Quantifying the heterogeneous impacts of the urban built environment on traffic carbon emissions

Quantifying the heterogeneous impacts of the urban built environment on traffic carbon emissions: New insights from machine learning techniques

Zhi, Danyue (Beijing Jiaotong University; Technische Universität München)
Zhao, Hepeng (China Mobile Group Design Institute Co., Ltd.)
Chen, Yan (Beijing Jiaotong University)
Song, Weize (Tsinghua University)
Song, Dongdong (Beijing Jiaotong University)
Yang, Y. (TU Delft Transport and Planning; Beijing Jiaotong University)

2024

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, we extract 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, we develop 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 has a significant influence on PCCE when its value is between 62% and 78%. Our results also reveal the nonlinear relationships and interactive effects between these features, providing the implications of urban morphological planning and carbon emission reduction.

Heterogeneous impact
Machine learning
Traffic carbon
Urban built environment

http://resolver.tudelft.nl/uuid:1e058c45-670b-48bb-8645-9ad735386b97

https://doi.org/10.1016/j.uclim.2023.101765

2024-05-24

2212-0955

Urban Climate, 53

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2024 Danyue Zhi, Hepeng Zhao, Yan Chen, Weize Song, Dongdong Song, Y. Yang