net metering, and emissions caps. The political explanation focuses on the influence of citizen ideology, government ideology, legislative professionalism, ...
The Politics of Renewable Portfolio Standards and Clean Energy Production in the States Hongtao Yi Askew School of Public Administration and Policy Florida State University
Abstract This paper investigates the relationship between state adoption of Renewable Portfolio Standards (RPS) policies, tax incentives and renewable energy production. The unique contribution of this paper is to analyze renewable energy development from a political perspective by investigating the influence of political and policy variables. Hypotheses relating to explanations based on policy, political and competing resources explanations are tested. The policy explanation investigates the influence of RPS, green power options, public benefits funds, net metering, and emissions caps. The political explanation focuses on the influence of citizen ideology, government ideology, legislative professionalism, as well as the activism of renewable energy interest groups, such as solar energy interest groups and wind energy interest groups. In addition, the effects of competing resources, such as nuclear, coal and hydro power are examined. The results provide substantial support for these hypotheses. Renewable portfolio standards, green power options, net metering and emission caps are each statistically significant and consistent with the expected relationship. The effect of RPS on the development of renewable energy capacity is substantial. We also found that citizen ideology and solar interest groups are important determinants for the deployment of renewable energy. We consider the development of renewable energy as a political process, in addition to a market process. Political mobilization of the renewable interest groups plays an important role in the deployment of renewable energy capacity.
Paper presented at the annual meeting of the American Political Science Association, Washington D.C. September 2010.
The Politics of Renewable Portfolio Standards and Clean Energy Production in the States
Introduction What are the determinants of renewable energy development? Do renewable portfolio standards (RPS) work? What are the marginal effects of the renewable portfolio standards on the development of clean energy? This paper begins to answer these important policy questions by investigating the relationship between state adoption of Renewable Portfolio Standards (RPS) policies and renewable energy production. RPS is a relatively new instrument to for promoting the renewable energy policy. Basically, it requires retail electricity suppliers to procure a certain minimum amount of qualified renewable energy. Through setting the proportion of electricity to be generated from renewable sources with a predesigned timetable, renewable energy’s share of electricity production can be incrementally increased. Since Iowa’s first adoption of RPS in 1983, 35 states have adopted this policy by October 2009. An important feature of RPS adoption is that it can be constantly revised or adjusted to set higher standards once the previous standards are achieved, thus providing a flexible policy instrument to stimulate the development of clean energy. Several studies conducted to investigate the determinants of RPS adoption in the states (Matisoff, 2008; Lyon and Yin, 2009; Yi, Feiock and Kassekert, 2009). However, the effect of RPS on the development of clean energy remains , except for the work of Kneifel (2009), Petersik(2004) and Chen, Wiser and Bolinger (2007). By beginning to fill this lacuna we contribute to the literatures on policy instruments and sustainability. Understanding the effect of RPS and its relative effects compared with other policy instruments on the development of clean
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energy can enhance the knowledge on interactions between science, technology and policy. This work also promises to provide useful information for policy makers in determining which policy instruments to utilize. The empirical analysis investigates the state factors that influence renewable energy resources and by modeling state renewable energy production. This allows us to identify how alternative policy tools, and changes in demand, as well as the use of RPS influence renewable energy production. The study of renewable energy politics, has been largely ignored by political scientists. In addition. Previous studies on the effects of RPS also ignoring the effects of multiple and simultaneous policy incentives. The consequence of not controlling for the other policy that is in place may be overestimation of the impact of RPS. After introducing the research question and an overview of current research on RPS, policy, political and competing resource explanations for renewable energy development are advanced. The policy variables include RPS, green power options, public benefits funds, net metering, emission caps for electricity and tax incentives. Political variables include citizen ideology, government ideology, legislative professionalism, as well as the activism of renewable energy interest groups, such as solar energy interest groups and wind energy interest groups. Competing resources, such as nuclear, coal and hydro power are also controlled. The modeling strategy is a random-effects GLS panel regression. After presenting the results,the impicatons of the finding for theory and practice are discussed.
Renewable Portfolio Standards and Clean Energy Production The transition towards more sustainable, low carbon states and communities requires the development and deployment of a range of new and existing energy technologies. The U.S.
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states are in the forefront of energy efficiency and renewable energy policy to address climate change. Tremendous attention has been paid toward climate change policy at the national and global levels, but the national government in the U.S. has done relatively little. In some instances, state governments have filled this vacuum, playing a leadership role in climate protection policy (Rabe 2004). Renewable Portfolio Standards (PRS) are a relatively new instrument for promoting carbon reduction and renewable energy. Thirty-five states and the District of Columbia have established renewable portfolio standards (RPS) since 1983, requiring electricity providers to supply a minimum percentage or amount of customer power from a renewable source of electricity. Basically, RPS requires the retail electricity suppliers to procure a certain minimum amount of qualified renewable energy. Through setting the proportion of electricity to be generated from renewable sources with a predesigned timetable, the share of renewable energy among the whole electricity production can be incrementally increased. For example, in Massachusetts, the Department of Energy Resources has adopted a RPS that required all retail electricity providers in the state to utilize new renewable energy sources for at least 1% of the power supply in 2003, the amount increased to 4% by 2009 (DSIRE 2009). An important feature of RPS adoption is that it could be constantly revised to set higher standards once the previous standards were achieved, providing a steady policy instrument stimulating the development of renewables. By 2007, eleven states have revised their RPS, most of which are getting more stringent (Wiser 2008). The attractiveness RPS lies in the tremendous potential benefits for adopting states in terms of both public and private benefit. First, the environmental benefits can be generated by decreasing GHG emissions and improving air quality, through increasing the percentage of
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renewables in the electricity portfolio (Jaccard, 2004; Berry and Jaccard, 2001; Lyon & Yin 2009). The basic argument is that, by substituting coals and natural gas with energy from renewable sources, GHG emissions will be reduced. Although the decrease in GHG emissions could only be achieved in a scenario when the increase of overall energy demand is smaller than the increase of energy supply by renewables, it is still an innovative step to take actions. RPS can also produce substantial economic benefits to states (Jaccard, 2004; Berry and Jaccard, 2001). Rabe (2006) argues that RPS is economically beneficial to the adopting state and consistent with the goals of economic development. Wiser (2008) shows that RPS can substantially motivate the renewable energy development. Yin & Powers (2009) argue that RPS is a substantive rather than symbolic policy in that RPS significantly and positively contributes to the development of renewable energy. The logical link between renewable energy development and economic development is the jobs created by the renewable energy industry. However, with the exception of Youm (2009), the policy literature has not examined the impacts of state energy policy on the creation of green jobs. Political benefits from promoting sustainable energy are also evident at least in some states (Feiock, Kassekert, Berry and Yi 2009). Additional benefit of RPS also include energy security issues (Jaccard, 2004; Berry and Jaccard, 2001). Given these benefits for adopting RPS, many states adopted RPS with the assumption that RPS could be effective in promoting the deployment of renewable energy. However, to what extent does RPS increase the deployment of renewable energy remains an empirical question, especially when different states have different configurations of various policy components in setting the RPS goals and implementation procedures. In this aspect, Kneifel(2009) has done an well-specified research on how the specific configurations of RPS could affect the results of renewable energy deployment.
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More importantly, many states have also adopted public benefit funds, emission caps for electricity industry, net metering programs, as well as numerous tax incentives, rebate programs, which could also contribute a large portion to the development of renewable energy. Therefore, to examine the effect of RPS, we should simultaneously examine the effects of other policies, otherwise we could get biased results. These factors are generally ignored by the current literature, due to the limits of data or practical considerations. Proposing the effects of these programs and testing these effects is the major effort made in this paper, with an aim to approach the renewable energy policies from a comprehensive framework.
Policies and Renewables Assessing the relative impacts of the various policy instruments on the deployment of renewable energy is the major task of this paper. Policy data collected by DSIRE (2010), adopting multiple renewable energy policy instruments is very popular among the U.S. states. Although some states also adopt rebates, loans and grant programs, these programs generally are not long-term commitments and constantly suffer from risks of being discontinued in situation of financial distress. Thus, we only look at the major sustained policies that can potentially contribute significantly to the development of renewable energy. The policy variables that we examine at are RPS, green power options, public benefits funds, net metering, emission caps for electricity and tax incentives. Renewable Portfolio Standards is one of the major policy instruments the states have adopted to stimulate the renewable energy industry. Among the various policy instruments, RPS is argued to be the most effective for promoting renewables (Palmer and Burtraw, 2005). Along the same line, Rader and Norgaard (1996) argue that RPS is “the most efficient means of
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correcting market imperfections and for moving toward sustainability”. In an econometric analysis, Kneifel (2009) found that RPS with either capacity or sales requirement increases renewable energy generation capacity in that state. Yin & Powers (2009) argue that RPS is a substantive rather than symbolic policy in that RPS significantly and positively contributes to the development of renewable energy. Given the body of empirical evidence, we argue that RPS could play a significant positive role in promoting renewables. H1: States with a RPS will have more renewable energy capacity than states without a RPS.
A green power option is a mandatory requirement on regulated electric utilities to offer consumers the option of purchasing electricity generated from renewable sources. This policy is sometimes complementary to renewable portfolio standards, but is of a voluntary nature. By 2010, nine states had adopted green power options as part of their renewable energy policy portfolio. Some empirical evidence shows that green power options could have a sizable impact on renewable capacity in a state (Kneifel, 2009). H2: States with a green power option for consumers will have more renewable energy capacity than states without a green power option.
Public benefit funds are another important policy instrument for supporting renewable energy. Most public benefit funds (PBFs) were developed in late 1990s to provide sustained support for renewable energy and energy efficiency (DSIRE, 2010). These funds are commonly supported through a surcharge on electricity consumption, which is also referred to as a "system benefits charge" (SBC). Since PBFs are a major source for funding rebate programs, loan programs, research and development, and energy education programs, we can expect that PBFs could also have a sizable impact on the deployment of renewable energy capacity. But previous
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study shows that the impact of PBF on wind energy development is not statistically significant (Menz and Vachon, 2006), due to the fact that some PBFs support both renewable energy and energy efficiency. Even with the evidence of insignificant coefficient of previous study, we still hypothesize that PBFs have a positive impact on renewable energy capacity, so that we can test the hypotheses with our new data. H3: States with a public benefits fund will have more renewable energy capacity than states without a public benefits fund.
Emission caps for electric utilities is another relevant policy that shapes the institutional environment for the renewables. Basically, this policy places values on the externalities generated by the electricity generation, and limits the total amount of emissions of GHGs and toxic gases from the utilities. However, numerous studies show that the general environmental policies will not necessarily result in the development of renewable resources (Rader and Norgaard, 1996), and incorporation of externalities in California, Massachusetts and Wisconsin had little impact on the resource selection process, given that the cost advantage would dominate the whole process (EIA, 1995). H4: The renewable energy capacity in states with an emission caps for electric utilities will not be significantly different from states without an emission caps for electric utilities.
Net metering is another policy instrument that states adopted to stimulate the renewables. Some net metering requires utility companies to pay a certain amount per megawatt to customers who generate electricity in their residence. Forsyth, Pedden and Gagliano (2002) shown that net metering programs could offer minimal incentive for consumers to produce renewable energy. However, not all net metering programs are targeted towards the supply side of the renewable energy, instead, it could be treated as a demand side response program that could contribute to
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the reduction in consumption of energy, including that of renewable energy, especially when the distributed generation of renewable energy are not yet part of the renewable portfolio in that state. Since distributed generation of renewable energy is still implemented in a small scale, we could expect that net metering as a demand side program can play a role of reducing the renewable energy capacity. H5: States with a net metering program will have less renewable energy capacity than states without a net metering program.
In addition to the above-mentioned policy instruments, another important aspect of the state renewable energy policies is the financial incentives, most notably tax incentives, including property tax incentives, sales tax incentives, corporate income tax incentives and personal income tax incentives. In theory, the tax incentives could help adjust the market imperfections by stimulating the development of renewables. Many states adopt multiple policy incentives for renewables. The number of tax incentives existed in a state could be a symbol of the state’s commitment to renewables. H6: States with tax incentives will have more renewable energy capacity than states without tax incentives. H7: The more tax incentives a state has, the more renewable energy capacity will be deployed in that state.
Politics and Renewables Politics play an important role in the development of renewables. First, politics affects the adoption of the renewable energy policies, which constitute the institutional arrangement for the technological adoption and diffusion process (Stoutenborough & Beverlin 2008; Matisoff 2008; Menz and Vachon, 2006; Yi, Feiock and Kassekert, 2010). Second, democratic politics is a process with multiple points of participation through which different ideologies and interest
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groups could exercise their influence in the whole political and market process. Whether and to what extent renewable energy would be deployed can be much influenced by citizen ideology, government ideology, legislative professionalism as well as the activities of renewable interest groups. Citizen ideology index measures the mean position of citizens on the liberal-conservative continuum, and the government ideology measures the mean position of elected public officials on the same continuum (Berry et al.,1998). It is generally argued by scholars that the liberal ideology is associated with green policies and renewable energy programs (Stoutenborough & Beverlin 2008; Matisoff 2008; Yi, Feiock and Kassekert, 2010). H8: The more liberal the citizen ideology of a state, the more renewable energy capacity will be deployed in that state. H9: The more liberal the government ideology of a state, the more renewable energy capacity will be deployed in that state.
Legislative professionalism measures the level of professionalism of the legislative officials (Squire, 1992). Previous evidence shows that the higher level of legislative professionalism is associated with higher likelihood of environment program adoptions (Ringquist, 1993). Higher level of professionalism of the legislature could result in better design of the renewable energy policy, which could be more effective in achieving the pre-designed policy goals. H10: The more professional the legislature of a state, the more renewable energy capacity will be deployed in that state.
The influence of the interest groups could be essential in the development for renewable energy. Without the strong support of the renewable energy industry, renewable energy policy in a state could be symbolic. Since the renewable energy industry would benefit from the tax incentives and rebate programs, the renewable interest groups would lobby to substantiate the
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policies. For example, the RPS was strongly advocated by American Wind Energy Association (Rader and Norgaard, 1996). More importantly, in a state with active renewable interest groups, these interest groups are generally responsible for the development of renewable energy themselves. Thus, we can expect that states with active renewable energy interest groups would have more renewable energy deployed. H11: The more organizations registered as a member of Solar Energy Industries Association in a state, the more renewable energy capacity will be deployed in that state. H12: The more organizations registered as a member of American Wind Energy Association in a state, the more renewable energy capacity will be deployed in that state.
Competing Energy Sources and Renewables Competing energy sources refer to the capacity of existing electricity sources. The major competing energy sources that we examine are nuclear energy, hydro energy and coal energy. Nuclear energy has low marginal costs of producing base load electricity and no emissions (Kneifel, 2009). Given the potential risks of nuclear energy, renewable energy may be used to reduce the pressure from environmental interests, so that higher nuclear energy capacity could lead to higher renewable energy capacity. Kneifel (2009) argued that the influence of nuclear energy on the deployment of renewable energy could be either positive or negative. If economic factors are the major concerns, nuclear energy capacity could be substitutive to non-hydro renewable energy in meeting the emission requirement. Hydro energy also has low marginal costs of producing base load electricity and no emissions. Hydro energy deployment could be substitutive to the deployment of renewable energy. The coal industry is the interest group that suffered by the climate and renewable energy policy. In a state with a strong coal industry, we can expect that the deployment of renewable energy could face much resistance. H13: The more the hydro energy capacity deployed in a state, the less renewable energy capacity will be deployed in that state.
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H14: The more the coal energy capacity deployed in a state, the less renewable energy capacity will be deployed in that state.
Research Design and Measurement To test the hypotheses, we develop an econometric model based on a unique panel data set developed in Center for Sustainable Energy Governance at Florida State University, which covers most of the relevant policy, politics and energy variables for 50 states from 1990 to 2008. In this data set, we collected socio-economic variables, political ideology variables, interest groups variables and electricity generation capacity for difference sources. The time range of 1990 to 2008 allows us to have a full picture of the development of renewable energy, and a reliable test of the long-term effect of the relevant policy instruments. Dependent Variable The dependent variable is the renewable energy capacity (RE_CAP)for the electricity generation in a state, measured in megawatts. Although the renewable energy capacity does not equal to the renewable energy production, and RPS is designed toward the renewable production in most states, approximating the renewable energy production with the renewable capacity could still be a good measurement, if our goal is to test the relative effectiveness of the policy instruments. Some previous research also uses the renewable capacity as the dependent variable to test the effects of RPS (Kneifel, 2009). The renewable energy that we defined includes solar thermal and photovoltaic, geothermal, wind and biomass. Nuclear energy and hydro energy are excluded from our definition due to the potential negative effect of these two sources of energy. The data is collected from Energy Information Administration (EIA).
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Independent Variables The policy variables, renewable portfolio standards (RPS), green power options(GREEN_POWER), public benefit funds (PBF), net metering (NET_MET), and emissions cap for electricity (EMI_CAP) are measured as dummy variables, with 1 denoting there is such a policy in that state, and 0 denoting there is no such policy in that state. Previous research has classified RPS into different categories, so as to test the influence of different configurations of RPS on the renewables (Kneifel, 2009). But for the purpose of this paper to test the relative effects of different policy instruments, coding the variable as a dummy should suffice to capture the effects. These data were collected from DSIRE (2009) website. Tax incentives (TAX_INCT) is measured by an index of adoptions of corporate income tax incentives, property tax incentives, personal income tax incentives and sales tax incentives. We assume the additive effects of tax incentives on the deployment of renewables, that is, the number of tax incentives existed in a state could be a symbol of the state’s commitment to renewables. The data on tax incentives were also collected from DSIRE (2009) website. The data on citizen ideology (CITIIDEO) and government ideology (GOVTIDEO) were collected from Fording’s website. The legislative professionalism (LEGPROF) data were gathered from the (Squire 2007), and extrapolated to the recent two years. The interest groups variables include the solar interest groups (SOLAR_INT) and wind interest groups (WIND_INT), which are measured by the number of entities registered as a member of Solar Energy Industries Association (SEIA) and American Wind Energy Association (AWEA) respectively. The data on solar and wind interest groups are gathered from the websites of SEIA and AWEA.
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The data on the capacity of competing energy sources, nuclear energy (NUCLEAR), hydro energy (HYDRO) and coal energy (COAL) are gathered from EIA. For the control variables, we included population density, per capita income, the percent days of sunshine and the average wind speed in a state. The data on population density (POPDENSITY) and per capita income (PCINC)were collected from Bureau of Economic Analysis (BEA). The percent days of sunshine (PER_SUN) is measured by percentage of days that are sunny. The average wind speed (WIND_SPEED) is measured by miles per hour. The percent days of sunshine and average wind speed were collected from census bureau. Table 1 presents the summary values of the variables.
Table 1: Univariate Statistics
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------RE_CAP |
950
257.19
830.23
0
7536
RPS |
950
.20
.40
0
1
GREEN_POWER |
950
.03
.18
0
1
PBF |
950
.19
.39
0
1
NET_MET |
950
.34
.48
0
1
EMI_CAP |
950
.04
.19
0
1
TAX_INCT |
950
1.08
1.16
0
4
CITIIDEO |
950
49.25
14.87
8.45
95.97
GOVIDEO |
950
48.11
25.44
0
97.92
LEGPROF |
950
.20
.13
.03
.66
SOLAR_INT |
950
8.66
17.98
0
124
WIND_INT |
950
310.14
31.56
262
423
NUCLEAR |
950
2129.22
2670.21
0
13734
COAL |
950
6717.76
6529.19
0
25276
HYDRO |
950
1523.31
3297.83
0
20810
POPDENSITY |
950
174.84
233.17
.97
1056.38
PCINC |
950
27405.83
7754.65
13208.33
56272
WIND_SPEED |
950
9.25
1.65
5.8
12.9
Per_SUN |
950
56.68
9.47
23.29
81.10
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Methods Given the data structure we have is a panel data set, the modeling strategy that we adopt is a random-effects GLS regression, which explores differences in error variances (Greene, 2003). In this case, we assume that the unobserved individual heterogeneity to be uncorrelated with the included explanatory variables and that the individual specific constant terms are randomly distributed across cross-sectional units. The model can be formulated as:
yi t = x΄i t β + α + ui + εi t Since the variance components are unknown, a Feasible Generailized Least Squares (FGLS) procedure is applied to estimate the parameters (Greene, 2003) . A Lagrange multiplier test for the random effects model would be performed to test whether the random effects model should be used (Breusch and Pagan, 1980).
Results and Discussion The results of the statistical analysis of the random-effects GLS regression model are shown in Table 2. The R-squared value of the model is 0.87. Although the R-squared value in this model could not be interpreted as in OLS regression, it still indicates that the model has a good model fit. A Breusch and Pagan Lagrange multiplier test is performed for this model, and a Lagrange multiplier test statistic of 1546.94 is obtained. The test statistic far exceeds the 95 percent critical value for chi-squared with one degree of freedom. We conclude that the classical regression model with a single constant term is inappropriate for these data, and the randomeffects GLS regression model is necessary. The results of the model support some of our hypotheses. For the policy related hypotheses, the effects of renewable portfolio standards, green power options, and emission caps 14
for electricity are statistically significant and consistent with the expected signs. The effect of RPS on the development of renewable energy capacity is positively significant. States with a RPS will generally have 122.47 megawatts more renewable energy capacity than states without RPS, controlling for the effects of other policy instruments, political conditions, other competing resources, time effect and other socio economic variables. The effect of green power options on the renewables is even more substantial. States with a green power option will have 263.02 megawatts more renewable energy capacity than states without such an option, controlling for other variables. This is reasonable and consistent with the previous findings, partly because the green power options are used as a supplement of RPS in some states. Consistent with our theoretical hypotheses, the coefficient of net metering is statistically significant with a negative sign. In general, states with a net metering program will have 103.18 megawatts less renewable energy capacity than states without such an option, controlling for other variables. This indicates that net metering program, if not supplemented with the wide spread adoption of distributed generation, will play a role of demand side program that reduces the consumption of renewable energy, which further limits the generation of renewables. The coefficient of emission caps for electricity is -134.47, which means that states with an emission cap for electricity will have 134.47 megawatts less renewable energy capacity than
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Table 2: Determinants of State Clean Energy Production Dependent Variable: The Clean Energy Generation Capacity in the States
Independent Variables Policy Variables Renewable Portfolio Standards Green Power Options Tax Incentives Public Benefit Funds Net Metering Emission Caps for Electricity Political Variables Citizen Ideology Government Ideology Legislative Professionalism Solar interest groups Wind interest groups Competing Energy Sources Nuclear Capacity Hydro Capacity Coal Capacity Controls Percent Days of Sunshine Average Wind Speed Population Density Per Capita Income Constant
Random-Effects GLS Regression 122.47*** (26.90) 263.02*** (46.33) -11.73 (11.58) -59.86** (29.85) -103.18*** (23.69) -134.47*** (41.21) 2.82** (1.25) -0.92** (0.41) 19.79 (184.03) 42.20*** (2.6) 1.06 (1.39) -0.03** (0.01) 0.001 (0.006) -0.005 (0.01) -6.90* (3.62) 9.41 (20.81) -0.55*** (0.15) 0.01*** (0.002) -436.69 (425.12) 0.87 900
R-Squared Observations * p < 0.10; ** p < 0.05; *** p < 0.01 (two-tailed) (Standard errors are given in parentheses)
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states without such an option, controlling for other variables. This is an interesting result, indicating that the relationship between the general environmental policy and the specific renewable energy policy might not always be complementary, and could be substitutive. Further research is needed to further examine the relationship between the generic environmental policy and renewable energy policy instruments. The coefficients on public benefit funds is statistically significant, but opposite to our theoretical predictions. In addition, the effect of tax incentives on the renewables is not statistically significant. The reasons for the unexpected results might be that the public benefit funds might also be used to support energy efficiency, therefore, the measurement of public benefit funds might not be accurate when it could be used to support both renewable energy and energy efficiency. For tax incentives, a possible scenario might be that the share of distributed generation of renewable energy is still small compared with the centralized generation of renewables. The measurement of the renewable generation capacity is in the unit of megawatts, and thus the effect of the net metering and tax incentives might be obscured by the data structure itself. Further studies are needed to keep track of the marginal effects of these policy instruments, with the expectation that the effects of tax incentives would become bigger with the increasing renewable capacity in the states. For the politics-related hypotheses, the results support the effects of citizen ideology and solar interest groups. The coefficient of citizen ideology is positively significant, indicating that the more liberal the citizen ideology in a state, the more renewable energy capacity will be deployed in that state. However, the coefficient of government ideology is negatively significant, which is unexpected. This might be a result of the influences of the outlier states, California and Texas, which are controlled by conservative governments and have large capacity of renewable
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energy deployed. The coefficient of legislative professionalism is also not statistically significant. The interest group hypotheses are partly supported. The solar interest group’s influence is positively significant. The impact is so large that with an additional solar interest group in a state, 42.20 megawatts more renewable energy capacity is expected in that state, controlling for the effects of other variables. The coefficient of wind energy interest groups is not significant, but the sign is consistent with the hypothesis that the activities of wind energy interest groups would increase the renewable energy capacity in that state. This indicates that political mobilization of the renewable interest groups play an important role in the deployment of renewable energy capacity, and the renewable energy development is itself a political process. The hypotheses on the competing energy resources are also partly supported. The coefficient of nuclear energy is statistically significant, while those of hydro and coal energy are not. Nuclear energy capacity shows a substitutive pattern to the renewable energy capacity, indicating that economic concerns outweigh the environmental concerns when decision makers are faced with a choice between nuclear and clean energy sources. The coefficients on the hydro and coal energy capacity are not statistically significant.
Conclusions In this paper, we investigated the marginal effects of the various policy instruments on the development of renewable energy, taking into account the influences of politics and socioeconomic conditions. With a unique panel data set that we collected in Center for Sustainable Energy Governance in Florida State University, which covers most of the relevant policy, politics and energy variables for 50 states from 1990 to 2008, we tested the hypotheses about policy and political determinants of the development of renewable energy. We found that 18
renewable portfolio standards positively stimulates the deployment of renewable energy. This finding supports the arguments made by various advocates of RPS as the most effective instrument for the renewable energy development (Palmer and Burtraw, 2005; Rader and Norgaard, 1996). The positive effect of green power option is also worth attention. If RPS and green power option are used together, the positive effect would be even bigger. In our study, we did not find significant effect of the tax incentives. As argued in the discussion section, further studies are needed to keep track of the marginal effects of these policy instruments, with the expectation that the effects of tax incentives would become bigger with the increasing renewable capacity in the states. For the politics-related hypotheses, we found that citizen ideology and solar interest groups are important determinants for the deployment of renewable energy. The contribution that we made here is that we consider the development of renewable energy as also a political process, in addition to a market process. Political mobilization of the renewable interest groups plays an important role in the deployment of renewable energy capacity. For the future research, we will focus on the political aspect of the renewable energy development, investigating how the state level political network structure, especially the energy policy network, affects the deployment of the renewable energy. Instead of a simple measurement of the interest groups, we will apply network analysis to study how and in what ways the policy network and interactions among policy actors matter.
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