The configuration of the urban built environment is critical for promoting sustainability and achieving carbon neutrality. However, existing studies mostly use linear and spatial econometric models to investigate the relationship between urban built environments and traffic carbon dioxide (CO2) emissions; in-depth studies exploring the heterogeneous impacts of related features on traffic CO2 emission by interpretive machine learning models are scarce.
Hence, new research extracts four dimensionless features to depict the size, compactness, irregularity, and isolation of built-up areas and road network-related features (i.e., average cluster coefficient, road topological density, and road geometric density), respectively. Subsequently, the researchers developed an interpretive machine-learning framework based on the extracted features related to the urban built-up areas and road networks.
The interpretive results of the proposed framework uncover that urban morphological features, especially population density (POP), GDP per capita (GDPpc), and urban physical compactness (UPC), have a heterogeneous impact on the per capita traffic emission (PCCE) across different cities. GDPpc is more like a linear relationship with PCCE, and UPC significantly influences PCCE when its value is between 62% and 78%. The results also reveal the nonlinear relationships and interactive effects between these features, providing the implications of urban morphological planning and carbon emission reduction.