Stability of International Environmental Agreements under Isoelastic Utility
Bibliographic record
Abstract
There is now a growing consensus that ratifying International Environmental Agreements (IEAs) is the most effective way to tackle transboundary pollution problems. While the social benefit function (SBF) critically affects emission choices as well as decisions to ratify IEAs, the related economic literature has mainly concentrated on scenarios where the marginal SBF is linear. Using climatic data, I find that the linear marginal SBF case does not match data and isoelastic SBFs fit data better. In the more realistic, but not yet explored, context of isoelastic SBFs, I reconsider incentives to ratify IEAs. My analysis gives rise to novel conclusions. For instance, changes in the scale of damages do not affect the level of cooperation. When the scale of damages is small, variations of the SBF parameter reveal that large coalitions including the coalition of all countries are stable, but only when the potential gain from cooperation is sufficiently high.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".