Climate change and trade agreements: friends or foes?
Bibliographic record
Abstract
The Intergovernmental Panel on Climate Change (IPCC) has shone a spotlight on the devastating humanitarian consequences the world can expect if global warming exceeds 1.5°C. Despite the 2015 Paris Agreement, most countries’ climate policies show a chronic lack of ambition and the world remains on track for temperature increases of more than 3°C. Against this backdrop, the world needs transformative solutions. In climate policy discussions, relatively little attention is paid to the global trade architecture. Bilateral, regional or World Trade Organisation (WTO) trade agreements could help to meet climate goals—for example, by removing tariffs and harmonising standards on environmental goods and services, and eliminating distortionary and poorly designed subsidies on fossil fuels and agriculture. Despite the potential for trade–climate synergies, the weight of historical evidence is heavy in the other direction. Universal tariff reduction has increased trade in carbon-intensive and environmentally destructive products, such as fossil fuels and timber, more than it has for environmental goods. In some cases FTAs can also shrink the “policy space” available to countries to pursue environmental goals, for example if they prohibit, or are perceived to prohibit, a country’s ability to distinguish between products according to emissions released during their production. This report assesses the degree to which the WTO and four contemporary free trade agreements (FTAs) —CPTPP, EU–Singapore, EU–Canada and Korea–Australia—support seven opportunities for boosting climate-friendly trade flows.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".