Urban environmental performance and it's driving factors in China: Based on the super-efficiency DEA and Panel regressive analysis
Bibliographic record
Abstract
With the sustained and rapid growth of China's urbanization level,the contradiction between the urban development and environmental protection is becoming aculeate increasingly; environmental efficiency in the course of urbanization has become the social focus gradually. Based on the connotation of environmental performance( EP) defined by World Business Council for sustainable development( WBCSD),we evaluated the urban environmental performance of 31 provincial capitals and cities specially designated in the State Plan in 2007 and2011 respectively by using Super-efficiency model of Data Envelopment Analysis( DEA),and then the impacts of driving factors on EP were analyzed by applying panel data model. The results indicate that there were only few cities in China that their environment performance is DEA efficient,most of the cities were in poor coordination of economy-environment and there was still more room for environmental performance. From 2007 to 2011,most of the city's environmental performance had improved,and some cities had been significantly improved,but there were still a few of the city's environmental performance reduced,including Shanghai,Lhasa,Nanchang,Hefei and Shijiazhuang. The impact of financial power of urban government on urban EP was not significant. Industrial structure,population scale,and economic growth had great negative influence on EP,but the openness of urban had positive influence on EP.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".