Urgency of Climate Change through the lens of COVID-19 Pandemic: the case of heat-related mortality
Bibliographic record
Abstract
The COVID-19 pandemic and climate change are both urgent global health concerns. However, their impact on human lives has not been compared on the same scale. In this study, we compared mortality due to heat with COVID-19 in 38 cities worldwide, considering different levels of global warming (+1°C, +1.5°C, +2°C, and +3°C). Our findings reveal that even at a global warming level of +1.0ºC, 6 cities are already at a point where heat-related deaths could equal COVID-19 deaths within 15 years. Regardless of high or low COVID-19 mortality in the cities, the number of years to reach the level of COVID-19 mortality decreases with higher global warming levels. In 18.4% to 47.4% of the cities, heat-related mortality is projected to equal COVID-19 mortality within 15 years, ranging from +1.0ºC to +3.0ºC of global warming. The vulnerability to climate change varies among regions, with European, Mediterranean, and North American cities experiencing a significant rise in heat-related mortality with higher global warming levels. It is important to note that the given number of years represents the time required to reach COVID-19 mortality. However, unlike the peak and decline of COVID-19, climate change-driven heat-related deaths will persistently worsen unless substantial adaptation measures are taken. This emphasizes the crucial need to integrate climate change into public health discourse and policy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".