Bridging the ideological gap? How fairness perceptions mediate the effect of revenue recycling on public support for carbon taxes in the United States, Canada and Germany
Bibliographic record
Abstract
Abstract Carbon taxes are frequently advocated as a means of reducing greenhouse gas (GHG) emissions, yet their political feasibility remains a challenge. To enhance their political appeal, carbon tax proponents have proposed revenue recycling as a means of alleviating public concern with this instrument's visible costs. Analyzing data from identical survey‐experiments administered in the United States, Canada, and Germany, we examine the extent to which returning revenues to the public has the potential to broaden the political acceptability of carbon taxes across ideological and national contexts. While public opinion is sensitive to the cost attributes of carbon taxes, we find that in some cases, opposition to carbon taxes can be offset by a reduction in income taxes. However, these effects tend to be modest in size, limited to some ideological groups, and varied across countries. Moreover, we demonstrate that fairness perceptions are a crucial mechanism linking revenue recycling to carbon tax support in all countries examined.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".