Increasing Heat‐Stress Inequality in a Warming Climate
Bibliographic record
Abstract
Abstract Adaptation is key to minimizing heatwaves' societal burden; however, our understanding of adaptation capacity across the socioeconomic spectrum is incomplete. We demonstrate that observed heatwave trends in the past four decades were most pronounced in the lowest‐quartile income region of the world resulting in >40% higher exposure from 2010 to 2019 compared to the highest‐quartile income region. Lower‐income regions have reduced adaptative capacity to warming, which compounds the impacts of higher heatwave exposure. We also show that individual contiguous heatwaves engulfed up to 2.5‐fold larger areas in the recent decade (2010–2019) as compared to the 1980s. Widespread heatwaves can overwhelm the power grid and nullify the electricity dependent adaptation efforts, with significant implications even in regions with higher adaption capacity. Furthermore, we compare projected global heatwave exposure using per‐capita gross domestic product as an indicator of adaptation capacity. Hypothesized rapid adaptation in high‐income regions yields limited changes in heatwave exposure through the 21st century. By contrast, lagged adaptation in the lower‐income region translates to escalating heatwave exposure and increased heat‐stress inequality. The lowest‐quartile income region is expected to experience 1.8‐ to 5‐fold higher heatwave exposure than each higher income region from 2060 to 2069. This inequality escalates by the end of the century, with the lowest‐quartile income region experiencing almost as much heatwave exposure as the three higher income regions combined from 2090 to 2099. Our results highlight the need for global investments in adaptation capabilities of low‐income countries to avoid major climate‐driven human disasters in the 21st century.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".