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The Influence of the Built Environment of Neighborhoods on Residents’ Low-Carbon Travel Mode

Author

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  • Caiyun Qian

    (School of Architecture, Nanjing Tech University, Nanjing 211800, China)

  • Yang Zhou

    (School of Architecture, Nanjing Tech University, Nanjing 211800, China)

  • Ze Ji

    (School of Architecture, Nanjing Tech University, Nanjing 211800, China)

  • Qing Feng

    (School of Architecture, Nanjing Tech University, Nanjing 211800, China)

Abstract

Motor vehicle travel is one of the causes of aggravation of CO 2 emission, environmental issues and urban problems. The advocation of low-carbon travel is necessary for the achievement of low-carbon city construction and sustainable development in the future. Many studies have shown that built environment tends to influence residents’ travel behavior, and most studies are demonstrated from the macro level of metropolis. However, from the perspective of neighborhoods, much less attention has been paid, especially in developing countries including China. This study chooses 15 neighborhoods in the main districts of Nanjing in China, taking the location of neighborhoods and residents’ socio-economic attributes into consideration, to examine the effects of residential built environment on residents’ mode choice of different travel types, and to propose the recommended values for the most significant variables. The residential built environment attributes are from three dimensions of land use, road network system and transit facilities. The method of this study is three-step and successive. Primarily, a correlation analysis model is applied to initially examine the role that residents’ socio-economic attributes and residential built environment attributes play on residents’ low-carbon travel of three different travel types respectively. Primary significant attributes from these two aspects are preliminarily screened out for the re-screening in the next step. In addition, the study uses multivariate logit regression modeling approach, with significant socio-economic attributes as concomitant variables, to further re-screen out the key variables of built environment. Furthermore, a unary linear regression model is applied to propose the recommended values for the key built environment variables.

Suggested Citation

  • Caiyun Qian & Yang Zhou & Ze Ji & Qing Feng, 2018. "The Influence of the Built Environment of Neighborhoods on Residents’ Low-Carbon Travel Mode," Sustainability, MDPI, vol. 10(3), pages 1-26, March.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:823-:d:136458
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    References listed on IDEAS

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    Cited by:

    1. Le Tang & Fengqin Zhou & Xueliang Feng & Yali Luo, 2018. "Collective Civic Petitions in Urban Neighborhoods: A Comparative Study between Two Different-Tier Chinese Cities," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    2. Garfield Wayne Hunter & Gideon Sagoe & Daniele Vettorato & Ding Jiayu, 2019. "Sustainability of Low Carbon City Initiatives in China: A Comprehensive Literature Review," Sustainability, MDPI, vol. 11(16), pages 1-37, August.
    3. Xiaohuan Xie & Shiyu Qin & Zhonghua Gou & Ming Yi, 2020. "Can Green Building Promote Pro-Environmental Behaviours? The Psychological Model and Design Strategy," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
    4. Yu, Le & Xie, Binglei & Chan, Edwin H.W., 2019. "Exploring impacts of the built environment on transit travel: Distance, time and mode choice, for urban villages in Shenzhen, China," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 132(C), pages 57-71.

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