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Record W4407576694 · doi:10.4337/9781035326570.00026

Climate change and trade regulation

2025· book-chapter· en· W4407576694 on OpenAlexaboutno aff
Maria Panezi

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeEnvironmental scienceEconomicsGeologyOceanography

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.255
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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