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Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA
Open accessAbstract
The rapidly increasing demand for generative artificial intelligence (AI) models requires extensive server installation with sustainability implications in terms of the compound energy–water–climate impacts. Here we show that the deployment of AI servers across the United States could generate an annual water footprint ranging from 731 to 1,125 million m3 and additional annual carbon emissions from 24 to 44 Mt CO2-equivalent between 2024 and 2030, depending on the scale of expansion. Other factors, such as industry efficiency initiatives, grid decarbonization rates and the spatial distribution of server locations within the United States, drive deep uncertainties in the estimated water and carbon footprints. We show that the AI server industry is unlikely to meet its net-zero aspirations by 2030 without substantial reliance on highly uncertain carbon offset and water restoration mechanisms. Although best practices may reduce emissions and water footprints by up to 73% and 86%, respectively, their effectiveness is constrained by current energy infrastructure limitations. These findings underscore the urgency of accelerating the energy transition and point to the need for AI companies to harness the clean energy potential of Midwestern states. Coordinating efforts of private actors and regulatory interventions would ensure the competitive and sustainable development of the AI sector. The rapid expansion of AI server installations in the United States poses sustainability challenges in terms of water usage and carbon emissions. A study now quantifies these potential impacts and outlines coordinated mitigation strategies for the AI sector to achieve net-zero.
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“Scenario-based modeling identifies that without coordinated efficiency improvements and regional planning, AI-driven data center growth has the potential to exacerbate water stress in already constrained watersheds [26].”
From Navigating Environmental Concerns Confronting U.S. Data Centers Related to Water Quantity and Quality (2026) · cites this paper as [26]
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Metadata
- Authors
- Xiao, T., Nerini, F. F., Matthews, H. D., Tavoni, M., & You, F.
- Affiliations
- Cornell University (US)
- University of Oxford (GB)
- Concordia University (CA)
- RFF-CMCC European Institute on Economics and the Environment (IT)
- Corresponding author
- You, Fengqi
- Venue
- Nature Sustainability
- Published
- 2025-11-10
- Volume
- 8
- Issue
- 12
- Pages
- 1541-1553
- ISSN
- 2398-9629 (ISSN-L)
- OpenAlex
- W4416067151
- Cited by
- 121
- References
- 32
- Citations by year
- 2026: 117 · 2025: 4
- FWCI
- 26.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Sustainability (publishedVersion, cc-by-nc-nd)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
- Funders
- National Science Foundation (1643244)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Environmental Science › Environmental Engineering › Environmental Impact and Sustainability
- Keywords
- generative AI, water footprint, CO2 emissions, net-zero pathways, grid decarbonization, carbon offsetting, water restoration, energy transition, United States, AI sustainability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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