Explaining public support for net-zero climate policy instruments: Perceptions of distributive fairness under competing frames
Bibliographic record
Abstract
Understanding public support for net-zero climate policy instruments is crucial for policy implementation and durability. Using survey data from a Canadian sample (n = 2362), we examine support for six net-zero policies, focusing on the roles of distributive fairness, effectiveness, and message framing. Consistent with prior research, we find that fairness perceptions are the strongest predictor of support, followed by effectiveness, though fairness judgments vary across policies. Notably, opposition to a zero-emission vehicle mandate and to a lesser extent an electric appliance mandate rivaled that of a consumer carbon tax , challenging assumptions that regulatory policies face less resistance. Distributive fairness perceptions were most influenced by expected impacts on future generations, low-income households, and rural communities, while those who prioritize equality and need-based justice principles were less likely to view policies as fair. Finally, pro-policy message frames did not shift policy support when positioned against a competing anti-policy frame, adding to the evidence that compelling counter arguments can neutralize otherwise persuasive frames. These findings highlight the need for policymakers to integrate fairness considerations into policy design and communication strategies to enhance the long-term feasibility of net-zero policy instruments.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".