Influence mechanisms of built environment factors on residential electricity carbon emissions under carbon peaking pressure: a case study of Wuqing District, Tianjin, China.

In light of the "dual carbon" objectives, examining residential electricity carbon emissions (RECE) is crucial for urban low-carbon advancement. This research investigates 146 micro-scale residential zones in Wuqing District, Tianjin, emphasizing the influence of building environmental factors (BEF) on RECE. This study innovatively employs AMI smart meter data from 2021 to 2023 to establish a framework of "spatial statistics-machine learning-mechanism analysis," addressing the constraints of macro-level studies that frequently fail to provide specific policy recommendations. RECE is spatially and temporally depicted in ArcGIS. At the same time, a random forest model is used to assess the relative significance of BEFs, thereby suggesting micro-level residential carbon-reduction planning solutions. Key findings: (1) From 2021 to 2023, residential electricity carbon emission intensity (RECEI) and average annual household electricity carbon emissions (AHECE) demonstrated a trend toward concentrated, contiguous residential units with high building density, indicative of ongoing emissions reduction pressures in residential sectors; (2) High-emission units display notable spatial clustering and diffusion, primarily concentrated in the southern region of the study area, whereas low-carbon units are mainly located in peripheral zones; (3) In terms of driving factors, transportation compactness (TC) is the predominant influence, while adjusted land area (SA) and building density (BD) exert a moderating effect on residential electricity carbon emissions, suggesting that traditional high-intensity development metrics still possess potential for carbon mitigation. This paper proposes targeted low-carbon development strategies for residential electricity consumption based on these findings: optimizing integrated transportation networks, judiciously regulating building density and floor area ratio, systematically planning public service facilities, and augmenting green infrastructure. These optimization pathways are based on the inherent mechanisms linking transportation connectivity, spatial development intensity, and household power-related carbon emissions. They offer a pragmatic foundation for the design and development of low-carbon communities while concurrently facilitating regional carbon-reduction initiatives.