Enhancing fairness in the Paris Agreement: lessons from the Montreal and Kyoto protocols and the path ahead
Bibliographic record
Abstract
Broad participation and compliance are key elements of any successful international environmental agreement (IEA). Notwithstanding the participation of a significant number of countries in the Paris Agreement, the level of compliance remains challenging. This article investigates the potential role of fairness in enhancing compliance under the Paris Agreement. It draws lessons from the Montreal and Kyoto protocols that could assist the Paris Agreement in incentivising countries. The article discusses the operationalisation of the common but differentiated responsibilities and respective capabilities (CBDR-RC) principle in different treaties. A formulaic approach to interpreting the CBDR-RC principle and the imposition of restrictions on non-compliant parties could be effective ways of promoting compliance with the Paris Agreement. Besides, market-based solutions are considered as economic approaches to incentivising countries to meet the climate target. The importance of market-based solutions is supported by the findings from a worldwide survey among international delegates negotiating the Paris Agreement.
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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 | 0.041 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".