Built environment interventions for emission mitigation: A machine learning analysis of travel-related CO2 in a developing city
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DOI: 10.1016/j.jtrangeo.2023.103632
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Cited by:
- Chun Yin & Yiyi Chen & Bindong Sun, 2024. "Nonlinear relationships of commuting and built environments surrounding residences and workplaces with obesity," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
- Tao, Tao & Zhong, Haotian, 2024. "Income moderates the nonlinear influence of built environment attributes on travel-related carbon emissions," Journal of Transport Geography, Elsevier, vol. 120(C).
- Ma, Xinwei & Tian, Xiaolin & Jin, Zejin & Cui, Hongjun & Ji, Yanjie & Cheng, Long, 2024. "Evaluation and determinants of metro users' regularity: Insights from transit one-card data," Journal of Transport Geography, Elsevier, vol. 118(C).
- Zhao, Chuyun & Tang, Jinjun & Kong, Xiangxin & Yu, Tianjian & Li, Zhitao, 2024. "Emission analysis of multi-mode public transportation based on joint choice model considering built environment factors," Energy, Elsevier, vol. 309(C).
- Yang, Shuo & Zhou, Leyu & Liu, Chang & Sun, Shan & Guo, Liang & Sun, Xiaoli, 2024. "Examining multiscale built environment interventions to mitigate travel-related carbon emissions," Journal of Transport Geography, Elsevier, vol. 119(C).
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Keywords
GHG emissions; Threshold effect; Gradient boosting decision trees (GBDT); Machine learning; Polycentric development; Low-carbon travel;All these keywords.
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