Local Government Competition and Regional Green Development in China: The Mediating Role of Environmental Regulation
Bibliographic record
Abstract
Green development is an important way to meet the challenges of ecological and environmental protection and economic growth, as well as an inevitable choice to realize China's sustainable development in the new era. The Chinese economic system is such that local government competition has become a key factor affecting regional green development under the current leadership. Based on the inter-provincial panel data of 30 provinces in mainland China from 1997 to 2017, this paper uses the total-factor non-radial directional distance function and slack-based measure data envelopment analysis (SBM-DEA) to measure the green development efficiency of the provinces. Additionally, it also uses the Malmquist-Luenberger (ML) index to decompose green development efficiency and analyzes its internal driving factors. Finally, taking environmental regulation as a mediating variable, this paper empirically analyzes the influence mechanism of local government competition on green development efficiency from three perspectives including growth competition, fiscal competition and investment competition. The study found that: the green development efficiency of Chinese regions showed a downward trend, with significant regional differences; technological progress is the key factor to improve the efficiency of green development, and its role gradually decreases from eastern to western and central regions; pure technical efficiency has become a bottleneck restricting the improvement of green development efficiency, while scale efficiency shows significant regional differences; the growth competition, fiscal competition and investment competition of local government all have a significant inhibitory effect on the efficiency of green development. This paper puts forward policy suggestions supporting enterprise technology research and development, optimizing energy conservation and emission reduction as well as improving the local government performance evaluation system for green development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".