The heterogeneous effects of the built environment on commuting and non-commuting carbon emission: evidence from Wuhan, China

Despite growing attention to urban travel carbon emissions, the differentiated drivers of commuting and non-commuting behaviors remain underexplored. Using Wuhan as a case, this study applies time-geographic theory and interpretable machine learning to examine nonlinear relationships shaping travel-related carbon emissions (TCE) across commuting and non-commuting groups. Results show commuting TCE is primarily influenced by socioeconomic factors such as gender and income, reflecting rigid constraints, while non-commuting TCE is more responsive to built environment features, especially POI density and transit accessibility. A shared low-emission window exists, with optimal POI density (100–400 items/km 2 ) and intersection density (<15/km 2 ). Interaction patterns indicate that built-environment associations vary across socioeconomic groups. The analysis also highlights Wuhan’s spatial duality: morphological polycentrism coexists with functional monocentrism, while non-commuting TCE is more closely associated with community-scale services. These findings suggest the need for differentiated, group-specific low-carbon strategies, contributing new evidence for integrating behavioral heterogeneity into transport and land use policies.