IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v110y2023ics0966692323001047.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692323001047
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2023.103632?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Alex Anas & Richard Arnott & Kenneth A. Small, 1998. "Urban Spatial Structure," Journal of Economic Literature, American Economic Association, vol. 36(3), pages 1426-1464, September.
    2. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    3. Mattioli, Giulio & Philips, Ian & Anable, Jillian & Chatterton, Tim, 2019. "Vulnerability to motor fuel price increases: Socio-spatial patterns in England," Journal of Transport Geography, Elsevier, vol. 78(C), pages 98-114.
    4. Daniel W. Apley & Jingyu Zhu, 2020. "Visualizing the effects of predictor variables in black box supervised learning models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1059-1086, September.
    5. Xu, Yiming & Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2021. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 170-188.
    6. Hui Cheng & David Shaw, 2018. "Polycentric development practice in master planning: the case of China," International Planning Studies, Taylor & Francis Journals, vol. 23(2), pages 163-179, April.
    7. Marlon G. Boarnet & Xize Wang & Douglas Houston, 2017. "Can New Light Rail Reduce Personal Vehicle Carbon Emissions? A Before‐After, Experimental‐Control Evaluation In Los Angeles," Journal of Regional Science, Wiley Blackwell, vol. 57(3), pages 523-539, June.
    8. Ding, Chuan & Cao, Xinyu (Jason) & Næss, Petter, 2018. "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 107-117.
    9. Jiawen Yang & Steven French & James Holt & Xingyou Zhang, 2012. "Measuring the Structure of U.S. Metropolitan Areas, 1970–2000," Journal of the American Planning Association, Taylor & Francis Journals, vol. 78(2), pages 197-209.
    10. Yuanqing Wang & Liu Yang & Sunsheng Han & Chao Li & T. V. Ramachandra, 2017. "Urban CO2 emissions in Xi’an and Bangalore by commuters: implications for controlling urban transportation carbon dioxide emissions in developing countries," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(7), pages 993-1019, October.
    11. Ron N. Buliung & Pavlos S. Kanaroglou, 2006. "Urban Form and Household Activity‐Travel Behavior," Growth and Change, Wiley Blackwell, vol. 37(2), pages 172-199, June.
    12. Brand, Christian & Goodman, Anna & Rutter, Harry & Song, Yena & Ogilvie, David, 2013. "Associations of individual, household and environmental characteristics with carbon dioxide emissions from motorised passenger travel," Applied Energy, Elsevier, vol. 104(C), pages 158-169.
    13. Lee, Sungwon & Lee, Bumsoo, 2014. "The influence of urban form on GHG emissions in the U.S. household sector," Energy Policy, Elsevier, vol. 68(C), pages 534-549.
    14. Marlon G. Boarnet & Xize Wang, 2019. "Urban spatial structure and the potential for vehicle miles traveled reduction: the effects of accessibility to jobs within and beyond employment sub-centers," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(2), pages 381-404, April.
    15. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
    16. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.
    17. Tan, Jijun & Xiao, Junji & Zhou, Xiaolan, 2019. "Market equilibrium and welfare effects of a fuel tax in China: The impact of consumers' response through driving patterns," Journal of Environmental Economics and Management, Elsevier, vol. 93(C), pages 20-43.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).
    3. 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).
    4. 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).
    5. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
    2. Gao, Kun & Yang, Ying & Gil, Jorge & Qu, Xiaobo, 2023. "Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility," Journal of Transport Geography, Elsevier, vol. 110(C).
    3. Ding, Chuan & Cao, Xinyu & Yu, Bin & Ju, Yang, 2021. "Non-linear associations between zonal built environment attributes and transit commuting mode choice accounting for spatial heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 22-35.
    4. Hu, Songhua & Xiong, Chenfeng & Chen, Peng & Schonfeld, Paul, 2023. "Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    5. Yang, Hongtai & Luo, Peng & Li, Chaojing & Zhai, Guocong & Yeh, Anthony G.O., 2023. "Nonlinear effects of fare discounts and built environment on ridesplitting adoption rates," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    6. Liu, Jixiang & Wang, Bo & Xiao, Longzhu, 2021. "Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach," Journal of Transport Geography, Elsevier, vol. 92(C).
    7. LEE, Sungwon & LEE, Bumsoo, 2020. "Comparing the impacts of local land use and urban spatial structure on household VMT and GHG emissions," Journal of Transport Geography, Elsevier, vol. 84(C).
    8. Tao, Tao & Cao, Jason, 2022. "Examining motivations for owning autonomous vehicles: Implications for land use and transportation," Journal of Transport Geography, Elsevier, vol. 102(C).
    9. Zhang, Xiaojian & Zhao, Xilei, 2022. "Machine learning approach for spatial modeling of ridesourcing demand," Journal of Transport Geography, Elsevier, vol. 100(C).
    10. Ashik, F.R. & Sreezon, A.I.Z. & Rahman, M.H. & Zafri, N.M. & Labib, S.M., 2024. "Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach," Journal of Transport Geography, Elsevier, vol. 116(C).
    11. Tao, Tao & Cao, Jason, 2023. "Exploring nonlinear and collective influences of regional and local built environment characteristics on travel distances by mode," Journal of Transport Geography, Elsevier, vol. 109(C).
    12. Li, Wu & Zhao, Shengchuan & Ma, Jingwen & Nielsen, Otto Anker & Jiang, Yu, 2023. "Book-ahead ride-hailing trip and its determinants: Findings from large-scale trip records in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    13. Liu Yang & Yuanqing Wang & Yujun Lian & Zhongming Guo & Yuanyuan Liu & Zhouhao Wu & Tieyue Zhang, 2022. "Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China," IJERPH, MDPI, vol. 19(21), pages 1-14, October.
    14. Laviolette, Jérôme & Morency, Catherine & Waygood, E.O.D., 2022. "A kilometer or a mile? Does buffer size matter when it comes to car ownership?," Journal of Transport Geography, Elsevier, vol. 104(C).
    15. Su, Shiliang & Wang, Zhuolun & Li, Bozhao & Kang, Mengjun, 2022. "Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications," Journal of Transport Geography, Elsevier, vol. 104(C).
    16. Yin, Chun & Cao, Jason & Sun, Bindong & Liu, Jiahang, 2023. "Exploring built environment correlates of walking for different purposes: Evidence for substitution," Journal of Transport Geography, Elsevier, vol. 106(C).
    17. Pu Lyu & Yongjie Lin & Yuanqing Wang, 2019. "The impacts of household features on commuting carbon emissions: a case study of Xi’an, China," Transportation, Springer, vol. 46(3), pages 841-857, June.
    18. Tao, Tao & Cao, Jason, 2024. "Ineffective built environment interventions: How to reduce driving in American suburbs?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    19. 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).
    20. Haseeb, Attiya & Mitra, Raktim, 2023. "Do environmentally sustainable travel behaviours contribute to transportation-related social exclusion?," Journal of Transport Geography, Elsevier, vol. 113(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jotrge:v:110:y:2023:i:c:s0966692323001047. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.