Mapping the Evolution of Artificial Intelligence (AI) in Sustainable Finance: A Bibliometric Analysis

Gurdas Singh (), Poonam Rani () and Mohammed Abul Khair ()
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Gurdas Singh: Maharaja Ranjit Singh Punjab Technical University
Poonam Rani: Chandigarh University
Mohammed Abul Khair: Al-Baha University

Chapter Chapter 4 in Green Horizons, 2025, pp 55-76 from Springer

Abstract: Abstract The primary objective of the present study is to examine the current academic research on the evolution of sustainable finance and AI in scientific research. The authors have combed through the Scopus database and 1081 papers were extracted from the 2020–2023 year. The authors employed the statistical program R-studio and VOSviewer software for conducting bibliometric analysis using methods such as scientific mapping (co-authorship analysis, co-word analysis, bibliographic coupling) and performance analysis (most influential authors, publications, countries, institutions, and journals). The scientific mapping reveals four niche topics that prior research has focused on such as blockchain as a future of sustainable finance, digital innovation in sustainable entrepreneurship, and blockchain technology in supply chain management. In addition, the findings offer a comprehensive look at the interdisciplinary nature of the subject. Through the presentation of fresh perspectives and important ideas, the authors aimed to enhance the theoretical advancement of artificial intelligence's application in promoting sustainability within the financial sector. Artificial intelligence approaches are gaining popularity as effective alternatives to traditional methods, showing promising results. This article has both theoretical and practical implications, providing researchers with an overview of the theoretical development and intellectual framework for future studies in this field.

Keywords: Artificial intelligence (AI); Sustainable finance; Supply chain; Blockchain; Bibliometric analysis (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-6495-5_4

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DOI: 10.1007/978-981-96-6495-5_4

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