Inequality in human exposure to future climate extremes
Bibliographic record
Abstract
Future climate extremes are expected to worsen existing inequalities in human exposure, yet the specific disparities across income groups are not well understood. We investigate how future floods, heatwaves, droughts, and compound hot-dry events will impact high- and low-income countries under various shared socioeconomic pathways (SSPs). We find that low-income countries are projected to experience more severe exposure to these events, primarily due to accelerated population growth rather than climate change. Exposure inequality between high- and low-income countries decreases as event severity increases, with the effects of population growth diminishing and the impact of climate change becoming more pronounced. While compound hot-dry events have a greater overall impact compared to single events, the inequality in exposure to these events is less pronounced. These findings underscore the need for targeted adaptation strategies that address both demographic drivers and the spatial-temporal dynamics of extreme events to effectively manage socioeconomic risks. Climate extremes are expected to worsen global population exposure, but disparities across income groups remain unclear. This study shows low-income countries face greater exposure to future floods, heatwaves, droughts, and compound events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".