Balancing contributions and rewards: a DEA approach for fair carbon emission abatement allocation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fairness is imperative in implementing carbon emission abatement (CEA) allocation schemes. This study introduces a new data envelopment analysis (DEA) methodology for the fair distribution of CEA among decision-making units (DMUs), taking into account their individual fairness. First, we establish a model to determine the maximum CEA potential for each DMU. Subsequently, an environmental efficiency evaluation model is presented to estimate a DMU’s maximum potential desirable output increment (referred to as individual reward) based on its CEA level (defined as individual contribution). The individual fairness index is then defined as the ratio of individual reward to individual contribution. A convergence of individual fairness indexes among DMUs indicates higher perceived fairness in the CEA allocation. To promote fairness, we propose a centralized CEA allocation model that maximizes the minimum individual fairness index among DMUs, aiming to minimize disparities. Our contribution lies in formulating the concept of individual fairness within the DEA-based CEA allocation paradigm and introducing an approach to generate a CEA allocation result that embodies fairness. Lastly, the proposed approach is applied to a case study involving 38 OECD countries, demonstrating its superiority in achieving equitable CEA allocation results.
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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 | 0.024 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it