Renewable Energy and Government Support: Time to ‘Green’ the SCM Agreement?
Bibliographic record
Abstract
Abstract Many governments provide subsidies to shift from ‘dirty’ but cheap fossil fuels to ‘clean’ but expensive renewable energy. Recently, public incentives in the renewable energy sector have been challenged through both dispute settlement procedures of the World Trade Organization and domestic countervailing duty investigations. One may expect that trade frictions in this field will intensify over time. This article argues that the Agreement on Subsidies and Countervailing Measures – a multilateral trade treaty on subsidization and anti-subsidy measures – should be revised to give more policy space to national authorities in implementing their low-carbon programmes. The Appellate Body made a few climate-friendly interpretations in Canada–Renewable Energy/Canada–Feed-In Tariff Program. It is now members’ turn to carry out meaningful rule-making reforms. This article explores some ways to ‘green’ the existing disciplines.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.020 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 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".