The Efficiency of Voluntary Pollution Abatement when Countries can Commit
Bibliographic record
Abstract
In this paper, we characterize a mechanism for reducing pollution emissions in which countries, acting non-cooperatively, commit to match each others’ abatement levels and may subsequently engage in emissions quota trading. The analysis shows that the mechanism leads to efficient outcomes. The level of emissions is efficient, and if the matching abatements process includes a quota trading stage, the marginal benefits of emissions are also equalized across countries. Given the equilibrium matching rates, the initial allocation of emission quotas (before trading) reflects each country’s marginal valuation for lower pollution relative to its marginal benefit from emissions. These results hold for any number of countries, in an environment where countries have different abatement technologies and different benefits from emissions, and even if the emissions of countries are imperfect substitutes in each country’s damage function. In a dynamic two-period setting, the mechanism achieves both intra-temporal and inter-temporal efficiency. We extend the model by assuming that countries are voluntarily contributing to an international public good, in addition to undertaking pollution abatements, and find that the level of emissions may be efficient even without any matching abatement commitments, and the marginal benefits of emissions may be equalized across countries even without quota trading.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".