A Bibliometric Mapping of Artificial Intelligence Applications in Climate Change Research

Khairul Hafezad Abdullah; Zulkiffly Baharom

doi:10.21664/2238-8869.2025v14i3.7927

Authors

Khairul Hafezad Abdullah Universiti Utara Malaysia https://orcid.org/0000-0003-3759-6541
Zulkiffly Baharom Universiti Utara Malaysia https://orcid.org/0009-0002-0310-7893

DOI:

https://doi.org/10.21664/2238-8869.2025v14i3.7927

Keywords:

artificial Intelligence, climate change, bibliometric analysis, environmental science, machine learning

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.

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