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

Author

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  • 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
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    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

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