Assessing potential for policy feedback from renewable energy incentive programs

Climate advocates see policy feedback as crucial for securing decarbonization. This theory posits that legislation influences electorate attitudes and behaviors, potentially creating constituencies supporting future policy expansion. However, little research directly examines whether climate legislation creates effective electoral constituencies. This study focuses on financial incentive programs for renewable energy, a cornerstone of U.S. energy transition policy, particularly the Inflation Reduction Act (IRA). The research develops a framework for analyzing policy feedback potential, incorporating (1) partisan elite preferences, (2) beneficiary volume, (3) beneficiary partisanship, and (4) beneficiary distribution across electoral districts. The framework is then applied to New York State's solar PV incentive program, a significant policy influencing the broader energy transition.

The study draws on policy feedback theory, highlighting its role in shaping political action and influencing future policy trajectories. Examples like Cold War military spending and the Social Security Act illustrate how policy feedback can entrench policies. Prior research suggests broad public benefits from incentive programs may create political support for energy transition policies, especially in polarized environments. However, existing studies primarily focus on beneficiary volume and partisanship, neglecting the geographical distribution and elite preferences. The study addresses this gap by explicitly incorporating political geography and partisan elite preferences into the analysis of policy feedback.

The study analyzes New York State's solar PV incentive program, which provided financial incentives to over 140,000 households. The analysis considers three types of electoral districts: U.S. House of Representatives, NYS Assembly, and NYS Senate. The research uses three datasets: NYSERDA records identifying program beneficiaries, property files from ATTOM Data Solutions, and New York State's voter registration file. A probabilistic record-linking algorithm merges these datasets to create a household-level dataset. Negative binomial count models are used to analyze the relationship between Republican vote share and the number of PV installations, controlling for population density, homeownership rate, and median income. Matched samples compare the partisanship of adopting and non-adopting households within each district, using linear probability models to analyze partisan composition of PV adopters, accounting for primary election participation and affiliations with minor parties. The analysis focuses on Republican-led districts to assess the potential for influencing officials who might oppose such policies.

The analysis reveals that Republican-led districts have more solar PV installations than Democratic-led districts. However, this relationship disappears after controlling for population density and homeownership, suggesting these factors drive the higher adoption in Republican districts, not partisan preference. Within districts, PV adopters skew Democratic; even in Republican-led districts, PV adopters are significantly more likely to be Democrats than randomly selected non-adopters. This suggests that, despite higher volume in Republican districts, the partisan composition might hinder the creation of a broad, bipartisan pro-solar constituency. Linear probability models confirm that Republicans are less likely to install solar PV than Democrats, a gap even more pronounced among primary voters. While Republican PV adopters still represent a significant portion in many Republican-led districts, the Democratic skew raises concerns about the efficacy of the program in creating broad, cross-partisan support.

The findings highlight the complexity of policy feedback in a polarized political environment. While the volume of solar installations in Republican districts suggests potential for policy feedback, the strong Democratic skew within these districts indicates that simply increasing adoption numbers might not be sufficient to build cross-partisan support. Effective policy feedback necessitates managing both resource (material benefits) and interpretive (perceived benefits and political learning) effects, addressing the politicization of the issue. The results challenge the simplistic assumption that increased adoption automatically translates into widespread political support for renewable energy policies.

This study provides a framework for analyzing the policy feedback potential of renewable energy incentive programs. While the analysis shows higher adoption in Republican districts, the Democratic skew within these districts suggests that achieving bipartisan support requires careful consideration of both the material benefits and the way the policy is perceived by different partisan groups. Future research should explore how policy benefits affect attitudes and behavior, particularly across partisan lines, and investigate the role of organizations in mobilizing support for broader energy transition policies. The study's limitations include its focus on New York State and the lack of direct measurement of changes in political behavior among beneficiaries.

The study focuses on a single state (New York), which limits generalizability. The data used has some limitations in terms of complete address information for all PV projects. Also, the study does not directly measure changes in political behavior among beneficiaries, only their partisanship at the time of adoption. Future research should address these limitations by expanding the geographic scope and directly measuring the effect of the incentive program on political attitudes and behavior.