Bibliographic record
Abstract
This article discusses the relationship between climate change and trade policy in North America. While the emerging narrative includes some failed promises, fragmentation, and a misplaced emphasis on competitiveness at the expense of progress in dealing with climate change, there are still opportunities to replace existing legal arrangements with ones that take the climate change dimensions of trade more seriously. The first part briefly discusses the World Trade Organization framework of trade and climate change through the lens of Sustainable Development Goal 13. The second part looks at the early days of the North American Free Trade Agreement and the promise for innovation contained therein. The third part examines the replacement of NAFTA by the United States–Mexico–Canada Agreement. The article then turns to three sections outlining opportunities that exist in the North American trade context to explore innovative approaches in transnational/international climate change and trade regulation. The first opportunity outlined is green procurement in the context of the USMCA and the WTO Government Procurement Agreement. The second opportunity emerges from the longstanding softwood lumber disputes between the United States and Canada. The third opportunity is Border Carbon Adjustments which can follow the adoption of a national carbon price. The USMCA emerges as a critical agreement among many. Both the impact on the environment and the footprint from this extensive commercial integration, as well as the opportunity for massive changes if coordinated regulatory frameworks were to be adopted to tackle climate change, are enormous.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".