描述
开 本: 16开纸 张: 胶版纸包 装: 平装-胶订是否套装: 否国际标准书号ISBN: 9787513062213
Contents
PrefaceⅠ
List of TablesⅨ
List of FiguresⅪ
Chapter 1 Introduction1
Chapter 2 Policy Introduction: the
California Solar Initiative7
1)The Joint Staff Report8
2)Megawatt-Triggering Mechanism10
3)Incentive Application Process13
Chapter 3 Optimal Subsidy Design with
Stochastic Learning: A Dynamic
Programming Evaluation of the California
Solar Initiative17
1)Introduction17
2)The California Solar Initiative: Policy
in Retrospect21
(1)CSI Target and Budget Setting21
(2)Megawatt-Triggering Mechanism22
(3)CSI Performance23
3)Modeling and Parameterization25
(1)Model Setup25
(2)Parameterization28
4)Results39
(1)Analytic Results39
(2)Deterministic Case41
(3)Stochastic Case51
5)Conclusions57
Chapter 4 Incentive Pass-through for
Residential Solar Systems in California60
1)Introduction60
2)Literature Review63
3)Methods and Data67
(1)Structural Modeling68
(2)Reduced-form Regression73
(3)Data74
4)Results81
(1)Structural Modeling81
(2)Reduced-form Approach87
5)Conclusions91
Chapter 5 Analyzing Incentive Pass-through
for the California Solar Initiative:
A Regression Discontinuity Design95
1)Introduction95
2)CSI Policy Design and Suitability for RD
Analysis98
3)Methods and Data100
(1)Methods100
(2)Data104
4)Results112
(1)Time Discontinuity112
(2)Geographic Discontinuity122
5)Conclusions127
Chapter 6 Conclusion130
Appendix134
Bibliography137
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 science—William
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 dioxide(CO2). 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 levels—city, state, regional, and national—have
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 recipients—the 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-side(learning-by-doing) and demand-side(peer
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 Initiative(CSI)—in 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 duration(for the same budget). In particular, I find that the optimal
rebate schedule would start not at $2.5/W as it actually did in CSI, but
instead at $4.2/W; the effective policy period would be only three years
instead of the realized period of six years. This optimal(i.e., most cost
effective) solution results in total PV adoption of 32.2MW(8.1%) 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 CSI’s design goal of
providing policy certainty and also finds a near-perfect incidence in CSI.
Long-term credible commitment as reflected through CSI’s capacity-triggered
step changes in rebates along with policy and data transparency are important
factors for CSI’s 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 literature(mostly 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 photovoltaic(PV) 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 China’s 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 Laboratory(LBNL) 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
Universities(Disciplines) 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.
Chapter 1 Introduction
Human-induced climate change, with its
potentially catastrophic impacts on weather patterns, water resources,
ecosystems, and agricultural production(IPCC WG2, 2014), is the toughest global
problem of modern times(Dow and Downing, 2011). Based on the latest projection
by the Intergovernmental Panel on Climate Change(IPCC), global surface
temperature has been increasing almost linearly in the past four decades or so,
and the temperature change is highly likely to exceed 2℃ by the end
of the 21st century(IPCC WG1, 2014). Such change is likely to cause significant
global GDP losses; the well-known Stern Report(Stern, 2007) estimated that for
a global mean temperature change of 2℃~3℃, the
potential global GDP loss would be around 1%~2%. This number may look small,
however, no single country wishes to bear the burden alone.
While the concentration of greenhouse
gases(GHGs) from the human activities is the largest driver of the observed
climate change(EPA, 2014a), tracing the sources of GHGs reveals that in the United
States, electricity generation produces the largest share of GHGs. In 2012,
this sector emits 32% of GHGs in the United States, followed by the
transportation sector at 28%(EPA, 2014b). Obviously, different electricity
generation technologies tend to have very different emission rates. Based on a
recent review report(WNA, 2011) that summarizes life-cycle assessments of the
GHG emission intensity for different generation technologies, solar
photovoltaic(PV) only emits 85 tons of CO2e per GWh, while the numbers for
natural gas and coal are 500 and 888 respectively.
Unfortunately, electricity generation costs
do not necessarily reflect the differences among technologies in terms of GHG
emission intensity, within a market where there is no price for carbon or GHGs.
This is the so-called environmental externality problem, i.e. the GHG emitters
do not need to pay for the environmental damages that they cause. A natural
cure for this externality problem is to put a price on carbon or all GHGs, i.e.
a Pigovian tax(Pigou, 1920). Nevertheless, few countries have chosen this path;
instead, most of them have come to support renewable energy technologies
directly. As pointed out in the IPCC mitigation report, impeding catastrophic
climate change necessitates the widespread deployment of renewable energy
technologies for reducing the emissions of heat-trapping gases, especially
carbon di-oxide(CO2)(IPCC WG3, 2014).
The solar PV industry has been growing very
rapidly in the last decade. According to the International Energy Agency(IEA),
since 2000 solar PV has had the fastest growth rate among renewable energy
technologies worldwide(IEA, 2010). While the global annual installed PV
capacity was less than 0.3 gigawatts(GW) in 2000, this number surpassed 38GW in
2013(EPIA, 2014). Reflecting the rapid growth in deployment, global investment
in solar energy technologies has been over $100 billion since 2000(Statista,
2014). Deployment in the United States has also grown rapidly from around
0.004GW of newly installed capacity in 2000 to more than 4GW installed in 2013
alone(Sherwood, 2013; SEIA/GTM, 2014).
A key driving force behind the growth of
solar PV has been the myriad of government incentive programs promoting solar
deployment(Arvizu et al., 2011; Kirkegaard et al., 2010; REN21, 2014; Timilsina
et al., 2011), often motivated by a desire to address various market failures
such as: environmental externalities as mentioned above(Baumol and Oates, 1988;
Bezdek, 1993; Painuly, 2002; Stavins, 2008), learning-by-doing, innovation
spillover effects, and peer effects in the PV industry(Arrow, 1962; Gillingham
and Sweeney, 2012; McDonald and Schrattenholzer, 2001; van Benthem et al.,
2008; Verdolini and Galeotti, 2011). Other factors driving policy decisions to
support solar include the potential benefits of energy resource diversity(i.e.
energy security) and the potential of new jobs and increased economic activity
in the solar sector(Fischer and Preonas, 2010). In addition, since most of the
incentive programs affect the demand side, the induced innovation engendered by
these demand-pull policies brings in additional benefits to society(Hickes,
1932; Jaffe and Newell, 2002; Lanzi and Sue Wing, 2011; Nemet, 2009a; Popp et
al., 2010). As solar deployment increases rapidly due to these demand-pull
policies, solar modules, the key component of a PV system, have experienced a
cost reduction by a factor of over 100 since the 1950s(Maycock, 2002; Nemet,
2006), with recent prices as low as 60~70 cents per Watt(GTM Research, 2014).
Direct policy instruments that support
solar PV deployment can take many forms, including feed-intariffs(FiT),
renewable portfolio standards, investment tax credits(ITC), upfront rebates,
net metering, favorable financing, mandatory access, and public investment. Indirect
policy tools also exist such as carbon tax and cap-and-trade. The relative
merits of these instruments have been broadly studied and debated(Fischer and
Newell, 2008; Fullerton and Melcalf, 2001; Nordhaus, 1992; Pizer, 1999;
Vollebergh and van der Werf, 2014; Weitzman, 1974). While recognizing the
complexity of the problem, this dissertation decides to focus on one of the
policy tools–the upfront rebates program, though from several different
perspectives. The understanding of the design features and effectiveness of
this policy tool provides a strong foundation to study inter-policy
relationships in the future.
Upfront rebates directly speak to the high
capital cost problem facing potential PV adopters, which is one of the major
barriers to the diffusion of renewable energy technologies(Beck and Martinot,
2004; Hoff, 2006; Sawin, 2004; Verbruggen et al., 2010). Though the average PV
installation price has come down dramatically in recent years(Barbose et al.,
2014), a typical residential PV system in the U.S.
(4kW) still costs around $20,000 on a
pre-rebate basis. In the U.S., the upfront rebate only exists at the state
level or below, and governments usually base their rebate on PV system
production(i.e. performance), capacity, or both. Production-based subsidies
encourage better siting, configuration, and operation and
maintenance(O&M), thus maximizing potential production by tying the
incentives to system performance; whereas capacity-based subsidies address the
capital cost problem directly and play a significant role in attracting lower
PV capacity customers and small projects(Barbose et al., 2006; Black, 2006;
Connor et al., 2009; Hoff, 2006; IPCC, 2011).
For both production-based and
capacity-based incentives, setting an appropriate incentive level is always a
major challenge for policymakers. Since the PV technology is evolving rapidly,
it becomes difficult to set up the incentive at the right level: too high an
incentive level would attract too many applications leading to a run on the program’s
budget, while too low a level would do little to induce market growth. Chapter
3 tackles this problem in the framework of dynamic programming, which has been
applied before in the literature to tackle similar problems. I use the biggest
state-level rebate program in the Unites States, the California Solar
Initiative(CSI), as the central example for its empirical focus. The
availability of rich data for CSI and its significant scale offer a good
opportunity to examine the problem in detail. While Chapter 2 introduces the
CSI policy, Chapter 3 provides several key insights regarding subsidy policy
design focusing on its cost effectiveness.
Another important perspective on the
question of subsidy policy design looks at the redistribution effect. This effect
is concerned with where the subsidy finally ends up, and whether it benefits
consumers or suppliers more. This is an important and much studied question in
public economics, i.e. the so-called subsidy incidence or incentive
pass-through question. However, despite CSI’s significant program budget(over
$2 billion), there are few studies that carefully study at the incentive
pass-through question for CSI. Chapter 4 fills in the gap and adopts two
approaches to answer this question: structural modeling based on the conduct
parameter approach and a reduced-form regression analysis. In my analysis I
view these two approaches as being complementary to each other, since different
underlying assumptions and data requirements are involved.
In Chapter 5 I employ an as-if natural
experiment design to re-examine the incentive pass-through question using a
regression discontinuity(RD) design. Under certain assumptions, the RD design
could improve the internal validity of research similar to randomized control
experiments(Imbens and Lemieux 2008; Lee, 2008). That is one of major reasons
for the increasing popularity and adoption of this method in economics and
other areas in the social sciences. As for CSI, the pre-determined incentive
level stepwise changes and the geographic borders between two neighboring
utilities provide good opportunities to apply the RD design. As a result, the
derived incentive pass-through rate can be claimed as causal effects, a further
robustness check to estimates from Chapter 4, while the latter complements
Chapter 5 by providing external validity to the pass-through results. The
results from these two chapters have direct implications for subsidy policy
design. A complete pass-through rate indicates that the subsidy has benefited
fully the intended recipient, i.e. the consumers, and that the induced market
competition by the subsidy policy is probably high.
Chapter 6 concludes the dissertation, while
synthesizing findings from the core Chapters 3~5, upon which the dissertation
is centered. It further discusses fruitful research directions as next steps.
The conclusion is kept short by choice, since there are corresponding
conclusion sections in each of the core chapters. Overall, this dissertation
makes both empirical and methodological contributions to the public policy
literature, especially in policy design and evaluation. First of all, it serves
as a thorough empirical study of incentive policy design, from both a
cost-effectiveness perspective(Chapter 3) and a redistribution point of view(Chapter
4 and 5). Second, methodologically Chapter 3 extends the deterministic dynamic
programming framework to further incorporate the stochastic learning-by-doing
phenomenon. The considerations of various PV demand functional forms and policy
flexibility as well as policy certainty are also new to the literature. Third,
Chapters 4 and 5 examine the incentive pass-through question from multiple
angles, and they are quite comprehensive in looking at this specific problem
for solar PV. Lastly, Chapter 5
also develops several adaptions of the RD
design to fit the PV price data, as they proved to be important in removing
potential biases in the estimation process.
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