Cooperative control of regional transboundary air pollutants
Bibliographic record
Abstract
After food and water, clean air is essential for continuity of life on the planet. Complex trans-boundary air pollution problem is often linked to sustenance and eutrophication. Air pollution problem has no universal conventions like Montreal and Kyoto protocols in case of refrigerants, except regional cooperative solutions. Polluter-pays principle is typically restricted to the source country. Under article 16, International Environment Law mentions the polluter-pay principle without any legitimate power to mitigate the transboundary air pollution risks. Mongolia, Botswana, and Pakistan are ranked as the most polluted countries, although their accumulative emissions are lesser than any of the coal power producing countries. China, South Africa, and India produce 68–79% of their electricity using coal-fired power plants, emitting harmful pollutants into the common air. Regional winds drive upwind smog into their adjacent downwind countries like Pakistan. This paper compares the published transboundary air pollutant flows data of the most polluted countries (Mongolia, Botswana, Pakistan) with the cleanest ones (Estonia, Mauritius, Australia). Air pollution externality problem can only be solved either by regional cooperation or global environment law which yet does not exist. Drawing an analogy from water contamination laws, this work proposes an extension of the polluter-pays principle to transboundary air pollutants to compensate the public losses. We should collectively go for the international environmental law as we cannot divide air like land. Industrialization near the border may be banned to avoid air pollutant migrations to neighbors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".