Public support for carbon pricing policies and revenue recycling options: a systematic review and meta-analysis of the survey literature
Bibliographic record
Abstract
Abstract Since public support is critical for implementing carbon pricing policies, we conduct a systematic review and meta-analysis to examine the survey-based literature on change in public support for direct and indirect carbon pricing policies with and without revenue recycling options. Following a comprehensive and transparent machine-learning assisted screening of the literature, our dataset comprises 35 studies containing 70 surveys across 26 countries with over 100,000 respondents. We find that the introduction of any type of revenue recycling option increases public support for carbon pricing. Results from our meta-regression indicate that green spending (i.e. using revenues for climate-friendly projects) is the only revenue recycling option associated with a statistically significant increase in public support. Our findings moreover suggest that the effects may depend on which region the survey was carried out, highlighting the need for additional research in countries in the regions of Africa and Latin America and the Caribbean.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".