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on data centre water use has often been inconsistent making comparisons between operators and regions difficult 3 to standardise operational reporting the green grid proposed the metric water usage effectiveness wue defined as annual site water use divided by it equipment energy use 20 wue does not by itself measure local water stress source sustainability or all upstream water impacts 20 3 studies of ai water use also distinguish between water withdrawal and water consumption a 22 research on ai specific water use has argued that the water footprint of ai systems can be difficult to observe and may vary substantially by location cooling design and electricity source a 2025 communications of the acm article summarised methods for estimating ai water footprints and emphasised the distinction between water withdrawal and water consumption 22 a 2025 report by the international energy agency said that global water consumption by data centres was around 560 billion litres in 2023 expected to rise to 12 000 billion litres in 2030 of the water consumed in 2023 about two thirds was associated with energy generation and one quarter with cooling with the remainder related to semiconductor and microchip manufacturing 23 a lawrence berkeley national laboratory report from 2024 suggests an indirect water footprint of 800 billion litres in 2023 from electricity generation with direct water consumption of 66 billion litres across all data centres in the united states with an unspecified amount due to ai 24 however there is high variation in water use based on workload with server efficiency playing a large role 25 based on estimates of water intensity and power draw a 2025 article by de vries gao suggested the total water footprint of ai systems could reach between 312 5 and 764 6 billion litres that year 26 the same year li and colleagues estimated that global ai water withdrawal could reach 4 2 6 6 billion cubic metres 4 200 6 600 billion litres in 2027 under the scenarios examined in their article 22 using gpt 3 released by openai in 2020 as an example they estimated that training the model in microsoft s us data centres could consume about 700 000 litres of onsite water and about 5 4 million litres in total when offsite electricity related water use was included they also estimated that 10 50 medium length gpt 3 responses could consume about 500 ml of water depending on when and where the model was deployed 22 published prompt level estimates have also varied by system and accounting framework the 2025 google study on gemini assistant serving reported a median text prompt estimate of about 0 26 ml under its framework 5 an article by shumba et al about water efficiency in african data centers suggest when writing a medium length email of 120 200 words llama3 70b and gpt 4 could consume about 0 12 liters and 2 6 litres of water respectively while writing a ten page report could use 0 6 and 53 litres 27 location can materially affect the significance of data centre water use research on u s data centres found that one fifth of servers direct water footprint came from moderately to highly water stressed watersheds while nearly half of servers were fully or partially powered by plants located in water stressed regions 28 a 2025 reuters report citing data from verisk maplecroft and naturefinance said that an average mid sized data centre uses about 1 4 million litres of water per day for cooling and that phoenix would experience a 32 increase in annual water stress if currently planned data centres come online 29 water use also occurs upstream in semiconductor fabrication which relies on large quantities of ultrapure water 30 e waste edit main article electronic waste ai systems depend on specialised computing hardware and rapid turnover in servers and accelerators may contribute to rising e waste 4 the world health organization identified e waste as a growing environmental and public health issue 31 the primary contributor to e waste from ai is the high performance hardware used in data centers which can contain hazardous materials like lead mercury and chromium 4 ai data center components are also swapped frequently with companies expected to completely replace their hardware every three years to remain competitive 32 recycling ai hardware can also be more difficult than typical consumer electronics due to the possibility of storing sensitive data 33 a 2024 study in nature computational science estimated that generative ai could add between 1 2 and 5 million tonnes of e waste by 2030 under the scenarios examined by the authors 4 in the study s higher end scenarios this would represent up to 12 of projected global e waste by 2030 4 the authors also reported that circular economy strategies along the generative ai value chain could reduce ai related e waste generation by 16 86 4 a study from 2026 instead estimated that by 2030 ai servers would generate 131 0 to 224 8 thousand tonnes of e waste per year 34 deep learning and ai systems have also been suggested to help with e waste tracking collection and management 35 36 37 38 39 mining edit ai hardware depends on complex supply chains for metals minerals and manufactured components unctad has reported that the expansion of digital infrastructure increases demand for raw materials and raises environmental and distributional concerns linked to extraction processing and manufacturing 40 specialised chips used in ai systems can depend on supply chains involving critical minerals and other materials whose extraction and processing may have significant environmental and social effects these impacts are not unique to ai but may increase as demand for ai related hardware grows 40 social impact and environmental justice edit the commonly adopted symbol of ai resistance used in protests and online campaigns after concerns of environmental impact the environmental effects of ai related infrastructure are not distributed evenly research on data centres in the united states has found that their environmental footprints vary by region and may intersect with local electricity systems water availability and existing environmental burdens 28 in that study one fifth of servers direct water footprint came from moderately to highly water stressed watersheds while nearly half of servers were fully or partially powered by plants located in water stressed regions 28 concerns have also been raised about local air pollution permitting and grid stress in communities hosting ai related facilities and associated power infrastructure in 2025 civil rights and environmental groups challenged permits connected to an xai facility in the memphis area arguing that air pollution burdens could fall disproportionately on historically overburdened neighbourhoods the dispute has been the subject of regulatory and legal proceedings 41 42 43 climate solutions edit despite concerns about its environmental footprint ai has been used in environmental and climate related applications including weather forecasting earth observation and optimisation in transport and energy systems in weather forecasting peer reviewed studies have reported strong results for some ai based forecasting systems under specific evaluation frameworks a 2023 nature paper on pangu weather reported strong medium range forecasting performance relative to a leading numerical weather prediction system in the study s evaluation 44 ai has also been used in research on extreme weather and climate event modelling 45 ai has also been proposed for mitigation oriented optimisation google s green light project for example uses traffic data and machine learning to recommend traffic signal timing adjustments intended to reduce stop and go traffic and associated emissions at intersections 46 whether ai produces net environmental benefits at large scale remains an open question because outcomes depend on deployment choices rebound effects additional infrastructure demand and the extent to which electricity and cooling systems are decarbonised 1 conflict on the use of ai for environmental research edit there is ongoing debate over the balance between the possible environmental benefits of ai applications and the environmental costs of scaling ai systems this includes discussion of transparency efficiency rebound effects and the extent to which ai related infrastructure growth may offset environmental gains from specific applications 2 5 policy and regulation edit united states edit in the united states proposals have been introduced to study and standardise reporting on ai s environmental impacts the artificial intelligence environmental impacts act of 2024 s 3732 introduced in the senate in february 2024 would require a federal study on the environmental impacts of ai direct the national institute of standards and technology to convene a consortium on measurement and standards and establish a voluntary reporting system 7 policy on ai is split between federal and state governments federal regulations have been minimal with president trump saying on the ai gov website the united states is in a race to achieve global dominance in artificial intelligence whoever has the largest ai ecosystem will set the global standards and reap broad economic and security benefits 47 state policy edit local and state governments have looked to address environmental and infrastructural impacts as of 2026 at least 27 states are considering or have put legislation related to data center development with california ohio and utah being the first to pass legislation this legislation requires data center developers to bear the costs of new energy infrastructure some states are also pushing for legislation requiring data centers to report water use an issue not addressed by the federal government states are looking to focus on data collection related to data center water use 48 a 2025 study published by nature sustainability estimated that ai servers in the united states could require approximately 731 to 1 125 million cubic meters of water annually by the year 2030 with 24 to 44 million metric tons of carbon emissions this study found that current growth trajectories are unlikely to meet net zero targets without policy 49 european union edit in the european union the energy efficiency directive introduced reporting obligations for large data centres the european commission has stated that a european database collects information relevant to the energy performance and water footprint of data centres and that a delegated regulation sets out the information and key performance indicators for the reporting scheme 6 eu member states also maintain national ai strategies some of which include references to sustainability energy efficiency or environmental applications of ai france edit france s ai strategy documents have discussed ai in relation to ecological transition and environmental applications including the use of digital infrastructure and data for environmental policy 50 germany edit germany s national ai strategy includes sections on the environmental impacts of ai and on research into energy efficient and sustainable ai applications 51 italy edit italy s national ai strategy documents include sustainability related priorities and discuss ai applications in areas such as environment infrastructure and sustainable development goals 52 see also edit workplace impact of artificial intelligence environmental impact of bitcoin environmental impact of computers digital decarbonisation notes edit water withdrawal refers to freshwater taken from ground or surface sources either temporarily or permanently 21 water consumption is often defined as water withdrawal minus water discharge such as water evaporated or otherwise removed from the immediate water environment 21 references edit 1 2 3 4 executive summary energy and ai international energy agency paris iea 2025 retrieved 2 april 2026 1 2 3 4 strubell emma ganesh ananya mccallum andrew 2019 energy and policy considerations for deep learning in nlp proceedings of the 57th annual meeting of the association for computational linguistics pp 3645 3650 arxiv 1906 02243 doi 10 18653 v1 p19 1355 1 2 3 4 mytton david 2021 data centre water consumption npj clean water 4 1 11 doi 10 1038 s41545 021 00101 w hdl 10044 1 86841 1 2 3 4 5 6 7 wang peng zhang ling yu tzachor asaf chen wei qiang 2024 e waste challenges of generative artificial intelligence nature computational science 4 11 818 823 doi 10 1038 s43588 024 00712 6 1 2 3 4 5 6 7 8 elsworth cooper huang keguo patterson david schneider ian sedivy robert goodman savannah townsend ben ranganathan parthasarathy dean jeff vahdat amin gomes ben manyika james 21 august 2025 measuring the environmental impact of delivering ai at google scale arxiv 2508 15734 cs ai 1 2 3 energy performance of data centres european commission 2026 retrieved 2 april 2026 1 2 text s 3732 118th congress 2023 2024 artificial intelligence environmental impacts act of 2024 congress gov library of congress 1 february 2024 retrieved 2 april 2026 lambert katherine luccioni sasha 25 june 2026 from cradle to cloud a life cycle review of ai s environmental footprint acm p 518 540 arxiv 2605 05416 doi 10 1145 3805689 3812401 isbn 979 8 4007 2596 8 retrieved 27 july 2026 you josh 7 february 2025 how much energy does chatgpt use epoch ai retrieved 11 november 2025 1 2 luccioni sasha jernite yacine strubell emma 2024 power hungry processing watts driving the cost of ai deployment proceedings of the 2024 acm conference on fairness accountability and transparency pp 85 99 arxiv 2311 16863 doi 10 1145 3630106 3658542 isbn 979 8 4007 0450 5 we did the math on ai s energy footprint here s the story you haven t heard mit technology review 20 may 2025 retrieved 12 november 2025 tomlinson bill black rebecca w patterson donald j torrance andrew w 2024 the carbon emissions of writing and illustrating are lower for ai than for humans scientific reports 14 3732 doi 10 1038 s41598 024 54271 x woo nolan h 2025 a comparative study of ai and human programming on environmental sustainability scientific reports 15 39182 doi 10 1038 s41598 025 24658 5 pmc 12603251 pmid 41214116 bhattarai abha lerman rachel 25 december 2025 10 charts that show where the economy is heading 3 ai related investments the washington post source msci cite news cs1 maint url status link alšauskas oskaras 2024 world energy outlook 2024 pdf international energy agency paris france efficiency improvement of ai related computer chips 2008 2023 international energy agency paris iea 17 october 2024 retrieved 2 february 2026 1 2 3 gomes ben brandt kate 2 july 2024 our 2024 environmental report the keyword google retrieved 2 april 2026 1 2 3 patterson david gonzalez joseph hölzle urs le quoc liang chen munguia lluis miquel rothchild daniel so david r texier maud dean jeff 2022 the carbon footprint of machine learning training will plateau then shrink 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