A MODEL INTERCOMPARISON OF THE WELFARE EFFECTS OF REGIONAL COALITIONS FOR AMBITIOUS CLIMATE MITIGATION TARGETS
Bibliographic record
Abstract
This paper presents the overall and distributional welfare effects of alternative multi-regional emissions trading coalitions relative to unilateral action. It focusses on meeting Paris Agreement pledges and more emissions reduction targets consistent with 2∘C and 1.5∘C temperature pathways in 2030. The results from seven computable general equilibrium (CGE) models are compared. Across all models, welfare gains are highest with a global market and increase with the stringency of targets. All regional coalitions also show overall welfare gains, although lower gains than the global market. The models show more variability in the gains by a participant. Depending on the model, participants may benefit more from some regional arrangements than from a global market or face modest losses compared to the domestic reductions alone, due to interactions between carbon targets and fossil fuel markets. The scenario with a joint China–European Union emissions trading system in all sectors is consistently favorable for participants and provides the highest economic gains per unit of emissions abated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".