Machine Learning Approaches Reveal Highly Heterogeneous Air Quality Co-benefits of the Energy Transition

Overview

Journal iScience

Publisher Cell Press

Date 2023 Sep 8

PMID 37680462

Authors

Da Zhang

Qingyi Wang

Shaojie Song

Simiao Chen

Mingwei Li

Lu Shen

Siqi Zheng

Bofeng Cai

Shenhao Wang

Haotian Zheng

Affiliations

Soon will be listed here.

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 (PM) 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/tCO and a standard deviation of $38/tCO. Reducing rural and residential coal use offers the highest co-benefits with a mean of $151/tCO. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition toward a carbon-neutral energy system.

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