Is Support for International Climate Action Conditional on Perceptions of Reciprocity? Evidence from Three Population-Based Survey Experiments in Canada, the US, and Norway
Bibliographic record
Abstract
The collective action problem hampering a globally coordinated international response to climate change is well known. In countries such as Canada, the US and Norway, political elites have frequently invoked the language of fairness as an excuse to limit domestic and international commitments, arguing that a fair international solution would also constrain large emitters like China. In this context, this paper asks whether citizen support for multilateral climate policies also depends on whether other countries are seen to reciprocate. We implemented three population-based survey experiments in the US, Canada, and Norway, asking subjects whether they think their country should commit to emission reductions at the climate talks in Warsaw, Poland in 2013. In each country, a randomly assigned portion of the sample was presented with a statement suggesting that another large, identified country -- typically China -- may choose not to cooperate. We find that support for signing a new international climate agreement is to varying degrees conditioned by participation from China. In Canada, mention of the chance that China may not sign the international treaty does not significantly alter public support for Canadian participation in an international climate treaty. In contrast, public support for international climate action is more conditional in Norway, where support for signing a new treaty declines significantly if China's participation is not assured. The US is in a middle position. This suggests that country size and dependence on fossil fuels may be more important than national traditions for multilateral cooperation in predicting support for unilateral climate action.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".