Assessing potential for policy feedback from renewable energy incentive programs
Bibliographic record
Abstract
Climate advocates look optimistically to policy feedback as a mechanism for locking-in a decarbonization policy trajectory, but little research has examined whether and how climate legislation creates constituencies that could provide future political support. This article focuses on incentive programs supporting investment in solar PV and the potential for policy feedback through participating households. We first develop a framework of feedback potential that considers the volume and partisanship of incentive program beneficiaries and their distribution across electoral districts. We then apply the framework to New York State’s solar PV incentive program, which enabled over 140,000 households to install solar PV. We find that the number of solar PV incentive beneficiaries is positively associated with Republican vote share, suggesting potential for a strong pro-solar constituency in the pivotal, Republican-led districts. Within electoral districts, however, beneficiaries skew Democratic, raising questions about the direction of policy feedback. The results carry implications for the kind of politics that incentives in the Inflation Reduction Act may set in motion in the coming years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".