Environmental Impact Assessments and Trade Agreements: An Analysis of US, Canadian, and EU Practices
Bibliographic record
Abstract
Abstract With trade and the environment becoming increasingly interconnected, environmental impact assessments (EIAs) of trade negotiations help to integrate environmental considerations into trade-related treaty making by evaluating potential risks and opportunities, addressing public concerns, and facilitating the introduction of response measures. Despite international efforts, such ‘trade EIAs’ have not yet been universally adopted. At the domestic level, the United States, Canada, and the European Union have pioneered the use of EIAs through their institutionalized procedures for over 20 years. This article examines and compares the relevant practices of these three jurisdictions to identify major patterns and to discuss the pros and cons of existing differences in this area. It argues that the time-tested experience of these jurisdictions could provide benchmarks for consideration in promoting the widespread implementation of trade EIAs through global and regional trade regimes.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.032 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".