Inequalities in Air Pollution Exposure and Attributable Mortality in a Low Carbon Future
Bibliographic record
Abstract
Abstract Understanding the costs and benefits of climate change mitigation and adaptation options is crucial to justify and prioritize future decarbonization pathways to achieve net zero. Here, we quantified the co‐benefits of decarbonization for air quality and public health under scenarios that aim to limit end‐of‐century warming to 2°C and 1.5°C. We estimated the mortality burden attributable to ambient PM 2.5 exposure using population attributable fractions of relative risk, incorporating projected changes in population demographics. We found that implementation of decarbonization scenarios could produce substantial global reductions in population exposure to PM 2.5 pollution and associated premature mortality, with maximum health benefits achieved in Asia around mid‐century. The stringent 1.5ºC‐compliant decarbonization scenario (SSP1‐1.9) could reduce the PM 2.5 ‐attributable mortality burden by 29% in 2050 relative to a middle‐of‐the‐road scenario (SSP2‐4.5), averting around 2.9 M annual deaths worldwide. While all income groups were found to benefit from improved air quality through a combination of decarbonization and air pollution controls, the smallest health benefits are experienced by the low‐income population. The disparity in PM 2.5 exposure across income groups is projected to reduce by 2100, but a 30% disparity between high‐ and low‐income groups persists even in the strongest mitigation scenario. Further, without additional and targeted air quality measures, low‐ and lower‐middle‐income populations (predominantly in Africa and Asia) will continue to experience PM 2.5 exposures that are over three times the World Health Organization Air Quality Guideline.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".