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Artificial intelligence, cloud computing, blockchain, and the energy market in the era of energy transition

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  • Wang, Kai-Hua
  • Jiang, Xin-Yu
  • Tang, Yun

Abstract

This study explores the spillover effects between artificial intelligence (AI), cloud computing (CC), blockchain (BC), and eight traditional and renewable energies using cross-quantilogram and quantile time–frequency connectedness approaches. The findings reveal a distinct inverse relationship between the lowest energy return quantile and the highest digital technology quantile. We also find that spillovers change over time, frequency, and quantile levels with hydrogen energy remaining a primary risk transmitter and crude oil as a receiver. Additionally, while digital technologies may impose short-term negative shocks on the energy market, their influence on hedging risks and stabilization is more prominent in comparison. Furthermore, the dependence between AI, CC, BC, and energy indices was insignificant before the COVID-19 pandemic but increased significantly afterward. This analysis is based on high-frequency data from Wind. This study extends the literature by constructing a comprehensive spillover network integrating digital technologies and energy markets. In addition, we conduct a dynamic analysis of asymmetric spillovers across short- and long-term time horizons, while also emphasizing the hedging and stabilizing functions of digital technology within energy markets. Our findings provide actionable insights for promoting digital transformation and strengthening international cooperation.

Suggested Citation

  • Wang, Kai-Hua & Jiang, Xin-Yu & Tang, Yun, 2025. "Artificial intelligence, cloud computing, blockchain, and the energy market in the era of energy transition," Energy Economics, Elsevier, vol. 151(C).
  • Handle: RePEc:eee:eneeco:v:151:y:2025:i:c:s0140988325008059
    DOI: 10.1016/j.eneco.2025.108975
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