Role of climate goals and clean-air policies on reducing future air pollution deaths in China: a modelling study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».