A Bibliometric Analysis and Research Landscape of Machine Learning Applications in Greenhouse Gas Emissions

Samuel-Soma M. Ajibade (), Anthonia Oluwatosin Adediran (), Muhammed Basheer Jasser (), Angela Lee Siew Hoong (), Faizah Mohammed Bashir (), Yakubu Aminu Dodo () and Olumide Simeon Ogunnusi ()
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Samuel-Soma M. Ajibade: School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University, Selangor, Malaysia; & Research Centre for Nanomaterials and Energy Technology, Sunway University, Selangor, Malaysia,
Anthonia Oluwatosin Adediran: Department of Real Estate, Faculty of Built Environment, Universiti Malaya, Kuala Lumpur, Malaysia,
Muhammed Basheer Jasser: School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University, Selangor, Malaysia; & Research Centre for Human-Machine Collaboration, Faculty of Engineering and Technology, Sunway University, Selangor, Malaysia,
Angela Lee Siew Hoong: School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University, Selangor, Malaysia,
Faizah Mohammed Bashir: Department of Decoration and Interior Design Engineering, College of Engineering, University of Hail, Hail, Kingdom of Saudi Arabia,
Yakubu Aminu Dodo: Department of Architectural Engineering, College of Engineering, Najran University, Najran, Kingdom of Saudi Arabia,
Olumide Simeon Ogunnusi: Department of Computer Science, The Federal Polytechnic, Ekiti State, Nigeria.

International Journal of Energy Economics and Policy, 2026, vol. 16, issue 1, 1163-1173

Abstract: This paper observes machine learning (ML) application in greenhouse gas (GHG) emission through a holistic bibliometric study, highlighting publication trends, influential contributors, and emerging research themes. Data were extracted from the Scopus database covering the period of 2008-2024. Bibliometric mapping was further performed to identify publication outputs, institutional contributions, collaboration patterns, and keyword co-occurrence networks. The analysis demonstrates a rapid growth in ML-GHG studies, with dominance of conference papers and journal articles. China and the United States are leading contributors especially in support of their institutions such as the U.S. Department of Energy and the Chinese Academy of Sciences. Research themes keep evolving from broad climate-related cases toward some advanced ML applications, including carbon capture optimization and real-time emissions monitoring. The study depends solely on Scopus data; it excluded relevant publications from other databases. Hence, expanding the data sources could offer a more inclusive perspective. The insights from the research can guide researchers, policymakers and industry practitioners on a deeper understanding of global trends as well as the identification of myriad opportunities for collaboration in applying ML for GHG/climate change mitigation. This research offers one of the first organized bibliometric overviews/summaries of ML applications in GHG mitigation, presenting a clearer picture of the field's evolution, significant actors, and research hotspots.

Keywords: Greenhouse Gas Emissions; Green Energy; Machine Learning; Climate Change; Mitigation; Bibliometric Analysis; Sustainable Energy (search for similar items in EconPapers)
Date: 2026
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DOI: 10.32479/ijeep.22023

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