Informing Risk Hotspots and Critical Mitigations for Rainstorms Using Machine Learning: Evidence from 268 Chinese Cities.

Climate change is exacerbating rainstorms, increasing the risk of flooding and threatening urban sustainability, which could undermine climate action. Here, we propose a machine learning-based framework to assess heterogeneous risks and identify critical mitigation measures for rainstorms across 268 Chinese cities. Nighttime light serves as a proxy for urban functionality, and meteorological, socio-economic, and infrastructural factors are incorporated to uncover underlying impact mechanisms.

Economic Growth Facilitates Household Fuel Use Transition to Reduce PM-Related Deaths in China.

Exposure to ambient and indoor particle matter (PM) leads to millions of premature deaths in China. In recent years, indoor air pollution and premature deaths associated with polluting fuel cooking demonstrate an abrupt decline. However, the driving forces behind the mortality change are still unclear due to the uncertainty in household fuel use prediction.