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Does Haze Drive Pro-Environmental and Energy Conservation Behaviors? Evidence from the Beijing-Tianjin-Hebei Area in China

Author

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  • Reeko Watanabe

    (School of Regional Design, Utsunomiya University, 7-1-2 Yoto, Utsunomiya City, Tochigi 321-0904, Japan)

  • Tsunemi Watanabe

    (School of Economics and Management, Kochi University of Technology, 2-22 Eikokuji, Kochi City, Kochi 782-0003, Japan)

Abstract

Humans conduct themselves in relation to energy use; energy use has degraded air quality, as reflected by haze occurrence in countries such as China. Improving the population’s involvement in environmental and energy conservation necessitates understanding their motivation to behave under haze. Considering the social problems caused by haze conditions in China, this study used people’s risk perception as a basis to determine their motivations to perform pro-environmental and energy-saving behaviors. We analyzed motivation from privately and publicly oriented perspectives as well as adaptive and mitigative behavioral viewpoints. Motivation-related data were collected through face-to-face discussion and a survey of 506 respondents in the Beijing-Tianjin-Hebei area, which is one of the most heavily polluted regions in China. We conducted multiple regression analysis to determine the extent to which socio-demographic characteristics and risk perception concerning haze predict motivation and actual behavior. Results showed that these factors explain 36.8% and 30.5% of privately and publicly oriented motivations, respectively, but more strongly explain more adaptive (i.e., privately oriented; 55.0%) than mitigating (i.e., publicly oriented; 8.8%) behaviors. Although the residents are motivated to behave equally for private and public purposes in initial conservation efforts, they tend to exhibit adaptive behavior more frequently than mitigating behaviors. These results serve as a reference in encouraging China’s residents to act pro-environmentally and use energy conservatively, thereby contributing to environmental and energy saving education for the society.

Suggested Citation

  • Reeko Watanabe & Tsunemi Watanabe, 2020. "Does Haze Drive Pro-Environmental and Energy Conservation Behaviors? Evidence from the Beijing-Tianjin-Hebei Area in China," Sustainability, MDPI, vol. 12(23), pages 1-17, November.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:9972-:d:452959
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    as
    1. Peng Cheng & Jiuchang Wei & Yue Ge, 2017. "Who should be blamed? The attribution of responsibility for a city smog event in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(2), pages 669-689, January.
    2. Barr, Stewart & Gilg, Andrew W & Ford, Nicholas, 2005. "The household energy gap: examining the divide between habitual- and purchase-related conservation behaviours," Energy Policy, Elsevier, vol. 33(11), pages 1425-1444, July.
    3. Rajagopal, 2015. "Reasoned Action and Planned Behavior," Palgrave Macmillan Books, in: The Butterfly Effect in Competitive Markets, chapter 2, pages 30-65, Palgrave Macmillan.
    4. Jianhua Xu & Cheryl S.F. Chi & Kejun Zhu, 2017. "Concern or apathy: the attitude of the public toward urban air pollution," Journal of Risk Research, Taylor & Francis Journals, vol. 20(4), pages 482-498, April.
    5. Susan J. Elliott & Donald C. Cole & Paul Krueger & Nancy Voorberg & Sarah Wakefield, 1999. "The Power of Perception: Health Risk Attributed to Air Pollution in anUrban Industrial Neighbourhood," Risk Analysis, John Wiley & Sons, vol. 19(4), pages 621-634, August.
    6. Hori, Shiro & Kondo, Kayoko & Nogata, Daisuke & Ben, Han, 2013. "The determinants of household energy-saving behavior: Survey and comparison in five major Asian cities," Energy Policy, Elsevier, vol. 52(C), pages 354-362.
    7. Liu, Zhaoyang & Hanley, Nick & Campbell, Danny, 2020. "Linking urban air pollution with residents’ willingness to pay for greenspace: A choice experiment study in Beijing," Journal of Environmental Economics and Management, Elsevier, vol. 104(C).
    8. Maria Kopsakangas-Savolainen & Artti Juutinen, 2013. "Energy consumption and savings: A survey-based study of Finnish households," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 2(1), pages 71-92, March.
    9. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    10. Michael K. Lindell & David J. Whitney, 2000. "Correlates of Household Seismic Hazard Adjustment Adoption," Risk Analysis, John Wiley & Sons, vol. 20(1), pages 13-26, February.
    11. Osberghaus, Daniel & Finkel, Elyssa & Pohl, Max, 2010. "Individual Adaptation to Climate Change: The Role of Information and Perceived Risk," MPRA Paper 26569, University Library of Munich, Germany.
    12. Ke Wang & Yingnan Liu, 2014. "Can Beijing fight with haze? Lessons can be learned from London and Los Angeles," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 72(2), pages 1265-1274, June.
    13. L. Smillie & A. Blissett, 2010. "A model for developing risk communication strategy," Journal of Risk Research, Taylor & Francis Journals, vol. 13(1), pages 115-134, January.
    14. Silver, William S. & Mitchell, Terence R. & Gist, Marilyn E., 1995. "Responses to Successful and Unsuccessful Performance: The Moderating Effect of Self-Efficacy on the Relationship between Performance and Attributions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 62(3), pages 286-299, June.
    15. Šćepanović, Sanja & Warnier, Martijn & Nurminen, Jukka K., 2017. "The role of context in residential energy interventions: A meta review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1146-1168.
    16. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    17. Sütterlin, Bernadette & Brunner, Thomas A. & Siegrist, Michael, 2011. "Who puts the most energy into energy conservation? A segmentation of energy consumers based on energy-related behavioral characteristics," Energy Policy, Elsevier, vol. 39(12), pages 8137-8152.
    18. Jiuchang Wei & Weiwei Zhu & Dora Marinova & Fei Wang, 2017. "Household adoption of smog protective behavior: a comparison between two Chinese cities," Journal of Risk Research, Taylor & Francis Journals, vol. 20(7), pages 846-867, July.
    19. Wang, Lingling & Watanabe, Tsunemi, 2019. "Effects of environmental policy on public risk perceptions of haze in Tianjin City: A difference-in-differences analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 199-212.
    20. Qing Tian & Jennifer L. Robertson, 2019. "How and When Does Perceived CSR Affect Employees’ Engagement in Voluntary Pro-environmental Behavior?," Journal of Business Ethics, Springer, vol. 155(2), pages 399-412, March.
    21. Terje Aven & Ortwin Renn, 2010. "Risk Management and Governance," Risk, Governance and Society, Springer, number 978-3-642-13926-0, March.
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