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

Author

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  • 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
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    1. 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.
    2. 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.
    3. Jukka Heinonen & Seppo Junnila, 2011. "A Carbon Consumption Comparison of Rural and Urban Lifestyles," Sustainability, MDPI, vol. 3(8), pages 1-16, August.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
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