Estimating air quality co-benefits of energy transition using machine learning
This work addresses the need for accessible and precise pollution estimation for policymakers and researchers in environmental science, though it is incremental as it applies a novel method to an existing bottleneck.
The study tackled the problem of estimating air quality co-benefits from energy transition by developing a machine learning framework to simulate PM2.5 concentrations from fossil energy use data, revealing highly heterogeneous health benefits in China with a mean of $34/tCO2 and standard deviation of $84/tCO2.
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 novel and succinct 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 data set. The accessibility and applicability of this framework show great potentials of machine learning approaches for integrated assessment studies. Applications of the framework with Chinese data reveal highly heterogeneous health benefits of reducing fossil fuel use in different sectors and regions in China with a mean of \$34/tCO2 and a standard deviation of \$84/tCO2. Reducing rural and residential coal use offers the highest co-benefits with a mean of \$360/tCO2. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition towards a carbon-neutral energy system.