Shaming Paris: A Political Economy of Climate Commitments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We use a formal model to explore leaders’ incentives to set climate commitments and subsequently exert downstream mitigation effort. Since the Paris Agreement asks countries to make unilateral voluntary commitments, we investigate the domestic factors motivating climate pledges. We study a country with electoral competition between two parties, Green and Brown, who first make commitments to reduce emissions and then implement policies to meet their commitments. Voters anticipate the equilibrium policies each party will implement given the pledge. If downstream mitigation policies are insufficient relative to the commitment, the government is “shamed” by the international community. Several incentive channels arise when parties make commitments, as they have policy and electoral value. Parties can use commitments to tie the opposition’s hands to implement preferential policies in the future. If parties care only about winning elections, they will exploit commitments to serve electoral needs, which paradoxically leads anti-environmental parties to implement more ambitious commitments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it