IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v183y2022ics0040162522004772.html
   My bibliography  Save this article

Multi-group analysis on the mechanism of residents' low-carbon behaviors in Beijing, China

Author

Listed:
  • Wang, Chao
  • Zhan, Jinyan
  • Wang, Huihui
  • Yang, Zheng
  • Chu, Xi
  • Liu, Wei
  • Teng, Yanmin
  • Liu, Huizi
  • Wang, Yifan

Abstract

Understanding the factors that influence low-carbon behaviors can help policy makers formulate and implement low-carbon regulations and policies. Limited research has been conducted on the mechanisms that influence low-carbon behaviors for multi-groups with different demographic factors. To address this research gap, a questionnaire survey was carried out in Beijing, China. A hypothetical structural model of low-carbon behaviors was built, and a structural equation modeling was used to verify the hypotheses. The main results are as follows. Firstly, the residents of the study area have relatively high levels of low-carbon attitudes and behaviors. The average scores of the items ranged from 3.31 to 4.72, and those of the internal factors were higher than those of external factors and behaviors. Secondly, the proposed hypothetical structural model is valid, and all of the eight hypotheses were accepted. Personal and social norms have significant, positive, and direct effects on both private and public low-carbon behaviors. Thirdly, demographic factors (i.e., gender, age, family size, marital status, education, income, and owning private car) play an important role in the mechanisms that influence low-carbon behaviors. Specific policy implications were proposed, which can contribute to the promotion of low-carbon behaviors and the achievement of low-carbon development.

Suggested Citation

  • Wang, Chao & Zhan, Jinyan & Wang, Huihui & Yang, Zheng & Chu, Xi & Liu, Wei & Teng, Yanmin & Liu, Huizi & Wang, Yifan, 2022. "Multi-group analysis on the mechanism of residents' low-carbon behaviors in Beijing, China," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:tefoso:v:183:y:2022:i:c:s0040162522004772
    DOI: 10.1016/j.techfore.2022.121956
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162522004772
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2022.121956?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Geng, Jichao & Long, Ruyin & Chen, Hong & Li, Wenbo, 2017. "Exploring the motivation-behavior gap in urban residents’ green travel behavior: A theoretical and empirical study," Resources, Conservation & Recycling, Elsevier, vol. 125(C), pages 282-292.
    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. Asensio, Omar Isaac & Delmas, Magali A., 2016. "The dynamics of behavior change: Evidence from energy conservation," Journal of Economic Behavior & Organization, Elsevier, vol. 126(PA), pages 196-212.
    4. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    5. Yongliang Yang & Yiyang Guo & Suqing Luo, 2020. "Consumers’ Intention and Cognition for Low-Carbon Behavior: A Case Study of Hangzhou in China," Energies, MDPI, vol. 13(21), pages 1-19, November.
    6. Zhang, Fan & Deng, Xiangzheng & Phillips, Fred & Fang, Chuanglin & Wang, Chao, 2020. "Impacts of industrial structure and technical progress on carbon emission intensity: Evidence from 281 cities in China," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    7. Wan, Bingyue & Tian, Lixin & Zhu, Naiping & Gu, Liqin & Zhang, Guangyong, 2018. "A new endogenous growth model for green low-carbon behavior and its comprehensive effects," Applied Energy, Elsevier, vol. 230(C), pages 1332-1346.
    8. Andrew D. Grotzinger & Mijke Rhemtulla & Ronald Vlaming & Stuart J. Ritchie & Travis T. Mallard & W. David Hill & Hill F. Ip & Riccardo E. Marioni & Andrew M. McIntosh & Ian J. Deary & Philipp D. Koel, 2019. "Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits," Nature Human Behaviour, Nature, vol. 3(5), pages 513-525, May.
    9. 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.
    10. 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.
    11. Vainio, Annukka & Paloniemi, Riikka, 2014. "The complex role of attitudes toward science in pro-environmental consumption in the Nordic countries," Ecological Economics, Elsevier, vol. 108(C), pages 18-27.
    12. Jin, Gui & Guo, Baishu & Deng, Xiangzheng, 2020. "Is there a decoupling relationship between CO2 emission reduction and poverty alleviation in China?," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    13. Claudia F. Nisa & Jocelyn J. Bélanger & Birga M. Schumpe & Daiane G. Faller, 2019. "Meta-analysis of randomised controlled trials testing behavioural interventions to promote household action on climate change," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    14. Girod, Bastien & Mayer, Sebastian & Nägele, Florian, 2017. "Economic versus belief-based models: Shedding light on the adoption of novel green technologies," Energy Policy, Elsevier, vol. 101(C), pages 415-426.
    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. Zhan, Jinyan & Wang, Chao & Wang, Huihui & Zhang, Fan & Li, Zhihui, 2024. "Pathways to achieve carbon emission peak and carbon neutrality by 2060: A case study in the Beijing-Tianjin-Hebei region, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Tan, Yang & Ying, Xiaoyu & Ge, Jian & Gao, Weijun & Zhang, Li & Wang, Shuai, 2024. "Driving role of perceived psychological factors in households’ low-carbon behaviors: A study based on the Chinese household carbon generalized system of preferences," Energy, Elsevier, vol. 303(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. Chun-Hsi Vivian Chen & Yu-Cheng Chen, 2021. "Assessment of Enhancing Employee Engagement in Energy-Saving Behavior at Workplace: An Empirical Study," Sustainability, MDPI, vol. 13(5), pages 1-18, February.
    2. Zhang, Wenqing & Liu, Liangliang, 2022. "Exploring non-users' intention to adopt ride-sharing services: Taking into account increased risks due to the COVID-19 pandemic among other factors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 180-195.
    3. Nieves García-de-Frutos & José Manuel Ortega-Egea & Javier Martínez-del-Río, 2018. "Anti-consumption for Environmental Sustainability: Conceptualization, Review, and Multilevel Research Directions," Journal of Business Ethics, Springer, vol. 148(2), pages 411-435, March.
    4. Pothitou, Mary & Hanna, Richard F. & Chalvatzis, Konstantinos J., 2016. "Environmental knowledge, pro-environmental behaviour and energy savings in households: An empirical study," Applied Energy, Elsevier, vol. 184(C), pages 1217-1229.
    5. Jing Wu & Shen Wang & Yuling Liu & Xuesong Xie & Siyi Wang & Lianhong Lv & Hong Luo, 2023. "Measurement of Tourism-Related CO 2 Emission and the Factors Influencing Low-Carbon Behavior of Tourists: Evidence from Protected Areas in China," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    6. Bin Wang & Jionghua Li & Ao Sun & Yongming Wang & Dianting Wu, 2019. "Residents’ Green Purchasing Intentions in a Developing-Country Context: Integrating PLS-SEM and MGA Methods," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    7. Spandagos, Constantine & Yarime, Masaru & Baark, Erik & Ng, Tze Ling, 2020. "“Triple Target” policy framework to influence household energy behavior: Satisfy, strengthen, include," Applied Energy, Elsevier, vol. 269(C).
    8. Martinho, Graça & Pires, Ana & Portela, Gonçalo & Fonseca, Miguel, 2015. "Factors affecting consumers’ choices concerning sustainable packaging during product purchase and recycling," Resources, Conservation & Recycling, Elsevier, vol. 103(C), pages 58-68.
    9. Jia, Ning & Li, Liying & Ling, Shuai & Ma, Shoufeng & Yao, Wang, 2018. "Influence of attitudinal and low-carbon factors on behavioral intention of commuting mode choice – A cross-city study in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 108-118.
    10. Liu, Diyi & Du, Huibin & Southworth, Frank & Ma, Shoufeng, 2017. "The influence of social-psychological factors on the intention to choose low-carbon travel modes in Tianjin, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 42-53.
    11. Qin, Yong & Xu, Zeshui & Wang, Xinxin & Škare, Marinko, 2022. "Green energy adoption and its determinants: A bibliometric analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    12. Qingyin Li & Meilin Dai & Yongli Zhang & Rong Wu, 2023. "The Effect of Public Traffic Accessibility on the Low-Carbon Awareness of Residents in Guangzhou: The Perspective of Travel Behavior," Land, MDPI, vol. 12(10), pages 1-20, October.
    13. Shi, Dan & Wang, Lei & Wang, Zhenxia, 2019. "What affects individual energy conservation behavior: Personal habits, external conditions or values? An empirical study based on a survey of college students," Energy Policy, Elsevier, vol. 128(C), pages 150-161.
    14. Zierler, Rupert & Wehrmeyer, Walter & Murphy, Richard, 2017. "The energy efficiency behaviour of individuals in large organisations: A case study of a major UK infrastructure operator," Energy Policy, Elsevier, vol. 104(C), pages 38-49.
    15. Morgane Innocent & Agnès François-Lecompte & Nolwenn Roudaut, 2020. "Comparison of human versus technological support to reduce domestic electricity consumption in France," Post-Print hal-02450849, HAL.
    16. Yu Wang & Shanyong Wang & Jing Wang & Jiuchang Wei & Chenglin Wang, 2020. "An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model," Transportation, Springer, vol. 47(1), pages 397-415, February.
    17. Kowalska-Pyzalska, Anna & Maciejowska, Katarzyna & Suszczyński, Karol & Sznajd-Weron, Katarzyna & Weron, Rafał, 2014. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," Energy Policy, Elsevier, vol. 72(C), pages 164-174.
    18. Bu, Yan & Wang, Erda & Möst, Dominik & Lieberwirth, Martin, 2022. "How population migration affects carbon emissions in China: Factual and counterfactual scenario analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    19. Fang, Xingming & Wang, Lu & Sun, Chuanwang & Zheng, Xuemei & Wei, Jing, 2021. "Gap between words and actions: Empirical study on consistency of residents supporting renewable energy development in China," Energy Policy, Elsevier, vol. 148(PA).
    20. Belaïd, Fateh & Joumni, Haitham, 2020. "Behavioral attitudes towards energy saving: Empirical evidence from France," Energy Policy, Elsevier, vol. 140(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:tefoso:v:183:y:2022:i:c:s0040162522004772. 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: http://www.sciencedirect.com/science/journal/00401625 .

    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.