Inequalities in air pollution exposure and attributable mortality in a low carbon future
Bibliographic record
Abstract
Abstract \n \nUnderstanding 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 PM2.5 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 PM2.5 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 PM2.5-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 PM2.5 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 PM2.5 exposures that are over three times the World Health Organization Air Quality Guideline. \n \nKey Points \n \nDecarbonization has the potential to generate substantial health co-benefits by averting millions of premature deaths associated with PM2.5 exposure across all income groups \n \nThe low-income population is predicted to experience the smallest health benefits of decarbonization and continue to be exposed to PM2.5 concentrations that are over three times that of the World Health Organization Air Quality Guideline \n \nUnder a decarbonization future pathway, the global socioeconomic disparity in PM2.5 exposure reduces but persists at around 30% by the end of the century \n \nPlain Language Summary \n \nImplementation of decarbonization strategies to mitigate future climate change can provide additional benefits or “co-benefits” through improved air quality and public health. Quantifying these benefits and how they manifest across different world regions and income groups is essential to incentivize climate action. In this work we have quantified the air pollution health co-benefits for three different possible future scenarios: one “middle-of-the-road” scenario and two decarbonization scenarios. We found that by following a future decarbonization pathway instead of a “middle-of-the-road” pathway, can generate substantial air quality and public health benefits worldwide, particularly in Asia around 2050. While all income groups were found to benefit from improved air quality through decarbonization, the smallest health benefits are experienced by the low-income population. Inequalities in air pollution exposure between the lower-income and high-income groups were found to reduce rapidly under a decarbonization pathway, but persist through to 2100 even under the strongest mitigation. Further, without additional and targeted air quality measures, low- and lower-middle-income populations (predominantly in Africa and Asia) will continue to experience air pollution levels that exceed the World Health Organization Air Quality Guideline.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".