IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i4p1292-d142583.html
   My bibliography  Save this article

Measurements and Factors That Influence the Carbon Capability of Urban Residents in China

Author

Listed:
  • Qianwen Li

    (School of Management, China University of Mining and Technology, Da Xue Road 1, Xuzhou 221116, China)

  • Ruyin Long

    (School of Management, China University of Mining and Technology, Da Xue Road 1, Xuzhou 221116, China)

  • Hong Chen

    (School of Management, China University of Mining and Technology, Da Xue Road 1, Xuzhou 221116, China)

Abstract

Due to the rapid growth in residential energy consumption, there is an urgent need to reduce carbon emissions from the consumer side, which requires improvements in the carbon capability of urban residents. In this study, previous investigations of carbon capability were analyzed and classified into four dimensions: carbon knowledge capability, carbon motivation capability, carbon behavior capability, and carbon management capability. According to grounded theory, a quantitative research model was constructed of the carbon capability of urban residents in Jiangsu, which was used to conduct a questionnaire survey. SPSS 19.0 and LatentGOLD were employed to process the questionnaire data and the carbon capability of the residents was evaluated. The results showed that the residents of Jiangsu Province could be divided into six groups based on their different carbon capabilities, where these six major groups accounted for 28.19%, 21.21%, 18.33%, 15.84%, 9.88%, and 6.55% of the total sample. Gender, age, occupation, and educational level had significant effects on the carbon capabilities of residents, whereas the annual household income and household population had no significant effects. According to the characteristics of each cluster based on the four carbon capability dimensions, the six clusters were designated as “balanced steady cluster”, “self-restraint cluster”, “fully backward cluster”, “comprehensive leading cluster”, “slightly cognitive cluster”, and “restrain others cluster”. Quantitative analysis showed that 61.93% of the residents of Jiangsu reached the qualified rate for the carbon capability but the excellent rate was only 15.84%. Relevant policy implications are suggested based on these conclusions.

Suggested Citation

  • Qianwen Li & Ruyin Long & Hong Chen, 2018. "Measurements and Factors That Influence the Carbon Capability of Urban Residents in China," Sustainability, MDPI, vol. 10(4), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1292-:d:142583
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/4/1292/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/4/1292/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jichao Geng & Ruyin Long & Hong Chen & Ting Yue & Wenbo Li & Qianwen Li, 2017. "Exploring Multiple Motivations on Urban Residents’ Travel Mode Choices: An Empirical Study from Jiangsu Province in China," Sustainability, MDPI, vol. 9(1), pages 1-16, January.
    2. Arief Anshory Yusuf & Budy P. Resosudarmo, 2007. "On the Distributional Effect of Carbon Tax in Developing Countries: The Case of Indonesia," Working Papers in Economics and Development Studies (WoPEDS) 200705, Department of Economics, Padjadjaran University, revised Aug 2007.
    3. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    4. Alem, Yonas & Beyene, Abebe D. & Köhlin, Gunnar & Mekonnen, Alemu, 2016. "Modeling household cooking fuel choice: A panel multinomial logit approach," Energy Economics, Elsevier, vol. 59(C), pages 129-137.
    5. 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.
    6. Edward Castronova, 2004. "Achievement Bias in the Evolution of Preferences," Journal of Bioeconomics, Springer, vol. 6(2), pages 195-226, May.
    7. Langevin, Jared & Gurian, Patrick L. & Wen, Jin, 2013. "Reducing energy consumption in low income public housing: Interviewing residents about energy behaviors," Applied Energy, Elsevier, vol. 102(C), pages 1358-1370.
    8. Qianwen Li & Ruyin Long & Hong Chen & Jichao Geng, 2017. "Low Purchase Willingness for Battery Electric Vehicles: Analysis and Simulation Based on the Fault Tree Model," Sustainability, MDPI, vol. 9(5), pages 1-20, May.
    9. Edward Castronova, "undated". "Achievement Bias in the Evolution of Preferences," Gruter Institute Working Papers on Law, Economics, and Evolutionary Biology 2-1-1010, Berkeley Electronic Press.
    10. Steg, Linda, 2008. "Promoting household energy conservation," Energy Policy, Elsevier, vol. 36(12), pages 4449-4453, December.
    11. Cameron, Trudy Ann, 1985. "A Nested Logit Model of Energy Conservation Activity by Owners of Existing Single Family Dwellings," The Review of Economics and Statistics, MIT Press, vol. 67(2), pages 205-211, May.
    12. Sardianou, Eleni, 2007. "Estimating energy conservation patterns of Greek households," Energy Policy, Elsevier, vol. 35(7), pages 3778-3791, July.
    13. Wang, Xuesong & You, Shikai & Wang, Ling, 2017. "Classifying road network patterns using multinomial logit model," Journal of Transport Geography, Elsevier, vol. 58(C), pages 104-112.
    14. Steven Cooke & Jesse Vermaire, 2015. "Environmental studies and environmental science today: inevitable mission creep and integration in action-oriented transdisciplinary areas of inquiry, training and practice," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 5(1), pages 70-78, March.
    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. Junling Wang & Lihong Qin & Hanfang Chu, 2023. "Evaluation of Carbon Emission and Carbon Contribution Capacity Based on the Beijing–Tianjin–Hebei Region of China," Sustainability, MDPI, vol. 15(7), pages 1-26, March.
    2. Lili Kusumawati & Erni Setyowati & Agus Budi Purnomo, 2021. "Practical-Empirical Modeling on Envelope Design towards Sustainability in Tropical Architecture," Sustainability, MDPI, vol. 13(5), pages 1-23, March.
    3. Dongxiao Niu & Gengqi Wu & Zhengsen Ji & Dongyu Wang & Yuying Li & Tian Gao, 2021. "Evaluation of Provincial Carbon Neutrality Capacity of China Based on Combined Weight and Improved TOPSIS Model," Sustainability, MDPI, vol. 13(5), pages 1-18, March.

    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. Ang, James B. & Fredriksson, Per G. & Sharma, Swati, 2020. "Individualism and the adoption of clean energy technology," Resource and Energy Economics, Elsevier, vol. 61(C).
    2. Licheng Sun & Qunwei Wang & Shilong Ge, 2018. "Urban resident energy-saving behavior: a case study under the A2SC framework," 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. 91(2), pages 515-536, March.
    3. Balta-Ozkan, Nazmiye & Le Gallo, Julie, 2018. "Spatial variation in energy attitudes and perceptions: Evidence from Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2160-2180.
    4. 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.
    5. Wang, Zhaohua & Zhang, Bin & Zhang, Yixiang, 2012. "Determinants of public acceptance of tiered electricity price reform in China: Evidence from four urban cities," Applied Energy, Elsevier, vol. 91(1), pages 235-244.
    6. Bowen Qin & Ge Song, 2022. "Internal Motivations, External Contexts, and Sustainable Consumption Behavior in China—Based on the TPB-ABC Integration Model," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    7. 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).
    8. Yih-Liang Chan, David & Yang, Kuang-Han & Lee, Jenq-Daw & Hong, Gui-Bing, 2010. "The case study of furnace use and energy conservation in iron and steel industry," Energy, Elsevier, vol. 35(4), pages 1665-1670.
    9. Feiyu Chen & Hong Chen & Jiahui Yang & Ruyin Long & Qianwen Li, 2018. "Impact of Information Intervention on the Recycling Behavior of Individuals with Different Value Orientations—An Experimental Study on Express Delivery Packaging Waste," Sustainability, MDPI, vol. 10(10), pages 1-20, October.
    10. Hong, Gui-Bing & Ma, Chih-Ming & Chen, Hua-Wei & Chuang, Kai-Jen & Chang, Chang-Tang & Su, Te-Li, 2011. "Energy flow analysis in pulp and paper industry," Energy, Elsevier, vol. 36(5), pages 3063-3068.
    11. Corradi, Nicola & Priftis, Konstantinos & Jacucci, Giulio & Gamberini, Luciano, 2013. "Oops, I forgot the light on! The cognitive mechanisms supporting the execution of energy saving behaviors," Journal of Economic Psychology, Elsevier, vol. 34(C), pages 88-96.
    12. Véronique Vasseur & Anne-Francoise Marique, 2019. "Households’ Willingness to Adopt Technological and Behavioral Energy Savings Measures: An Empirical Study in The Netherlands," Energies, MDPI, vol. 12(22), pages 1-25, November.
    13. 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.
    14. Chen, Chien-fei & Xu, Xiaojing & Adua, Lazarus & Briggs, Morgan & Nelson, Hannah, 2022. "Exploring the factors that influence energy use intensity across low-, middle-, and high-income households in the United States," Energy Policy, Elsevier, vol. 168(C).
    15. Boudet, Hilary & Ardoin, Nicole M. & Flora, June & Armel, K. Carrie & Desai, Manisha & Robinson, Thomas N., 2014. "Energy behaviours of northern California Girl Scouts and their families," Energy Policy, Elsevier, vol. 73(C), pages 439-449.
    16. Salem, Mohammed Z. & Ertz, Myriam & Sarigӧllü, Emine, 2021. "Demarketing strategies to rationalize electricity consumption in the Gaza Strip-Palestine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    17. Qianwen Li & Ruyin Long & Hong Chen & Jichao Geng, 2017. "Low Purchase Willingness for Battery Electric Vehicles: Analysis and Simulation Based on the Fault Tree Model," Sustainability, MDPI, vol. 9(5), pages 1-20, May.
    18. Hong, Gui-Bing & Su, Te-Li & Lee, Jenq-Daw & Hsu, Tsung-Chi & Chen, Hua-Wei, 2010. "Energy conservation potential in Taiwanese textile industry," Energy Policy, Elsevier, vol. 38(11), pages 7048-7053, November.
    19. 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).
    20. Jin Zhang & Lianrui Ma & Jinkai Li, 2021. "Why Low-Carbon Publicity Effect Limits? The Role of Heterogeneous Intention in Reducing Household Energy Consumption," Energies, MDPI, vol. 14(22), pages 1-17, November.

    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:jsusta:v:10:y:2018:i:4:p:1292-:d:142583. 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.