Globalization’s Vehicle: The Evolution and Future of Emission Regulation in the icao and imo in Comparative Assessment
Bibliographic record
Abstract
Global aviation and shipping offer important lessons for the regulation of air pollution and greenhouse gas emissions. In a relatively short period, each industry has, through international organizations, achieved seemingly effective, universally adopted rules. This article explores the historical development and prospects for air-pollution regulation by examining and comparing state delegation of rule making to the International Civil Aviation Organization and the International Maritime Organization. It is argued that the success experienced in the regulation of the two industries’ air emissions would not have been accomplished without their multilateral organizations. That is because of the complexity of technical regulation and a lack of capacity in many states to design and implement regulations. The article examines the development of now-comprehensive air-pollution regulation by the two organizations. The lessons for regulatory design are canvassed, including for relevance to greenhouse-gas control and the emerging framework of global atmospheric protection. The question of how to assess the effectiveness of the regulation is considered in an effort to understand how progress in the two industries might be sustained. Improved regulation, it is concluded, will depend on the two governing regimes adopting measures from each other and ensuring that they are included in an emerging global atmospheric governance framework.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".