Bibliographic record
Abstract
Some believe that relatively lenient environmental standards give developing countries a comparative advantage in pollution–intensive goods. Thus, freer trade will harm their environment. This paper brings together the literature on openness and growth, and on the environmental Kuznet’s curve, to demonstrate that the opposite may be true. A simultaneous–equations system is derived which incorporates multiple effects of trade liberalization on the environment. Estimation using pooled provincial data on Chinese water pollution, suggests that freer trade aggravates environmental damage via the terms of trade, but mitigates it via income growth. Simulations suggest that the net effect in China was beneficial. JEL Classification: Fl3, Q28, 0l9 Est–ce que la libéralisation du commerce est nuisible pour l’environnement? Un nouveau test. Certains croient que des normes environnementales relativement peu contraignantes donnent un avantage comparatif aux pays en voie de développement dans la production de biens qui polluent intensivement. Donc, un commerce plus libre contribuera à nuire à l’environnement. Ce mémoire synthétise la littérature spécialisée sur l’ouverture des marchés et la croissance, ainsi que sur la courbe de Kuznets, pour montrer que l’inverse est vrai. On dérive un système d’équations simultanées qui incorpore les multiples effets de la libéralisation du commerce sur l’environnement. La calibration de ce système, en utilisant de manière intégrée les données provinciales de pollution de l’eau en Chine, suggère qu’un commerce plus libre aggrave l’état de l’environnement par le truchement du jeu des termes d’échange, mais que cet effet est mitigé par l’effet de croissance des revenus. Des simulations suggèrent que l’effet net en Chine est positif.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 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".