IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v134y2024ics0140988324003219.html
   My bibliography  Save this article

A win-win relationship? New evidence on artificial intelligence and new energy vehicles

Author

Listed:
  • Gu, Jianqiang
  • Wu, Zhan
  • Song, Yubing
  • Nicolescu, Ana-Cristina

Abstract

Investigating the vital role of artificial intelligence is essential to develop the electric vehicle market. This study utilises the wavelet-based QQR methodology to seize the dynamic correlation of artificial intelligence index (AII) and electric vehicle indicator (EVI). Based on quantitative deliberations, the favourable effects of AII on EVI at low-low and high-high quantiles and adverse impacts at high-low and low-high quantiles in the short run confirm the role of artificial intelligence in facilitating the electric vehicle market. However, the favourable effect of AII at medium to high quantiles on EVI at low quantiles refutes it because of the crowding-out effect. Conversely, the positive impact of EVI at medium to high quantiles on AII at low to medium quantiles ascertains the crowding-out effect of electric vehicles, while AII at medium to high quantiles cannot agree on it due to safety and convenience needs. In the mid-to-long term, the interactions of AII and EVI are gradually weakened, and speculative behaviours, crowding-out effects, and safety concerns drive the different cases. Therefore, a win-win situation between them does not always hold, and recommendations are being offered to enhance the significance of artificial intelligence in electric vehicles under the new round of scientific and technological revolution.

Suggested Citation

  • Gu, Jianqiang & Wu, Zhan & Song, Yubing & Nicolescu, Ana-Cristina, 2024. "A win-win relationship? New evidence on artificial intelligence and new energy vehicles," Energy Economics, Elsevier, vol. 134(C).
    • Handle: RePEc:eee:eneeco:v:134:y:2024:i:c:s0140988324003219
    DOI: 10.1016/j.eneco.2024.107613
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324003219
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2024.107613?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Artificial intelligence; Electric vehicles; Win-win relationship; Dynamic;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q21 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Demand and Supply; Prices
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    Statistics

    Access and download statistics

    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:eee:eneeco:v:134:y:2024:i:c:s0140988324003219. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    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.