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Built environment interventions for emission mitigation: A machine learning analysis of travel-related CO2 in a developing city

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  • Shao, Qifan
  • Zhang, Wenjia
  • Cao, Xinyu (Jason)
  • Yang, Jiawen

Abstract

The transport sector accounts for more than one-fifth of global CO2 emissions. Reducing fossil fuel consumption and travel-related CO2 emissions (TCE) is a major approach to mitigating global climate change. Urban planners worldwide propose to promote low-carbon travel by changing the built environment. Therefore, understanding the relationships between built environment variables and TCE is key to the development of land use and transportation policies. Using 2019 regional household travel data from Zhongshan, a polycentric urban area in China, this study developed a gradient boosting decision trees model to estimate the relative importance of built environment variables in predicting TCE and their nonlinear associations with TCE. Built environment variables collectively contribute nearly half of the predictive power to predicting TCE, suggesting the potential of built environment interventions. Among them, location accessibility to city-level and township-level centers and population density are the top-three important features in predicting TCE. Furthermore, most built environment variables show threshold relationships with TCE. The results suggest that polycentric development, intensification of town centers, and densification of street networks are conducive to TCE mitigation. These findings inform planners of effective ranges of built environment variables to promote low-carbon travel.

Suggested Citation

  • Shao, Qifan & Zhang, Wenjia & Cao, Xinyu (Jason) & Yang, Jiawen, 2023. "Built environment interventions for emission mitigation: A machine learning analysis of travel-related CO2 in a developing city," Journal of Transport Geography, Elsevier, vol. 110(C).
    • Handle: RePEc:eee:jotrge:v:110:y:2023:i:c:s0966692323001047
    DOI: 10.1016/j.jtrangeo.2023.103632
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