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Dynamic connectedness of quantum computing, artificial intelligence, and big data stocks on renewable and sustainable energy

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  • Ghaemi Asl, Mahdi
  • Ben Jabeur, Sami
  • Nammouri, Hela
  • Bel Hadj Miled, Kamel

Abstract

This research aims to evaluate the accuracy of the long-term relationship between renewable and sustainable energy sectors and emerging technologies, including quantum computing, artificial intelligence (AI), and big data. Using a novel methodology that integrates the Time-Varying Parameter Vector Autoregressive (TVP-VAR) frequency connectedness approach with Long Short-Term Memory (LSTM) neural networks, the study examines the long-term interconnectedness, considering the dynamic nature of coefficients and covariance structures. The analysis spans from May 14, 2018, to September 6, 2023. It focuses on six critical clusters within the sustainable and renewable energy sectors: clean energy, green energy, solar energy, the water industry, wind energy, and the low-carbon industry. Additionally, the study explores two contemporary technology domains, AI and big data, alongside quantum computing. The findings reveal that AI and its associated technologies generally exhibit weaker connections to the renewable and sustainable energy sectors. However, specific pairs, such as those involving business intelligence and AI, show notable interconnectedness. Overall, quantum computing entities demonstrate lower levels of connectedness than the AI/significant data sector, with Microsoft standing out for its solid and broad connections to renewable and sustainable industries. Further analysis identifies distinct patterns, with AI and related technologies showing strong long-term memory connections with renewables and green energies. At the same time, platforms centered on business intelligence and AI display comparatively weaker long-term ties. Among the quantum computing companies, IBM and Google have shown superior performance through specific subsectors. Finally, this study offers valuable insights into the evolving dynamics and interconnectedness at the intersection of renewable and sustainable energies, quantum computing, and the AI/big data industries. The findings support strategic decision-making in sustainable energy transitions and underscore the significance of industry-specific factors in shaping long-term collaborations.

Suggested Citation

  • Ghaemi Asl, Mahdi & Ben Jabeur, Sami & Nammouri, Hela & Bel Hadj Miled, Kamel, 2024. "Dynamic connectedness of quantum computing, artificial intelligence, and big data stocks on renewable and sustainable energy," Energy Economics, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:eneeco:v:140:y:2024:i:c:s0140988324007254
    DOI: 10.1016/j.eneco.2024.108017
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    Cited by:

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    3. Naveed Khan & Anam Tariq & Syed Zulfiqar Ali Shah & Hassan Javed, 2026. "Quantile time–frequency connectedness and spillover between artificial intelligence, clean energy, and traditional asset classes: insights and portfolio implications," Future Business Journal, Springer, vol. 12(1), pages 1-39, December.

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    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • 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
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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