Do economic trade-offs matter in climate policy support? Survey evidence from the United Kingdom and Australia
Bibliographic record
Abstract
Countries vary in their success in decoupling greenhouse gas emissions from economic growth to meet emissions reduction targets. Using a web-based survey of citizens in the United Kingdom (n = 1009) and Australia (n = 1029), with different decoupling rates, this study assesses levels of citizen support for different types of climate policies, beliefs in trade-offs between emissions reduction and economic growth, and associations between these emissions-economy trade-off beliefs and support for climate policies. The results show compulsory policies, including carbon taxes and bans, receive the highest opposition. There is little variation between the studied countries for climate policy support and emissions-economy trade-off beliefs. The results also show that citizens who are agnostic about economic growth support policies the most. Therefore, decision-makers should focus on communicating climate policies’ economic and social benefits for the economic growth-concerned citizens to increase overall policy support. • Comparative case study of climate policy support in the UK and Australia. • A country's decoupling rate has a minimal effect on climate policy support. • Agnosticism about economic growth leads to higher climate policy support. • Beliefs about emissions and economic trends matter for climate policy support. • Promoting economic and social benefits of climate policy is important.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".