Low-carbon energy production for sustainable development: a bibliometric analysis and SWOT appraisal of artificial intelligence contributions

Abstract

Artificial intelligence (AI) is positioned as a strategic enabler of low-carbon and renewable energy systems, yet the knowledge base at this intersection has expanded more rapidly than it has consolidated. This study examines the evolving landscape through a Scopus-based bibliometric analysis combined with a SWOT appraisal to clarify how AI is contributing to low-carbon and renewable energy production for sustainable development. The review integrates descriptive performance analysis and science mapping, including bibliographic coupling, citation, co-authorship, co-citation, and keyword co-occurrence. Available evidence were extended through a coded core sample used for strategic interpretation. The findings show that the field has moved from an emerging niche into a rapidly expanding research domain, with annual scientific output rising from 239 documents in 2021 to 2,366 in 2025. Its technical core is strongly concentrated around machine learning and deep learning, particularly in forecasting/prediction and optimization. The corpus contains 5,192 documents linked to sustainable development.