Reserving the right to say no? Equilibria around hard trade‐sustainability commitments in power‐asymmetric contexts
Bibliographic record
Abstract
Abstract When will stringent sustainability commitments (not) be a stumbling block in the negotiation of trade agreements? Although the existing literature has explored the determinants of the design of sustainability provisions in trade agreements, few works have explored when countries will accept/reject those provisions once their content cannot be changed. Based on insights from game theory, we flesh out the conditions under which there will be an equilibrium in favor of hard sustainability provisions in trade deals. We then present empirical illustrations related to Mexico's participation in the United States–Mexico–Canada Agreement (USMCA) and Brazil's participation in the EU‐Mercosur trade negotiations. Our model shows that (1) fears of partner opportunism, (2) the costs of nonparticipation in trade deals, and (3) costs of adjustments to hard trade‐sustainability commitments are key to understanding whether a compromise can arise on trade and strong sustainability commitments. The model highlights what sorts of concessions ought to be made for negotiations to prosper. The findings point to how the changing structure of trade governance may affect the decision‐making process of Global South countries. The paper concludes with recommendations and avenues for further research.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".