Quantitative Ecological Risk Analysis by Evaluating China's Eco-Efficiency and Its Determinants
Bibliographic record
Abstract
Industrial production can produce large amounts of harmful by-products, causing serious pollution and ecological risk. In addition, if government regulations are subjected into the industries, huge cost risk will be faced. This article adopts a two-stage slack-based undesirable-output data envelope analysis (DEA) model to measure the eco-efficiency of China. In the first stage, we analyze the eco-efficiency of each province of China, and in the second stage, we employed a truncated bootstrap method to understand the determinants of eco-efficiency. The results indicate that whereas the eco-efficiency of the eastern region was the highest, that of the western region was the lowest. The western region's economy lagged behind that of other regions, and its environment suffered from heavy pollution. It was found that the level of industrialization did not contribute to eco-efficiency. However, promotion of the service industry, investment for the environment, and regional innovation have positive effects on eco-efficiency.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".