Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition

Da Zhang; Qingyi Wang; Shaojie Song; Simiao Chen; Mingwei Li; Lu Shen; Siqi Zheng; Bofeng Cai; Shenhao Wang; Haotian Zheng

iScience (Sep 2023)

Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition

Da Zhang,
Qingyi Wang,
Shaojie Song,
Simiao Chen,
Mingwei Li,
Lu Shen,
Siqi Zheng,
Bofeng Cai,
Shenhao Wang,
Haotian Zheng

Affiliations

Da Zhang: Institute of Energy, Economy, and Environment, Tsinghua University, Beijing, China; Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA; Corresponding author
Qingyi Wang: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Shaojie Song: State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment Health Research, Tianjin 300350, China; Harvard-China on Energy, Economy, and Environment, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Simiao Chen: Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany; Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
Mingwei Li: Institute of Energy, Economy, and Environment, Tsinghua University, Beijing, China; Center for Policy Research on Energy and the Environment, Princeton University, Princeton, NJ, USA
Lu Shen: Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Siqi Zheng: Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA
Bofeng Cai: Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, China; Corresponding author
Shenhao Wang: Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA; Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
Haotian Zheng: State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment Health Research, Tianjin 300350, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China; Corresponding author

Journal volume & issue: Vol. 26, no. 9
p. 107652

Abstract

Read online

Summary: 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.

Published in iScience

ISSN: 2589-0042 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Science
Website: http://www.cell.com/iscience/home

About the journal

Abstract

Keywords