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

Mahdi Ghaemi Asl, Sami Ben Jabeur (), Hela Nammouri () and Kamel Miled
Additional contact information
Sami Ben Jabeur: UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University)
Hela Nammouri: UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University)

Post-Print from HAL

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.

Keywords: Renewable and Sustainable Energies; Quantum Computing; Artificial Intelligence; Big Data; TVP-VAR Frequency Connectedness Approach; Long Short-Term Memory.; mémoire à long terme.; approche de la connectivité des fréquences TVP-VAR; intelligence artificielle; informatique quantique; Énergies renouvelables et durables (search for similar items in EconPapers)
Date: 2024-12
References: Add references at CitEc
Citations:

Published in Energy economics, 2024, 140, pp.108017. ⟨10.1016/j.eneco.2024.108017⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05238464

DOI: 10.1016/j.eneco.2024.108017

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().