Transitioning from climate ambitions to climate actions through public health policy initiatives
Bibliographic record
Abstract
Policies to implement climate-forcing pollution emission reductions have often been stymied by economic and political divisiveness. However, certain uncontested nonregret public health policies that also carry climate-forcing cobenefits with them could provide more achievable policy pathways to accelerate the implementation of climate mitigation. An International Society for Environmental Epidemiology Policy Committee endorsed pre-28th Conference of the Parties climate meeting workshop brought together experts on environment, diet, civic planning, and health to review current understanding of public health policy approaches that provide climate change mitigation cobenefits by also reducing greenhouse gas emissions. Promising public health policy areas identified as also providing climate mitigation cobenefits included: improving air quality through stronger regulation of harmful combustion-related air pollutants, advancing healthier plant-based public food procurement programs, promoting more sustainable transport options, developing healthier infrastructure (e.g., combustion-free buildings), and reducing the use of climate forcing substances in healthcare. It is concluded that cities, states, and nations, when aided by involved health professionals, can advance many practical public health, diet, and civic planning policies to improve health and well-being that will also serve to translate climate mitigation ambitions into action.
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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 | 0.044 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".