Role of climate goals and clean-air policies on reducing future air pollution deaths in China: a modelling study
Bibliographic record
Abstract
Background Over 3 million people die every year from diseases caused by exposure to outdoor PM 2·5 air pollution, and more than a quarter of these premature deaths occur in China. In addition to clean-air policies that target pollution emissions, climate policies aimed at reducing fossil-fuel CO 2 emissions (eg, to avoid 1·5°C of warming) might also greatly improve air quality and public health. However, no comprehensive accounting of public health outcomes has been done under different energy pathways and local clean-air management decisions in China. We aimed to develop an integrated method for quantifying the health co-benefits from different climate, energy, and clean-air policy scenarios and to assess the relationship between climate and clean-air policies and future health burdens in China, where an ageing population will further exacerbate the effects of air pollution. Methods For this modelling study, we used a China-focused integrated assessment model and a dynamic emission projection model to project future Chinese air quality in scenarios spanning a range of global climate targets (1·5°C, 2°C, national determined contributions [NDC], unambitious, baseline, and 4·5°C) and national clean-air actions (termed 2015-pollution, current-pollution, and ambitious-pollution). We then evaluated the health effects of PM 2·5 air pollution in the scenario matrix using the air quality model and the latest epidemiological concentration–response functions from the 2019 Global Burden of Diseases, Injuries, and Risk Factors Study. Findings We found that, without ambitious climate mitigation (eg, under current NDC pledge), Chinese deaths related to PM 2·5 air pollution might not always decrease—and might often grow—by 2050 compared with the base year of 2015, regardless of clean-air policies and air quality improvements. For example, in the scenario that tracks China's current NDC pledge and uses the best available pollution control technologies (the ambitious-pollution and NDC goals scenario), PM 2·5 -related deaths in China would decrease slightly by 2030 to 1·23 million per year (95% CI 0·95–1·51) from 1·25 million (1·04–1·46) in 2015, but would not decrease further by 2050 (1·21 million, 0·86–1·60) despite substantial and continuous improvements in population-weighted air quality (from 27·2 μg/m 3 in 2030 to 16·0 μg/m 3 in 2050). The contrary trends of improving air quality and increasing PM 2·5 -related deaths in many of our scenarios revealed the extent to which extra efforts are needed to compensate for the increasing age of China's population in the future. With the scenarios that included ambitious clean-air policies and met international climate goals to avoid 1·5°C and 2°C of warming (the ambitious-pollution-2°C goals scenario and the ambitious-pollution-1·5°C goals scenario), we observed substantial decreases in China's PM 2·5 -related deaths of 0·32–0·55 million deaths compared with NDC goals in 2050, and age-standardised death rates decreased by 10·2–14·2 deaths per 100 000 population per year. Interpretation Our results show that ambitious climate policies (ie, limiting global average temperature rise to well below 2°C) and low-carbon energy transitions coupled with stringent clean-air policies are necessary to substantially reduce the human health effects from air pollution in China, regardless of socioeconomic assumptions. Our findings could help policy makers understand the crucial links between climate policy and public health. Funding The National Natural Science Foundation of China.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".