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.
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?
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.
“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]
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
The role of artificial intelligence in achieving the Sustainable Development Goals
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020) · Nature Communications · doi:10.1038/s41467-019-14108-y · Cited by 3,294 · see citing works
Self-citationPower provisioning for a warehouse-sized computer
Fan, X., Weber, W.-D., & Barroso, L. A. (2007) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/1250662.1250665 · Cited by 1,813 · 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
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 · cited 1× · see citing works
“Previous research has developed bottom-up and top-down meth-ods to assess energy–water–carbon outcomes of servers 19–21 , but these approaches face challenges with the rise of AI servers.”
Assessing 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 growing energy footprint of artificial intelligence
de Vries, A. (2023) · Joule · doi:10.1016/j.joule.2023.09.004 · Cited by 597 · see citing works
Aligning artificial intelligence with climate change mitigation
Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01377-7 · Cited by 570 · see citing works
U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Lopez, A., Roberts, B., Heimiller, D., Blair, N., & Porro, G. (2012) · doi:10.2172/1047328 · Cited by 301 · 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
Measuring the Carbon Intensity of AI in Cloud Instances
Dodge, J., Prewitt, T., Tachet des Combes, R., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A. S., Smith, N. A., DeCario, N., & Buchanan, W. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533234 · Cited by 257 · see citing works
Toward a Systematic Survey for Carbon Neutral Data Centers
Cao, Z., Zhou, X., Hu, H., Wang, Z., & Wen, Y. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3161275 · Cited by 208 · see citing works
Generative AI’s environmental costs are soaring — and mostly secret
Crawford, K. (2024) · Nature · doi:10.1038/d41586-024-00478-x · Cited by 175 · see citing works
Renewable energy certificates threaten the integrity of corporate science-based targets
Bjørn, A., Lloyd, S. M., Brander, M., & Matthews, H. D. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01379-5 · Cited by 161 · see citing works
Self-citationICT sector electricity consumption and greenhouse gas emissions – 2020 outcome
Malmodin, J., Lövehagen, N., Bergmark, P., & Lundén, D. (2024) · Telecommunications Policy · doi:10.1016/j.telpol.2023.102701 · Cited by 160 · see citing works
Characterization and prediction of deep learning workloads in large-scale GPU datacenters
Hu, Q., Sun, P., Yan, S., Wen, Y., & Zhang, T. (2021) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3458817.3476223 · Cited by 158 · see citing works
Deep Learning Workload Scheduling in GPU Datacenters: A Survey
Ye, Z., Gao, W., Hu, Q., Sun, P., Wang, X., Luo, Y., Zhang, T., & Wen, Y. (2023) · ACM Computing Surveys · doi:10.1145/3638757 · Cited by 131 · see citing works
Jens, M. (2014) · doi:10.1111/jiec.12145 · Cited by 115 · see citing works
Millstein, D. E., Wiser, R. H., Mills, A., Bolinger, M. A., Seel, J., & Jeong, S. (2021) · Joule · doi:10.1016/j.joule.2021.05.009 · Cited by 115 · see citing works
Estimating the environmental impact of Generative-AI services using an LCA-based methodology
Berthelot, A., Caron, E., Jay, M., & Lefèvre, L. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.01.098 · Cited by 103 · see citing works
Characterizing Power Management Opportunities for LLMs in the Cloud
Patel, P., Choukse, E., Zhang, C., Goiri, Í., Warrier, B., Mahalingam, N., & Bianchini, R. (2024) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3620666.3651329 · Cited by 93 · see citing works
Using life cycle assessment to drive innovation for sustainable cool clouds
Alissa, H. A., Nick, T. A., Raniwala, A., Herranz, A. A., Frost, K., Manousakis, I., Lio, K., Warrier, B., Oruganti, V., diCaprio, T. J., et al. (2025) · Nature · doi:10.1038/s41586-025-08832-3 · Cited by 89 · 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
Estimating the Energy Use and Efficiency Potential of U.S. Data Centers
Masanet, E. R., Brown, R. E., Shehabi, A., Koomey, J. G., & Nordman, B. (2011) · Proceedings of the IEEE · doi:10.1109/jproc.2011.2155610 · Cited by 87 · 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
The carbon emissions of writing and illustrating are lower for AI than for humans
Tomlinson, B., Black, R. W., Patterson, D. J., & Torrance, A. W. (2024) · Scientific Reports · doi:10.1038/s41598-024-54271-x · Cited by 77 · see citing works
Farfan, J., & Lohrmann, A. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117225 · Cited by 44 · see citing works
Data Center Sustainability: Revisits and Outlooks
Cao, Z., Zhou, X., Wu, X., Zhu, Z., Liu, T., Neng, J., & Wen, Y. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3281583 · Cited by 42 · see citing works
Huawei Technologies Sweden AB, S. 9., & Andrae, A. S. G. (2019) · International Journal of Green Technology · doi:10.30634/2414-2077.2019.05.06 · Cited by 35 · see citing works
Reaching carbon neutrality requires energy-efficient training of AI
Guan, L. (2024) · Nature · doi:10.1038/d41586-024-00200-x · Cited by 18 · 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
AI & robotics briefing: Data centres’ huge ‘water footprint’ becomes clear amid AI boom
Krämer, K. (2023) · Nature · doi:10.1038/d41586-023-03768-y · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- Jeon, M. et al. Analysis of large-scale multi-tenant GPU clusters for DNN training workloads. In 2019 USENIX Annual Technical Conference (USENIX ATC 19), 947-960 (2019). https://www.usenix. org/conference/atc19/presentation/jeon
- Weng, Q. et al. Beware of Fragmentation: scheduling GPU-sharing workloads with fragmentation gradient descent. In 2023 USENIX Annual Technical Conference (USENIX ATC 23), 995-1008 (2023). https://www.usenix.org/conference/atc23/presentation/weng
- Hu, Q. et al. Characterization of large language model development in the datacenter. In 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24), 709-729 (2024). https://www.usenix.org/conference/nsdi24/ presentation/hu Analysis https://doi.org/10.1038/s41893-025-01681-y
- Worldwide Quarterly AI Infrastructure Tracker (IDC, 2024); https://www.idc.com/getdoc.jsp?containerId=IDC_P37251
- Kung, F. See Generative AI's Impact on the AI Server Market to 2025 (TrendForce, 2024); https://files.futurememorystorage. com/proceedings/2024/20240808_BMKT-301-1_KUNG.pdf
- Wu, C.-J. et al. Sustainable AI: environmental implications, challenges and opportunities. Proc. Mach. Learn. Syst. 4, 795-813 (2022).
Cited by
Sorted by citations
Chouksey, A., Rajan, A. K., Gurjar, V., Tiwari, R., & Mishra, P. K. (2026) · Global Environmental Change Advances · doi:10.1016/j.gecadv.2025.100029 · Cited by 16 · see citing works
Rare-Earth Exposure and Bottlenecks in AI Data Centers: Cost and Schedule RiskPreprint
He, Q., Shan, R., Qu, C., Tang, Y., & Zou, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6232318 · Cited by 12 · see citing works
Friese, S., Nguyen‐Trung, K., Powell, S., & Morgan, D. L. (2026) · Qualitative Inquiry · doi:10.1177/10778004261429393 · Cited by 11 · see citing works
Xu, X., Wang, Y., Deng, Y., Jiang, M., & Shi, V. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124765 · Cited by 11 · see citing works
From computation to environmental cost the resource burden of artificial intelligence
Falk, S., Corrêa, N. K., Luccioni, A. S., Biber‐Freudenberger, L., & van Wynsberghe, A. (2026) · Communications Earth & Environment · doi:10.1038/s43247-026-03537-5 · Cited by 8 · see citing works
The Carbon Cost of Intelligence: A Domain-Specific Framework for Measuring AI Energy and Emissions
Kaur, R., Kundu, T., Park, K., & Pinsky, E. (2026) · Energies · doi:10.3390/en19030642 · Cited by 7 · see citing works
Marmouzi, O., Oumaira, I., & Khaddar, M. A. E. (2026) · Sustainability · doi:10.3390/su18084115 · Cited by 9 · see citing works
Devadas, R. M., & Sowmya, T. (2026) · Energy Informatics · doi:10.1186/s42162-026-00651-8 · Cited by 3 · see citing works
Can renewable energy meet the surging power demand of artificial intelligence? A systematic review
Yoo, S., Choi, S., & Kim, D. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.117213 · Cited by 4 · see citing works
Global data–water symbiosis reduces AI infrastructure's carbon and water footprint
Wang, A., Zhang, C., Xu, T., Savic, D. A., Jiang, J., Xiao, P., He, C., Tao, Y., Daigger, G. T., & Ren, N. (2026) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2026.100702 · Cited by 7 · see citing works
Does stringent climate policy decouple economic growth from greenhouse gas emissions?
Thombs, R. P., & Jorgenson, A. K. (2025) · Social Forces · doi:10.1093/sf/soaf217 · Cited by 2 · see citing works
Reducing Energy Footprint of LLM Inference Through FPGA-Based Heterogeneous Computing Platforms
Monteiro, T. C., & Guerrieri, A. (2026) · Electronics · doi:10.3390/electronics15051052 · Cited by 2 · see citing works
Zhao, R., Hu, J., Sun, Y., Yang, H., Yu, H., Zhao, C., Wang, Q., Li, Z., Duan, F., Yang, C., et al. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117535 · Cited by 2 · see citing works
Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology
Doo, F. X., Davis, M., Poff, J. A., Lui, Y. W., Haines, K., & Towbin, A. J. (2026) · Radiology Artificial Intelligence · doi:10.1148/ryai.250982 · Cited by 2 · see citing works
Creely, E. (2026) · AI in Education · doi:10.3390/aieduc2020020 · Cited by 3 · see citing works
Bitzenis, A., Κουτσουπιάς, Ν., & Nosios, M. (2026) · Sustainability · doi:10.3390/su18136705 · Cited by 2 · see citing works
Strategies and design for increasing AI sustainability
Chien, A. A., Gupta, U., Ren, S., Sriraman, A., & Tomlinson, B. (2026) · Nature Reviews Clean Technology · doi:10.1038/s44359-026-00195-w · Cited by 2 · 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
AI-Carbon-Energy: Spillover effects and drivers in interconnected markets
Zhang, M., Pan, Y., Su, B., & Zhou, D. (2025) · iScience · doi:10.1016/j.isci.2025.114541 · Cited by 1 · see citing works
Applying One Health Bioethical Principles to Climate Change: The Planetary Patient Paradigm
Lewczak, Z. A., & Mitchell, M. G. (2026) · Cureus · doi:10.7759/cureus.104411 · Cited by 1 · see citing works
History of chatbots and their emerging applications in textiles, polymers, and sustainability
Al-Amin, M., Hossain, M. E., Ali, M. S., Salam, A., Ali, A., Ullah, A., Alam, N., Akhter, M. N., & Mahmood, S. A. (2026) · Sustainable materials and technologies · doi:10.1016/j.susmat.2026.e01967 · Cited by 2 · see citing works
Izuagie, T., Oyem, I. M., Umar, B. N., Isah, A., Abdulsalami, I. O., & Oyem, H. H. (2026) · Sustainability Science and Technology · doi:10.1088/2977-3504/ae565e · Cited by 1 · see citing works
Li, P., De, G., Zhang, J., 邹绍辉, Liu, H., & Li, Y. (2026) · Energy Economics · doi:10.1016/j.eneco.2026.109346 · Cited by 1 · see citing works
Cousins, J. J. (2026) · npj Urban Sustainability · doi:10.1038/s42949-026-00389-y · Cited by 1 · see citing works
Zhou, K., Yang, Z., & Hu, R. (2026) · Communications Sustainability · doi:10.1038/s44458-026-00080-4 · Cited by 1 · see citing works
Johnson, J. X., Wade, C., Blackhurst, M. F., DeCarolis, J. F., de Queiroz, A. R., Posen, I. D., & Jaramillo, P. (2026) · Environmental Research Letters · doi:10.1088/1748-9326/ae6c3d · Cited by 2 · see citing works
Domingo, J. L., & Shomar, B. (2026) · Human and Ecological Risk Assessment An International Journal · doi:10.1080/10807039.2026.2670401 · Cited by 1 · see citing works
Zhou, K., Yang, Z., & Gao, Y. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124739 · Cited by 1 · see citing works
Chen, J., Tian, T., Wong, C. W. Y., & Lai, K.-H. (2026) · Sustainable Futures · doi:10.1016/j.sftr.2026.101918 · Cited by 1 · see citing works
Thompson-Olson, M., Michael, D., & Saluja, J. (2026) · American Journal of Lifestyle Medicine · doi:10.1177/15598276261449784 · Cited by 1 · see citing works
Imagined futures of retail and consumption
Shahriar, H., & Wulff, G. (2026) · The International Review of Retail Distribution and Consumer Research · doi:10.1080/09593969.2026.2675863 · Cited by 1 · see citing works
The Great AI Acceleration: Problems of Scale in Artificial Intelligence and the Anthropocene
Wickberg, A. (2026) · Environmental History · doi:10.1086/741655 · Cited by 1 · see citing works
Wang, Q., Huang, T., Liu, Q., & Na, J. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100840 · Cited by 1 · see citing works
Tian, X., Guo, W., & Liao, J. (2026) · Sustainability · doi:10.3390/su18147252 · Cited by 1 · see citing works
Multiscale Design Principles for Nanocomposite Hydrogel-Based Triboelectric Nanogenerators
Choo, J., Na, J., Lee, S., & Park, J. (2026) · Nano Letters · doi:10.1021/acs.nanolett.6c02504 · Cited by 1 · see citing works
Kumar, S., Pasimeni, F., Bolton, M., Hadfield, P., & Raven, R. P. J. M. (2026) · Energy Research & Social Science · doi:10.1016/j.erss.2026.104895 · Cited by 1 · see citing works
Zhang, X., He, X., Li, M., Liang, J., Wu, L., & Yang, Y. (2026) · Sustainability · doi:10.3390/su18178828 · Cited by 1 · see citing works
Artificial intelligence data centers could reach one percent of global electricity demand by 2030
Chen, D., Zhou, Z., Cai, Y., Qin, J., Katchova, A. L., & Chen, L. (2026) · Communications Sustainability · doi:10.1038/s44458-026-00152-5 · Cited by 1 · see citing works
Adverse Environmental and Public Health Effects of Artificial Intelligence: A Narrative ReviewPreprint
Domingo, J. L. (2026) · Qeios · doi:10.32388/x3o2n6.3 · Cited by 1 · see citing works
Planetary Health Ethics Needs to Consider the Emerging Role of Artificial Intelligence
Irfan, B., Hepp, P., Elmore, M., & Fiske, A. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2657903 · Cited by 1 · see citing works
Adverse Environmental and Public Health Effects of Artificial Intelligence: A Comprehensive ReviewPreprint
Domingo, J. L. (2026) · Qeios · doi:10.32388/x3o2n6
Thermal Management Innovations for Energy-Efficient A.I. Data Centres
Ekpe, I. C., Babatunde, O., Ale, A., Oladipupo, S. I., Adelekan, D. S., & Oluwafunmilayo, J. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202668401007
Adverse Environmental and Public Health Effects of Artificial Intelligence: A Narrative ReviewPreprint
Domingo, J. L. (2026) · Qeios · doi:10.32388/x3o2n6.2
Moreau, L., Bernard, C. D., Martin, P. L., & Diallo, A. E. (2025) · World Journal of Environmental Biosciences · doi:10.51847/apwuwikxqv
Data centers told to pitch in as storms and cold weather boost power demand
Luke, N., & Harrison, C. (2026) · doi:10.64628/aai.a6j47yyfn
AI as Political Infrastructure: A Diamond Model of Political AI Ethics
Bozdağ, A. A. (2026) · Philosophy & Technology · doi:10.1007/s13347-026-01058-9
Data Centred: the Shifting Landscape of Canada's Digital InfrastructurePreprint
Carlo, A., & Rolheiser, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6464099
Kalmar, A. F., Stevens, G., Saxena, S., González‐Pizarro, P., & Verdonck, P. (2026) · European Journal of Anaesthesiology · doi:10.1097/eja.0000000000002393
Adams, S. T. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-6592-3.ch010
Water stress and its sensitivity to demands across the Contiguous United States
Kim, S., & Devineni, N. (2026) · Communications Sustainability · doi:10.1038/s44458-026-00072-4
WANG, Q., HUANG, T., & Liu, Q. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6619349
MacKay, M., Khalid, F., Dunn, E., & McWhirter, J. E. (2026) · Advances in Public Health · doi:10.1155/adph/7677566
Dynamical Symbiosis of Solar Cell and Memristor
Kumar, J., Kim, S., Rivera‐Sierra, G., & Bisquert, J. (2026) · ACS Energy Letters · doi:10.1021/acsenergylett.6c00713
¿Cuánto pesan las nubes? El gran problema energético de normalizar el uso de IA
Lamo, P., & Carolina, G. C. (2026) · doi:10.64628/aao.uejgus7dx
Apaji, A. N., Uzuh, F. D., Okemmiri, S., & Abere, D. V. (2026) · Environmental Science and Pollution Research · doi:10.1007/s11356-026-37838-1
Buribayeva, M., Khamzina, Z. A., & Buribayev, Y. A. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1781993 · Cited by 3 · see citing works
Analysing the drivers of a sustainable bioeconomy paradigm
Ríos, M. S., Solé, T. T., & Perdiguer, M. F. (2026) · Frontiers in Sustainability · doi:10.3389/frsus.2026.1794059
Terawatts for Petabytes: Exploring the impact of AI data centres on Europe’s net zero goals
Hemmati, M., & Charitopoulos, V. M. (2026) · Systems and Control Transactions · doi:10.69997/sct.164210
Rajeev, S., Yogi, K., Kunchapu, V. A., Yang, Y., Lattupalli, H. K., Schiffres, S. N., Wei, T., Rangarajan, S., & Sammakia, B. G. (2026) · Journal of Electronic Packaging · doi:10.1115/1.4072231
The Water Footprint of Artificial Intelligence
Agrawal, P., & Yadav, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2761-5.ch004
Legal Battles Over Water for AI
Nandal, N., Bhattacharya, R., Sharma, A., Paliwal, D. S., & Godha, D. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2761-5.ch006
Vaz, H. M. L. (2026) · Revista Científica/FAP · doi:10.33871/19805071.2026.34.1.11814
Keller, A. A. (2026) · Research Square · doi:10.21203/rs.3.rs-9962169/v1
Huang, X., Chen, Y., Zhao, D., & Liu, C. (2026) · Emerging Markets Finance and Trade · doi:10.1080/1540496x.2026.2704830
Fokoué, E. (2026) · Sustainability · doi:10.3390/su18157545 · Cited by 1 · see citing works
Tâm, N. V., Hong, P. V., & Phong, V. V. (2026) · Building Research & Information · doi:10.1080/09613218.2026.2695750
RHODES: Robust Optimization for Uncertainty-Aware Design of CO 2 -Efficient Computing Systems
Elgamal, M., Mahmoud, A., Wei, G.-Y., Brooks, D. M., & Hills, G. (2026) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca66397.2026.00028
Li, Z., Wang, M., Zhou, T., & Zhang, C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7244749
Schnase, J. L., Carroll, M., Montesano, P., & Seamster, V. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.06.741224
Su, L., Guo, Y., Mo, X., & Zhang, L. (2026) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2026.132702
Light-Induced Neuron-Like Bursts
Kumar, J., Fenollosa, R., Rivera‐Sierra, G., Kim, S., & Bisquert, J. (2026) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.6c01968
Johnson, P. S., & Eberl, M. (2026) · Human Organization · doi:10.1080/00187259.2026.2714301
Apaolaza, V., Paredes, M. R., Eletxigerra, A., & Hartmann, P. (2026) · International Journal of Consumer Studies · doi:10.1111/ijcs.70248
Advancing Sustainable Development Through Artificial Intelligence (AI)
Bonakdari, H., Ghaderpour, E., & Gumière, S. J. (2026) · Sustainability · doi:10.3390/su18168486
Coldicutt, R. (2026) · Australian Planner · doi:10.1080/07293682.2026.2717138
AI-Enabled Business Models for Sustainability
Ramachandran, R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7699-8.ch003
Yang, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7357559
Walther, C. (2026) · Research Square · doi:10.21203/rs.3.rs-10531049/v1
Lu, Y., Yi, Y., Gao, H., & Zhang, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7386102
Adzhar, S. N. A., Munian, K., Mazian, M. A., Aqmal-Naser, M., & Azmir, I. A. (2026) · Aquaculture Aquarium Conservation & Legislation · doi:10.67784/0001
Jang, B., Song, J., Koo, J., & Yoon, S. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105189
Albatayneh, R. (2026) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2026.109144
Posadas-Paredes, T., Zavala-Vega, D. K., Márquez, C. R., & Ponce‐Ortega, J. M. (2026) · Resources · doi:10.3390/resources15090115
Wang, X. P., Zhu, H., Zhu, J., Dong, Z. Y., Yang, X., & Li, J. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.117460
Adapting Regional Power Generation Mix to Climate Change: Global Efforts, Challenges, and Gaps
Razeghi-Jahromi, D., Khajehpour, H., & Gardumi, F. (2026) · Current Sustainable/Renewable Energy Reports · doi:10.1007/s40518-026-00295-y
Wang, J., Xu, Q., Wang, M., Yang, B., & Wang, Z. (2026) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2026.3733880
Quintana, Y., Schleyer, T., Benis, A., Fraser, H., Joseph, A. L., Holl, F., & Campbell, E. A. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag140
Qiu, N., Xu, D., Sahakian, B., Langley, C., Eils, R., Marciano, L., Montag, C., Halpern, J., Yeo, B. T. T., Yip, P. S. F., et al. (2026) · Psychotherapy and Psychosomatics · doi:10.1159/pps/aemag015
Charting a human-Centered future: AI's promise and challenges for occupational therapy
Pellerin, M., Malfitano, A. P. S., Francis, J., Teng, M. Y., & Jacobs, K. (2026) · Work · doi:10.1177/10519815261490547
Wang, X., Sun, F., & Anderson, M. (2026) · AATCC Journal of Research · doi:10.1177/24723444261488123
One Quarter of Global Data Centers are Located in Water-Stressed Areas
Rosa, L., Sangiorgio, M., Scanlon, B. R., Mazzotti, M., Berry, J. A., & Michalak, A. M. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.6c08088 · Cited by 2 · see citing works
Rocha, A., Ferreira, J. P., de Oliveira, P. C., Alves, M., & Sousa, A. J. (2026) · Computers · doi:10.3390/computers15100647
The case for integrated assessment to design data centers of the future
Bergerson, J. A., Dowling, A., Javeline, D., Han, X., Lee, Y. S., Legatzke, H., Ostermann, S. L., Schaefer, J. L., Zaman, R., & Agrawal, A. (2026) · Cell Reports Sustainability · doi:10.1016/j.crsus.2026.100865
The Discovery of Quasicrystals: Reflections on Theory Change and Correct Attribution
Ramírez-de-Arellano, J. M. (2026) · Histories · doi:10.3390/histories6040058
From Gen Z Skepticism to Environmental Accountability for Artificial Intelligence
Rahimi, M. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.6c11139
From Participation to Exposure: NFTs and the Socio-Environmental Tensions of Digital Innovation
Oberhauser, M., Lima, V., & Chamakiotis, P. (2026) · AJIS. Australasian journal of information systems/AJIS. Australian journal of information systems/Australian journal of information systems · doi:10.3127/ajis.v30.6562
Cunha, A., Nataraj, C., Lorenzo, L. M., Panetto, H., Scotti, A., & Vargas, J. V. C. (2026) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-026-06873-x
- 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 ›
Are you an author, or do you see a problem with this page? Request a review
