How environmental treaties contribute to global health governance
Bibliographic record
Abstract
BACKGROUND: Recent work in international relations theory argues that international regimes do not develop in isolation, as previously assumed, but evolve as open systems that interact with other regimes. The implications of this insight's for sustainable development remains underexplored. Even thought environmental protection and health promotion are clearly interconnected at the impact level, it remains unclear how global environmental governance interacts with global health governance at the institutional level. In order to fill this gap, this article aims to assess how environmental treaties contribute to global health governance. METHODS AND RESULTS: To assess how environmental treaties contribute to global health governance, we conducted a content analysis of 2280 international environmental treaties. For each of these treaties, we measure the type and number of health-related provisions in these treaties. The result is the Health and Environment Interplay Database (HEIDI), which we make public with the publication of this article. This new database reveals that more than 300 environmental treaties have health-related provisions. CONCLUSIONS: We conclude that the global environmental regime contributes significantly to the institutionalization of the global health regime, considering that the health regime includes itself very few treaties focusing primarily on health. When reflecting on how global governance can improve population health, decision makers should not only consider the instruments available to them within the realm of global health institutions. They should broaden their perspectives to integrate the contribution of other global regimes, such as the global environmental regime.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".