Bibliographic record
Abstract
The linking of emission permit markets allows participants in different systems to purchase allowances from each other for the purpose of domestic compliance. A recent paper studied the efficiency gains generated in multilateral linkings between permit markets, and concluded that, despite the linking of all jurisdictions maximizing gains, it is not likely to emerge as it is not the most preferred option by all participants. We formulate the linking problem as a cooperative game and show that the linking of all jurisdictions satisfies core stability criteria. Thus, no subset of jurisdictions would benefit from creating their separate market, and the gains will be maximized. We then extend our analysis to arbitrary partitions and farsightedness level and analyze the stable linking of markets between Australia, Canada, the EU, South Korea, and the U.S. Our results indicate that the most likely stable configuration includes a market that links Australia, the EU, and the U.S., and another in which Canada is linked with South Korea. This scenario leaves about 15% of the potential gains unrealized. To mitigate this issue, we suggest that efficiency gains from market linkage be allocated according to the Shapley value, in which case our results suggest that we would see stable linking of all five jurisdictions and thus increase the efficiency.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".