A Bibliometric Mapping of Artificial Intelligence Applications in Climate Change Research

Artificial intelligence (AI) in climate change study is a huge step forward in understanding, predicting, and dealing with environmental problems in the modern research world. This bibliometric study examines the evolution and landscape of artificial intelligence (AI) applications in climate change research from 1996 to 2024. The current analysis covers 399 publications based on two leading databases, Web of Science (WoS) and Scopus. ScientoPy is used to evaluate and analyse publication patterns, whereas VOSviewer generates network visualisation. Results in this study depicted substantial research growth of previous publications, with WoS demonstrating a high annual growth rate of 20.5 and Scopus showing significant recent activity with 61.7% publications in the last years. The key findings also indicated that Sustainability was the leading publisher, the Chinese Academy of Sciences was the top contributing institution, and Environmental Sciences & Ecology was the dominant subject area. This analysis also granted three main keyword clusters: AI methodologies, climate science concepts, and application domains. This study presents an exhaustive bibliometric examination of artificial intelligence implementations in climate change benefits for various stakeholders. Notwithstanding the constraints of concentrating on Anglophone publications (English-language journals), the results furnish significant perspectives regarding the domain’s evolution, principal contributors, and prospective trajectories. The results are relevant for researchers, practitioners, and policymakers to harness AI technologies to climate change research and environmental analysis.

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Publication Details

Journal
Fronteiras Journal of Social Technological and Environmental Science
Published
2025-09-04
DOI
https://doi.org/10.21664/2238-8869.2025v14i3.7927
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
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article

A Bibliometric Mapping of Artificial Intelligence Applications in Climate Change Research

Zulkiffly Baharom, Khairul Hafezad Abdullah
Fronteiras Journal of Social Technological and Environmental Science
Air Quality Monitoring and Forecasting
article

A Bibliometric Mapping of Artificial Intelligence Applications in Climate Change Research

Zulkiffly Baharom, Khairul Hafezad Abdullah
article en

Abstract

Artificial intelligence (AI) in climate change study is a huge step forward in understanding, predicting, and dealing with environmental problems in the modern research world. This bibliometric study examines the evolution and landscape of artificial intelligence (AI) applications in climate change research from 1996 to 2024. The current analysis covers 399 publications based on two leading databases, Web of Science (WoS) and Scopus. ScientoPy is used to evaluate and analyse publication patterns, whereas VOSviewer generates network visualisation. Results in this study depicted substantial research growth of previous publications, with WoS demonstrating a high annual growth rate of 20.5 and Scopus showing significant recent activity with 61.7% publications in the last years. The key findings also indicated that Sustainability was the leading publisher, the Chinese Academy of Sciences was the top contributing institution, and Environmental Sciences & Ecology was the dominant subject area. This analysis also granted three main keyword clusters: AI methodologies, climate science concepts, and application domains. This study presents an exhaustive bibliometric examination of artificial intelligence implementations in climate change benefits for various stakeholders. Notwithstanding the constraints of concentrating on Anglophone publications (English-language journals), the results furnish significant perspectives regarding the domain’s evolution, principal contributors, and prospective trajectories. The results are relevant for researchers, practitioners, and policymakers to harness AI technologies to climate change research and environmental analysis.

Fronteiras Journal of Social Technological and Environmental ScienceVol. 14(3)
Universiti Teknologi MARA (MY), Northern University of Malaysia (MY)
Openalex Percentile: Top 78%
Air Quality Monitoring and Forecasting
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