The environmental footprint of data centers in the United States
Open accessAbstract
Much of the world’s data are stored, managed, and distributed by data centers. Data centers require a tremendous amount of energy to operate, accounting for around 1.8% of electricity use in the United States. Large amounts of water are also required to operate data centers, both directly for liquid cooling and indirectly to produce electricity. For the first time, we calculate spatially-detailed carbon and water footprints of data centers operating within the United States, which is home to around one-quarter of all data center servers globally. Our bottom-up approach reveals one-fifth of data center servers direct water footprint comes from moderately to highly water stressed watersheds, while nearly half of servers are fully or partially powered by power plants located within water stressed regions. Approximately 0.5% of total US greenhouse gas emissions are attributed to data centers. We investigate tradeoffs and synergies between data center’s water and energy utilization by strategically locating data centers in areas of the country that will minimize one or more environmental footprints. Our study quantifies the environmental implications behind our data creation and storage and shows a path to decrease the environmental footprint of our increasing digital footprint.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A little more than half (52%) of total data centre emissions is attributable to the Northeast, Southeast, and Central United States.
“A little more than half (52%) of total data centre emissions is attributable to the Northeast, Southeast, and Central United States, which have a high concentration of thermoelectric power plants as well as a big number of data centres.”
From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [144]
Total greenhouse gas emissions attributed to data centres in 2018 were 3.15×10^7 tons CO2-eq, accounting for about 0.5% of total GHG emissions in the United States [144].
“Total Greenhouse Gas Emissions (GHG) attributed to data centres in 2018 were 3.15 9 107 tons CO2-eq, accounting for about 0.5% of total GHG emissions in the United States [144].”
From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [144]
Almost 30% of the emissions from the data centre sector occur in the Central United States, which depends significantly on coal and natural gas to satisfy its energy needs.
“Almost 30% of the emissions from the data centre sector occur in the Central United States, which depends significantly on coal and natural gas to satisfy its energy needs.”
From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [144]
Environmental footprint analyses of AI-focused infrastructure show that direct water use for cooling now represents a substantial share of total water demand, alongside significant indirect water consumption embedded in electricity generation.
“Environmental footprint analyses of AI-focused infrastructure show that direct water use for cooling now represents a substantial share of total water demand, alongside significant indirect water consumption embedded in electricity generation (Figure 2) [25].”
From Navigating Environmental Concerns Confronting U.S. Data Centers Related to Water Quantity and Quality (2026) · cites this paper as [25]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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-citationUnited 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 · cited 5× · see citing works
“Though the amount of data center computing workloads has increased nearly 550% between 2010 and 2018, data center electricity consumption has only risen by 6% due to dramatic improvements in energy efficiency and storage-drive density across the industry [1, 3].”
BackgroundMethodInfluentialSelf-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
Boulay, A.-M., Bare, J., Benini, L., Berger, M., Lathuillière, M. J., Manzardo, A., Margni, M., Motoshita, M., Núñez, M., Pastor, A. V., et al. (2018) · The International Journal of Life Cycle Assessment · doi:10.1007/s11367-017-1333-8 · Cited by 849 · see citing works
Ebrahimi, K., Jones, G. F., & Fleischer, A. S. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.12.007 · Cited by 781 · see citing works
Estimated use of water in the United States in 2015
Dieter, C. A., Maupin, M. A., Caldwell, R. R., Harris, M. A., Ivahnenko, T. I., Lovelace, J. K., Barber, N. L., & Linsey, K. S. (2018) · U.S. Geological Survey circular/U.S. Geological Survey Circular · doi:10.3133/cir1441 · Cited by 760 · see citing works
Ridoutt, B. G., & Pfister, S. (2009) · Global Environmental Change · doi:10.1016/j.gloenvcha.2009.08.003 · Cited by 639 · see citing works
Trends in worldwide ICT electricity consumption from 2007 to 2012
Van Heddeghem, W., Lambert, S., Lannoo, B., Colle, D., Pickavet, M., & Demeester, P. (2014) · Computer Communications · doi:10.1016/j.comcom.2014.02.008 · Cited by 533 · see citing works
Macknick, J., Newmark, R. L., Heath, G. A., & Hallett, K. C. (2011) · doi:10.2172/1009674 · Cited by 286 · see citing works
Characterizing changes in drought risk for the United States from climate change
Strzepek, K. M., Yohe, G., Neumann, J. E., & Boehlert, B. B. (2010) · Environmental Research Letters · doi:10.1088/1748-9326/5/4/044012 · Cited by 280 · see citing works
Greenhouse Gases Emissions from Wastewater Treatment Plants: Minimization, Treatment, and Prevention
Campos, J. L., Valenzuela‐Heredia, D., Pedrouso, A., del Río, Á. V., Belmonte, M., & Mosquera‐Corral, A. (2016) · Journal of Chemistry · doi:10.1155/2016/3796352 · Cited by 268 · see citing works
Cooling Systems in Data Centers: State of Art and Emerging Technologies
Capozzoli, A., & Primiceri, G. (2015) · Energy Procedia · doi:10.1016/j.egypro.2015.12.168 · Cited by 238 · see citing works
Machine Learning Applications for Data Center Optimization
Gao, J. (2014) · Cited by 227 · see citing works
Data center growth in the United States: decoupling the demand for services from electricity use
Shehabi, A., Smith, S. J., Masanet, E. R., & Koomey, J. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aaec9c · Cited by 219 · see citing works
Self-citationBest Practices for Data Centers: Lessons Learned from Benchmarking 22 Data Centers
Greenberg, S., Mills, E., Tschudi, B., & Rumsey, P. (2006) · Cited by 214 · see citing works
Macknick, J., Sattler, S., Averyt, K. B., Clemmer, S., & Rogers, J. (2012) · Environmental Research Letters · doi:10.1088/1748-9326/7/4/045803 · Cited by 183 · see citing works
USDOE Office of Energy Efficiency and Renewable Energy (EERE), R. P. O. W. E. T. O., Macknick, J., Newmark, R. L., Heath, G. A., & Hallett, K. C. (2011) · doi:10.2172/1219149 · Cited by 183 · see citing works
Water scarcity and fish imperilment driven by beef production
Richter, B. D., Bartak, D., Caldwell, P. V., Davis, K. F., Debaere, P., Hoekstra, A. Y., Li, T., Marston, L. T., McManamay, R. A., Mekonnen, M. M., et al. (2020) · Nature Sustainability · doi:10.1038/s41893-020-0483-z · Cited by 160 · see citing works
Self-citationEvaporative cooling of water in a mechanical draft cooling tower
Fisenko, S. P., Brin, A. A., & Petruchik, A. I. (2003) · International Journal of Heat and Mass Transfer · doi:10.1016/s0017-9310(03)00409-5 · Cited by 154 · see citing works
Reitz, M. D., Sanford, W. E., Senay, G. B., et al. (2017) · JAWRA Journal of the American Water Resources Association · doi:10.1111/1752-1688.12546 · Cited by 153 · see citing works
Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers
Zheng, J., Chien, A. A., & Suh, S. (2020) · Joule · doi:10.1016/j.joule.2020.08.001 · Cited by 139 · see citing works
Intelligent Placement of Datacenters for Internet Services
Goiri, Í., Le, K., Guitart, J., Torres, J., & Bianchini, R. (2011) · Proceedings of the ... International Conference on Distributed Computing Systems/Proceedings of the International Conference on Distributed Computing Systems · doi:10.1109/icdcs.2011.19 · Cited by 133 · see citing works
High‐Resolution Water Footprints of Production of the United States
Marston, L. T., Ao, Y. Z., Konar, M., et al. (2018) · Water Resources Research · doi:10.1002/2017wr021923 · Cited by 128 · see citing works
Self-citationSectoral contributions to surface water stress in the coterminous United States
Averyt, K. B., Meldrum, J. R., Caldwell, P. V., et al. (2013) · Environmental Research Letters · doi:10.1088/1748-9326/8/3/035046 · Cited by 116 · see citing works
The location as an energy efficiency and renewable energy supply measure for data centres in Europe
Depoorter, V., Oró, E., & Salom, J. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.11.067 · Cited by 105 · see citing works
Virtual water transfers of the US electric grid
Chini, C. M., Djehdian, L. A., Lubega, W. N., & Stillwell, A. S. (2018) · Nature Energy · doi:10.1038/s41560-018-0266-1 · Cited by 94 · see citing works
Direct and indirect urban water footprints of the U nited S tates
Chini, C. M., Konar, M., & Stillwell, A. S. (2016) · Water Resources Research · doi:10.1002/2016wr019473 · Cited by 92 · see citing works
The Water Footprint of Data Centers
Ristic, B., Madani, K., & Makuch, Z. (2015) · Sustainability · doi:10.3390/su70811260 · Cited by 81 · see citing works
Water consumption from hydroelectricity in the United States
Grubert, E. A. (2016) · Advances in Water Resources · doi:10.1016/j.advwatres.2016.07.004 · Cited by 76 · see citing works
Reducing water scarcity by improving water productivity in the United States
Marston, L. T., Lamsal, G., Ancona, Z. H., et al. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/ab9d39 · Cited by 73 · see citing works
Self-citationData center design and location: Consequences for electricity use and greenhouse-gas emissions
Shehabi, A., Masanet, E. R., Price, H., Horvath, A., & Nazaroff, W. W. (2010) · Building and Environment · doi:10.1016/j.buildenv.2010.10.023 · Cited by 69 · see citing works
Self-citationLiquid immersion cooling technology with natural convection in data center
Matsuoka, M., Matsuda, K., & Hideo, K. (2017) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet.2017.8071539 · Cited by 61 · see citing works
Colett, J. S., Kelly, J. C., & Keoleian, G. A. (2015) · Journal of Industrial Ecology · doi:10.1111/jiec.12268 · Cited by 45 · see citing works
Stanford, H. W., III (2012) · CRC Press eBooks · doi:10.1201/b11510-14 · Cited by 39 · see citing works
Water and Carbon Footprints of Electricity Are Sensitive to Geographical Attribution Methods
Siddik, M. A. B., Chini, C. M., & Marston, L. T. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c00176 · Cited by 27 · see citing works
Self-citationGrubert, E. A. (2019) · The Electricity Journal · doi:10.1016/j.tej.2019.106692 · Cited by 24 · see citing works
Saving energy by insulating pipe components: on steam & hot water distribution systems
Hart, G. H. (2011) · ASHRAE journal · Cited by 20 · see citing works
Maurice, E., Dandres, T., Moghaddam, R. F., et al. (2014) · Advances in computer science research · doi:10.2991/ict4s-14.2014.35 · Cited by 16 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Lawrence A 2020 Rack density is rising (available at: https:// journal.uptimeinstitute.com/rack-density-is-rising/)
- Colett J S, Kelly J C and Keoleian G A 2016 Using nested average electricity allocation protocols to characterize electrical grids in life cycle assessment J. Ind. Ecol. 20 29-41 · DOI
Cited by
Sorted by citations
Energy efficiency in cloud computing data centers: a survey on software technologies
Katal, A., Dahiya, S., & Choudhury, T. (2023) · Cluster Computing · doi:10.1007/s10586-022-03713-0 · Cited by 516 · see citing works
“Total Greenhouse Gas Emissions (GHG) attributed to data centres in 2018 were 3.15 9 107 tons CO2-eq, accounting for about 0.5% of total GHG emissions in the United States [144].”
BackgroundInfluentialLi, P., Yang, J., Islam, M. A., & Ren, S. (2025) · Communications of the ACM · doi:10.1145/3724499 · Cited by 176 · see citing works
Xiao, T., Nerini, F. F., Matthews, H. D., Tavoni, M., & You, F. (2025) · Nature Sustainability · doi:10.1038/s41893-025-01681-y · Cited by 121 · see citing works
The water use of data center workloads: A review and assessment of key determinants
Lei, N., Lu, J., Shehabi, A., & Masanet, E. (2025) · Resources, Conservation and Recycling · doi:10.1016/j.resconrec.2025.108310 · Cited by 79 · see citing works
Self-citationWang, Q., Qi, Y., & Li, R. (2026) · Sustainable Development · doi:10.1002/sd.70200 · Cited by 55 · see citing works
A Water Efficiency Dataset for African Data Centers
Shumba, N., Tshekiso, O., Li, P., Fanti, G., & Ren, S. (2025) · Proceedings of the ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies · doi:10.1145/3715335.3735483 · Cited by 10 · see citing works
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
- Siddik, M. A. B., Shehabi, A., & Marston, L.
- Affiliations
- Virginia Tech (US)
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Marston, Landon
- Venue
- Environmental Research Letters
- Publisher
- IOP Publishing
- Published
- 2021-05-21
- Volume
- 16
- Issue
- 6
- Pages
- 064017
- ISSN
- 1748-9326 (ISSN-L)
- OpenAlex
- W3158784237
- Cited by
- 281
- References
- 38
- Citations by year
- 2026: 100 · 2025: 81 · 2024: 36 · 2023: 39 · 2022: 22 · 2021: 3
- FWCI
- 7.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Environmental Research Letters (publishedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- VTechWorks (Virginia Tech) (submittedVersion, cc-by)
- Funders
- National Science Foundation (ACI-1639529)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Environmental Science › Environmental Engineering › Environmental Impact and Sustainability
- Physical Sciences › Environmental Science › Water Science and Technology › Water-Energy-Food Nexus Studies
- Keywords
- data centers, carbon footprint, water footprint, greenhouse gas emissions, water stress, energy consumption, liquid cooling, power plant location, data center siting, digital footprint, United States
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 ›
Are you an author, or do you see a problem with this page? Request a review
