 2 learning-by-doing implies that governments can encourage innovation during the deployment phase with demand-pull measures. This dissertation focuses in particular, on buydowns, defined as demand-pull subsidies intended to launch long-term commercial markets for immature technologies that cannot yet compete. That such measures have the potential to encourage innovation begs the question of whether governments should intervene and, if so, how, and to what extent. The question is controversial for supply-push measures and often not even asked for demandpull measures. On the supply-push side, empirical work suggests that private spending on research, development, and demonstration (RD 2 ) falls short of the social optimum. 4 In addition to spillovers among competitors within an industry (e.g. reverse-engineering), inter-industry spillovers (e.g. firms using improved inputs from suppliers) play a major role. Based on a survey of six empirical studies, Jones and Williams (1998) concludes that accounting for inter-industry spillovers raises the average annualized
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