Climate and trade policies: from silos to integration
Bibliographic record
Abstract
This paper investigates linkages between trade and climate policies by examining commitments made in preferential trade agreements (PTAs) and Nationally Determined Contributions (NDCs) under the Paris Agreement. While environmental protection and economic growth are often perceived as conflicting policy goals, PTAs and NDCs have the potential to encourage mutually supportive approaches to climate and trade governance. Building upon three recent datasets, the paper locates a sample of 21 countries in a typology of four issue-linkage strategies across both types of instruments: policy integration, policy silos, asymmetry in favour of trade policy, and asymmetry in favour of climate policy. It finds that countries that reveal a preference for strong linkage with climate in their PTAs typically do not reveal a preference for strong trade linkage in their NDCs, and vice versa. No state from the sample favours strong policy integration. After sketching out possible explanations for this observation, the paper concludes that policy-makers have significant room for enhancing synergies between trade and climate commitments and that scholars have a role to play in this endeavour. Key policy insightsThere is substantial untapped potential for simultaneously promoting trade and tackling the climate crisis across borders in future NDCs and PTAs.In future NDCs, trade provisions, e.g. the reduction of trade barriers for climate-friendly goods and services, should be strengthened in national climate plans.Countries should make better use of climate provisions in their PTAs, e.g. to encourage their trade partners to commit to binding climate objectives and foster exchanges of climate-friendly goods and services.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".