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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".