Residential contribution to air pollution–associated health impacts is critical, but inadequately addressed because of data gaps. Here, we fully model the effects of residential energy use on emissions, outdoor and indoor PM_2.5 concentrations, exposure, and premature deaths using updated energy data. We show that the residential sector contributed only 7.5% of total energy consumption but contributed 27% of primary PM_2.5 emissions; 23 and 71% of the outdoor and indoor PM_2.5 concentrations, respectively; 68% of PM_2.5 exposure; and 67% of PM_2.5-induced premature deaths in 2014 in China, with a progressive order of magnitude increase from sources to receptors. Biomass fuels and coal provided similar contributions to health impacts. These findings are particularly true for rural populations, which contribute more to emissions and face higher premature death risks than urban populations. The impacts of both residential and nonresidential emissions are interconnected, and efforts are necessary to simultaneously mitigate both emission types.

Rural residential energy consumption in China is experiencing a rapid transition towards clean energy, nevertheless, solid fuel combustion remains an important emission source. Here we quantitatively evaluate the contribution of rural residential emissions to PM_2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) and the impacts on health and climate. The clean energy transitions result in remarkable reductions in the contributions to ambient PM_2.5, avoiding 130,000 (90,000–160,000) premature deaths associated with PM_2.5 exposure. The climate forcing associated with this sector declines from 0.057 ± 0.016 W/m² in 1992 to 0.031 ± 0.008 W/m² in 2012. Despite this, the large remaining quantities of solid fuels still contributed 14 ± 10 μg/m³ to population-weighted PM_2.5 in 2012, which comprises 21 ± 14% of the overall population-weighted PM_2.5 from all sources. Rural residential emissions affect not only rural but urban air quality, and the impacts are highly seasonal and location dependent.