An additive super-efficiency DEA approach to measuring regional environmental performance in China
Bibliographic record
Abstract
It is one of the issues of current concern for international research engaged in data envelopment analysis (DEA) that how to achieve more accurate results of environmental and energy efficiency evaluation. Past studies about the application of DEA to environmental performance measurement often follow the concept of undesirable factors. In the context, slacks-based measure of super-efficiency, a non-radial super-efficiency model compared to the traditional radial super-efficiency DEA models, offers a remarkable alternative, largely due to their ability to deal with ranking the performance of efficient decision-making units (DMUs). This paper extends super-efficiency approach to the additive super-efficiency DEA approach with undesirable outputs to measuring environmental performance. Unlike the traditional radial super-efficiency DEA suffering from infeasibility, the additive super-efficiency models in the context of undesirable factors are always feasible. A case study of regional environmental performance in China is also presented by applying the proposed the additive super-efficiency DEA approach. The results show that the environmental efficiency scores of inefficient provinces were slightly lowered in China, and identified regions where these provinces have the capacity to develop without damaging overall performance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".