Public Health Adaptation to Climate Change in Large Cities
Bibliographic record
Abstract
Climate change will have significant impacts on human health, and urban populations are expected to be highly sensitive. The health risks from climate change in cities are compounded by rapid urbanization, high population density, and climate-sensitive built environments. Local governments are positioned to protect populations from climate health risks, but it is unclear whether municipalities are producing climate-adaptive policies. In this article, we develop and apply systematic methods to assess the state of public health adaptation in 401 urban areas globally with more than 1 million people, creating the first global baseline for urban public health adaptation. We find that only 10% of the sampled urban areas report any public health adaptation initiatives. The initiatives identified most frequently address risks posed by extreme weather events and involve direct changes in management or behavior rather than capacity building, research, or long-term investments in infrastructure. Based on our characterization of the current urban health adaptation landscape, we identify several gaps: limited evidence of reporting of institutional adaptation at the municipal level in urban areas in the Global South; lack of information-based adaptation initiatives; limited focus on initiatives addressing infectious disease risks; and absence of monitoring, reporting, and evaluation.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".