Transboundary pollution control and competitiveness concerns in a two-country differential game
Bibliographic record
Abstract
We analyze a transboundary pollution control problem in a heterogeneous two-country differential game setting in which regulators care for the implications of environmental policies on the competitiveness. We characterize the noncooperative and the cooperative solutions, showing that under both scenarios, in presence of competitiveness considerations, heterogeneous countries will generally set different carbon taxes. This suggests, while implementing a mitigation policy is necessary to combat climate change, a universally homogeneous policy may not be optimal. Moreover, when countries are symmetric, except for their degree of competitiveness concerns, under noncooperation introduction of such concerns lowers the abatement policies in both countries, however, the self-effect is stronger than the cross-effect. Nevertheless, under cooperation, an increase in country j's competitiveness concerns leads to more stringent policies in country i, while the self-effect could be either positive or negative. The latter result emphasizes the importance of cooperation to tackle pollution in the presence of competitiveness concerns.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".