A systematic review of current AI techniques used in the context of the SDGs
Open accessAbstract
Abstract This study aims to explore the application of artificial intelligence (AI) in the resolution of sustainability challenges, with a specific focus on environmental studies. Given the rapidly evolving nature of this field, there is an urgent need for more frequent and dynamic reviews to keep pace with the innovative applications of AI. Through a systematic analysis of 191 research articles, we classified AI techniques applied in the field of sustainability. Our review found that 65% of the studies applied supervised learning methods, 18% employed unsupervised learning, and 17% utilized reinforcement learning approaches. The review highlights that artificial neural networks (ANN), are the most commonly applied AI techniques in sustainability contexts, accounting for 23% of the reviewed methods. This comprehensive overview of AI techniques identifies key trends and proposes new research avenues to address the complex issue of achieving the Sustainable Development Goals (SDGs). Graphic abstract
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The rapid pace of AI development limits the review's ability to comprehensively capture the full spectrum of available AI-driven decision-support solutions and their real-time capabilities.
“Many AI-driven decision-support tools evolve faster than academic research cycles, which limits the ability to comprehensively capture the full spectrum of available solutions and their real-time capabilities.”
From Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review (2026) · cites this paper as [56]
In “Limitations and future work” (page unavailable), the review says rapid AI development may make its discussions and conclusions outdated and describes the work as a snapshot in time. It does not specifically say that decision-support tools outpace academic research cycles or that the review cannot capture the full range of available solutions and their real-time capabilities. (LLM, conf=0.86)
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References 231 · Cited by 55
Research works printed but not linked: 7
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- Authors
- Greif, L., Röckel, F., Kimmig, A., & Ovtcharova, J.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Corresponding author
- Greif, Lucas
- Venue
- International Journal of Environmental Research
- Published
- 2024-10-24
- Volume
- 19
- Issue
- 1
- ISSN
- 1735-6865 (ISSN-L)
- 2008-2304
- OpenAlex
- W4403706069
- Cited by
- 55
- References
- 231
- Citations by year
- 2026: 31 · 2025: 23 · 2024: 1
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International Journal of Environmental Research (publishedVersion, cc-by)
- PUBLISSO (German National Library of Medicine) (submittedVersion, cc-by)
- Funders
- Bundesministerium für Wirtschaft und Klimaschutz (03LB2041E, 03LB3058B)
- Karlsruher Institut für Technologie (KIT)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Sociology and Political Science › Innovation, Sustainability, Human-Machine Systems
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Sustainability and Technology
- Keywords
- artificial intelligence, Sustainable Development Goals, sustainability challenges, environmental studies, supervised learning, unsupervised learning, reinforcement learning, artificial neural networks, machine learning applications
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