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Open AccessOpen Access JUSTC Management 01 August 2022

Evolutionary game analysis of low-carbon behavior credit supervision of logistics enterprises

  • School of Management, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.52396/JUSTC-2022-0070
More Information
  • Author Bio:

    Yu Dong is an Associate Professor with the University of Science and Technology of China (USTC) and a Vice President of Anhui University of Science & Technology. He received his Ph.D. degree in Management from the USTC. His main research directions are decision science and operations management, and he is interested in studing game theory in management science issues

  • Corresponding author: E-mail: ydong@ustc.edu.cn
  • Received Date: 21 April 2022
  • Accepted Date: 18 May 2022
    • Available Online: 01 August 2022
    Abstract

  • Abstract

    Based on the low-carbon obligation fulfillment of Chinese logistics enterprises, this study constructs a tripartite evolutionary game model to analyze the evolutionary process of the interaction between the local government, logistics enterprises and the public in the process of low-carbon behavior credit supervision. Then using Netlogo software, a parameter simulation experiment is conducted to determine the optimal policy for improving the effect of supervision. The results are as follows: ① The combined influence of the local government and the public can effectively change the strategic choice of enterprises and promote the low-carbon behavior of enterprises. ② In terms of improving the effect of supervision, reducing the cost of government supervision would have a highly significant effect, and reducing the cost of the public would be counterproductive. ③ Increasing the government subsidies to enterprises and the government’s fines to enterprises both have a significant effect, and the effect of improving the former is better. However, increasing the severity of higher-level governments punishing local governments will reduce the stability of the system. ④ Supervision can be more effective by increasing the public’s impact on enterprises’ earnings rather than by increasing government subsidies to the public.

    Graphical abstract

    Research methods and conclusions based on evolutionary game model.

    Abstract

    Based on the low-carbon obligation fulfillment of Chinese logistics enterprises, this study constructs a tripartite evolutionary game model to analyze the evolutionary process of the interaction between the local government, logistics enterprises and the public in the process of low-carbon behavior credit supervision. Then using Netlogo software, a parameter simulation experiment is conducted to determine the optimal policy for improving the effect of supervision. The results are as follows: ① The combined influence of the local government and the public can effectively change the strategic choice of enterprises and promote the low-carbon behavior of enterprises. ② In terms of improving the effect of supervision, reducing the cost of government supervision would have a highly significant effect, and reducing the cost of the public would be counterproductive. ③ Increasing the government subsidies to enterprises and the government’s fines to enterprises both have a significant effect, and the effect of improving the former is better. However, increasing the severity of higher-level governments punishing local governments will reduce the stability of the system. ④ Supervision can be more effective by increasing the public’s impact on enterprises’ earnings rather than by increasing government subsidies to the public.

    • Public Summary

    • Reducing government costs can significantly promote the low-carbon behavior of logistics enterprises, while reducing public costs is counterproductive.
    • Increasing the low-carbon subsidy of local governments to logistics enterprises has the best effect, and increasing the punishment of higher-level governments to local governments has the worst effect.
    • The effect of increasing the public’s impact on the income of enterprises is the best, and the effect of increasing the government’s subsidy to the public is the worst.

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    • References(24)
  • [1]
    Yang W Y, Li T, Cao X S. Examining the impacts of socio-economic factors, urban form and transportation development on CO2 emissions from transportation in China: A panel data analysis of China’s provinces. Habitat International, 2015, 49 (5): 212–220. doi: 10.1016/j.habitatint.2015.05.030
    [2]
    Ma J, Liu Z L, Chai Y W. The impact of urban form on CO2 emission from work and non-work trips: The case of Beijing, China. Habitat International, 2015, 47 (12): 1–10. doi: 10.1016/j.habitatint.2014.12.007
    [3]
    Li C, Zan D L. An empirical study on carbon emission measurement and decomposition model of my country’s logistics and transportation industry. Resource Development & Market, 2015, 31 (10): 1197–1199, 1213. doi: 10.3969/j.issn.1005-8141.2015.10.009
    [4]
    Wang F Z, Shen Z Z. Research on substitution utility and urbanization utility of energy consumption in logistics industry. Chinese Journal of Management Science, 2016, 24 (9): 45–52. doi: 10.16381/j.cnki.issn1003-207x.2016.09.006
    [5]
    Yang G H. System dynamics analysis of low-carbon logistics development. Logistics Sci-Tech, 2012, 35 (12): 32–35. doi: 10.3969/j.issn.1002-3100.2012.12.011
    [6]
    Royer S J, Ferrón S, Wilson S T, et al. Production of methane and ethylene from plastic in the environment. PLoS ONE, 2018, 13 (8): e0200574. doi: 10.1371/journal.pone.0200574
    [7]
    Zhai Y P. Greening of express packaging. China Logistics & Purchasing, 2016, 1: 47–48.
    [8]
    Qu W X, Ma M Q, Miao Z M. Research on the problems and countermeasures of express green packaging. China Storage & Transport, 2022 (4): 155–157. doi: 10.3969/j.issn.1005-0434.2022.04.076
    [9]
    Yang F H, Pan X. Analysis of the development path of express packaging under the trend of green logistics. China Logistics & Purchasing, 2021 (23): 76–77. doi: 10.16079/j.cnki.issn1671-6663.2021.23.042
    [10]
    Li L H, Huang J P, Li L J, et al. System model and simulation of logistics cluster based on the synergistic network. Systems Engineering, 2022, 40 (2): 98–108.
    [11]
    Van Dender K. Energy policy in transport and transport policy. Energy Policy, 2009, 37 (10): 3854–3862. doi: 10.1016/j.enpol.2009.07.008
    [12]
    Yang Y, Xu X Y. Research on the evolution of low-carbon behavior of logistics enterprises considering carbon tax policy. Journal of Safety and Environment, 2021, 21 (4): 1750–1758. doi: 10.13637/j.issn.1009-6094.2020.0790
    [13]
    Yu L J, Chen Z Q. Research on the green innovation diffusion mechanism of logistics enterprises based on evolutionary game. Operations Research and Management Science, 2018, 27 (12): 193–199. doi: 10.12005/orms.2018.0296
    [14]
    Lu L, Zhang Y. Research on low-carbon logistics government supervision strategy based on evolutionary game. Mathematics in Practice and Theory, 2022, 52 (1): 64–84.
    [15]
    Cai Y. Research on the influence of external pressure on the green management behavior of logistics enterprises. Thesis. Beijing: Beijing University of Posts and Telecommunications, 2021.
    [16]
    Du J G, Wang M, Chen X Y, et al. Study on evolution of enterprise’s environmental behavior under public participation. Operations Research and Management Science, 2013, 22 (1): 244–251. doi: 10.3969/j.issn.1007-3221.2013.01.037
    [17]
    Chen W D, Yang R Y. Government regulation, public participation and environmental governance satisfaction: An empirical analysis based on CGSS2015 data. Soft Science, 2018, 32 (11): 49–53. doi: 10.13956/j.ss.1001-8409.2018.11.11
    [18]
    Fu J Y, Geng Y Y. Public participation, regulatory compliance and green development in China based on provincial panel data. Journal of Cleaner Production, 2019, 230: 1344–1353. doi: 10.1016/j.jclepro.2019.05.093
    [19]
    Deng W J, Ma S H, Guan X. Duopoly enterprises’ strategies for consumer environmental awareness under carbon-emission-trading mechanism. Chinese Journal of Management Science, 2017, 25 (12): 17–26. doi: 10.16381/j.cnki.issn1003-207x.2017.12.003
    [20]
    Ren H X. Focusing on the goal of carbon peaking and carbon neutrality, accelerating the green and low-carbon transformation of the logistics industry. China Logistics & Purchasing, 2021 (17): 11–12. doi: 10.16079/j.cnki.issn1671-6663.2021.17.002
    [21]
    Wang Y L, Guo W B. Thoughts on promoting the construction of credit system under the collaborative governance model. Macroeconomic Management, 2018 (10): 52–57.
    [22]
    Zhang G X, Zhang X T, Cheng S J, et al. Signaling game model of government and enterprise based on the subsidy policy for energy saving and emission reduction. Chinese Journal of Management Science, 2013, 21 (4): 129–136.
    [23]
    Friedman D. Evolutionary games in economics. Econometrica, 1991, 59 (3): 637–666.
    [24]
    Cui M. Tripartite evolutionary game analysis of environmental credit supervision under the background of collaborative governance. Systems Engineering:Theory & Practice, 2021, 41 (3): 713–726. doi: 10.12011/SETP2020-0480
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    Figure  1.  Game interaction mechanism among the three parties.

    Figure  2.  Local government behavior dynamic evolution trend.

    Figure  3.  Logistics enterprise behavior dynamic evolution trend.

    Figure  4.  Public behavior dynamic evolution trend.

    Figure  5.  Color distinction of six agents.

    Figure  6.  Simulation diagram of regulatory development stages.

    Figure  7.  Simulation diagram of decreasing ${{C}_{G}},{{C}_{E}},{{C}_{M}}.$

    Figure  8.  Simulation diagram of increasing $\alpha ,\theta ,\mu .$

    Figure  9.  Simulation diagram of increasing $\Delta P,H,\Delta M .$

    [1]
    Yang W Y, Li T, Cao X S. Examining the impacts of socio-economic factors, urban form and transportation development on CO2 emissions from transportation in China: A panel data analysis of China’s provinces. Habitat International, 2015, 49 (5): 212–220. doi: 10.1016/j.habitatint.2015.05.030
    [2]
    Ma J, Liu Z L, Chai Y W. The impact of urban form on CO2 emission from work and non-work trips: The case of Beijing, China. Habitat International, 2015, 47 (12): 1–10. doi: 10.1016/j.habitatint.2014.12.007
    [3]
    Li C, Zan D L. An empirical study on carbon emission measurement and decomposition model of my country’s logistics and transportation industry. Resource Development & Market, 2015, 31 (10): 1197–1199, 1213. doi: 10.3969/j.issn.1005-8141.2015.10.009
    [4]
    Wang F Z, Shen Z Z. Research on substitution utility and urbanization utility of energy consumption in logistics industry. Chinese Journal of Management Science, 2016, 24 (9): 45–52. doi: 10.16381/j.cnki.issn1003-207x.2016.09.006
    [5]
    Yang G H. System dynamics analysis of low-carbon logistics development. Logistics Sci-Tech, 2012, 35 (12): 32–35. doi: 10.3969/j.issn.1002-3100.2012.12.011
    [6]
    Royer S J, Ferrón S, Wilson S T, et al. Production of methane and ethylene from plastic in the environment. PLoS ONE, 2018, 13 (8): e0200574. doi: 10.1371/journal.pone.0200574
    [7]
    Zhai Y P. Greening of express packaging. China Logistics & Purchasing, 2016, 1: 47–48.
    [8]
    Qu W X, Ma M Q, Miao Z M. Research on the problems and countermeasures of express green packaging. China Storage & Transport, 2022 (4): 155–157. doi: 10.3969/j.issn.1005-0434.2022.04.076
    [9]
    Yang F H, Pan X. Analysis of the development path of express packaging under the trend of green logistics. China Logistics & Purchasing, 2021 (23): 76–77. doi: 10.16079/j.cnki.issn1671-6663.2021.23.042
    [10]
    Li L H, Huang J P, Li L J, et al. System model and simulation of logistics cluster based on the synergistic network. Systems Engineering, 2022, 40 (2): 98–108.
    [11]
    Van Dender K. Energy policy in transport and transport policy. Energy Policy, 2009, 37 (10): 3854–3862. doi: 10.1016/j.enpol.2009.07.008
    [12]
    Yang Y, Xu X Y. Research on the evolution of low-carbon behavior of logistics enterprises considering carbon tax policy. Journal of Safety and Environment, 2021, 21 (4): 1750–1758. doi: 10.13637/j.issn.1009-6094.2020.0790
    [13]
    Yu L J, Chen Z Q. Research on the green innovation diffusion mechanism of logistics enterprises based on evolutionary game. Operations Research and Management Science, 2018, 27 (12): 193–199. doi: 10.12005/orms.2018.0296
    [14]
    Lu L, Zhang Y. Research on low-carbon logistics government supervision strategy based on evolutionary game. Mathematics in Practice and Theory, 2022, 52 (1): 64–84.
    [15]
    Cai Y. Research on the influence of external pressure on the green management behavior of logistics enterprises. Thesis. Beijing: Beijing University of Posts and Telecommunications, 2021.
    [16]
    Du J G, Wang M, Chen X Y, et al. Study on evolution of enterprise’s environmental behavior under public participation. Operations Research and Management Science, 2013, 22 (1): 244–251. doi: 10.3969/j.issn.1007-3221.2013.01.037
    [17]
    Chen W D, Yang R Y. Government regulation, public participation and environmental governance satisfaction: An empirical analysis based on CGSS2015 data. Soft Science, 2018, 32 (11): 49–53. doi: 10.13956/j.ss.1001-8409.2018.11.11
    [18]
    Fu J Y, Geng Y Y. Public participation, regulatory compliance and green development in China based on provincial panel data. Journal of Cleaner Production, 2019, 230: 1344–1353. doi: 10.1016/j.jclepro.2019.05.093
    [19]
    Deng W J, Ma S H, Guan X. Duopoly enterprises’ strategies for consumer environmental awareness under carbon-emission-trading mechanism. Chinese Journal of Management Science, 2017, 25 (12): 17–26. doi: 10.16381/j.cnki.issn1003-207x.2017.12.003
    [20]
    Ren H X. Focusing on the goal of carbon peaking and carbon neutrality, accelerating the green and low-carbon transformation of the logistics industry. China Logistics & Purchasing, 2021 (17): 11–12. doi: 10.16079/j.cnki.issn1671-6663.2021.17.002
    [21]
    Wang Y L, Guo W B. Thoughts on promoting the construction of credit system under the collaborative governance model. Macroeconomic Management, 2018 (10): 52–57.
    [22]
    Zhang G X, Zhang X T, Cheng S J, et al. Signaling game model of government and enterprise based on the subsidy policy for energy saving and emission reduction. Chinese Journal of Management Science, 2013, 21 (4): 129–136.
    [23]
    Friedman D. Evolutionary games in economics. Econometrica, 1991, 59 (3): 637–666.
    [24]
    Cui M. Tripartite evolutionary game analysis of environmental credit supervision under the background of collaborative governance. Systems Engineering:Theory & Practice, 2021, 41 (3): 713–726. doi: 10.12011/SETP2020-0480
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