Estimating air quality co-benefits of energy transition using machine learning

TL;DR

This paper introduces a machine learning framework that accurately estimates air quality improvements from energy transitions, revealing significant health co-benefits across sectors and regions, especially from reducing rural coal use.

Contribution

A novel machine learning approach for direct PM2.5 estimation from energy data, enabling efficient assessment of air quality and health benefits without complex models.

Findings

High heterogeneity in health benefits across sectors and regions

Rural coal reduction yields the highest co-benefits

Average benefit of $34 per ton of CO2 reduced

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 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.