  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.
