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Lookup NU author(s): Dr Manuel HerreraORCiD, Dr Xiang XieORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The rapid expansion of artificial intelligence (AI) and cloud computing is creating a significant but often overlooked impact on global water resources. This paper presents a global assessment of water consumption in AI-driven data centres, distinguishing between water consumption for operational use at the facility, off-site water consumption related to electricity generation, and embodied water consumption associated with hardware manufacturing and supply chains. To anticipate future demand, a scenario-based probabilistic forecasting framework inspired by Bayesian methods is developed, combining sparse empirical data with expert-informed assumptions and policy-relevant growth trajectories for the years 2030 and 2050. Results suggest that, without mitigation, global water consumption associated with data centres could increase more than seven times by mid-century, with cooling-related operational consumption accounting for the majority of demand. Several mitigation pathways are identified, including improvements in cooling efficiency, adoption of alternative technologies, and infrastructure planning that takes into account regional water availability. A sensitivity analysis highlights the strong influence of compute growth and efficiency trends on future outcomes. The findings offer a transparent and adaptable basis for aligning AI infrastructure development with long-term water sustainability goals.
Author(s): Herrera M, Xie X, Menapace A, Zanfei A, Brentan BM
Publication type: Article
Publication status: Published
Journal: Journal of Cleaner Production
Year: 2025
Volume: 526
Print publication date: 01/10/2025
Online publication date: 17/09/2025
Acceptance date: 30/08/2025
Date deposited: 17/09/2025
ISSN (print): 0959-6526
ISSN (electronic): 1879-1786
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.jclepro.2025.146528
DOI: 10.1016/j.jclepro.2025.146528
Data Access Statement: The data and code that support the findings of this study are openly available in the GitHub repository at: https://github.com/manuelhf/waterforAI
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