Tax expenditures as tools for state-level climate action in the U.S.
Bibliographic record
Abstract
State governments in the United States offer a wide range of tax incentives to encourage the use of green technologies and support the energy transition away from fossil fuels. These tax incentives offer a politically palatable middle ground that subsidizes more environmentally friendly policies while also giving the appearance of reducing taxes. In this paper, we evaluate the extent of climate-related tax expenditures (i.e. the amount of foregone revenue from tax preferences) at the state level by collecting data from U.S. state budget documents and classifying and comparing the different types of tax expenditures across various dimensions. Our analysis yields three key findings: (1) ‘climate-friendly’ tax expenditures at the state level account for only about 0.1% of aggregate state tax collections and are negligible relative to federal incentives historically, (2) energy subsidies make up more than half of the incentives in dollar terms, and (3) both climate and fossil fuel subsidies exhibit substantial longevity due to the infrequency with which governments revisit or re-evaluate them. While these results suggest that there may be opportunities to better utilize tax incentives for climate policy, tax expenditures must be integrated more strategically into the broader landscape of budget and tax policy to efficiently advance climate goals.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".