Evaluation and optimization of ecological compensation fairness in prefecture-level cities of Anhui province
Bibliographic record
Abstract
Abstract Scientific evaluation and continuous optimization of the fairness of ecological compensation are conducive to improving the effect of air pollution control. However, relevant research in this field is in its infancy. Based on the data on urban-scale PM2.5 concentration and ecological compensation from the third quarter of 2018 to the fourth quarter of 2020, this study takes 16 prefecture-level cities in Anhui Province as the research area and uses the Granger causality test to determine the PM2.5 overflow paths of each city. Moreover, using 2020 as an example, the PM2.5 spillover effect of each city is calculated, and the haze Gini coefficient of Anhui Province is obtained. According to the empirical results, the ecological compensation policy for PM2.5 control in Anhui Province is in a relatively equal fairness range (0.295). On this basis, combined with the scatter diagram of ecological compensation and spillover effect, it is suggested to reduce the ecological compensation of five cities, including Maanshan and Xuancheng, while the ecological compensation of the remaining 11 cities should be increased. Two feasible optimization schemes, i.e., annual adjustment and regular adjustment, are proposed for environmental regulators to choose.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".