The Green Barrier of Trade Effect of EU's Environmental Regulations on Textile and Clothing Exported from China Based on Panel Data Analysis on China's Four Types of Textile and Clothing Exported to Eleven of the EU Member Countries: 1990-2006
Bibliographic record
Abstract
By using gravity model,panel data analysis is conducted for China's blended staple fiber cloth, cotton shirts for men,synthetic fiber suit for men,and miscellaneous cotton socks exported to eleven of the European Union (EU) member countries from the year of 1990 to 2006,in order to investigate whether EU's environmental regulation package has green barrier of trade effect on China's export of textile and clothing (TC). An approach of vertically assigned value is developed by the authors for stringency of environmental regulations (SER),and the SER is assigned various values added according to the strength of the impact of environmental regulation on TC export,and hence a breakthrough has been achieved regarding the econometric analysis in time series on more than one environmental regulation measures of different types adopted at different time.It's pointed out by the authors that the variable setting must reflect the characteristic of the questioned products,and two dummies are set to reflect the three phases of quota restriction particularly against China's TC export.The result shows that the questioned EU's environmental regulation measures has constituted green barrier of trade to the questioned TC exported from China.An evidence based on empirical test is provided by this result to support the argument that EU's environmental regulations do constitute green barrier of trade.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".