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Exploring the Spatial Heterogeneity and Influence Factors of Daily Travel Carbon Emissions in Metropolitan Areas: From the Perspective of the 15-min City

Author

Listed:
  • Liang Guo

    (School of Architecture & Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    The Key Laboratory of Urban Simulation for Ministry of Natural Resources, Wuhan 430074, China)

  • Wenjun Cheng

    (School of Architecture & Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    The Key Laboratory of Urban Simulation for Ministry of Natural Resources, Wuhan 430074, China)

  • Chang Liu

    (School of Architecture & Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    The Key Laboratory of Urban Simulation for Ministry of Natural Resources, Wuhan 430074, China)

  • Qinghao Zhang

    (School of Architecture & Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    The Key Laboratory of Urban Simulation for Ministry of Natural Resources, Wuhan 430074, China)

  • Shuo Yang

    (School of Architecture & Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    The Key Laboratory of Urban Simulation for Ministry of Natural Resources, Wuhan 430074, China)

Abstract

Most of the residents’ daily travel is concentrated within their 15-min walking distance. In China, derived from the 15-min city concept, the 15-min walkable area is often referred to as the 15-min pedestrian-scale neighborhood, and it has become a basic planning unit. Understanding the factors that influence the built environment of the 15-min pedestrian-scale neighborhood on the residents’ daily travel carbon emissions is critical to reduce urban carbon emissions. There may be spatial heterogeneity in daily travel carbon emissions as a dependent variable due to the spatial heterogeneity of built environment factors. Therefore, this study used data from the Wuhan City Resident Travel Survey to describe the spatial pattern of daily travel carbon emissions among Wuhan residents. The study examined the spatial heterogeneity of daily travel carbon emissions and explored the spatial differentiation of the built environment’s impact on daily travel carbon emissions within the 15-min pedestrian-scale neighborhood of the residents using spatial autocorrelation analysis and multi-scale geo-weighted regression (MGWR). The results indicate that Wuhan residents’ daily travel carbon emissions show an increasing circle structure from the center outward. In general, built environment elements in the 15-min pedestrian-scale neighborhood are closely related to the daily travel carbon emissions, and the direction and degree of impact of the built environment varies spatially. This study provides empirical evidence for controlling transportation carbon emissions.

Suggested Citation

  • Liang Guo & Wenjun Cheng & Chang Liu & Qinghao Zhang & Shuo Yang, 2023. "Exploring the Spatial Heterogeneity and Influence Factors of Daily Travel Carbon Emissions in Metropolitan Areas: From the Perspective of the 15-min City," Land, MDPI, vol. 12(2), pages 1-22, January.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:2:p:299-:d:1042325
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    References listed on IDEAS

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    1. Ding, Chuan & Cao, Xinyu & Wang, Yunpeng, 2018. "Synergistic effects of the built environment and commuting programs on commute mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 104-118.
    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. Wang, Dongeen & Lin, Tao, 2014. "Residential self-selection, built environment, and travel behavior in the Chinese context," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(3), pages 5-14.
    4. Brownstone, David & Golob, Thomas F., 2009. "The impact of residential density on vehicle usage and energy consumption," Journal of Urban Economics, Elsevier, vol. 65(1), pages 91-98, January.
    5. Peng Zang & Hualong Qiu & Fei Xian & Linchuan Yang & Yanan Qiu & Hongxu Guo, 2022. "Nonlinear Effects of the Built Environment on Light Physical Activity among Older Adults: The Case of Lanzhou, China," IJERPH, MDPI, vol. 19(14), pages 1-15, July.
    6. Cao, Xinyu (Jason) & Mokhtarian, Patricia L. & Handy, Susan L., 2009. "The relationship between the built environment and nonwork travel: A case study of Northern California," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(5), pages 548-559, June.
    7. J. Keith Ord & Arthur Getis, 2001. "Testing for Local Spatial Autocorrelation in the Presence of Global Autocorrelation," Journal of Regional Science, Wiley Blackwell, vol. 41(3), pages 411-432, August.
    8. Cervero, Robert B., 2013. "Linking urban transport and land use in developing countries," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(1), pages 7-24.
    9. Chen Li & Heng Li & Xionghe Qin, 2022. "Spatial Heterogeneity of Carbon Emissions and Its Influencing Factors in China: Evidence from 286 Prefecture-Level Cities," IJERPH, MDPI, vol. 19(3), pages 1-29, January.
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