Mapping the Landscape: Bibliometric Analysis of Sustainable Applications in Artificial Intelligence and Machine Learning

Abstract

This study employs a comprehensive bibliometric approach to dissect the landscape of sustainable applications within the interdisciplinary realm of Artificial Intelligence and Machine Learning. Through systematic retrieval from major academic databases and rigorous analysis, we map out the current research landscape. Initial screening yielded 447 relevant articles, which underwent thorough bibliometric scrutiny, including citation, co-authorship, keyword, and co-citation analyses. Our screening criteria ensured the inclusion of 191 articles directly relevant to our study objectives. Notably, India and the United States emerged as leaders in research output, with India boasting the highest document count and citations, while the United States wielded significant citation impact despite fewer documents. Turkey stood out for its impactful research relative to document count, suggesting emerging influence. Furthermore, co-authorship and organization analyses unveiled intricate collaborative networks. Saudi Arabia exhibited strong collaborative ties, mirrored by Italy, while isolated entities like Uttaranchal Institute of Technology underscored limited collaboration despite research activity. A co-occurrence analysis of keywords highlighted central themes such as sustainability, AI/ML technologies, and their potential applications in addressing global challenges like climate change and smart city development. Finally, a detailed bibliographic coupling analysis illuminated the interconnectedness of seminal sources, emphasizing the varying degrees of citation frequency and collaborative influence within the research domain. Overall, our analysis provides a holistic view of the global research landscape in sustainable AI and ML applications, underscoring collaborative dynamics, key players, and avenues for future investigation.