Shining a light on fossil fuel subsidies at the WTO: how NGOs can contribute to WTO notification and surveillance
Bibliographic record
Abstract
Abstract Fossil fuel subsidies undermine efforts to mitigate climate change, and they damage the trading system. Multilateral discussion is hampered by inconsistent definitions and incomplete data, which could increase the risks of WTO disputes. Members do not notify such subsidies as much as they should under the Agreement on Subsidies and Countervailing Measures (ASCM), which limits the usefulness of the SCM Committee. The reports of the Trade Policy Review Mechanism on individual countries and on the trading system draw on a wider range of sources, creating an opportunity for non-governmental organizations (NGOs) to provide the missing data from publicly available sources. We suggest a new template that could be used for such third-party notifications. The objective is to shine a light on all fossil fuel subsidies that cause market distortions, especially trade distortions. The result should be better, more comparable data for the Secretariat, governments, and researchers, providing the basis for better-informed discussion of the incidence of fossil fuel subsidies and rationale for their use.
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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 | 0.236 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".