Urban residential buildings make large contributions to energy consumption. Energy consumption per square meter is most widely used to measure energy efficiency in urban residential buildings. This study aims to explore whether it is an appropriate indicator. An extended STIRPAT model was used based on the survey data from 867 households. Here we present that building area per household has a dilution effect on energy consumption per square meter. Neglecting this dilution effect leads to a significant overestimation of the effectiveness of building energy savings standards. Further analysis suggests that the peak of energy consumption per square meter in China’s urban residential buildings occurred in 2012 when accounting for the dilution effect, which is 11 years later than it would have occurred without considering the dilution effect. Overall, overlooking the dilution effect may lead to misleading judgments of crucial energy-saving policy tools, as well as the ongoing trend of residential energy consumption in China.

The accelerated flow of resources and the population has resulted in the coexistence of the expansion and shrinkage of cities. The shrinking city is not a new thing, but it is a new problem that needs to be solved urgently in China. Urban resources reveal the direct cause of the shrinking city: attraction, which reflects the competition for external resources and the endowment of the intrinsic resources of cities. Therefore, this paper established the Urban Resource Degree (URD) model to measure the urban resource degree of the shrinking city. Factors were then selected to analyze the ways in which they influence the shrinking cities. Given the spillover effect and heterogeneity of the influencing factors, a Spatial Durbin Model (SDM) and a Spatiotemporal Geographically Weighted Regression Model (GTWR) were used to conduct a spatial spillover (SSA) and spatial heterogeneity (SHA) analysis of the URD of the shrinking cities in Northeast China. The results show that the ability gap to compete for and control resources between prefecture-level shrinking cities and county-level shrinking cities is narrowed. From the SSA and SHA perspectives, the influence factors can be categorized into three types: “High West–Low East and Negative–Positive Spillover”, “Low West and High East Positive–Negative Spillover”, “Low Northwest and High Southeast Negative Spillover”. Finally, policy implications are proposed to provide support for policy-making.