The International Energy Agency (IEA) emphasizes that using real building energy use data (RBEUD) to reflect the actual condition of buildings and inform policy-making is the most effective way to reduce buildings’ carbon emissions. However, based on IEA’s evaluation, regional and national building stock data are limited and lacking. Especially for China, the lack of RBEUD in buildings has limited our ability to address the energy performance gap (EPG). In this research, EPG refers to the difference between regulated energy consumption by design standards and actual energy usage. EPG makes it difficult to develop buildings that are energy-efficient. Therefore, this study aims to gather and analyze RBEUD in order to understand the role of occupants’ behavior in explaining the EPG of energy-efficient residential buildings in China. The results suggest that the actual consumption of residential buildings is less than 1/5–1/3 of the theoretical limits. The heat pump and air conditioner’s actual schedules and setpoint settings are the significant drivers that explain the EPG. In addition, the presentation of a database of 1128 households provides actual usage behavior parameters for policy-makers to improve the accuracy of building energy forecasting models.

The role of urban residential buildings (URBs) in the carbon reduction goal of China is becoming increasingly important because of the rising energy consumption and carbon emission of such buildings in the region. Considering the increasing spatial interaction of the carbon emission of URBs (URBCE) in the region, this study investigates the influence of climate and economic factors on the URBCE in North and South China. First, the URBCE is calculated by using a decomposition energy balance table based on the carbon emission coefficient of electric and thermal power, thereby improving the estimation of the basic data of URBCE. Second, the influence of economic and climatic factors on the URBCE intensity in 30 provinces of China is explored by using a spatial econometric model. Results show that the URBCE intensity in China had a spatial autocorrelation from 2000 to 2016. Climatic and economic factors have great differences in the degree and direction of influencing the URBCE intensity in the country. Formulating emission reduction policies for climate or economic zones is more scientific and effective than developing national policies. Among these factors, urbanization rate, climate, and GDP per capita have a significant positive impact on the URBCE intensity in the region, whereas other factors have varying degrees of negative impact. In addition, climate, consumption level, and building area have significant spatial spillover effects on URBCE intensity, whereas other factors do not pass the significance test. Relevant conclusions should be given special attention by policymakers.

Social progress and economic development has resulted in the need to focus on the impacts of building microenvironment on the energy consumption in office buildings. The concept of a building’s microenvironment was introduced to understand the local microclimate around a building that is formed by the surrounding urban green spaces, the distribution of roads, and building proximity. For this research, we adopted a regression analysis to quantify the impacts of building microenvironment on energy consumption in office buildings. Taking the government office buildings of Guangdong Province as an example, we measured the building microenvironment through the urban green space density, road density, and number of points of interest (POI) around the buildings. The results showed that when the green space density increased by one unit, the energy consumption in government office buildings was reduced by 0.277%. Moreover, an increase of 1% in road density and in the number of POI increased the energy consumption in government office buildings by 0.288% and 0.048%, respectively. Furthermore, we discussed the heterogeneous impacts of building microenvironment on the energy consumption in government office buildings at varying scale levels. Green space and road density had less impact on the energy consumption in larger buildings, whereas the number of POI had no significant impact on small-scale buildings but did have a significant impact on large-scale buildings. There were also some limitations in the study. The data were limited to government office buildings, and did not include panel data, as well as it lacked building characteristics such as orientation, floor height, and building materials. In addition, it was impossible to evaluate the impacts of meteorological factors such as wind speed and thermal radiation on energy consumption in buildings. Nonetheless, our study demonstrates that energy-aware urban planning and design have the potential to unlock energy efficiency for cities worldwide.