Transatlantic convergence of preferential trade agreements environmental clauses
Bibliographic record
Abstract
Abstract The United States and the European Union include several environmental clauses in their respective preferential trade agreements (PTAs). Building on an exhaustive and fine-grained dataset of PTAs’ environmental clauses, this article makes two contributions. First, it shows that the United States and the European Union have initially favored different approaches to environmental protection in their PTAs. The United States’ concerns over regulatory sovereignty and level playing field have led to a legalistic and adversarial approach, while the European Union's concerns for policy coherence have led to a more procedural and cooperative approach. Second, this article provides evidence that European and American trade negotiators have gradually converged on a shared set of environmental norms. Although the United States and the European Union initially pursued different objectives, they learned from each other and drew similar lessons. As a result, recent American agreements have become more European-like, and European agreements have become more Americanized. This article concludes that U.S. and E.U. approaches, far from being incompatible, can usefully be combined and reinforce each other.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".