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.