Residential energy consumptions of both electricity and fuels are associated with emissions of many air pollutants. Temporally and spatially resolved energy consumption data are scarce in China, which are critical for a better understanding of their environmental impacts. In this study, a space-for-time substitution method was proposed and two models for predicting fuel and electricity consumptions in residential sector of China were developed using provincial data. It was found that fuel consumption was not directly proportional to heating degree day and was also affected by heating day, defined as the number of days when heating is required in a year. The models were validated against a set of historical annual data and two sets of survey data on seasonal variations. The models were applied to predict spatial and temporal variations of residential energy consumptions and emissions of various pollutants and to predict net effects of climate warming on energy consumptions and pollutant emissions. The emissions of black carbon (BC), carbon monoxide (CO), and polycyclic aromatic carbons (PAHs) in winter were significantly higher than those in other seasons. For the emissions of carbon dioxide (CO2), sulfur dioxide (SO2), and nitrogen oxides (NOx), there were two peaks in winter and summer, with the latter increasing gradually over years. It was predicted that per-capita residential energy consumptions would reach 0.43, 0.33, and 0.26toe/cap in 2050 for IPCC scenarios of A1B, B1, and A2, respectively. Climate warming in the future would lead to less residential fuel but more electricity consumptions. Consequently, emissions of BC, CO, and PAHs would decrease mainly in cold climate zones, while emissions of CO2, SO2, and NOx would increase largely in southeast China.
