Contents
PrefaceⅠ
List of TablesⅨ
List of FiguresⅪ
Chapter 1 Introduction1
Chapter 2 Policy Introduction： the California Solar Initiative7
1The Joint Staff Report8
2Megawatt-Triggering Mechanism10
3Incentive Application Process13
Chapter 3 Optimal Subsidy Design with Stochastic Learning： A Dynamic
Programming Evaluation of the California Solar Initiative17
1Introduction17
2The California Solar Initiative： Policy in Retrospect21
1CSI Target and Budget Setting21
2Megawatt-Triggering Mechanism22
3CSI Performance23
3Modeling and Parameterization25
1Model Setup25
2Parameterization28
4Results39
1Analytic Results39
2Deterministic Case41
3Stochastic Case51
5Conclusions57
Chapter 4 Incentive Pass-through for Residential Solar Systems in California60
1Introduction60
2Literature Review63
3Methods and Data67
1Structural Modeling68
2Reduced-form Regression73
3Data74
4Results81
1Structural Modeling81
2Reduced-form Approach87
5Conclusions91
Chapter 5 Analyzing Incentive Pass-through for the California Solar Initiative：
A Regression Discontinuity Design95
1Introduction95
2CSI Policy Design and Suitability for RD Analysis98
3Methods and Data100
1Methods100
2Data104
4Results112
1Time Discontinuity112
2Geographic Discontinuity122
5Conclusions127
Chapter 6 Conclusion130
Appendix134
Bibliography137
內容試閱：
Preface
Human-induced climate change, with its potentially catastrophic impacts on weather patterns, water resources, ecosystems, and agricultural production, is the toughest global problem of modern times. One of the 2018 Nobel Prize winner in economic scienceWilliam Nordhaus is awarded for his economic analysis of climate change. Impeding catastrophic climate change necessitates the widespread deployment of renewable energy technologies for reducing the emissions of heat-trapping gases, especially carbon dioxideCO2. However, the deployment of renewable energy technologies is plagued by various market failures, such as environmental externalities from fossil fuels, technology learning-by-doing, innovation spillover effects, and peer effects. In efforts to address these market failures, governments at all levelscity, state, regional, and nationalhave instituted various subsidies for promoting the adoption of renewable energy technologies. Since public resources are limited and have competing uses, it is important to ask： how cost-effective are renewable energy subsidies? And are the subsidies even reaching the intended recipientsthe adopters of renewable energy technologies? In this book, I choose to answer these two research questions with a focus on the biggest solar subsidy programs in California.
On cost-effectiveness, all programs to incentivize the adoption of renewable energy technologies run into the same key question： what is the optimal rebate schedule in the face of volatile product prices and the need for policy certainty? Answering this question requires careful attentions to both supply-sidelearning-by-doing and demand-sidepeer effects market dynamics. Then I use dynamic programming to analyze the effectiveness of the largest state-level solar PV subsidy program in the U.S.the California Solar InitiativeCSIin maximizing the cumulative PV installation in California under a budget constraint. I find that previous studies overestimated learning-by-doing in the solar industry. Consistent with other studies, I also find that peer effects are a significant demand driver in the California solar market. The main implication of this empirical finding in the dynamic optimization context is that it forces the optimal solution towards higher subsidies in earlier years of the program, and, hence, leads to a lower program durationfor the same budget. In particular, I find that the optimal rebate schedule would start not at $2.5W as it actually did in CSI, but instead at $4.2W; the effective policy period would be only three years instead of the realized period of six years. This optimali.e., most cost effective solution results in total PV adoption of 32.2MW8.1% higher than that installed under CSI, while using the same budget. Furthermore, I find that the optimal rebate schedule starts to look like the implemented CSI in a policy certainty scenario where the variation of periodic subsidy-level changes is constrained, and thus creating policy certainty. Finally, introduction of stochastic learning-by-doing as a way to better capture the dynamic nature of learning in markets for new products does not yield significantly different results compared to the deterministic case.
Another key question related to the redistribution effect of the CSI program is： to what degree have the direct PV incentives in California been passed through from installers to PV customers? I address this question by carefully examining the residential PV market in California with multiple quantitative methods. Specifically, I apply a structural-modeling approach, a reduced-form regression analysis, and regression discontinuity designs to estimate the incentive pass-through rate for the CSI. The results consistently show a high average pass-through rate of direct incentives of nearly 100%, though with regional differences among California counties and utilities.While these results could have multiple explanations, they suggest a relatively competitive market and a smoothly operating subsidy program.
Combining evidence from the optimal subsidy policy design and the incentive pass-through analysis, this research lends credibility to the cost-effectiveness of CSI given CSIs design goal of providing policy certainty and also finds a near-perfect incidence in CSI. Long-term credible commitment as reflected through CSIs capacity-triggered step changes in rebates along with policy and data transparency are important factors for CSIs smooth and cost-effective functioning. Though CSI has now wound down because final solar capacity targets have been reached, the performance of CSI is relevant not only as an ex-post analysis in California, but potentially has broader policy implications for other solar incentive programs in other states and countries such as China.
This book is a reprint of my Ph.D dissertation at the University of Texas at Austin back in 2014. Although four years have passed since then, much of its content is still relevant for readers in China. Firstly, it shows how a serious Ph.D dissertation in the United States looks like, from which one might guess how many efforts are involved behind. Secondly, by comparing it to more recent literaturemostly working papers, the observations and conclusions made in my dissertation still stand correct. For example, more and more papers start to show that the incentive pass-through rate for solar photovoltaicPV subsidy programs is high or complete, though at first sight this conclusion may seem odd to some people. Thirdly, since PV subsidies have played a key role in promoting China to be the world-largest PV market, more research should be conducted on Chinas PV subsidies in the terms of policy evaluation and potential adjustment. For instance, how to avoid the sudden change of 530-policy in China? In all three aspects, this book can be taken as a good starting point.
While preparing this manuscript, I would like to acknowledge those who have helped me along the way. Firstly, I am grateful to have Dr. Varun Rai join in the LBJ School at the University of Texas at Austin, then become my advisor and inspire many of my ideas. His generous help and unlimited support have encouraged me to try different approaches to answering important questions. We have shared very long working hours on meeting deadlines together, and discussed research and teaching philosophy, among other things, during our shared road trips to Houston, Texas. I also want to thank Dr. Kenneth Flamm to enroll me and be my academic advisor at the beginning. I am awe-inspired by his extraordinary knowledge of the semiconductor industry, and I in particular acknowledge his financial support for my research during the first few years after I came to the U.S. My sincere thanks go to Dean Chandler Stolp, a great mentor and teacher, who helped me tremendously during my transition to doctoral candidacy. I would also love to thank Dr. Jay Zarnikau for his valuable and timely feedback on several of my papers, Dr. Ross Baldick for his passion about everything and generosity with his time to discuss things with me, and Dr. Eric Bickel for pushing me to make my dissertation more and more rigorous.
I have bothered many people for help with editing, and I would like to thank all of them here, including Carlos Olmedo, Jarett Zuboy, Vivek Nath, Ariane Beck, Trevor Udwin, Erik Funkhouser, Matthew Stringer, Cale Reeves, and Tobin McKearin. I also want to thank Scott Robinson for his GIS help along the way.
Dr. Ryan Wiser from the Lawrence Berkeley National LaboratoryLBNL has helped me a lot for not only funding me to conduct part of my dissertation research, but also providing me his many insights on the solar PV industry. Naim Darghouth and Galen Barbose, both from LBNL, have helped me a lot to get to know their data.
I thank China Scholarship Council for their financial support during my Ph.D life, and thank my Chinese colleges here at UT Austin, Liangfei Qiu, Hao Hang, Fang Tang, Zhu Chen, Yumin Li, and Zhufeng Gao for making my Ph.D life more colorful.
Lastly, I would like to thank my then-girlfriend and now wife, Fang Cong, for her love and support through my Ph.D life; without her, I probably will finish my dissertation a couple of months earlier. Also, I want to thank my family for fully supporting me going abroad and forgiving me for not being around.
The publication of this work has been supported by the MOF and MOE specific fund of Building World-Class UniversitiesDisciplines and Fostering characteristic Development received by Renmin University of China in 2018.The author would also like to acknowledge the help from editor Jingjing Chen and editor Chenggong Jing at the Intellectual Property Publishing House Co., Ltd. Their editing has made this book more readable.
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