Along with the improvement of social productivity and living standard, residential buildings generate a growing portion of carbon emissions, especially during the operation stage. However, energy use behaviors are usually ignored in carbon emission calculation. This study focuses on calculating carbon emissions during the operation stage for residential buildings based on the characteristics of energy use behaviors in different regions. Firstly, we investigated energy use behaviors in dwellings across three cities in China: Xi’an, Shanghai and Fuzhou. Then, we established calibrated carbon emission models and optimization models with different green building measures for residential buildings. The results of this research reveal a significant disparity between the energy usage habits of residents in different climate regions. The carbon emissions of residential electricity bills in Xi’an, Shanghai and Fuzhou are 13.6 kgCO₂/(m²·a) (excluding central heating), 29.3 kgCO₂/(m²·a) and 17.2 kgCO₂/(m²·a), respectively. Equipment carbon emissions account for 32.2%–64.1% of the total. In comparison to the model based on internal standard setting, the accuracy of the models using actual internal has improved by 25.9%–37.4%. The three-star green building methods have the highest carbon reduction rate among different star buildings, the emission reduction rates are around 30%. This study’s findings are useful for carbon emission calculation and green building design of residential buildings in the future.

To prevent COVID-19 outbreaks, many indoor environments are increasing the volume of fresh air and running air conditioning systems at maximum power. However, it is essential to consider the comfort of indoor occupants and energy consumption simultaneously when controlling the spread of infection. In this study, we simulated the energy consumption of a three-storey office building for postgraduate students and teachers at a university in Beijing. Based on an improved Wells-Riley model, we established an infection risk-energy consumption model considering non-pharmaceutical interventions and human comfort. The infection risk and building energy efficiency under different room occupancy rates on weekdays and at weekends, during different seasons were then evaluated. Energy consumption, based on the real hourly room occupancy rate during weekdays was 43%–55% lower than energy consumption when dynamic room occupancy rate was not considered. If all people wear masks indoors, the total energy consumption could be reduced by 32%–45% and the proportion of energy used for ventilation for epidemic prevention and control could be reduced by 22%–36% during all seasons. When only graduate students wear masks in rooms with a high occupancy, total energy consumption can be reduced by 15%–25%. After optimization, compared with the strict epidemic prevention and control strategy (the effective reproductive number R_t = 1 in all rooms), energy consumption during weekdays (weekends) in winter, summer and transition seasons, can be reduced by 45% (74%), 43% (69%), and 55% (78%), respectively. The results of this study provide a scientific basis for policies on epidemic prevention and control, carbon emission peak and neutrality, and Healthy China 2030.