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Impact of Residential Self-Selection on Low-Carbon Behavior: Evidence from Zhengzhou, China

Author

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  • Jingfei Zhang

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475004, China)

  • Lijun Zhang

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475004, China)

  • Yaochen Qin

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475004, China)

  • Xia Wang

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475004, China)

  • Zhicheng Zheng

    (Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Kaifeng 475004, China
    College of Environment and Planning, Henan University, Kaifeng 475004, China)

Abstract

Current resident lifestyles pose a significant threat to urban sustainable development. Therefore, low-carbon behavior is receiving increasing attention from scholars and policy makers. Ascertaining residential self-selection is essential in order to study the relationship between the built environment and travel behavior. While several studies have explored the relationship between the urban form, socioeconomic factors, and travel behavior, only a few of them have studied the impact of self-selection on household energy consumption and other forms of consumption, which are also contribute to household carbon emissions. Using large-scale field surveys of 1,485 households and high-resolution images, sourced from Google Maps in 2018, of Zhengzhou city, the present study estimated the low-carbon level of three kinds of behavior: daily energy use at home, daily travel, and daily consumption. The study investigated the influence factors on low-carbon behavior using the hierarchical linear model. We found that residential self-selection impacts both energy use and daily travel. Residents in some built environments consumed less energy at home and contributed less CO 2 emissions through daily travel than others. In particular, individual-level variables significantly affected the low-carbon energy use behavior. The female, elderly, highly educated, married, and working-class residents with children had higher levels of low-carbon energy use. Community-level variables significantly affected the level of low-carbon travel and low-carbon consumption. If residents lived in areas with high density, mixed land use, and high accessibility, their travel mode and consumption behavior would entail low carbon emissions. There is a relationship between individual variables and community variables. Different individual attributes living in the same built environment have different impacts on low-carbon behaviors.

Suggested Citation

  • Jingfei Zhang & Lijun Zhang & Yaochen Qin & Xia Wang & Zhicheng Zheng, 2019. "Impact of Residential Self-Selection on Low-Carbon Behavior: Evidence from Zhengzhou, China," Sustainability, MDPI, vol. 11(23), pages 1-17, December.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6871-:d:293669
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    References listed on IDEAS

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    1. Abdul Pinjari & Ram Pendyala & Chandra Bhat & Paul Waddell, 2007. "Modeling residential sorting effects to understand the impact of the built environment on commute mode choice," Transportation, Springer, vol. 34(5), pages 557-573, 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. Ding, Zhihua & Wang, Guangqiang & Liu, Zhenhua & Long, Ruyin, 2017. "Research on differences in the factors influencing the energy-saving behavior of urban and rural residents in China–A case study of Jiangsu Province," Energy Policy, Elsevier, vol. 100(C), pages 252-259.
    4. Mokhtarian, Patricia L. & Cao, Xinyu, 2008. "Examining the impacts of residential self-selection on travel behavior: A focus on methodologies," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 204-228, March.
    5. Patricia L. Mokhtarian & Michael N. Bagley, 2002. "The impact of residential neighborhood type on travel behavior: A structural equations modeling approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 36(2), pages 279-297.
    6. Ana Ramos & Xavier Labandeira & Andreas Löschel, 2016. "Pro-environmental Households and Energy Efficiency in Spain," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(2), pages 367-393, February.
    7. Poruschi, Lavinia & Ambrey, Christopher L., 2016. "On the confluence of city living, energy saving behaviours and direct residential energy consumption," Environmental Science & Policy, Elsevier, vol. 66(C), pages 334-343.
    8. Mills, Bradford & Schleich, Joachim, 2010. "What's driving energy efficient appliance label awareness and purchase propensity?," Energy Policy, Elsevier, vol. 38(2), pages 814-825, February.
    9. Jarass, Julia & Scheiner, Joachim, 2018. "Residential self-selection and travel mode use in a new inner-city development neighbourhood in Berlin," Journal of Transport Geography, Elsevier, vol. 70(C), pages 68-77.
    10. Lin, Tao & Wang, Donggen & Guan, Xiaodong, 2017. "The built environment, travel attitude, and travel behavior: Residential self-selection or residential determination?," Journal of Transport Geography, Elsevier, vol. 65(C), pages 111-122.
    11. Daniel G Chatman, 2009. "Residential Choice, the Built Environment, and Nonwork Travel: Evidence Using New Data and Methods," Environment and Planning A, , vol. 41(5), pages 1072-1089, May.
    12. Tim Schwanen & Patricia L. Mokhtarian, 2007. "Attitudes toward travel and land use and choice of residential neighborhood type: Evidence from the San Francisco bay area," Housing Policy Debate, Taylor & Francis Journals, vol. 18(1), pages 171-207, January.
    13. Chaudhury, Habib & Campo, Michael & Michael, Yvonne & Mahmood, Atiya, 2016. "Neighbourhood environment and physical activity in older adults," Social Science & Medicine, Elsevier, vol. 149(C), pages 104-113.
    14. Bai, Yin & Liu, Yong, 2013. "An exploration of residents’ low-carbon awareness and behavior in Tianjin, China," Energy Policy, Elsevier, vol. 61(C), pages 1261-1270.
    15. Xinyu (Jason) Cao & Patricia L. Mokhtarian & Susan L. Handy, 2008. "Examining the Impacts of Residential Self‐Selection on Travel Behaviour: A Focus on Empirical Findings," Transport Reviews, Taylor & Francis Journals, vol. 29(3), pages 359-395, October.
    16. Gadenne, David & Sharma, Bishnu & Kerr, Don & Smith, Tim, 2011. "The influence of consumers' environmental beliefs and attitudes on energy saving behaviours," Energy Policy, Elsevier, vol. 39(12), pages 7684-7694.
    17. 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.
    18. Cao, Xinyu & Mokhtarian, Patricia & Handy, Susan, 2008. "Examining The Impacts of Residential Self-Selection on Travel Behavior: Methodologies and Empirical Findings," Institute of Transportation Studies, Working Paper Series qt08x1k476, Institute of Transportation Studies, UC Davis.
    19. Wendy Bohte & Kees Maat & Bert van Wee, 2009. "Measuring Attitudes in Research on Residential Self‐Selection and Travel Behaviour: A Review of Theories and Empirical Research," Transport Reviews, Taylor & Francis Journals, vol. 29(3), pages 325-357, February.
    20. Donggen Wang & Tao Lin, 2019. "Built environment, travel behavior, and residential self-selection: a study based on panel data from Beijing, China," Transportation, Springer, vol. 46(1), pages 51-74, February.
    21. Combs, Tabitha S. & Rodríguez, Daniel A., 2014. "Joint impacts of Bus Rapid Transit and urban form on vehicle ownership: New evidence from a quasi-longitudinal analysis in Bogotá, Colombia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 272-285.
    22. Xinyu (Jason) Cao, 2009. "Disentangling the influence of neighborhood type and self-selection on driving behavior: an application of sample selection model," Transportation, Springer, vol. 36(2), pages 207-222, March.
    23. Yang, Shu & Zhang, Yanbing & Zhao, Dingtao, 2016. "Who exhibits more energy-saving behavior in direct and indirect ways in china? The role of psychological factors and socio-demographics," Energy Policy, Elsevier, vol. 93(C), pages 196-205.
    24. von Borgstede, Chris & Andersson, Maria & Johnsson, Filip, 2013. "Public attitudes to climate change and carbon mitigation—Implications for energy-associated behaviours," Energy Policy, Elsevier, vol. 57(C), pages 182-193.
    25. Brounen, Dirk & Kok, Nils & Quigley, John M., 2013. "Energy literacy, awareness, and conservation behavior of residential households," Energy Economics, Elsevier, vol. 38(C), pages 42-50.
    26. Kroesen, Maarten & Handy, Susan & Chorus, Caspar, 2017. "Do attitudes cause behavior or vice versa? An alternative conceptualization of the attitude-behavior relationship in travel behavior modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 190-202.
    27. Li, Jun & Zhang, Dayong & Su, Bin, 2019. "The Impact of Social Awareness and Lifestyles on Household Carbon Emissions in China," Ecological Economics, Elsevier, vol. 160(C), pages 145-155.
    28. Cao, Xinyu (Jason) & Xu, Zhiyi & Fan, Yingling, 2010. "Exploring the connections among residential location, self-selection, and driving: Propensity score matching with multiple treatments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 797-805, December.
    29. Nejat, Payam & Jomehzadeh, Fatemeh & Taheri, Mohammad Mahdi & Gohari, Mohammad & Abd. Majid, Muhd Zaimi, 2015. "A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 843-862.
    30. Zang, Peng & Lu, Yi & Ma, Jing & Xie, Bo & Wang, Ruoyu & Liu, Ye, 2019. "Disentangling residential self-selection from impacts of built environment characteristics on travel behaviors for older adults," Social Science & Medicine, Elsevier, vol. 238(C), pages 1-1.
    31. Schwanen, Tim & Mokhtarian, Patricia L., 2005. "What Affects Commute Mode Choice: Neighborhood Physical Structure or Preferences Toward Neighborhoods?," University of California Transportation Center, Working Papers qt4nq9r1c9, University of California Transportation Center.
    32. Joachim Scheiner & Christian Holz-Rau, 2007. "Travel mode choice: affected by objective or subjective determinants?," Transportation, Springer, vol. 34(4), pages 487-511, July.
    33. Thøgersen, John & Grønhøj, Alice, 2010. "Electricity saving in households--A social cognitive approach," Energy Policy, Elsevier, vol. 38(12), pages 7732-7743, December.
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