DATACENTERS environmental impact of artificial intelligenc
page 3 / 4
== Mining ==
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.
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.
== Social impact and environmental justice ==
The environmental effects of AI-related infrastructure are not distributed evenly. Research on U.S. data centres has found that their environmental footprints vary by region and may intersect with local electricity systems, water availability and existing environmental burdens. 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.
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.
== Climate solutions ==
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. AI has also been used in research on extreme weather and climate-event modelling.
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.
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.
=== Conflict on the use of AI for environmental research ===
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.
== Policy and regulation ==
=== United States ===
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. 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.”
==== State Policy ====
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.
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.