International Trade and the Negotiability of Global Climate Change Agreements
Bibliographic record
Abstract
Country incentives to participate in cooperative arrangements which either fully or partially internalize climate change externalities from carbon emissions involve critical asymmetries.Small countries trade off own country costs of carbon mitigation actions against their own benefits from global improvements in climate which benefit all.Small countries thus have limited incentive to participate as their actions, while costly to them, have a significant impact on global temperature change which mainly benefits others.Here we build on the work of Shapley and Shubik (1969) which suggests that the core of a global warming game without transferable utility may be empty and use numerical simulation methods to analyse country incentives to participate in carbon emission limitation negotiations using a micro global warming structure related to that used by Uzawa(2003).We discuss how the presence of international trade in goods affects the willingness of countries to join international negotiations on climate change.We calibrate our simulation structure to business as usual scenarios for the period 2006-2036.We go significantly beyond the PAGE model relied on in the Stern (2006) report in capturing multi-country interactive effects on the benefit side of climate change mitigation.We show how the perceived severity of global climate change damage influences participation decisions, and importantly how international trade makes participation more likely.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 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".