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Open AccessOpen Access JUSTC Management 01 August 2022

Evolutionary game analysis of promoting the development of green logistics under government regulation

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

Cite this:
https://doi.org/10.52396/JUSTC-2022-0067
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 USTC. His main research directions are decision science and operation management, and he is interested in the study of game theory in management science issues

  • Corresponding author: E-mail: ydong@ustc.edu.cn
  • Received Date: 18 April 2022
  • Accepted Date: 23 May 2022
    • Available Online: 01 August 2022
    Abstract

  • Abstract

    Due to the strong negative externalities of traditional logistics, the green logistics that developed from traditional logistics has the advantages of saving resources and protecting the environment. However, in the competitive market environment, enterprises will not implement green logistics based on their own revenues and competitiveness and, instead, will choose the best choice from the actions of a series of internal and external factors. To explore the effect of various factors on the implementation of green logistics by enterprises, this study constructs a tripartite evolutionary game model of the governments, logistics enterprises, and users from the perspective of the participants in the process of logistics greening and analyzes the evolutionarily stable strategies of each participant under different situations. Netlogo software is used to simulate and analyze the initial willingness of the participants, the intensity of government subsidies and fines, and the probability that the enterprises’ speculative behaviors are founded on the system’s evolutionary paths and results. The results demonstrate that the initial willingness of the governments, logistics enterprises, and users to participate has different effects on the evolutionary results of the system. Government subsidy and fine measures significantly impact the strategic choices of enterprises and users. Compared with users, enterprises are more sensitive to government subsidies, and compared with fines, government subsidies have a greater impact on enterprises’ behavior choices. Moreover, the governments should strengthen the publicity of green logistics, formulate judgement standards and an evaluation system for green enterprise logistics, and restrain the speculative behaviors of enterprises.

    Graphical abstract

    Influence degree of different parameters on logistics greening.

    Abstract

    Due to the strong negative externalities of traditional logistics, the green logistics that developed from traditional logistics has the advantages of saving resources and protecting the environment. However, in the competitive market environment, enterprises will not implement green logistics based on their own revenues and competitiveness and, instead, will choose the best choice from the actions of a series of internal and external factors. To explore the effect of various factors on the implementation of green logistics by enterprises, this study constructs a tripartite evolutionary game model of the governments, logistics enterprises, and users from the perspective of the participants in the process of logistics greening and analyzes the evolutionarily stable strategies of each participant under different situations. Netlogo software is used to simulate and analyze the initial willingness of the participants, the intensity of government subsidies and fines, and the probability that the enterprises’ speculative behaviors are founded on the system’s evolutionary paths and results. The results demonstrate that the initial willingness of the governments, logistics enterprises, and users to participate has different effects on the evolutionary results of the system. Government subsidy and fine measures significantly impact the strategic choices of enterprises and users. Compared with users, enterprises are more sensitive to government subsidies, and compared with fines, government subsidies have a greater impact on enterprises’ behavior choices. Moreover, the governments should strengthen the publicity of green logistics, formulate judgement standards and an evaluation system for green enterprise logistics, and restrain the speculative behaviors of enterprises.

    • Public Summary

    • This paper constructs an evolutionary game model, discusses the green behaviors of logistics enterprises from a dynamic perspective, and analyzes the evolutionary mechanism of logistics enterprises implementing green logistics.
    • Considering the evolutionary game model composed of governments, enterprises and users, we analyze the evolutionary paths and results of green logistics implementation by enterprises under government regulation from a systematic perspective, which can better portray the interactions among the three parties.
    • Using ABM simulation method and Netlogo software, we quantitatively study the micro-mechanism of implementing green logistics in enterprises from the bottom-up perspective of microinteraction to macroemergence.

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    • References(34)
  • [1]
    Mafini C, Muposhy A. The impact of green supply chain management in small and medium enterprises: Cross-sectional evidence. Journal of Transport and Supply Chain Management, 2017, 11: 270–281. doi: 10.4102/jtscm.v11i0.270
    [2]
    Liu Z Y, Sun X L, Xue J L. Prominent problems faced by the development of green logistics in my country and countermeasures. Economic Review Journal, 2018 (5): 97–101. doi: 10.16528/j.cnki.22-1054/f.201805097
    [3]
    Liu H H, Chen X L, Wang X F. Research on the threshold effect of urbanization and logistics green total factor productivity. Review of Economy and Management, 2020, 36 (2): 123–132. doi: 10.13962/j.cnki.37-1486/f.2020.02.012
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    Mckinnon A, Browne M, Piecyk M, et al. Green Logistics: Improving the Environmental Sustainability of Logistics. London: Kogan Page, 2015.
    [5]
    Besbes W, Dhouib D, Wassan N, et al. Solving Transport Problems: Towards Green Logistics. Hoboken, NJ: Wiley, 2019.
    [6]
    Murphy P R, Poist R F. Comparative view of logistics and marketing practitioners regarding interfunctional co-ordination. International Journal of Physical Distribution and Logistics Management, 1996, 26 (8): 15–28. doi: 10.1108/09600039610128249
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    Wu H J, Dunn S C. Environmentally responsible logistics systems. International Journal of Physical Distribution and Logistics Management, 1995, 25 (2): 20–38. doi: 10.1108/09600039510083925
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    State Administration of Quality and Technical Supervision. National Standard of the People’s Republic of China (GB/T 18354–2001) : Logistics Terms. Beijing: China Standards Press, 2001.
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    Zheng W Y, Meng Y P. A linear programming model of green reverse logistics network under government guidance: Taking express packaging as an example. Journal of Central China Normal University (Natural Sciences), 2017, 51 (4): 518–525. doi: 10.19603/j.cnki.1000-1190.2017.04.017
    [10]
    You M H, Yan M L, He M Z. Exploration of green logistics capacity expansion model driven by 5G applications: Taking Cainiao and Jingdong as examples. Journal of Commercial Economics, 2020 (19): 103–106. doi: 10.3969/j.issn.1002-5863.2020.19.025
    [11]
    Zha F. Green logistics management strategy from the perspective of ecological environment protection. Environmental Engineering, 2021, 39 (9): 254.
    [12]
    Chen L S. Double greens, analysis on the two characters of green logistics. China Business and Market, 2008, 22 (11): 17–20. doi: 10.3969/j.issn.1007-8266.2008.11.005
    [13]
    Feng G Z. Modern Logistics and Supply chain. Xi’an: Xi’an Jiaotong University Press, 2003.
    [14]
    Pigou A C. The Economics of Welfare. 4th edition. London: Macmillan, 1960.
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    North D C. Institutions, Institutional Change, and Economic Performance. New York: Cambridge University Press, 1990.
    [16]
    Gong X, Jing L B. Review of the theory and policy research on the development of green logistics. Modern Economic Research, 2017 (11): 126–132. doi: 10.3969/j.issn.1009-2382.2017.11.015
    [17]
    Li A B, Chen Y, Wang D P. An empirical study of the factors influencing the willingness to implement green coal logistics in China. Journal of Cleaner Production, 2020, 245: 118932. doi: 10.1016/j.jclepro.2019.118932
    [18]
    Liang Z M. Negative externalities of logistics and its government control: Series Ⅵ of regional logistics development and government governance transformation. Journal of Commercial Economics, 2014 (20): 19–21. doi: 10.3969/j.issn.1002-5863.2014.20.009
    [19]
    Agyabeng-Mensah Y, Afum E, Ahenkorah E. Exploring financial performance and green logistics management practices: Examining the mediating influences of market, environmental and social performances. Journal of Cleaner Production, 2020, 258: 120613. doi: 10.1016/j.jclepro.2020.120613
    [20]
    Chen D. Modern green logistics management and its strategies. China Population, Resources and Environment, 2001, 11 (2): 112–114.
    [21]
    Lin B F. Green logistics and its development strategy. Business and Management Journal, 2007 (20): 56–59.
    [22]
    Li H X, Gao L. Research on green logistics management under the background of low-carbon economy. Logistics Technology, 2012, 31 (15): 117–119.
    [23]
    Li S H, Wang X J. Research on the obstacles to the green development of enterprise logistics and countermeasures: From the perspective of the participants in the green development of enterprise logistics. Chinese Journal of Management Science, 2014, 22 (S1): 788–793. doi: 10.16381/j.cnki.issn1003-207x.2014.s1.115
    [24]
    Yu L J, Chen Z Q. Research on 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
    [25]
    Zhou Q L, Hu W, Huang Y J. An analysis of the exteriority of green physical distribution and the subjects’ games. Journal of Shenzhen University (Humanities & Social Sciences), 2007, 24 (2): 49–53.
    [26]
    Wang S L, Yan G L, Li Z. Evolutionary game analysis of reverse logistics. Journal of Systems Engineering, 2010, 25 (4): 520–525.
    [27]
    Xu J Z, Xu Y Y. Low-carbon technology innovation diffusion under government environmental regulation: Evolutionary game analysis based on prospect theory. Systems Engineering, 2015, 33 (2): 118–125.
    [28]
    Weibull J W. Evolutionary Game Theory. Cambridge, MA: MIT Press, 1995.
    [29]
    Friedman D. Evolutionary games in economics. Econometrica, 1991, 59: 637–666.
    [30]
    Selten R. A note on evolutionarily stable strategies in asymmetric animal conflicts. Journal of Theoretical Biology, 1980, 84 (1): 93–101. doi: 10.1016/S0022-5193(80)81038-1
    [31]
    Friedman D. On economic application of evolutionary game theory. Journal of Evolutionary Economics, 1998 (1): 15–43. doi: 10.1007/s001910050054
    [32]
    Shubik M. Game Theory Evolving: A Problem-Centered Introduction to Modeling Strategic Behavior, by Gintis H. Princeton, NJ: Princeton University Press, 2000. Reviewed by Shubik M. Journal of Economics, 2001, 73 (2): 207–209.
    [33]
    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
    [34]
    Wang X H, Ren X X. Research on platform e-commerce credit supervision mechanism based on evolutionary game. Systems Engineering: Theory & Practice, 2020, 40 (10): 2617–2630. doi: 10.12011/1000-6788-2019-1967-14
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    Figure  1.  Evolutionary path when ${x}_{0}={y}_{0}={z}_{0}=0.5.$

    Figure  2.  Evolutionary path when ${x}_{0}={y}_{0}=0.5,\;{z}_{0}=0.9.$

    Figure  3.  Evolutionary path when ${y}_{0}={z}_{0}=0.5,\;{x}_{0}=0.7.$

    Figure  4.  Evolutionary path when ${y}_{0}={z}_{0}=0.5,\;{x}_{0}=0.9.$

    Figure  5.  Evolutionary path when ${x}_{0}={z}_{0}=0.5,\;{y}_{0}=0.7 .$

    Figure  6.  Evolutionary path when ${x}_{0}={z}_{0}=0.5,\;{y}_{0}=0.9 .$

    Figure  7.  Evolutionary path when ${B}_{1}=2.7.$

    Figure  8.  Evolutionary path when ${B}_{1}=2.8.$

    Figure  9.  Evolutionary path when ${B}_{2}=1.4.$

    Figure  10.  Evolutionary path when ${B}_{2}=1.5.$

    Figure  11.  Evolutionary path when $A=2.9.$

    Figure  12.  Evolutionary path when $A=3.$

    Figure  13.  Evolutionary path when $\theta =0.6.$

    Figure  14.  Evolutionary path when $\theta =0.7.$

    [1]
    Mafini C, Muposhy A. The impact of green supply chain management in small and medium enterprises: Cross-sectional evidence. Journal of Transport and Supply Chain Management, 2017, 11: 270–281. doi: 10.4102/jtscm.v11i0.270
    [2]
    Liu Z Y, Sun X L, Xue J L. Prominent problems faced by the development of green logistics in my country and countermeasures. Economic Review Journal, 2018 (5): 97–101. doi: 10.16528/j.cnki.22-1054/f.201805097
    [3]
    Liu H H, Chen X L, Wang X F. Research on the threshold effect of urbanization and logistics green total factor productivity. Review of Economy and Management, 2020, 36 (2): 123–132. doi: 10.13962/j.cnki.37-1486/f.2020.02.012
    [4]
    Mckinnon A, Browne M, Piecyk M, et al. Green Logistics: Improving the Environmental Sustainability of Logistics. London: Kogan Page, 2015.
    [5]
    Besbes W, Dhouib D, Wassan N, et al. Solving Transport Problems: Towards Green Logistics. Hoboken, NJ: Wiley, 2019.
    [6]
    Murphy P R, Poist R F. Comparative view of logistics and marketing practitioners regarding interfunctional co-ordination. International Journal of Physical Distribution and Logistics Management, 1996, 26 (8): 15–28. doi: 10.1108/09600039610128249
    [7]
    Wu H J, Dunn S C. Environmentally responsible logistics systems. International Journal of Physical Distribution and Logistics Management, 1995, 25 (2): 20–38. doi: 10.1108/09600039510083925
    [8]
    State Administration of Quality and Technical Supervision. National Standard of the People’s Republic of China (GB/T 18354–2001) : Logistics Terms. Beijing: China Standards Press, 2001.
    [9]
    Zheng W Y, Meng Y P. A linear programming model of green reverse logistics network under government guidance: Taking express packaging as an example. Journal of Central China Normal University (Natural Sciences), 2017, 51 (4): 518–525. doi: 10.19603/j.cnki.1000-1190.2017.04.017
    [10]
    You M H, Yan M L, He M Z. Exploration of green logistics capacity expansion model driven by 5G applications: Taking Cainiao and Jingdong as examples. Journal of Commercial Economics, 2020 (19): 103–106. doi: 10.3969/j.issn.1002-5863.2020.19.025
    [11]
    Zha F. Green logistics management strategy from the perspective of ecological environment protection. Environmental Engineering, 2021, 39 (9): 254.
    [12]
    Chen L S. Double greens, analysis on the two characters of green logistics. China Business and Market, 2008, 22 (11): 17–20. doi: 10.3969/j.issn.1007-8266.2008.11.005
    [13]
    Feng G Z. Modern Logistics and Supply chain. Xi’an: Xi’an Jiaotong University Press, 2003.
    [14]
    Pigou A C. The Economics of Welfare. 4th edition. London: Macmillan, 1960.
    [15]
    North D C. Institutions, Institutional Change, and Economic Performance. New York: Cambridge University Press, 1990.
    [16]
    Gong X, Jing L B. Review of the theory and policy research on the development of green logistics. Modern Economic Research, 2017 (11): 126–132. doi: 10.3969/j.issn.1009-2382.2017.11.015
    [17]
    Li A B, Chen Y, Wang D P. An empirical study of the factors influencing the willingness to implement green coal logistics in China. Journal of Cleaner Production, 2020, 245: 118932. doi: 10.1016/j.jclepro.2019.118932
    [18]
    Liang Z M. Negative externalities of logistics and its government control: Series Ⅵ of regional logistics development and government governance transformation. Journal of Commercial Economics, 2014 (20): 19–21. doi: 10.3969/j.issn.1002-5863.2014.20.009
    [19]
    Agyabeng-Mensah Y, Afum E, Ahenkorah E. Exploring financial performance and green logistics management practices: Examining the mediating influences of market, environmental and social performances. Journal of Cleaner Production, 2020, 258: 120613. doi: 10.1016/j.jclepro.2020.120613
    [20]
    Chen D. Modern green logistics management and its strategies. China Population, Resources and Environment, 2001, 11 (2): 112–114.
    [21]
    Lin B F. Green logistics and its development strategy. Business and Management Journal, 2007 (20): 56–59.
    [22]
    Li H X, Gao L. Research on green logistics management under the background of low-carbon economy. Logistics Technology, 2012, 31 (15): 117–119.
    [23]
    Li S H, Wang X J. Research on the obstacles to the green development of enterprise logistics and countermeasures: From the perspective of the participants in the green development of enterprise logistics. Chinese Journal of Management Science, 2014, 22 (S1): 788–793. doi: 10.16381/j.cnki.issn1003-207x.2014.s1.115
    [24]
    Yu L J, Chen Z Q. Research on 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
    [25]
    Zhou Q L, Hu W, Huang Y J. An analysis of the exteriority of green physical distribution and the subjects’ games. Journal of Shenzhen University (Humanities & Social Sciences), 2007, 24 (2): 49–53.
    [26]
    Wang S L, Yan G L, Li Z. Evolutionary game analysis of reverse logistics. Journal of Systems Engineering, 2010, 25 (4): 520–525.
    [27]
    Xu J Z, Xu Y Y. Low-carbon technology innovation diffusion under government environmental regulation: Evolutionary game analysis based on prospect theory. Systems Engineering, 2015, 33 (2): 118–125.
    [28]
    Weibull J W. Evolutionary Game Theory. Cambridge, MA: MIT Press, 1995.
    [29]
    Friedman D. Evolutionary games in economics. Econometrica, 1991, 59: 637–666.
    [30]
    Selten R. A note on evolutionarily stable strategies in asymmetric animal conflicts. Journal of Theoretical Biology, 1980, 84 (1): 93–101. doi: 10.1016/S0022-5193(80)81038-1
    [31]
    Friedman D. On economic application of evolutionary game theory. Journal of Evolutionary Economics, 1998 (1): 15–43. doi: 10.1007/s001910050054
    [32]
    Shubik M. Game Theory Evolving: A Problem-Centered Introduction to Modeling Strategic Behavior, by Gintis H. Princeton, NJ: Princeton University Press, 2000. Reviewed by Shubik M. Journal of Economics, 2001, 73 (2): 207–209.
    [33]
    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
    [34]
    Wang X H, Ren X X. Research on platform e-commerce credit supervision mechanism based on evolutionary game. Systems Engineering: Theory & Practice, 2020, 40 (10): 2617–2630. doi: 10.12011/1000-6788-2019-1967-14
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