From non-cooperative CO2 abatement strategies to the optimal world cooperation: Results from the integrated MARKAL model
Bibliographic record
Abstract
In order to study the conditions for a world self-enforcing agreement on climate change, we model cooperative and non-cooperative world climate strategies with an integrated version of the world 15region techno-economic MARKAL model in which abatement costs and climate related damages are both included. Assuming interregional transfers to share the global gain of cooperation, our work adopts the point of view of dynamic partial equilibrium computation coupled with cooperative game-theoretic principles. The results illustrate how the climatic and economic gap between cooperation and noncooperation, the willingness of regions to cooperate, and the amount of side-payments, depend on the level and distribution of climate damages, the abatement costs, and the emission levels in the reference case. The internal (in)stability of farsighted coalitions without transfers (non-cooperation) is also analyzed. The current project appears to be the first one of the sort using a world, large and detailed technology explicit model such as MARKAL. 1 Research done with financial support from the Natural Sciences and Engineering Research Council of Canada and the Fonds Quebecois de recherche sur la Nature et les Technologies 2 GERAD and Universite du Quebec a Montreal 3 GERAD and McGill University
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".