The water use of data center workloads: A review and assessment of key determinants
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Water consumption per unit of computational output can vary by orders of magnitude depending on server efficiency, utilization, cooling technology, and geographic location.
“Research showed that water consumption per unit of computational output can vary by orders of magnitude depending on server efficiency, utilization, cooling technology, and geographic location [24].”
From Navigating Environmental Concerns Confronting U.S. Data Centers Related to Water Quantity and Quality (2026) · cites this paper as [24]
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Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates
Sobol′, I. M. (2001) · Mathematics and Computers in Simulation · doi:10.1016/s0378-4754(00)00270-6 · Cited by 6,389 · see citing works
The Case for Energy-Proportional Computing
Barroso, L. A., & Hölzle, U. (2007) · Computer · doi:10.1109/mc.2007.443 · Cited by 2,503 · see citing works
Recalibrating global data center energy-use estimates
Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020) · Science · doi:10.1126/science.aba3758 · Cited by 1,545 · see citing works
Self-citationOn Global Electricity Usage of Communication Technology: Trends to 2030
Andrae, A., & Edler, T. (2015) · Challenges · doi:10.3390/challe6010117 · Cited by 1,334 · see citing works
How to stop data centres from gobbling up the world’s electricity
Jones, N. (2018) · Nature · doi:10.1038/d41586-018-06610-y · Cited by 1,166 · see citing works
United States Data Center Energy Usage Report
Lawrence Berkeley National Laboratory (LBNL), B., Shehabi, A., USDOE, Smith, S., Sartor, D., Brown, R., Herrlin, M., Koomey, J., Masanet, E., Horner, N., et al. (2016) · doi:10.2172/1372902 · Cited by 1,040 · see citing works
Self-citationAssessing ICT global emissions footprint: Trends to 2040 & recommendations
Belkhir, L., & Elmeligi, A. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.12.239 · Cited by 970 · see citing works
The Water Footprint Assessment Manual
Aldaya, M. M., Chapagain, A. K., Hoekstra, A. Y., & Mekonnen, M. M. (2012) · Routledge eBooks · doi:10.4324/9781849775526 · Cited by 566 · see citing works
Mytton, D. (2021) · npj Clean Water · doi:10.1038/s41545-021-00101-w · Cited by 297 · see citing works
The environmental footprint of data centers in the United States
Siddik, M. A. B., Shehabi, A., & Marston, L. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/abfba1 · Cited by 281 · see citing works
Self-citationGrubert, E. A., & Sanders, K. T. (2018) · Environmental Science & Technology · doi:10.1021/acs.est.8b00139 · Cited by 116 · see citing works
Server liquid cooling with chiller-less data center design to enable significant energy savings
Iyengar, M., David, M. P., Parida, P. R., et al. (2012) · Annual IEEE Semiconductor Thermal Measurement and Management Symposium/Proceedings · doi:10.1109/stherm.2012.6188851 · Cited by 108 · see citing works
Regional water consumption for hydro and thermal electricity generation in the United States
Lee, U., Han, J., Elgowainy, A., & Wang, M. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.05.025 · Cited by 93 · see citing works
Statistical analysis for predicting location-specific data center PUE and its improvement potential
Lei, N., & Masanet, E. R. (2020) · Energy · doi:10.1016/j.energy.2020.117556 · Cited by 88 · see citing works
Self-citationThe Water Footprint of Data Centers
Ristic, B., Madani, K., & Makuch, Z. (2015) · Sustainability · doi:10.3390/su70811260 · Cited by 81 · see citing works
Lei, N., & Masanet, E. R. (2022) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2022.106323 · Cited by 81 · see citing works
Self-citationCharacteristics of low-carbon data centres
Masanet, E. R., Shehabi, A., & Koomey, J. (2013) · Nature Climate Change · doi:10.1038/nclimate1786 · Cited by 65 · see citing works
Self-citationTrends in Server Energy Proportionality
Ryckbosch, F., Polfliet, S., & Eeckhout, L. (2011) · Computer · doi:10.1109/mc.2011.130 · Cited by 52 · see citing works
Power usage effectiveness in data centers: overloaded and underachieving
Horner, N., & Azevedo, I. L. (2016) · The Electricity Journal · doi:10.1016/j.tej.2016.04.011 · Cited by 51 · see citing works
To better understand AI’s growing energy use, analysts need a data revolution
Masanet, E. R., Lei, N., & Koomey, J. (2024) · Joule · doi:10.1016/j.joule.2024.07.018 · Cited by 44 · see citing works
Self-citationA regional assessment of the water embedded in the US electricity system
Peer, R., Grubert, E. A., & Sanders, K. T. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab2daa · Cited by 39 · see citing works
Water efficiency management in datacenters: Metrics and methodology
Sharma, R. K., Shah, A. J., Bash, C. E., et al. (2009) · IEEE International Symposium on Sustainable Systems and Technology · doi:10.1109/issst.2009.5156773 · Cited by 21 · see citing works
Spatially and Temporally Detailed Water and Carbon Footprints of U.S. Electricity Generation and Use
Siddik, M. A. B., Shehabi, A., Rao, P., & Marston, L. T. (2024) · Water Resources Research · doi:10.1029/2024wr038350 · Cited by 10 · see citing works
Self-citationGeospatial assessment of water footprints for hyperscale data centers in the United States
Lei, N., Lu, J., Zhu, C., Cao, Z., Shehabi, A., & Masanet, E. R. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2600/17/172003 · Cited by 7 · see citing works
Self-citationAnsorge, L., & Stejskalová, L. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.124038 · Cited by 6 · see citing works
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Research works printed but not linked: 1
- ASHRAE, 2014. Liquid Cooling Guidelines for Datacom Equipment Centers, 2nd Edition, ASHRAE Datacom Series. Atlanta. https://www.zotero.org/google-docs/?Cri7Yc
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Estahbanati, S., Cutting, A. L., Chimchirian, R., Fusco, J., & Love, A. H. (2026) · Journal of Environmental Protection · doi:10.4236/jep.2026.174010 · Cited by 1 · see citing works
- Authors
- Lei, N., Lu, J., Shehabi, A., & Masanet, E.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- University of Illinois Chicago (US)
- University of California, Santa Barbara (US)
- Corresponding author
- Lei, Nuoa
- Venue
- Resources, Conservation and Recycling
- Publisher
- Elsevier BV
- Published
- 2025-06
- Volume
- 219
- Pages
- 108310
- ISSN
- 0921-3449 (ISSN-L)
- OpenAlex
- W4409433201
- Cited by
- 79
- References
- 25
- Citations by year
- 2026: 71 · 2025: 8
- FWCI
- 17.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Resources Conservation and Recycling (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- University of California
- US Department of Energy (DE-AC02\u201305CH11231)
- 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 › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- server energy efficiency, server utilization, cooling systems, climate zones, water-saving measures, digitalization
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