IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p16003-d989307.html
   My bibliography  Save this article

The Fuel Cycle Carbon Reduction Effects of New Energy Vehicles: Empirical Evidence Based on Regional Data in China

Author

Listed:
  • Anqi Chen

    (Economics and Management School, Wuhan University, Wuhan 430072, China)

  • Shibing You

    (Economics and Management School, Wuhan University, Wuhan 430072, China)

Abstract

With ever-growing energy demands and increasing greenhouse gas (GHG) emissions, carbon emission reduction has attracted worldwide attention. This article establishes a bottom-up method using regional data from 2010 to 2020 to quantify the carbon reduction effects of new energy vehicles (NEVs) in the fuel cycle. From this, a generalized Bass model with outstanding performance was created (with a goodness-of-fit of 99.7%) to forecast CO 2 emission reduction potential in 2030 and 2050. The results are as follows: (1) there are regional differences in the fuel cycle carbon reduction effects of NEVs in all six regions, with the Central China power grid having the strongest ability to reduce emissions, while the Northeast and Northwest grids have relatively low carbon reduction effects. (2) Battery electric vehicles (BEVs) have the strongest CO 2 emission reduction effect, while fuel cell vehicles (FCVs) have the most potential. (3) Under the baseline scenario, the carbon reduction of NEVs will be 2992 million tons in 2030 and reach 11,559 million tons in 2050, which is far from carbon neutrality. Further, policy implications, including the tailoring of policies to specific regions and upgrading the energy mix, are proposed to reduce further carbon emissions.

Suggested Citation

  • Anqi Chen & Shibing You, 2022. "The Fuel Cycle Carbon Reduction Effects of New Energy Vehicles: Empirical Evidence Based on Regional Data in China," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16003-:d:989307
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/16003/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/23/16003/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuan, Xiaodong & Cai, Yuchen, 2021. "Forecasting the development trend of low emission vehicle technologies: Based on patent data," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    2. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    3. Lander, Laura & Kallitsis, Evangelos & Hales, Alastair & Edge, Jacqueline Sophie & Korre, Anna & Offer, Gregory, 2021. "Cost and carbon footprint reduction of electric vehicle lithium-ion batteries through efficient thermal management," Applied Energy, Elsevier, vol. 289(C).
    4. Ren, Lei & Zhou, Sheng & Ou, Xunmin, 2020. "Life-cycle energy consumption and greenhouse-gas emissions of hydrogen supply chains for fuel-cell vehicles in China," Energy, Elsevier, vol. 209(C).
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Lin, Boqiang & Shi, Lei, 2022. "Do environmental quality and policy changes affect the evolution of consumers’ intentions to buy new energy vehicles," Applied Energy, Elsevier, vol. 310(C).
    7. Thakur Dhakal & Kyoung-Soon Min, 2021. "Macro Analysis and Forecast of Global Expansion of Electric Vehicles," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 15(1), pages 67-73.
    8. Dong, Feng & Liu, Yajie, 2020. "Policy evolution and effect evaluation of new-energy vehicle industry in China," Resources Policy, Elsevier, vol. 67(C).
    9. Massiani, Jérôme & Gohs, Andreas, 2015. "The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies," Research in Transportation Economics, Elsevier, vol. 50(C), pages 17-28.
    10. Qiao, Qinyu & Zhao, Fuquan & Liu, Zongwei & He, Xin & Hao, Han, 2019. "Life cycle greenhouse gas emissions of Electric Vehicles in China: Combining the vehicle cycle and fuel cycle," Energy, Elsevier, vol. 177(C), pages 222-233.
    11. Ma, Hongrui & Balthasar, Felix & Tait, Nigel & Riera-Palou, Xavier & Harrison, Andrew, 2012. "A new comparison between the life cycle greenhouse gas emissions of battery electric vehicles and internal combustion vehicles," Energy Policy, Elsevier, vol. 44(C), pages 160-173.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anqi Chen & Shibing You & Huan Liu & Jiaxuan Zhu & Xu Peng, 2023. "A Sustainable Road Transport Decarbonisation: The Scenario Analysis of New Energy Vehicle in China," IJERPH, MDPI, vol. 20(4), pages 1-18, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anqi Chen & Shibing You & Huan Liu & Jiaxuan Zhu & Xu Peng, 2023. "A Sustainable Road Transport Decarbonisation: The Scenario Analysis of New Energy Vehicle in China," IJERPH, MDPI, vol. 20(4), pages 1-18, February.
    2. Brito, Thiago Luis Felipe & Islam, Towhidul & Stettler, Marc & Mouette, Dominique & Meade, Nigel & Moutinho dos Santos, Edmilson, 2019. "Transitions between technological generations of alternative fuel vehicles in Brazil," Energy Policy, Elsevier, vol. 134(C).
    3. Nelly S. Kolyan & Alexander E. Plesovskikh & Roman V. Gordeev, 2023. "Predictive Assessment of the Potential Electric Vehicle Market and the Effects of Reducing Greenhouse Gas Emissions in Russia," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 22(3), pages 497-521.
    4. Park, Changeun & Lim, Sesil & Shin, Jungwoo & Lee, Chul-Yong, 2022. "How much hydrogen should be supplied in the transportation market? Focusing on hydrogen fuel cell vehicle demand in South Korea," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    5. Kumar, Rajeev Ranjan & Guha, Pritha & Chakraborty, Abhishek, 2022. "Comparative assessment and selection of electric vehicle diffusion models: A global outlook," Energy, Elsevier, vol. 238(PC).
    6. Kristof Decock & Koenraad Debackere & Anne- Mieke Vandamme & Bart Looy, 2020. "Scenario-driven forecasting: modeling peaks and paths. Insights from the COVID-19 pandemic in Belgium," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2703-2715, September.
    7. Shi, Yingying & Zeng, Yongchao & Engo, Jean & Han, Botang & Li, Yang & Muehleisen, Ralph T., 2020. "Leveraging inter-firm influence in the diffusion of energy efficiency technologies: An agent-based model," Applied Energy, Elsevier, vol. 263(C).
    8. Qi Wu & Shouheng Sun, 2022. "Energy and Environmental Impact of the Promotion of Battery Electric Vehicles in the Context of Banning Gasoline Vehicle Sales," Energies, MDPI, vol. 15(22), pages 1-18, November.
    9. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    10. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
    11. Yuri Peers & Dennis Fok & Philip Hans Franses, 2012. "Modeling Seasonality in New Product Diffusion," Marketing Science, INFORMS, vol. 31(2), pages 351-364, March.
    12. Constanza Fosco, 2012. "Spatial Difusion and Commuting Flows," Documentos de Trabajo en Economia y Ciencia Regional 30, Universidad Catolica del Norte, Chile, Department of Economics, revised Sep 2012.
    13. Guseo, Renato, 2016. "Diffusion of innovations dynamics, biological growth and catenary function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 1-10.
    14. Nenming Wang & Guwen Tang, 2022. "A Review on Environmental Efficiency Evaluation of New Energy Vehicles Using Life Cycle Analysis," Sustainability, MDPI, vol. 14(6), pages 1-35, March.
    15. Toka, Agorasti & Iakovou, Eleftherios & Vlachos, Dimitrios & Tsolakis, Naoum & Grigoriadou, Anastasia-Loukia, 2014. "Managing the diffusion of biomass in the residential energy sector: An illustrative real-world case study," Applied Energy, Elsevier, vol. 129(C), pages 56-69.
    16. Krishnan, Trichy V. & Feng, Shanfei & Jain, Dipak C., 2023. "Peak sales time prediction in new product sales: Can a product manager rely on it?," Journal of Business Research, Elsevier, vol. 165(C).
    17. Anna Brdulak & Grażyna Chaberek & Jacek Jagodziński, 2021. "BASS Model Analysis in “Crossing the Chasm” in E-Cars Innovation Diffusion Scenarios," Energies, MDPI, vol. 14(11), pages 1-16, May.
    18. Vakratsas, Demetrios & Kolsarici, Ceren, 2008. "A dual-market diffusion model for a new prescription pharmaceutical," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 282-293.
    19. Ruiz-Conde, Enar & Wieringa, Jaap E. & Leeflang, Peter S.H., 2014. "Competitive diffusion of new prescription drugs: The role of pharmaceutical marketing investment," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 49-63.
    20. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16003-:d:989307. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.