The International Regulation of Aviation Emissions: Putting Differential Treatment into Practice
Bibliographic record
Abstract
Given their rapidly increasing contribution to the climate change problem, calls for regulation of emissions from the international aviation sector have become stronger in recent years. The Kyoto Protocol has delegated the adoption of mitigation measures to the International Civil Aviation Organization (ICAO), with only modest results to date. A core challenge in crafting international regulation for international aviation emissions is the differential treatment of developed and developing countries in a sector that is otherwise characterised by equality of treatment. This article shows how the ICAO has struggled to find a balance between the two approaches, and traces the evolution of the European Union’s approach to differentiation, which included international aviation in its emissions trading system as of 2012. We argue that reconciling differential and equal treatment is likely to include the use of contextual norms applying differential treatment at the implementation stage, specifically through financial, technological, and capacity-building assistance arrangements.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".