Sustainability and Artificial Intelligence: Research Overview of Intersections and Applications
A bibliometric review published on arXiv analyzes 541 studies from the Web of Science database to map how artificial intelligence and sustainability research intersect. The paper, originally presented at the 2020 MSIEID conference and published via IEEE Xplore, identifies green and sustainable science and technology as a central bridging body of work across disciplines, journals, and key concepts. The findings matter because they highlight both the complexity of aligning AI development with environmental, social, and governance goals and suggest pathways for expanding interdisciplinary practice.
A preprint posted to arXiv presents a bibliometric overview of research at the intersection of artificial intelligence and sustainability, drawing on 541 records from the Web of Science database. The authors argue that AI and sustainability share the characteristics of 'wicked problems'—complex, interconnected, and dynamic challenges that resist straightforward solutions. The review identifies green and sustainable science and technology as an increasingly central field bridging multiple disciplines, key journals, and thematic concepts. The paper characterizes the AI-sustainability relationship as simultaneously necessary, challenging, and promising, reflecting both the urgency of addressing AI's environmental and social impacts and the difficulty of doing so. The authors conclude by calling for a diversification and expansion of the community of practice around AI for sustainable development, particularly regarding expected application areas and institutional involvement. The work was originally presented at the 2020 Management Science Informatization and Economic Innovation Development Conference in Guangzhou, China, and the final authenticated version is available through IEEE Xplore.
What's missing
The bibliometric sample is restricted to the Web of Science database, potentially excluding relevant literature indexed elsewhere (e.g., Scopus, Google Scholar). The paper does not quantitatively assess the quality or real-world impact of the identified research, only its bibliographic structure.
What different sources said
- arXiv cs.AICenter
Sustainability and Artificial Intelligence: Necessary, Challenging, and Promising Intersections
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