Estimating the urban heat-related mortality burden due to greenness: a global modelling study
Bibliographic record
Abstract
BACKGROUND: Heat exposure poses a substantial public health threat. Increasing greenness has been suggested as a mitigation strategy due to its cooling effect and potential to modify the heat-mortality association. This study aimed to comprehensively estimate the effects of increased greenness on heat-related deaths. METHODS: We applied a multistage meta-analytical approach to estimate the potential reduction in global heat-related deaths by increasing greenness in the warm season in 2000-19 in 11 534 urban areas. We used the enhanced vegetation index (EVI) to indicate greenness and a random forest model to predict daily temperatures in counterfactual EVI scenarios. In the factual EVI scenarios, daily mortality and weather variables from 830 locations in 53 countries were extracted from the Multi-Country Multi-City Collaborative Research Network and used to assess heat-mortality associations. These associations were then extrapolated to each urban area under both factual and counterfactual EVI scenarios based on meta-regression models. FINDINGS: We estimated that EVI increased by 10% would decrease the global population-weighted warm-season mean temperature by 0·08°C, EVI increased by 20% would decrease temperature by 0·14°C, and EVI increased by 30% would decrease temperature by 0·19°C. In the factual scenario, 3 153 225 (2·48%) of 127 179 341 total deaths could be attributed to heat exposure. The attributable fraction of heat-related deaths (as a fraction of total deaths) in 2000-19 would decrease by 0·67 (95% empirical CI 0·53-0·82) percentage points in the 10% scenario, 0·80 (0·63-0·97) percentage points in the 20% scenario, and 0·91 (0·72-1·10) percentage points in the 30% scenario, compared with the factual scenario. South Europe was modelled to have the largest decrease in attributable fraction of heat-related mortality. INTERPRETATION: This modelling study suggests that increased greenness could substantially reduce the heat-related mortality burden. Preserving and expanding greenness might be potential strategies to lower ambient temperature and reduce the health impacts of heat exposure. FUNDING: Australian Research Council and Australian National Health and Medical Research Council.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".