The Nexus Between Green Finance and Artificial Intelligence: A Systemic Bibliometric Analysis Based on Web of Science Database

Katerina Fotova Čiković (), Violeta Cvetkoska and Dinko Primorac
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Katerina Fotova Čiković: Department of Economy, University North, 48000 Koprivnica, Croatia
Violeta Cvetkoska: Faculty of Economics—Skopje, Ss. Cyril and Methodius University in Skopje, Blvd. Goce Delchev 9V, 1000 Skopje, North Macedonia
Dinko Primorac: Department of Economy, University North, 48000 Koprivnica, Croatia

JRFM, 2025, vol. 18, issue 8, 1-19

Abstract: The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems.

Keywords: green finance; artificial intelligence; sustainable finance; green fintech; bibliometric analysis; science mapping (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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