A decision aid tool for equity issues analysis in emission permit allocations
Bibliographic record
Abstract
In the long term, the Kyoto Protocol will be insufficient to stabilize the greenhouse gas (GHG) concentrations in the atmosphere; quantified commitments will also be essential for major developing countries (and the US). International cooperation mechanisms, such as permit trading systems, can help achieve global economic efficiency. However, the initial allocation of emission permits raises many debates on equity. The main objective is to propose a decision aid tool for decision makers, which is capable of providing relevant information on various equitable permit allocation schemes and burden sharing. A dynamic multicriteria model is proposed to share the global quantity of permits among 15 regions, taking into account multiple definitions of equity and regional interests. The World-MARKAL energy model is used to compute the gross reduction cost (before permit exchanges) for each region. Afterward, it is possible to calculate their net reduction costs (after permit exchanges) according to different allocation schemes. A realistic simulation of the tool provides examples of results, i.e. ranges of permit allocations and net costs for each region. Finally, some recommendations are proposed to policy makers to design a decision process adapted to the global context of negotiations.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".