FOREIGN DIRECT INVESTMENT AND AIR POLLUTION IN CHINA: EVIDENCE FROM CHINESE CITIES
Bibliographic record
Abstract
In order to gain deeper insight into the impacts of FDI on the air pollution situation in Chinese cities, I construct a simultaneous system. This system supposes the air pollution indicators to be determined by economic scale, industrial composition and technical characters of a city and in turn, FDI entry can affect the production scale, structure transformation and technical progress in pollution abatement activities. This system is tested for two air pollution cases in China: the annual average concentration of SO2 and total suspended particles (TSP). Based on a panel database of 80 cities (1993-2001), the system is estimated by the Generalized Method of Moment (GMM) estimator for simultaneous system. The fixed effect estimator and the method of Anderson and Hsiao (1982) are included to take into account the city’s specific effect and the potential first-order autocorrelation respectively. The results show that although there exist various channels through which FDI affects pollution, the impacts of FDI on pollution are mainly exerted through scale and technical effects. Corresponding to similar studies, the total environmental impacts of FDI in both pollution cases are proven to be very small.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".