Clash of Rationalities: Revisiting the Trade and Environment Debate in Light of WTO Disputes over Green Industrial Policy
Bibliographic record
Abstract
Climate Change has found its way into the World Trade Organization through the backdoor of the profitable and contentious trade in solar and wind energy technologies. In addition to the Ontario FIT dispute critically examined in this article, there are at least five other active disputes in Geneva over aspects of trade in wind and solar technologies, with more on the horizon. \n \n Solar panels constitute a significant part of China’s total export sales in the EU and for more than two years its trade has overheated EU-China relations. The anti-dumping and anti-subsidy investigations that resulted have now culminated in a tentative settlement to bring Chinese solar panel prices to a “sustainable” level. Winds have been blowing more strongly on the other side of the Atlantic, though in a similar direction, where the US imposed record high rates of anti-dumping and countervailing duties on Chinese (and Vietnamese) wind towers, as well as silicon solar panels. The political economy unfolding in both cases has been quite similar: on one side - claiming unfair trade allegedly committed by the Chinese exporters - are the import-competing manufacturers of Renewable Energy (`RE’) technology, and on the other side, the rest of the RE industry, particularly generators in whose interest it is to have access to the best and cheapest equipment, regardless of origin.
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 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 | 0.028 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.091 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 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".