Panel Data Analysis on the Influence of Environmental Regulations on the Inflow of Foreign Direct Investment in China
Bibliographic record
Abstract
From the micro level, this paper thoroughly investigates the influence of environmental regulations (ERS) on the inflow of foreign direct investment (FDI) in China. Firstly, the entropy method was adopted to comprehensively measure the ERS intensities of 283 Chinese cities at prefecture level and above in 2003-2016. Then, the Cournot model was utilized to analyze how ERS affects FDI. After that, fixed-effects model was employed to empirically examine the impacts of ERS intensities in eastern, central, and western regions on FDI inflow. The results show that: The regression results on nationwide, central, and western samples indicate that the influence of ERS variable was significantly negative. This means ERS is indeed an important consideration of foreign investors in location selection. Besides, stricter ERS hinders the inflow of FDI, which agrees with the pollution haven hypothesis. On eastern samples, stricter ERS promotes FDI inflow, that is, the situation in eastern region meets Porter hypothesis. Finally, several suggestions were presented for policymakers based on the empirical results.
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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 | 0.002 |
| 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".