The Global Commons through a Regional Lens: The Arctic Council on Short-Lived Climate Pollutants
Bibliographic record
Abstract
Abstract The regulation of short-lived climate pollutants (SLCPs) is widely seen as an important dimension of global atmospheric pollution control and climate change governance. SLCPs emitted outside the Arctic influence the Arctic atmosphere, Arctic communities, and the rate of ice melt. As an intergovernmental forum that brings together three of the world’s major petroleum producers (Russia, the United States, and Canada), the Arctic Council has a pivotal role in reducing the rate of Arctic warming through SLCP mitigation. This article explores the Arctic Council’s approach to SLCP mitigation. It begins by addressing the current status of black carbon and methane in international legal instruments, and goes on to explore the important regime linkages that are set in place through the Arctic Council’s Framework for Action on Enhanced Black Carbon and Methane Emission Reductions. The article suggests that the Arctic Council provides an experimental platform that may catalyze SLCP regulation not only in Arctic jurisdictions but also in Arctic Council observer states, such as China and India. The transnational and inclusive character of the Arctic Council’s constitutional framework and knowledge-generating mechanisms enables new pathways for global action on climate change and air pollution governance.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".