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Open AccessOpen Access JUSTC Management 25 September 2023

Optimizing convergence for dual-credit policy and carbon trading in the automobile sector: A bi-layer planning model

  • 1.

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

  • 2.

    School of Public Affairs, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.52396/JUSTC-2023-0038
More Information
  • Author Bio:

    Haonan He is an Associate Professor at Chang’an University. He received his Ph.D. degree in Business Administration from the University of Science and Technology of China in 2019. His research mainly focuses on decision optimization under uncertainty, energy and environmental efficiency assessment, and low-carbon economic statistics

    Jie Zhao is a Lecturer at Chang’an University. He received his Ph.D. degree in Computer Science from Shaanxi Normal University. His research mainly focuses on artificial intelligence, intelligent optimization and decision-making, and transportation science

  • Corresponding author: E-mail: zhaojie2012@chd.edu.cn
  • Received Date: 10 March 2023
  • Accepted Date: 20 May 2023
    • Available Online: 25 September 2023
    Abstract

  • Abstract

    A growing call has been made to convert the dual-credit policy to carbon trading to further unleash the carbon reduction potential of the automobile sector as China’s dual-carbon strategy progresses. However, controversy exists in academia about the convergence timing of the two policies. Therefore, this paper builds a bi-layer planning model to show the interaction between government policies and automakers’ production and R&D decisions, based on which to explore the optimal decision on carbon trading’s introduction timing and carbon quotas. The results show that the current is not the optimal time to bridge the two policies considering the price difference between carbon pricing and credits. Interestingly, we find that the reduction in carbon emissions per vehicle for new energy vehicles and conventional fuel vehicles has an opposite effect on the optimal timing of the introduction of carbon trading. Moreover, a comparison of the impact of new energy vehicle profits and carbon prices on the timing of introduction shows the former has a greater impact on the adoption of carbon trading in the automobile sector.

    Graphical abstract

    Research methods and conclusions based on a bi-layer planning model.

    Abstract

    A growing call has been made to convert the dual-credit policy to carbon trading to further unleash the carbon reduction potential of the automobile sector as China’s dual-carbon strategy progresses. However, controversy exists in academia about the convergence timing of the two policies. Therefore, this paper builds a bi-layer planning model to show the interaction between government policies and automakers’ production and R&D decisions, based on which to explore the optimal decision on carbon trading’s introduction timing and carbon quotas. The results show that the current is not the optimal time to bridge the two policies considering the price difference between carbon pricing and credits. Interestingly, we find that the reduction in carbon emissions per vehicle for new energy vehicles and conventional fuel vehicles has an opposite effect on the optimal timing of the introduction of carbon trading. Moreover, a comparison of the impact of new energy vehicle profits and carbon prices on the timing of introduction shows the former has a greater impact on the adoption of carbon trading in the automobile sector.

    • Public Summary

    • The interface problem between the dual-credit policy and carbon trading policy in the automobile sector is studied.
    • The optimal timing for the introduction of carbon trading policies to the automobile sector is determined.
    • Government and automakers’ decision interactions are captured through a bi-layer planning model.
    • NEV profits have a larger impact on carbon trading adoption in the automobile sector than carbon prices.

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    • References(51)
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    Li Y, Zhang Q, Liu B, et al. Substitution effect of new-energy vehicle credit program and corporate average fuel consumption regulation for green-car subsidy. Energy, 2018, 152: 223–236. doi: 10.1016/j.energy.2018.03.134
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    Nie Q, Zhang L, Tong Z, et al. Strategies for applying carbon trading to the new energy vehicle market in China: An improved evolutionary game analysis for the bus industry. Energy, 2022, 259: 124904. doi: 10.1016/j.energy.2022.124904
    [30]
    Yu P. Carbon tax/subsidy policy choice and its effects in the presence of interest groups. Energy Policy, 2020, 147: 111886. doi: 10.1016/j.enpol.2020.111886
    [31]
    Liao D, Tan B. An evolutionary game analysis of new energy vehicles promotion considering carbon tax in post-subsidy era. Energy, 2023, 264: 126156. doi: 10.1016/j.energy.2022.126156
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    Tian Y, Xiong S, Ma X, et al. Structural path decomposition of carbon emission: A study of China’s manufacturing industry. Journal of Cleaner Production, 2018, 193: 563–574. doi: 10.1016/j.jclepro.2018.05.047
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    Xu H, Pan X, Li J, et al. Comparing the impacts of carbon tax and carbon emission trading, which regulation is more effective? Journal of Environmental Management, 2023, 330: 117156. doi: 10.1016/j.jenvman.2022.117156
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    Figure  1.  Optimal government decision-making under the benchmark scenario. (a) Data represent the changes in social welfare. (b) Data represent the changes in the automakers’ profit.

    Figure  2.  The impact of the carbon quota ratio on enterprise decisions. Data represent the impact of governments’ carbon quota ratio decisions on automaker decisions.

    Figure  3.  Impact of $ e_1 $ and $ e_2 $ on CT introduction timing and carbon quota ratio. Data represent the impact of the single-vehicle carbon emissions of NEVs and CFVs on the optimal CT introduction timing and carbon quota ratio.

    Figure  4.  Impact of $ p_c $ and $ p_0 $ on CT introduction timing and carbon quota ratio. Data represent the impact of the carbon price and the initial profit of NEVs on government decisions.

    Figure  5.  The impact of $ n $ on CT introduction timing and carbon quota ratio. Data represent the impact of consumers’ low-carbon preference levels ($ n $) on the optimal CT introduction timing and carbon quota ratio.

    [1]
    Chen Z, Du H, Li J, et al. Achieving low-carbon urban passenger transport in China: Insights from the heterogeneous rebound effect. Energy Economics, 2019, 81: 1029–1041. doi: 10.1016/j.eneco.2019.06.009
    [2]
    Nie Q, Zhang L, Li S. How can personal carbon trading be applied in electric vehicle subsidies? A Stackelberg game method in private vehicles. Applied Energy, 2022, 313: 118855. doi: 10.1016/j.apenergy.2022.118855
    [3]
    Fan R, Bao X, Du K, et al. The effect of government policies and consumer green preferences on the R&D diffusion of new energy vehicles: A perspective of complex network games. Energy, 2022, 254: 124316. doi: 10.1016/j.energy.2022.124316
    [4]
    Liu J Y, Feng C. Marginal abatement costs of carbon dioxide emissions and its influencing factors: A global perspective. Journal of Cleaner Production, 2018, 170: 1433–1450. doi: 10.1016/j.jclepro.2017.09.216
    [5]
    Li S, Liu J, Wu J, et al. Spatial spillover effect of carbon emission trading policy on carbon emission reduction: Empirical data from transport industry in China. Journal of Cleaner Production, 2022, 371: 133529. doi: 10.1016/j.jclepro.2022.133529
    [6]
    Brewer T L. Black carbon emissions and regulatory policies in transportation. Energy Policy, 2019, 129: 1047–1055. doi: 10.1016/j.enpol.2019.02.073
    [7]
    Halat K, Hafezalkotob A. Modeling carbon regulation policies in inventory decisions of a multi-stage green supply chain: A game theory approach. Computers & Industrial Engineering, 2019, 128: 807–830. doi: 10.1016/j.cie.2019.01.009
    [8]
    Hong Z, Chu C, Zhang L L, et al. Optimizing an emission trading scheme for local governments: A Stackelberg game model and hybrid algorithm. International Journal of Production Economics, 2017, 193: 172–182. doi: 10.1016/j.ijpe.2017.07.009
    [9]
    Liu C M, Sun Z, Zhang J. Research on the effect of carbon emission reduction policy in China’s carbon emissions trading pilot. China Population, Resources and Environment, 2019, 29 (11): 49–58. (in Chinese) doi: 10.12062/cpre.20190619
    [10]
    Zhang X, Fan D. Research on the impact of the carbon emissions trading market on the efficiency of carbon emission reduction: An empirical analysis based on the double mediation effect. Science of Science and Management of S.&T., 2021, 42 (11): 20–38. (in Chinese)
    [11]
    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. doi: 10.1016/j.jclepro.2017.07.251
    [12]
    Fang G, Tian L, Liu M, et al. How to optimize the development of carbon trading in China—Enlightenment from evolution rules of the EU carbon price. Applied Energy, 2018, 211: 1039–1049. doi: 10.1016/j.apenergy.2017.12.001
    [13]
    Wang C, Wang W, Huang R. Supply chain enterprise operations and government carbon tax decisions considering carbon emissions. Journal of Cleaner Production, 2017, 152: 271–280. doi: 10.1016/j.jclepro.2017.03.051
    [14]
    Chen H, Qi S, Zhang J. Towards carbon neutrality with Chinese characteristics: From an integrated perspective of economic growth-equity-environment. Applied Energy, 2022, 324: 119719. doi: 10.1016/j.apenergy.2022.119719
    [15]
    Sun L, Cao X, Alharthi M, et al. Carbon emission transfer strategies in supply chain with lag time of emission reduction technologies and low-carbon preference of consumers. Journal of Cleaner Production, 2020, 264: 121664. doi: 10.1016/j.jclepro.2020.121664
    [16]
    Zhou Y, Hu F, Zhou Z. Pricing decisions and social welfare in a supply chain with multiple competing retailers and carbon tax policy. Journal of Cleaner Production, 2018, 190: 752–777. doi: 10.1016/j.jclepro.2018.04.162
    [17]
    Kong D, Xia Q, Xue Y, et al. Effects of multi policies on electric vehicle diffusion under subsidy policy abolishment in China: A multi-actor perspective. Applied Energy, 2020, 266: 114887. doi: 10.1016/j.apenergy.2020.114887
    [18]
    Shu T, Wu Q, Chen S, et al. Manufacturers’/remanufacturers’ inventory control strategies with cap-and-trade regulation. Journal of Cleaner Production, 2017, 159: 11–25. doi: 10.1016/j.jclepro.2017.05.021
    [19]
    Zhang Y J, Shi W, Jiang L. Does China’s carbon emissions trading policy improve the technology innovation of relevant enterprises? Business Strategy and the Environment, 2020, 29 (3): 872–885. doi: 10.1002/bse.2404
    [20]
    Wang M, Li Y, Li M, et al. Will carbon tax affect the strategy and performance of low-carbon technology sharing between enterprises? Journal of Cleaner Production, 2019, 210: 724–737. doi: 10.1016/j.jclepro.2018.10.321
    [21]
    Zhang L, Xue L, Zhou Y. How do low-carbon policies promote green diffusion among alliance-based firms in China? An evolutionary-game model of complex networks. Journal of Cleaner Production, 2019, 210: 518–529. doi: 10.1016/j.jclepro.2018.11.028
    [22]
    Zhao M, Sun T, Feng Q. Capital allocation efficiency, technological innovation and vehicle carbon emissions: Evidence from a panel threshold model of Chinese new energy vehicles enterprises. Science of the Total Environment, 2021, 784: 147104. doi: 10.1016/j.scitotenv.2021.147104
    [23]
    Du H, Chen Z, Peng B, et al. What drives CO2 emissions from the transport sector? A linkage analysis. Energy, 2019, 175: 195–204. doi: 10.1016/j.energy.2019.03.052
    [24]
    Bonsu N O. Towards a circular and low-carbon economy: Insights from the transitioning to electric vehicles and net zero economy. Journal of Cleaner Production, 2020, 256: 120659. doi: 10.1016/j.jclepro.2020.120659
    [25]
    Ma J, Hou Y, Yang W, et al. A time-based pricing game in a competitive vehicle market regarding the intervention of carbon emission reduction. Energy Policy, 2020, 142: 111440. doi: 10.1016/j.enpol.2020.111440
    [26]
    Yao M, Liu H, Feng X. The development of low-carbon vehicles in China. Energy Policy, 2011, 39 (9): 5457–5464. doi: 10.1016/j.enpol.2011.05.017
    [27]
    Li Y, Zhang Q, Liu B, et al. Substitution effect of new-energy vehicle credit program and corporate average fuel consumption regulation for green-car subsidy. Energy, 2018, 152: 223–236. doi: 10.1016/j.energy.2018.03.134
    [28]
    Li J, Ku Y, Liu C, et al. Dual credit policy: Promoting new energy vehicles with battery recycling in a competitive environment? Journal of Cleaner Production, 2020, 243: 118456. doi: 10.1016/j.jclepro.2019.118456
    [29]
    Nie Q, Zhang L, Tong Z, et al. Strategies for applying carbon trading to the new energy vehicle market in China: An improved evolutionary game analysis for the bus industry. Energy, 2022, 259: 124904. doi: 10.1016/j.energy.2022.124904
    [30]
    Yu P. Carbon tax/subsidy policy choice and its effects in the presence of interest groups. Energy Policy, 2020, 147: 111886. doi: 10.1016/j.enpol.2020.111886
    [31]
    Liao D, Tan B. An evolutionary game analysis of new energy vehicles promotion considering carbon tax in post-subsidy era. Energy, 2023, 264: 126156. doi: 10.1016/j.energy.2022.126156
    [32]
    Tian Y, Xiong S, Ma X, et al. Structural path decomposition of carbon emission: A study of China’s manufacturing industry. Journal of Cleaner Production, 2018, 193: 563–574. doi: 10.1016/j.jclepro.2018.05.047
    [33]
    Lindner S, Liu Z, Guan D, et al. CO2 emissions from China’s power sector at the provincial level: Consumption versus production perspectives. Renewable and Sustainable Energy Reviews, 2013, 19: 164–172. doi: 10.1016/j.rser.2012.10.050
    [34]
    Xu H, Pan X, Li J, et al. Comparing the impacts of carbon tax and carbon emission trading, which regulation is more effective? Journal of Environmental Management, 2023, 330: 117156. doi: 10.1016/j.jenvman.2022.117156
    [35]
    Luo W, Zhang Y, Gao Y, et al. Life cycle carbon cost of buildings under carbon trading and carbon tax system in China. Sustainable Cities and Society, 2021, 66: 102509. doi: 10.1016/j.scs.2020.102509
    [36]
    Jia Z, Lin B. Rethinking the choice of carbon tax and carbon trading in China. Technological Forecasting and Social Change, 2020, 159: 120187. doi: 10.1016/j.techfore.2020.120187
    [37]
    Hu X, Yang Z, Sun J, et al. Carbon tax or cap-and-trade: Which is more viable for Chinese remanufacturing industry? Journal of Cleaner Production, 2020, 243: 118606. doi: 10.1016/j.jclepro.2019.118606
    [38]
    Sun H, Yang J. Optimal decisions for competitive manufacturers under carbon tax and cap-and-trade policies. Computers & Industrial Engineering, 2021, 156: 107244. doi: 10.1016/j.cie.2021.107244
    [39]
    Chen Y, Wang C, Nie P, et al. A clean innovation comparison between carbon tax and cap-and-trade system. Energy Strategy Reviews, 2020, 29: 100483. doi: 10.1016/j.esr.2020.100483
    [40]
    Yin G, Zhou L, Duan M, et al. Impacts of carbon pricing and renewable electricity subsidy on direct cost of electricity generation: A case study of China’s provincial power sector. Journal of Cleaner Production, 2018, 205: 375–387. doi: 10.1016/j.jclepro.2018.09.108
    [41]
    Liebensteiner M, Haxhimusa A, Naumann F. Subsidized renewables’ adverse effect on energy storage and carbon pricing as a potential remedy. Renewable and Sustainable Energy Reviews, 2023, 171: 112990. doi: 10.1016/j.rser.2022.112990
    [42]
    Li G, Zheng H, Ji X, et al. Game theoretical analysis of firms’ operational low-carbon strategy under various cap-and-trade mechanisms. Journal of Cleaner Production, 2018, 197: 124–133. doi: 10.1016/j.jclepro.2018.06.177
    [43]
    Lou G, Ma H, Fan T, et al. Impact of the dual-credit policy on improvements in fuel economy and the production of internal combustion engine vehicles. Resources, Conservation and Recycling, 2020, 156: 104712. doi: 10.1016/j.resconrec.2020.104712
    [44]
    Ou S, Lin Z, Qi L, et al. The dual-credit policy: Quantifying the policy impact on plug-in electric vehicle sales and industry profits in China. Energy Policy, 2018, 121: 597–610. doi: 10.1016/j.enpol.2018.06.017
    [45]
    Meng W, Ma M, Li Y, et al. New energy vehicle R&D strategy with supplier capital constraints under China’s dual credit policy. Energy Policy, 2022, 168: 113099. doi: 10.1016/j.enpol.2022.113099
    [46]
    He H, Li S, Wang S, et al. Interaction mechanism between dual-credit pricing and automobile manufacturers’ electrification decisions. Transportation Research Part D: Transport and Environment, 2022, 109: 103390. doi: 10.1016/j.trd.2022.103390
    [47]
    Qiao Q, Zhao F, Liu Z, et al. Life cycle greenhouse gas emissions of electric vehicles in China: Combining the vehicle cycle and fuel cycle. Energy, 2019, 177: 222–233. doi: 10.1016/j.energy.2019.04.080
    [48]
    Luo Z, Chen X, Wang X. The role of co-opetition in low carbon manufacturing. European Journal of Operational Research, 2016, 253 (2): 392–403. doi: 10.1016/j.ejor.2016.02.030
    [49]
    Wang S, Chen K, Zhao F, et al. Technology pathways for complying with Corporate Average Fuel Consumption regulations up to 2030: A case study of China. Applied Energy, 2019, 241: 257–277. doi: 10.1016/j.apenergy.2019.03.092
    [50]
    Miller M, Modigliani F. The cost of capital, corporate finance and the theory of investment. American Economic Review, 1958, 48 (3): 261–297.
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