Party affiliation predicts homeowners’ decisions to install solar PV, but partisan gap wanes with improved economics of solar
Bibliographic record
Abstract
The perceived risk of climate change and the sense of urgency for an energy transition are both politically polarized, especially in the United States. Yet, we know relatively little about how political polarization affects consumer energy preferences and behaviors. Here, we use the case of residential solar photovoltaics (PV) in New York State to 1) measure the partisan gap in solar adoption rates and 2) test whether more favorable economics of solar PV mute the effect of political identity. Using household-level, longitudinal data that include nearly 63,000 completed residential PV projects, we find evidence of a partisan gap in PV adoption. Democratic homeowners are approximately 1.45 times as likely to adopt solar PV as Republican homeowners. Republicans' rate of adoption is the lowest of all measured groups, behind Independents, unaffiliated voters, and homeowners not registered to vote. Crucially, however, Republicans in our sample appear to be the most attuned to the changing economics and financing options of solar PV. Our estimates suggest that 1) as homeowners' electricity rate increases relative to its long-run average, the adoption gap between Democ-rats and Republicans narrows, 2) that Republican PV adopters obtain systems with higher expected economic value, and 3) Republicans take greater advantage of alternative financing models, like leases and power purchase agreements, especially when the upfront costs of solar are high. The results demonstrate that political identity affects consumers' participation in the energy transition, but local context, including the local economics of solar, may mitigate the effect of personal politics.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".