Data-Driven Multiscale Analysis of Household Electricity Carbon Emissions in High-Density Residential Area for Low-Carbon Urban Planning

Household electricity consumption is a major and growing source of urban carbon emissions in high-density residential area. However, its influencing mechanisms operate across multiple nested spatial scales and are not fully captured by single-scale analyses. Using electricity consumption data and a household survey from 42 high-density communities in Beijing, this study applies a hierarchical linear model (HLM) to examine how spatial form, socioeconomic attributes, and behavioral factors affect household electricity carbon emissions across urban districts, Ten-Minute Living Circles, residential areas, and Individual dwellings. The results indicate that dwelling-level characteristics exert the most direct influence, while residential-area-level spatial morphology provides an important contextual effect. In contrast, Ten-Minute Living Circles indicators show limited direct associations after accounting for hierarchical structure. Several spatial factors influence emissions through cross-level moderating mechanisms rather than isolated effects. These findings provide a data-driven multiscale analytical framework to support low-carbon planning and retrofit strategies in high-density residential area.