Health risks of warming of 1.5 °C, 2 °C, and higher, above pre-industrial temperatures
Bibliographic record
Abstract
Background : In response to the Paris Agreement under the United Nations Framework Convention on Climate Change, the research community was asked to estimate differences in sectoral-specific risks at 1.5 °C and 2 °C increases in global mean surface air temperature (SAT) above pre-industrial temperatures. Projections of the health risks of climate change typically focus on time periods and not on the magnitude of temperature change. Objective : Summarize projections of health risks associated with temperature extremes and occupational heat stress, air quality, undernutrition, and vector-borne diseases to estimate how these risks would differ at increases in warming of 1.5 °C, 2 °C, and higher. Methods : A comprehensive search strategy included English language publications since 2008 projecting health risks of climate change identified through established databases. Of 109 relevant publications, nearly all were for future time periods (e.g. in 2030 and 2050) rather than future SAT thresholds. Time periods were therefore converted to temperature changes based on the models and scenarios used. Results : Warming of 1.5 °C is reached in about the 2030s for all multi-model means under all scenarios and warming of 2 °C is reached in about the 2050s under most scenarios. Of the 40 studies projecting risks at 1.5 and 2 °C increases of SAT, risks were higher at 2 °C for adverse health consequences associated with exposures to high ambient temperatures, ground-level ozone, and undernutrition, with regional variations. Risks for vector-borne diseases could increase or decrease with higher global mean temperatures, depending on regional climate responses and disease ecology. Conclusions : The burden of many climate-sensitive health risks are projected to be greater at an increase of 2 °C SAT above pre-industrial temperatures than at 1.5 °C. Future projection studies should report results based on changes in global and regional mean SATs and time, to facilitate quantitative analyses of health risks and to inform the level of ambition and timing of adaptation interventions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".