Machine Learning for Sustainable Energy Supply Chain Management
El Mahdi LAGHRISSI, Mourad REHIOUI
Abstract. This bibliometric study examines publications from the Scopus database (2020–2026) to chart the conceptual terrain of machine learning applications in sustainable energy supply chain management. Using VOSviewer for network mapping and PRISMA 2020 protocols, it uncovers rapid expansion in research output alongside focused geographic contributions from leading nations. Keyword co-occurrence mapping reveals six thematic clusters encompassing artificial intelligence techniques, learning systems, materials applications, energy policies, economic linkages, and computing advancements, with primary engagement from computer science and engineering fields. Key challenges involve scattered methodologies, narrow regional scope, underdeveloped causal models, and limited circular economy focus. These findings establish a solid base for advancing scholarship and practical efforts linking computational tools to sustainable energy operations.
Keywords
Machine Learning, Sustainable Energy, Supply Chain Management, Bibliometric Analysis, VOSviewer, Renewable Energy, Artificial Intelligence, Energy Logistics
Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: El Mahdi LAGHRISSI, Mourad REHIOUI, Machine Learning for Sustainable Energy Supply Chain Management, Materials Research Proceedings, Vol. 64, pp 1011-1018, 2026
DOI: https://doi.org/10.21741/9781644904091-125
The article was published as article 125 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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