CO <sub>2</sub> emission efficiency evaluation and improvement from the perspective of non-uniqueness of equilibrium efficient frontier: evidence from China
Bibliographic record
Abstract
Accurately evaluating CO2 emission efficiency is crucial for capturing regional carbon emission levels and formulating effective reduction policies. Existing studies have typically analyzed CO2 emission efficiency under a single equilibrium efficient frontier (EEF). In this study, we propose an improved generalized EEF data envelopment analysis (GEEFDEA) method to explore CO2 emission efficiency across all feasible EEFs and propose targeted efficiency improvement paths. The improved GEEFDEA approach addresses the nonuniqueness of the resulting EEF in prior studies and strengthens the reliability and robustness of results. Additionally, it provides rich evaluation information and guarantees comprehensive conclusions. The empirical application of 30 provincial regions in China for 2022 indicate that: (1) In nearly half of the provinces, the maximum CO2 emission efficiency is below 1, with eastern provinces performing better; (2) The carbon efficiency and rank of each province vary across different EEFs, underscoring the importance of considering all EEFs; (3) Provinces can achieve their best rank either by making substantial CO2 emission adjustments in one step or through gradual changes, depending on its practical production. The findings of this paper provide a robust theoretical and empirical foundation for accurately measuring and improving CO2 emission efficiency.
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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 | 0.004 | 0.001 |
| 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 | 0.001 |
| 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".