IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i8p1607-d160603.html
   My bibliography  Save this article

Determining Multi-Layer Factors That Drive the Carbon Capability of Urban Residents in Response to Climate Change: An Exploratory Qualitative Study in China

Author

Listed:
  • Jia Wei

    (School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China
    Co-first author, these authors contributed equally to this work.)

  • Hong Chen

    (School of Management, China University of Mining and Technology, Xuzhou 221116, China
    Co-first author, these authors contributed equally to this work.)

  • Ruyin Long

    (School of Management, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

The active promotion of carbon abatement to mitigate global climate change and protect the environment and public health has become the international consensus. The carbon capability is a key index for measuring the potential reduction of the carbon emissions by urban residents, and thus encouraging residents to exhibit normal and autonomous low-carbon behavior has become an important issue. In this study, based on grounded theory, data from in-depth interviews were encoded at three levels to identify the multi-layer factors that drive the carbon capability of urban residents, and we constructed a theoretical model for policy intervention. The results showed that individual factors, organizational factors, social factors, and social demographic variables were the main variables that affected the carbon capability, and utility experience perception was the main intermediary variable that affected the carbon capability. There was an obvious gap between utility experience perception and carbon capability. Low carbon selection cost was an internal situational variable that regulated the relationship between these factors, and the policy situation and technical situation were external situational variables. There were two-way effects on the carbon capability and utility experience perception. Thus, we explored these driving factors and the role of the carbon capability model. The results of this study may facilitate targeted policy thinking and the development of an implementation path for government in order to formulate effective guiding policies to enhance the carbon capability of urban residents.

Suggested Citation

  • Jia Wei & Hong Chen & Ruyin Long, 2018. "Determining Multi-Layer Factors That Drive the Carbon Capability of Urban Residents in Response to Climate Change: An Exploratory Qualitative Study in China," IJERPH, MDPI, vol. 15(8), pages 1-19, July.
  • Handle: RePEc:gam:jijerp:v:15:y:2018:i:8:p:1607-:d:160603
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/8/1607/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/15/8/1607/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiang, Ping & Chen, Yihui & Xu, Bin & Dong, Wenbo & Kennedy, Erin, 2013. "Building low carbon communities in China: The role of individual’s behaviour change and engagement," Energy Policy, Elsevier, vol. 60(C), pages 611-620.
    2. Yu, Shiwei & Zhang, Junjie & Zheng, Shuhong & Sun, Han, 2015. "Provincial carbon intensity abatement potential estimation in China: A PSO–GA-optimized multi-factor environmental learning curve method," Energy Policy, Elsevier, vol. 77(C), pages 46-55.
    3. Yu, Shiwei & Wei, Yi-Ming & Fan, Jingli & Zhang, Xian & Wang, Ke, 2012. "Exploring the regional characteristics of inter-provincial CO2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization," Applied Energy, Elsevier, vol. 92(C), pages 552-562.
    4. Ahmed, Qureshi Intikhab & Lu, Huapu & Ye, Shi, 2008. "Urban transportation and equity: A case study of Beijing and Karachi," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(1), pages 125-139, January.
    5. Shuai Yang & Yu Wang & Wengang Ao & Yun Bai & Chuan Li, 2018. "Prediction and Analysis of CO 2 Emission in Chongqing for the Protection of Environment and Public Health," IJERPH, MDPI, vol. 15(3), pages 1-15, March.
    6. Tai-Yi Yu & Tai-Kuei Yu, 2017. "The Moderating Effects of Students’ Personality Traits on Pro-Environmental Behavioral Intentions in Response to Climate Change," IJERPH, MDPI, vol. 14(12), pages 1-20, November.
    7. Wei, Jia & Chen, Hong & Long, Ruyin, 2016. "Is ecological personality always consistent with low-carbon behavioral intention of urban residents?," Energy Policy, Elsevier, vol. 98(C), pages 343-352.
    8. Wei, Jia & Chen, Hong & Cui, Xiaotong & Long, Ruyin, 2016. "Carbon capability of urban residents and its structure: Evidence from a survey of Jiangsu Province in China," Applied Energy, Elsevier, vol. 173(C), pages 635-649.
    9. Wang, Qunwei & Zhou, Peng & Zhou, Dequn, 2012. "Efficiency measurement with carbon dioxide emissions: The case of China," Applied Energy, Elsevier, vol. 90(1), pages 161-166.
    10. Chen, Hong & Long, Ruyin & Niu, Wenjing & Feng, Qun & Yang, Ranran, 2014. "How does individual low-carbon consumption behavior occur? – An analysis based on attitude process," Applied Energy, Elsevier, vol. 116(C), pages 376-386.
    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. Ting Yue & Ruyin Long & Junli Liu & Haiwen Liu & Hong Chen, 2019. "Empirical Study on Households’ Energy-Conservation Behavior of Jiangsu Province in China: The Role of Policies and Behavior Results," IJERPH, MDPI, vol. 16(6), pages 1-16, March.
    2. Daoyan Guo & Xinping Wang & Taozhu Feng & Shuai Han, 2022. "Factors Influencing the Waste Separation Behaviors of Urban Residents in Shaanxi Province during the 14th National Games of China," IJERPH, MDPI, vol. 19(7), pages 1-14, April.
    3. Daoyan Guo & Hong Chen & Ruyin Long & Shaohui Zou, 2021. "Determinants of Residents’ Approach–Avoidance Responses to the Personal Carbon Trading Scheme: An Empirical Analysis of Urban Residents in Eastern China," IJERPH, MDPI, vol. 18(2), pages 1-15, January.
    4. Lingyun Mi & Yuhuan Sun & Lijie Qiao & Tianwen Jia & Yang Yang & Tao Lv, 2021. "Analysis of the Cause of Household Carbon Lock-In for Chinese Urban Households," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    5. Yi Chen & Yinrong Chen & Kun Chen & Min Liu, 2023. "Research Progress and Hotspot Analysis of Residential Carbon Emissions Based on CiteSpace Software," IJERPH, MDPI, vol. 20(3), pages 1-19, January.

    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. Jia Wei & Hong Chen & Ruyin Long, 2018. "Diffusion Paths and Guiding Policy for Urban Residents’ Carbon Identification Capability: Simulation Analysis from the Perspective of Relation Strength and Personal Carbon Trading," Sustainability, MDPI, vol. 10(6), pages 1-19, May.
    2. Wei, Jia & Chen, Hong & Long, Ruyin, 2016. "Is ecological personality always consistent with low-carbon behavioral intention of urban residents?," Energy Policy, Elsevier, vol. 98(C), pages 343-352.
    3. Wei, Jia & Chen, Hong & Cui, Xiaotong & Long, Ruyin, 2016. "Carbon capability of urban residents and its structure: Evidence from a survey of Jiangsu Province in China," Applied Energy, Elsevier, vol. 173(C), pages 635-649.
    4. 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.
    5. Zhang, Yue-Jun & Wang, Ao-Dong & Da, Ya-Bin, 2014. "Regional allocation of carbon emission quotas in China: Evidence from the Shapley value method," Energy Policy, Elsevier, vol. 74(C), pages 454-464.
    6. 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.
    7. Ye, Bin & Jiang, JingJing & Li, Changsheng & Miao, Lixin & Tang, Jie, 2017. "Quantification and driving force analysis of provincial-level carbon emissions in China," Applied Energy, Elsevier, vol. 198(C), pages 223-238.
    8. Dong, Huijuan & Dai, Hancheng & Geng, Yong & Fujita, Tsuyoshi & Liu, Zhe & Xie, Yang & Wu, Rui & Fujii, Minoru & Masui, Toshihiko & Tang, Liang, 2017. "Exploring impact of carbon tax on China’s CO2 reductions and provincial disparities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 596-603.
    9. Zhang, Kun & Zhang, Zong-Yong & Liang, Qiao-Mei, 2017. "An empirical analysis of the green paradox in China: From the perspective of fiscal decentralization," Energy Policy, Elsevier, vol. 103(C), pages 203-211.
    10. Feiyu Chen & Hong Chen & Xinru Huang & Ruyin Long & Hui Lu & Ting Yue, 2017. "Public Response to the Regulation Policy of Urban Household Waste: Evidence from a Survey of Jiangsu Province in China," Sustainability, MDPI, vol. 9(6), pages 1-23, June.
    11. 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.
    12. Yue-Jun Zhang & Jun-Fang Hao, 2015. "The allocation of carbon emission intensity reduction target by 2020 among provinces 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. 79(2), pages 921-937, November.
    13. Zhang, Yue-Jun & Hao, Jun-Fang & Song, Juan, 2016. "The CO2 emission efficiency, reduction potential and spatial clustering in China’s industry: Evidence from the regional level," Applied Energy, Elsevier, vol. 174(C), pages 213-223.
    14. Qin, Quande & Li, Xin & Li, Li & Zhen, Wei & Wei, Yi-Ming, 2017. "Air emissions perspective on energy efficiency: An empirical analysis of China’s coastal areas," Applied Energy, Elsevier, vol. 185(P1), pages 604-614.
    15. Chen, Zhenling & Zhang, Xiaoling & Ni, Guohua, 2020. "Decomposing capacity utilization under carbon dioxide emissions reduction constraints in data envelopment analysis: An application to Chinese regions," Energy Policy, Elsevier, vol. 139(C).
    16. Yu, Shiwei & Wei, Yi-Ming & Fan, Jingli & Zhang, Xian & Wang, Ke, 2012. "Exploring the regional characteristics of inter-provincial CO2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization," Applied Energy, Elsevier, vol. 92(C), pages 552-562.
    17. Qian, Yuan & Scherer, Laura & Tukker, Arnold & Behrens, Paul, 2020. "China's potential SO2 emissions from coal by 2050," Energy Policy, Elsevier, vol. 147(C).
    18. Xiaodong Zhu & Rongrong Gu & Bingbing Wu & Shunsuke Managi, 2017. "Does Hazy Weather Influence Earnings Management of Heavy-Polluting Enterprises? A Chinese Empirical Study from the Perspective of Negative Social Concerns," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    19. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    20. Thananya Janhuaton & Vatanavongs Ratanavaraha & Sajjakaj Jomnonkwao, 2024. "Forecasting Thailand’s Transportation CO 2 Emissions: A Comparison among Artificial Intelligent Models," Forecasting, MDPI, vol. 6(2), pages 1-23, June.

    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:gam:jijerp:v:15:y:2018:i:8:p:1607-:d:160603. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.