Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition
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1-s2.0-S2589004223017297-main.pdf
Description
Accepted version
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3.57 MB
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Author(s)
Zhang, Da
Wang, Qingyi
Song, Shaojie
Chen, Simiao
Li, Mingwei
Shen, Lu
Zheng, Siqi
Cai, Bofeng
Wang, Shenhao
Zheng, Haotian
Date Issued
September 2023
Journal
iScience
Publisher
Elsevier BV
Citation
Zhang, Da, Wang, Qingyi, Song, Shaojie, Chen, Simiao, Li, Mingwei et al. 2023. "Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition." iScience, 26 (9).
Version
Final published version
Abstract
Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. Simulating pollution concentration is a crucial step of the estimation, but traditional approaches often rely on complicated chemical transport models that require extensive expertise and computational resources. In this study, we develop a machine learning framework that is able to provide precise and robust annual average fine particle (PM2.5) concentration estimations directly from a high-resolution fossil energy use dataset. Applications of the framework with Chinese data reveal highly heterogeneous health benefits of avoiding premature mortality by reducing fossil fuel use in different sectors and regions in China with a mean of $19/tCO2 and a standard deviation of $38/tCO2. Reducing rural and residential coal use offers the highest co-benefits with a mean of $151/tCO2. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition toward a carbon-neutral energy system.
MIT Department
Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Massachusetts Institute of Technology. Media Laboratory
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DOI of Published Version
https://doi.org/10.1016/j.isci.2023.107652
