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Effect of personal carbon trading on EV adoption behavior based on a stochastic Petri net

  • 1.

    School of Economics and Management, Chang'an University, Xi'an 710064, China

  • 2.

    College of Transportation Engineering, Chang'an University, Xi'an 710064, China

  • 3.

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

Funds:  China Postdoctoral Science Foundation under grant 2020M683403; Fundamental Re-search Funds for the Central Universities under grant 300102230104; National Natural Science Foundation of China under grant 71804174 and 71974177; Science and Technology Planning Project of Shaanxi Province, China under grant 2020JQ-398; USTC Research Funds of the Double First-Class Initiative under grant YD2160002002.
Cite this:
https://doi.org/10.52396/JUST-2020-0024
More Information
  • Author Bio:

    He Haonan is a lecturer at Chang'an University. His research interests include low carbon behavior and environmental policy. He has published several articles in some high quality journals such as Transportation Research Part D, Journal of Retailing and Consumer Services, and Economic Modelling.

    Ren Wei is a graduate student at Chang'an University. His research focuses on the transportation network vulnerability and low-carbon transportation development.

    Wang Zuohanga graduate student at Chang'an University. His research mainly focuses on the transportation network vulnerability.

    Zhao Chenyong is currently a graduate student at Chang'an University. His research focuses on urban transportation planning and low-carbon transportation development.

    Fei Ma is a Professor at Chang'an University. His research interests include sustainability, transportation, and logistics management. He has published more than 30 research articles in many high quality journals such as Journal of Cleaner Production and International Journal of Sustainable Transportation.

  • Corresponding author: Wang Shanyong, is an Associate Professor at University of Science and Technology of China. His research interests include sustainable development and green management. He has published several articles in many high quality journals such as Energy and Energy Policy. E-mail: wsy1988@ustc.edu.cn
  • Publish Date: 31 January 2021
    Abstract

  • Abstract

    The increasing urgency of environmental issues and maturity of the upstream carbon trading schemes indicate that personal carbon trading (PCT) is likely to be implemented soon, which will significantly affect the green behavior of consumers. In this study, a stochastic Petri net (SPN) model was constructed to analyze the evolution of the residential EV adoption behavior under a PCT scheme and the impacts of the environmental awareness and the PCT scheme on the EV adoption behavior were quantified. The results of this work show that the introduction of PCT does not necessarily positively impact the EV adoption. An emission quota and “cap-and-trade” attributes can significantly increase the environmental awareness of consumers, which is a “double-edged sword” for the EV adoption behavior at this stage. Specifically, it raises questions about the actual low-carbon performance of EVs and changes in travel patterns while increasing the willingness of consumers to pay a premium for the low-carbon products. Therefore, the government should rationalize the strength of PCT policies and traditional incentives to maximize the goal of promoting the EV adoption. The results can aid in gaining a better understanding of the behavioral evolution of the consumer EV adoption under the PCT scheme and provide theoretical support for government policymaking and product design and pricing by EV companies.

    Abstract

    The increasing urgency of environmental issues and maturity of the upstream carbon trading schemes indicate that personal carbon trading (PCT) is likely to be implemented soon, which will significantly affect the green behavior of consumers. In this study, a stochastic Petri net (SPN) model was constructed to analyze the evolution of the residential EV adoption behavior under a PCT scheme and the impacts of the environmental awareness and the PCT scheme on the EV adoption behavior were quantified. The results of this work show that the introduction of PCT does not necessarily positively impact the EV adoption. An emission quota and “cap-and-trade” attributes can significantly increase the environmental awareness of consumers, which is a “double-edged sword” for the EV adoption behavior at this stage. Specifically, it raises questions about the actual low-carbon performance of EVs and changes in travel patterns while increasing the willingness of consumers to pay a premium for the low-carbon products. Therefore, the government should rationalize the strength of PCT policies and traditional incentives to maximize the goal of promoting the EV adoption. The results can aid in gaining a better understanding of the behavioral evolution of the consumer EV adoption under the PCT scheme and provide theoretical support for government policymaking and product design and pricing by EV companies.
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    • References(47)
  • [1]
    Others D B. BP statistical review of world energy. BP Statistical Review, London, UK, accessed, 2018, 6: 2018.
    [2]
    Rezvani Z, Jansson J, Bodin J. Advances in consumer electric vehicle adoption research: A review and research agenda. Transportation Research Part D: Transport and Environment, 2015, 34: 122-136.
    [3]
    Ensslen A, Gnann T, Jochem P, et al. Can product service systems support electric vehicle adoption? Transportation Research Part A: Policy and Practice, 2020,137: 343-359.
    [4]
    陈开朗. 新能源汽车行业中政府、企业和消费者三方的博弈分析. 经济研究导刊, 2015(12): 72-75.
    Chen K. Analysis of government, enterprise and consumer games in the new energy vehicle industry. Economic Research Guide, 2015(12): 72-75.
    [5]
    Liao F, Molin E, Timmermans H, et al. Consumer preferences for business models in electric vehicle adoption. Transport Policy, 2019, 73: 12-24.
    [6]
    Elma O. A dynamic charging strategy with hybrid fast charging station for electric vehicles. Energy, 2020, 202: 117680.
    [7]
    Fan J, He H, Wu Y. Personal carbon trading and subsidies for hybrid electric vehicles. Economic Modelling, 2016, 59: 164-173.
    [8]
    He H, Fan J, Li Y, et al. When to switch to a hybrid electric vehicle: A replacement optimisation decision. Journal of Cleaner Production, 2017, 148: 295-303.
    [9]
    Fawcett T, Parag Y. An introduction to personal carbon trading.Climate Policy, 2010, 10(4): 329-338.
    [10]
    He H, Wang S. Cost-benefit associations in consumer inventory problem with uncertain benefit. Journal of Retailing and Consumer Services, 2019, 51: 271-284.
    [11]
    Fawcett T. Personal carbon trading: A policy ahead of its time? Energy Policy, 2010, 38(11): 6868-6876.
    [12]
    Li J, Wang S, Fan J, et al. An equilibrium model of consumer energy choice using a personal carbon trading scheme based on allowance price. Journal of Cleaner Production, 2018, 204: 1087-1096.
    [13]
    Cao K, Xu X, Wu Q, et al. Optimal production and carbon emission reduction level under cap-and-trade and low carbon subsidy policies. Journal of Cleaner Production, 2017, 167: 505-513.
    [14]
    Pang Q, Li M, Yang T, et al. Supply chain coordination with carbon trading price and consumers' environmental awareness dependent demand. Mathematical Problems in Engineering, 2018(6): 1-11.
    [15]
    BECK M J, ROSE J M, HENSHER D A. Environmental attitudes and emissions charging: An example of policy implications for vehicle choice. Transportation Research Part A: Policy and Practice, 2013, 50: 171-182.
    [16]
    Bristow A L, Wardman M, Zanni A M, et al. Public acceptability of personal carbon trading and carbon tax. Ecological Economics, 2010, 69(9): 1824-1837.
    [17]
    Raux C, Croissant Y, Pons D. Would personal carbon trading reduce travel emissions more effectively than a carbon tax? Transportation Research Part D: Transport and Environment, 2015, 35: 72-83.
    [18]
    Li W, Long R, Chen H, et al. Would personal carbon trading enhance individual adopting intention of battery electric vehicles more effectively than a carbon tax? Resources, Conservation and Recycling, 2019, 149: 638-645.
    [19]
    Li W, Long R, Chen H, et al. Effects of personal carbon trading on the decision to adopt battery electric vehicles: Analysis based on a choice experiment in Jiangsu, China. Applied Energy, 2018, 209: 478-488.
    [20]
    Bows A, Anderson K, Upham P. Contraction & Convergence: UK Carbon Emissions and the Implications for UK Air Traffic. Manchester: Tyndall Centre for Climate Change Research, 2006.
    [21]
    Fawcett T. Carbon rationing and personal energy use. Energy & Environment, 2004, 15(6): 1067-1083.
    [22]
    Starkey R, Anderson K. Domestic Tradable Quotas: A Policy Instrument for Reducing Greenhouse Gas Emissions from Energy use. Norwich, UK: Tyndall centre for climate change research, 2005.
    [23]
    Roberts S, Thumim J. A rough guide to individual carbon trading-the ideas, the issues and the next steps. Report to Defra, Simon Roberts and Joshua Thumim Centre for Sustainable Energy, 2006.
    [24]
    Niemeier D, Gould G, Karner A, et al. Rethinking downstream regulation: California's opportunity to engage households in reducing greenhouse gases. Energy Policy, 2008, 36(9): 3436-3447.
    [25]
    Wadud Z. Personal tradable carbon permits for road transport: Why, why not and who wins? Transportation Research Part A: Policy and Practice, 2011, 45(10): 1052-1065.
    [26]
    Fan J, Wang S, Wu Y, et al. Buffer effect and price effect of a personal carbon trading scheme. Energy, 2015, 82: 601-610.
    [27]
    Nelson T, Kelley S, Orton F. A literature review of economic studies on carbon pricing and Australian wholesale electricity markets. Energy Policy, 2012, 49: 217-224.
    [28]
    Mcnamara D, Caulfield B. Examining the impact of carbon price changes under a personalised carbon trading scheme for transport. Transport Policy, 2013, 30: 238-253.
    [29]
    Li J, Wang S, Fan J, et al. Impact of personal carbon trading schemes on consumer energy consumption. Systems Engineering-Theory & Practice, 2016, 36(1): 77-85.
    [30]
    Zhao L, Wu S. Research on the theory framework and effects of carbon emission reduction of carbon household registration:Based on the comparative analysis of private cars control. China Economic Studies, 2017(2): 107-117.
    [31]
    Zhao L, Wu S. Approach to carbon-exhaust emission reduction through a comparative analysis based on the carbon trading and taxation from the private vehicles owners. Journal of Safety and Environment, 2017,17(2): 688-693.
    [32]
    Li L, Wang Z, Wang Q. Do policy mix characteristics matter for electric vehicle adoption? A survey-based exploration. Transportation Research Part D: Transport and Environment, 2020, 87: 102488.
    [33]
    Jeon C, Yoo J, Choi M K. The effect of social influence on consumers' hybrid electric vehicles adoption in Korea and China. 14th International Conference on Advanced Communication Technology. PyeongChang, South Korea: IEEE, 2012: 336-340.
    [34]
    Zhang X, Bai X. Incentive policies from 2006 to 2016 and new energy vehicle adoption in 2010-2020 in China. Renewable and Sustainable Energy Reviews, 2017, 70: 24-43.
    [35]
    Yun J. New energy vehicles. Traffic & Transportation, 2008, 24(2): 28-30.
    [36]
    He H, Wang C, Wang S, et al. Does environmental concern promote EV sales? Duopoly pricing analysis considering consumer heterogeneity. Transportation Research Part D: Transport and Environment, 2021, 91: 102695.
    [37]
    Ma J, Wang N, Kong D. Market Forecasting modeling study for new energy vehicle based on AHP and logit regression. Journal of Tongji University, 2009, 37(8): 1079-1084.
    [38]
    Wang Y, Wang Q. Factor saffecting Beijing residents' buying behavior of new energy vehicle: An integration of technology acceptance model and theory of planned behavior. Chinese Journal of Management Science, 2013,21(S2): 691-698.
    [39]
    Han L, Wang S, Zhao D, et al. The intention to adopt electric vehicles: Driven by functional and non-functional values. Transportation Research Part A: Policy and Practice, 2017, 103: 185-197.
    [40]
    Jang D C, Kim B, Lee S Y. A two-sided market platform analysis for the electric vehicle adoption: Firm strategies and policy design. Transportation Research Part D: Transport and Environment, 2018, 62: 646-658.
    [41]
    Zhang Z, Sun X, Ding N, et al. Life cycle environmental assessment of charging infrastructure for electric vehicles in China. Journal of Cleaner Production, 2019, 227: 932-941.
    [42]
    Kong D, Bi X. Impact of social network and business model on innovation diffusion of electric vehicles in China. Mathematical Problems in Engineering, 2014, Article ID: 230765.
    [43]
    Plötz P, Gnann T, Wietschel M. Modelling market diffusion of electric vehicles with real world driving data—Part I: Model structure and validation. Ecological Economics, 2014, 107: 411-421.
    [44]
    Schwoon M. A tool to optimize the initial distribution of hydrogen filling stations. Transportation Research Part D: Transport and Environment, 2007, 12(2): 70-82.
    [45]
    Xiang S, Ma T. Integration of ABM and GIS and its application in analysis of diffusion of alternative energy vehicles. Journal of Management Science of China, 2014, 17(1).
    [46]
    Wang Z, Li Y, Wang W. Evolving Model of information towards emergencies based on stochastic Petri net. Chinese Journal of Management Science, 2020,28(3): 113-121.
    [47]
    Wang S, Fan J, Zhao D, et al. Predicting consumers' intention to adopt hybrid electric vehicles: using an extended version of the theory of planned behavior model. Transportation, 2016, 43(1): 123-143.
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    [1]
    Others D B. BP statistical review of world energy. BP Statistical Review, London, UK, accessed, 2018, 6: 2018.
    [2]
    Rezvani Z, Jansson J, Bodin J. Advances in consumer electric vehicle adoption research: A review and research agenda. Transportation Research Part D: Transport and Environment, 2015, 34: 122-136.
    [3]
    Ensslen A, Gnann T, Jochem P, et al. Can product service systems support electric vehicle adoption? Transportation Research Part A: Policy and Practice, 2020,137: 343-359.
    [4]
    陈开朗. 新能源汽车行业中政府、企业和消费者三方的博弈分析. 经济研究导刊, 2015(12): 72-75.
    Chen K. Analysis of government, enterprise and consumer games in the new energy vehicle industry. Economic Research Guide, 2015(12): 72-75.
    [5]
    Liao F, Molin E, Timmermans H, et al. Consumer preferences for business models in electric vehicle adoption. Transport Policy, 2019, 73: 12-24.
    [6]
    Elma O. A dynamic charging strategy with hybrid fast charging station for electric vehicles. Energy, 2020, 202: 117680.
    [7]
    Fan J, He H, Wu Y. Personal carbon trading and subsidies for hybrid electric vehicles. Economic Modelling, 2016, 59: 164-173.
    [8]
    He H, Fan J, Li Y, et al. When to switch to a hybrid electric vehicle: A replacement optimisation decision. Journal of Cleaner Production, 2017, 148: 295-303.
    [9]
    Fawcett T, Parag Y. An introduction to personal carbon trading.Climate Policy, 2010, 10(4): 329-338.
    [10]
    He H, Wang S. Cost-benefit associations in consumer inventory problem with uncertain benefit. Journal of Retailing and Consumer Services, 2019, 51: 271-284.
    [11]
    Fawcett T. Personal carbon trading: A policy ahead of its time? Energy Policy, 2010, 38(11): 6868-6876.
    [12]
    Li J, Wang S, Fan J, et al. An equilibrium model of consumer energy choice using a personal carbon trading scheme based on allowance price. Journal of Cleaner Production, 2018, 204: 1087-1096.
    [13]
    Cao K, Xu X, Wu Q, et al. Optimal production and carbon emission reduction level under cap-and-trade and low carbon subsidy policies. Journal of Cleaner Production, 2017, 167: 505-513.
    [14]
    Pang Q, Li M, Yang T, et al. Supply chain coordination with carbon trading price and consumers' environmental awareness dependent demand. Mathematical Problems in Engineering, 2018(6): 1-11.
    [15]
    BECK M J, ROSE J M, HENSHER D A. Environmental attitudes and emissions charging: An example of policy implications for vehicle choice. Transportation Research Part A: Policy and Practice, 2013, 50: 171-182.
    [16]
    Bristow A L, Wardman M, Zanni A M, et al. Public acceptability of personal carbon trading and carbon tax. Ecological Economics, 2010, 69(9): 1824-1837.
    [17]
    Raux C, Croissant Y, Pons D. Would personal carbon trading reduce travel emissions more effectively than a carbon tax? Transportation Research Part D: Transport and Environment, 2015, 35: 72-83.
    [18]
    Li W, Long R, Chen H, et al. Would personal carbon trading enhance individual adopting intention of battery electric vehicles more effectively than a carbon tax? Resources, Conservation and Recycling, 2019, 149: 638-645.
    [19]
    Li W, Long R, Chen H, et al. Effects of personal carbon trading on the decision to adopt battery electric vehicles: Analysis based on a choice experiment in Jiangsu, China. Applied Energy, 2018, 209: 478-488.
    [20]
    Bows A, Anderson K, Upham P. Contraction & Convergence: UK Carbon Emissions and the Implications for UK Air Traffic. Manchester: Tyndall Centre for Climate Change Research, 2006.
    [21]
    Fawcett T. Carbon rationing and personal energy use. Energy & Environment, 2004, 15(6): 1067-1083.
    [22]
    Starkey R, Anderson K. Domestic Tradable Quotas: A Policy Instrument for Reducing Greenhouse Gas Emissions from Energy use. Norwich, UK: Tyndall centre for climate change research, 2005.
    [23]
    Roberts S, Thumim J. A rough guide to individual carbon trading-the ideas, the issues and the next steps. Report to Defra, Simon Roberts and Joshua Thumim Centre for Sustainable Energy, 2006.
    [24]
    Niemeier D, Gould G, Karner A, et al. Rethinking downstream regulation: California's opportunity to engage households in reducing greenhouse gases. Energy Policy, 2008, 36(9): 3436-3447.
    [25]
    Wadud Z. Personal tradable carbon permits for road transport: Why, why not and who wins? Transportation Research Part A: Policy and Practice, 2011, 45(10): 1052-1065.
    [26]
    Fan J, Wang S, Wu Y, et al. Buffer effect and price effect of a personal carbon trading scheme. Energy, 2015, 82: 601-610.
    [27]
    Nelson T, Kelley S, Orton F. A literature review of economic studies on carbon pricing and Australian wholesale electricity markets. Energy Policy, 2012, 49: 217-224.
    [28]
    Mcnamara D, Caulfield B. Examining the impact of carbon price changes under a personalised carbon trading scheme for transport. Transport Policy, 2013, 30: 238-253.
    [29]
    Li J, Wang S, Fan J, et al. Impact of personal carbon trading schemes on consumer energy consumption. Systems Engineering-Theory & Practice, 2016, 36(1): 77-85.
    [30]
    Zhao L, Wu S. Research on the theory framework and effects of carbon emission reduction of carbon household registration:Based on the comparative analysis of private cars control. China Economic Studies, 2017(2): 107-117.
    [31]
    Zhao L, Wu S. Approach to carbon-exhaust emission reduction through a comparative analysis based on the carbon trading and taxation from the private vehicles owners. Journal of Safety and Environment, 2017,17(2): 688-693.
    [32]
    Li L, Wang Z, Wang Q. Do policy mix characteristics matter for electric vehicle adoption? A survey-based exploration. Transportation Research Part D: Transport and Environment, 2020, 87: 102488.
    [33]
    Jeon C, Yoo J, Choi M K. The effect of social influence on consumers' hybrid electric vehicles adoption in Korea and China. 14th International Conference on Advanced Communication Technology. PyeongChang, South Korea: IEEE, 2012: 336-340.
    [34]
    Zhang X, Bai X. Incentive policies from 2006 to 2016 and new energy vehicle adoption in 2010-2020 in China. Renewable and Sustainable Energy Reviews, 2017, 70: 24-43.
    [35]
    Yun J. New energy vehicles. Traffic & Transportation, 2008, 24(2): 28-30.
    [36]
    He H, Wang C, Wang S, et al. Does environmental concern promote EV sales? Duopoly pricing analysis considering consumer heterogeneity. Transportation Research Part D: Transport and Environment, 2021, 91: 102695.
    [37]
    Ma J, Wang N, Kong D. Market Forecasting modeling study for new energy vehicle based on AHP and logit regression. Journal of Tongji University, 2009, 37(8): 1079-1084.
    [38]
    Wang Y, Wang Q. Factor saffecting Beijing residents' buying behavior of new energy vehicle: An integration of technology acceptance model and theory of planned behavior. Chinese Journal of Management Science, 2013,21(S2): 691-698.
    [39]
    Han L, Wang S, Zhao D, et al. The intention to adopt electric vehicles: Driven by functional and non-functional values. Transportation Research Part A: Policy and Practice, 2017, 103: 185-197.
    [40]
    Jang D C, Kim B, Lee S Y. A two-sided market platform analysis for the electric vehicle adoption: Firm strategies and policy design. Transportation Research Part D: Transport and Environment, 2018, 62: 646-658.
    [41]
    Zhang Z, Sun X, Ding N, et al. Life cycle environmental assessment of charging infrastructure for electric vehicles in China. Journal of Cleaner Production, 2019, 227: 932-941.
    [42]
    Kong D, Bi X. Impact of social network and business model on innovation diffusion of electric vehicles in China. Mathematical Problems in Engineering, 2014, Article ID: 230765.
    [43]
    Plötz P, Gnann T, Wietschel M. Modelling market diffusion of electric vehicles with real world driving data—Part I: Model structure and validation. Ecological Economics, 2014, 107: 411-421.
    [44]
    Schwoon M. A tool to optimize the initial distribution of hydrogen filling stations. Transportation Research Part D: Transport and Environment, 2007, 12(2): 70-82.
    [45]
    Xiang S, Ma T. Integration of ABM and GIS and its application in analysis of diffusion of alternative energy vehicles. Journal of Management Science of China, 2014, 17(1).
    [46]
    Wang Z, Li Y, Wang W. Evolving Model of information towards emergencies based on stochastic Petri net. Chinese Journal of Management Science, 2020,28(3): 113-121.
    [47]
    Wang S, Fan J, Zhao D, et al. Predicting consumers' intention to adopt hybrid electric vehicles: using an extended version of the theory of planned behavior model. Transportation, 2016, 43(1): 123-143.
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