Coupling Climate Damages and GHG Abatement Costs in a Linear Programming Framework
Bibliographic record
Abstract
The paper discusses the coupling of non-linear non-convex damage costs due to climate change with a cost-efficiency analysis based on a technical-economic linear programming model like MARKAL and studies the implications for the computation of cooperative and non-cooperative solutions. Our empirical analysis of climate damages based on different world emissions levels and paths prove (a) that the dependency of damages on the trajectory of emissions may be neglected, so that the only relevant variables are the cumulative emissions in each country, and (b) that a linear relationship links regional damages and cumulative global emissions. Based on these results, cooperative and non-cooperative equilibria can be much more easily calculated by solving local optimization problems in a case where international trade effects of GHG policies are neglected: given the linearity of damage functions, each country chooses its non-cooperative strategy by considering only the part of its own damage cost due to its own emissions; in the cooperative case, each country takes into account its contribution to the damages done to all countries. Of course, any cost-benefit conclusion that will be produced by this approach is fully dependent on the damage functions. Also, this approach may be extended to the case where trade effects are modeled.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".