Study warns AI infrastructure boom risks undermining climate targets

A new scientific analysis highlights the environmental costs of expanding AI infrastructure in the US, with potential emissions and water demands threatening progress on climate targets this decade, prompting calls for regional planning and efficiency measures.

A new scientific analysis warns that the rapid expansion of artificial intelligence infrastructure could significantly erode progress on climate targets this decade, producing greenhouse gas emissions and water demand on a scale normally associated with major industrial sectors. According to the study published in Nature Sustainability, AI servers deployed across the United States could emit between 24 million and 44 million metric tons of carbon dioxide annually by 2030, the authors say, a footprint the researchers liken to adding as many as ten million cars to the road. ^[1][2]

The paper attributes the surge primarily to the energy-intensive processes required to train and operate large language models and other generative AI systems now embedded in search engines, productivity suites, creative platforms and business operations. Training a single advanced model can produce the equivalent of hundreds of thousands of kilograms of CO2, the study notes, while continual inference , the computations run every time a user queries an AI , accounts for roughly 60% of total AI energy use. The researchers caution that efficiency gains alone will not offset the scale of growth projected. ^[1][2]

Water use emerges as a parallel and sometimes overlooked environmental cost. The Nature Sustainability analysis estimates U.S. AI data centres may consume between 731 million and 1,125 million cubic metres of water per year, a quantity comparable to the annual household water needs of up to ten million Americans. Industry reporting and technical analyses corroborate the magnitude of the problem: IEEE Spectrum reported that U.S. data‑centre direct water consumption reached about 17.5 billion gallons in 2023 and warned that demand could double or quadruple within a few years, placing heavy strain on local water systems. ^[1][2][3]

Independent research and sector surveys offer a similar, if sometimes broader, picture of AI’s resource appetite. A study from the VU Amsterdam Institute for Environmental Studies projected global AI power demand could reach roughly 23 gigawatts and consume hundreds of billions of litres of water within a few years, with CO2 emissions in the tens of millions of tonnes annually. Industry summaries estimate individual large facilities may use millions of gallons a day for cooling, reinforcing concerns about both direct cooling needs and the indirect water footprint embedded in electricity production. ^[5][4]

Data centres already account for a non-trivial share of global emissions, and the shift toward AI workloads is reshaping demand patterns. The lead study places data‑centre emissions at up to 3.7% of global greenhouse gases, and industry analysts expect AI workloads to expand their share of total data‑centre activity substantially. Financial‑sector forecasts cited in infrastructure reporting project that AI could double its share of the data‑centre market in the coming years, with power consumption rising steeply between 2023 and 2030. That trajectory has prompted warnings about grid stress, higher electricity prices and, in some scenarios, localised supply shortfalls. ^[1][6][7]

The environmental cost is not confined to operational energy and water. Construction of new facilities requires large quantities of steel, concrete and specialised equipment, adding embodied emissions before servers ever begin computing. The Nature Sustainability authors argue that relocating data centres to regions with abundant renewable power and lower water stress , for example, wind‑rich Midwestern states , could materially reduce impacts, but they also note the scale of infrastructure, transmission and permitting investment required to make such a shift. Even under optimistic renewables scenarios, millions of tonnes of emissions are likely to remain by 2030. ^[1][2]

The study’s findings complicate tech companies’ public commitments to net‑zero targets by 2030. According to the researchers, many corporate pledges appear difficult to meet without substantial reliance on carbon offsets, the validity and permanence of which remain debated. Policymakers and companies face choices about demand management, siting, grid investment and cooling technology, alongside stronger transparency about water and lifecycle impacts if the environmental consequences of AI are to be contained. Industry and academic sources cited in coverage urge a mix of efficiency improvements, regional planning, renewable procurement and tighter scrutiny of offsets to avoid undermining broader climate objectives. ^[1][2][3][6]

📌 Reference Map:

##Reference Map:

• ^[1] (NatureNews/A. Nazil) – Paragraph 1, Paragraph 2, Paragraph 3, Paragraph 6, Paragraph 7
• ^[2] (Nature Sustainability) – Paragraph 1, Paragraph 2, Paragraph 3, Paragraph 6, Paragraph 7
• ^[3] (IEEE Spectrum) – Paragraph 3, Paragraph 7
• ^[4] (AllAboutAI) – Paragraph 4
• ^[5] (VU Amsterdam Institute study reported by Tom’s Hardware) – Paragraph 4
• ^[6] (ITPro/Goldman Sachs forecasts) – Paragraph 5, Paragraph 7
• ^[7] (Le Monde) – Paragraph 5

Source: Fuse Wire Services