IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i6p1756-d149215.html
   My bibliography  Save this article

Diffusion Paths and Guiding Policy for Urban Residents’ Carbon Identification Capability: Simulation Analysis from the Perspective of Relation Strength and Personal Carbon Trading

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
    Co-first author, these authors contributed equally to this work.)

Abstract

On the consumption side, the key to carbon emission reduction is urban residents’ carbon capability. As it is the main bottleneck hindering carbon capability enhancement, the promotion of carbon identification capability is very important. This study establishes diffusion models of carbon identification capability from the perspectives of relation strength and personal carbon trading through weighted small-world theory, and it takes Chinese urban residents as the research object to make a simulation analysis. The results show that, at the initial stage, using a knowledge priority strategy to determine the sender of capability can bring about a higher capability growth rate for individuals, and the capability diffusion equilibrium of the network is also the highest. However, in the entire diffusion process, the strength priority model is the best to make the network reach the equilibrium quickly. After the introduction of personal carbon trading, the growth rate of the carbon identification capability increases significantly, and the network equilibrium becomes higher synchronously. More egoistic nodes and fewer altruistic nodes in the network are more favorable for the capability diffusion in the network, but they may bring about the risk that the network equilibrium becomes lower. Finally, the study puts forward suggestions to help with the improvement of residents’ carbon identification capability.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1756-:d:149215
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/6/1756/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/6/1756/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Li, Wenbo & Long, Ruyin & Chen, Hong, 2016. "Consumers’ evaluation of national new energy vehicle policy in China: An analysis based on a four paradigm model," Energy Policy, Elsevier, vol. 99(C), pages 33-41.
    3. Lenzen, Manfred & Wier, Mette & Cohen, Claude & Hayami, Hitoshi & Pachauri, Shonali & Schaeffer, Roberto, 2006. "A comparative multivariate analysis of household energy requirements in Australia, Brazil, Denmark, India and Japan," Energy, Elsevier, vol. 31(2), pages 181-207.
    4. Wang, Zhaohua & Feng, Chao, 2015. "Sources of production inefficiency and productivity growth in China: A global data envelopment analysis," Energy Economics, Elsevier, vol. 49(C), pages 380-389.
    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. 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.
    7. Michael Fritsch & Martina Kauffeld-Monz, 2010. "The impact of network structure on knowledge transfer: an application of social network analysis in the context of regional innovation networks," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 44(1), pages 21-38, February.
    8. Wang, Zhaohua & Yin, Fangchao & Zhang, Yixiang & Zhang, Xian, 2012. "An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China," Applied Energy, Elsevier, vol. 100(C), pages 277-284.
    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. 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.
    11. 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.
    12. Charles Raux & Yves Croissant & Damien Pons, 2015. "Would personal carbon trading reduce travel emissions more effectively than a carbon tax?," Post-Print halshs-01099917, HAL.
    13. Piergiuseppe Morone & Richard Taylor, 2004. "Knowledge diffusion dynamics and network properties of face-to-face interactions," Journal of Evolutionary Economics, Springer, vol. 14(3), pages 327-351, July.
    14. Ranran Yang & Ruyin Long, 2016. "Analysis of the Influencing Factors of the Public Willingness to Participate in Public Bicycle Projects and Intervention Strategies—A Case Study of Jiangsu Province, China," Sustainability, MDPI, vol. 8(4), pages 1-16, April.
    15. Andrew A. Wallace & Katherine N. Irvine & Andrew J. Wright & Paul D. Fleming, 2010. "Public attitudes to personal carbon allowances: findings from a mixed-method study," Climate Policy, Taylor & Francis Journals, vol. 10(4), pages 385-409, July.
    16. Jukka Heinonen & Seppo Junnila, 2011. "A Carbon Consumption Comparison of Rural and Urban Lifestyles," Sustainability, MDPI, vol. 3(8), pages 1-16, August.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. 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.
    4. Wei Li & Shuang Sun & Hao Li, 2015. "Decomposing the decoupling relationship between energy-related CO 2 emissions and economic growth 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 977-997, November.
    5. 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.
    6. 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.
    7. Dogterom, Nico & Ettema, Dick & Dijst, Martin, 2018. "Behavioural effects of a tradable driving credit scheme: Results of an online stated adaptation experiment in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 52-64.
    8. Yanbin Li & Zhen Li & Min Wu & Feng Zhang & Gejirifu De, 2018. "Regional-Level Allocation of CO 2 Emission Permits in China: Evidence from the Boltzmann Distribution Method," Sustainability, MDPI, vol. 10(8), pages 1-16, July.
    9. 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.
    10. Apergis, Nicholas & Chang, Tsangyao & Gupta, Rangan & Ziramba, Emmanuel, 2016. "Hydroelectricity consumption and economic growth nexus: Evidence from a panel of ten largest hydroelectricity consumers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 318-325.
    11. Yong Liu, 2019. "Residents’ Willingness and Influencing Factors on Action Personal Carbon Trading: A Case Study of Metropolitan Areas in Tianjin, China," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    12. Shuai, Chenyang & Shen, Liyin & Jiao, Liudan & Wu, Ya & Tan, Yongtao, 2017. "Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011," Applied Energy, Elsevier, vol. 187(C), pages 310-325.
    13. 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.
    14. Lu, Heli & Liu, Guifang, 2014. "Spatial effects of carbon dioxide emissions from residential energy consumption: A county-level study using enhanced nocturnal lighting," Applied Energy, Elsevier, vol. 131(C), pages 297-306.
    15. Yang, Shu & Cheng, Peng & Li, Jun & Wang, Shanyong, 2019. "Which group should policies target? Effects of incentive policies and product cognitions for electric vehicle adoption among Chinese consumers," Energy Policy, Elsevier, vol. 135(C).
    16. Shan, Yuli & Liu, Jianghua & Liu, Zhu & Xu, Xinwanghao & Shao, Shuai & Wang, Peng & Guan, Dabo, 2016. "New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors," Applied Energy, Elsevier, vol. 184(C), pages 742-750.
    17. 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.
    18. Zhang, Ning & Wang, Bing & Chen, Zhongfei, 2016. "Carbon emissions reductions and technology gaps in the world's factory, 1990–2012," Energy Policy, Elsevier, vol. 91(C), pages 28-37.
    19. 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.
    20. Age Poom & Rein Ahas, 2016. "How Does the Environmental Load of Household Consumption Depend on Residential Location?," Sustainability, MDPI, vol. 8(9), pages 1-18, August.

    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:6:p:1756-:d:149215. 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.