Scholarly record
ARTIFICIAL INTELLIGENCE AND ENVIRONMENTAL SUSTAINABILITY: A FIRST LOOK INTO CITIZENS- UNAWARENESS
Abstract
Artificial intelligence (AI) has significant potential to enhance resource efficiency and reduce environmental costs. Nevertheless, AI systems-especially large language models (LLM)-are energy-intensive and resource-demanding. The ecological cost stems not only from the energy required for training and inference, but also from the infrastructure needed to sustain AI operations, including data centers and cloud services. From a socio-psychological point of view, it is extremely relevant to understand what beliefs individuals hold on the relation between AI and sustainability. Surprisingly, no research has been conducted on the topic so far. Hence, we conducted a first exploration through 44 interviews in Italy. Results show that individuals held no significant beliefs about the relation between AI and sustainability, in terms of both perceived advantages and disadvantages. Hence, results suggest a widespread lack of awareness about AI-s sustainability, thus highlighting a significant cognitive and communicative gap. Additional cross-cultural research is needed to address a significant research gap and inform policies designed to enhance citizens- awareness of the environmental costs and benefits associated with advanced AI applications. This appears essential for cultivating a more reflective and knowledgeable approach to Green AI.
Publication Impact Profile
Publication details
References14
Fan, Z., Yan, Z., & Wen, S., Deep learning and artificial intelligence in sustainability: a review of SDGs, renewable energy, and environmental health. Sustainability, vol. 15, issue 18, pp 1-20, 2023, DOI: 10.3390/su151813493
Raihan, A., Paul, A., Rahman, M. S., Islam, S., Paul, P., & Karmakar, S., Artificial Intelligence (AI) for environmental sustainability: A concise review of technology innovations in energy, transportation, biodiversity, and water management. Journal of Technology Innovations and Energy, vol. 3, issue 2, pp 64-73, 2024, DOI: 10.56556/jtie.v3i2.953
Madkhali, A., & Sithole, S. T., Exploring the role of information technology in supporting sustainability efforts in Saudi Arabia. Sustainability, vol. 15, issue 16, pp 1-20, 2023, DOI: 10.3390/su151612375
Wright, D., Igel, C., Samuel, G., & Selvan, R., Efficiency Is Not Enough: A Critical Perspective on Environmentally Sustainable AI. Communications of the ACM, vol. 68, issue 7, 62-69, 2025, DOI: 10.1145/3724500
IEA, Energy and AI, International Energy Agency, Paris, 2025, Available at: https://www.iea.org/reports/energy-and-ai
Bashir, N., Donti, P., Cuff, J., Sroka, S., Ilic, M., Sze, V., Delimitrou, C., & Olivetti, E., The climate and sustainability implications of generative AI. An MIT exploration of generative AI, vol 3, issue 7, pp 1-45, 2024, DOI: 10.21428/e4baedd9.9070dfe7
Li, P., Yang, J., Islam, M. A., & Ren, S., Making AI less' thirsty': Uncovering and Addressing the Secret Water Footprint of AI Models. Communications of the ACM, vol. 68, issue 7, 54-61, 2025, DOI: 10.1145/3724499
Noviati, N. D., Maulina, S. D., & Smith, S., Smart grids: Integrating ai for efficient renewable energy utilization. International Transactions on Artificial Intelligence, vol. 3 issue 1, 1-10, 2024, DOI: 10.33050/italic.v3i1.644
Strubell, E., Ganesh, A., & McCallum, A., Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th annual meeting of the association for computational linguistics, pp. 3645-3650, 2019, DOI: 10.18653/v1/P19-1355
Yeh, S. C., Wu, A. W., Yu, H. C., Wu, H. C., Kuo, Y. P., & Chen, P. X., Public perception of artificial intelligence and its connections to the sustainable development goals. Sustainability, vol. 13, issue 16, pp 1-35, 2021, https://doi.org/10. 3390 /su13169165 DOI: 10.3390/su13169165
Modhvadia R., How do people feel about AI? A nationally representative survey of public attitudes to artificial intelligence in Britain. Ada Lovelace Institute and The Alan Turing Institute, 2023, Available at: https://www.adalovelaceinstitute.org/report/ public-attitudes-ai/
Barnett-Itzhaki, Z., & Tsoury, A., From Awareness to Action: A UK-Based Study on Public Perceptions of Digital Pollution. Sustainability, vol. 17, issue 17, pp 1-26, 2025, DOI: 10.3390/su17177839
Fishbein, M., & Ajzen, I., Predicting and Changing Behavior: The Reasoned Action Approach. Psychology Press, 2010, DOI: 10.4324/9780203838020
Braun, V., & Clarke, V., Using thematic analysis in psychology. Qualitative research in psychology, vol. 3, issue 2, 77-101, 2006, https://doi.org/10.1191/ 147808 8706 qp063oa DOI: 10.1191/1478088706qp063oa
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.

