Climate action for health: Inter‐regional engagement to share knowledge to guide mitigation and adaptation actions
Bibliographic record
Abstract
Abstract Climate change, attributable to human activity, is increasingly contributing to a global health crisis. The scale, nature and timing of adverse effects on physical and mental health, via direct and indirect pathways, vary within and between regions but there are common challenges that can be tackled by better integrated mitigation and adaptation actions. The actions described in this paper would have benefits for health if appropriately implemented, both by reducing the health risks of climate change and from the ancillary (co‐)benefits of mitigation such as from reduced air pollution as a result of phasing out fossil fuels. There are unprecedented health threats from climate change but also unprecedented opportunities to use scientific knowledge to inform policy and practice. Much can be done now to use the evidence already available to effect rapid and decisive action as well as generating new evidence to support effective policy development and implementation. This paper draws on an inter‐regional, inclusive, project by the InterAcademy Partnership, the global network of more than 140 academies of science, engineering and medicine, to summarise evidence available worldwide in order to help inform options for policy making. A particular focus is on clarifying climate change mitigation and adaptation solutions and their implementation for the benefit of the most vulnerable groups. The present authors actively participated in managing this project which encouraged academies to capture diverse impacts and policy options by evaluating and synthesising evidence from their own countries to inform policy for collective and customised action at national, regional and global levels. Using a systems‐based approach, recommendations from the project in this publication are transdisciplinary and multisectoral. Despite the accumulating evidence, protecting and improving human health have not yet become major focal points in global climate change policy discussions. Drawing on the IAP project outputs, we strongly recommend that health and health equity must now come to the foreground, accompanied by much greater allocation of climate finance to health‐related programmes.
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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 | 0.126 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.007 | 0.041 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".