Rising Emissions, Depleting Water and Vanishing Land—UN Scientists: AI Is Threatening Natural Resources for Billions | United Nations University

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Rising Emissions, Depleting Water and Vanishing Land—UN Scientists: AI Is Threatening Natural Resources for Billions

By 2030, AI's water use will match the needs of 1.3 billion people while its power use triples that of 650 million, UN University investigation warns

Date Published

3 Jun 2026

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Artificial intelligence is driving a surge in land, water and climate consequences cascading from the technology’s intense and fast-rising energy consumption; UN University scientists call for urgent, multi-stakeholder action in a new UNU-INWEH report

Richmond Hill, Ontario, Canada (3 June 2026) – By 2030, the global data centres powering artificial intelligence are projected to consume 945 terawatt-hours of electricity. This is nearly triple the combined annual electricity use of Pakistan, Bangladesh, and Nigeria—countries collectively home to more than 650 million people. Their associated water footprint will equal the basic annual domestic water needs of all 1.3 billion people in Sub-Saharan Africa, and their land footprint will exceed 14,500 square kilometers, roughly twice the Jakarta metropolitan area, home to more than 32 million people.<br>These stark findings are detailed in the new report, Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints, by the United Nations University Institute for Water, Environment and Health (UNU-INWEH). Researchers have previously warned about the greenhouse gas emissions of data centers before. But the UN scientists now argue that the environmental costs of AI and data centers cannot be understood through carbon emissions alone. In their report, they quantify the carbon, water and land footprints of AI's electricity use across the globe and highlight the big differences between these footprints in the world’s 20 largest data center hubs.<br>"This report is not a case against artificial intelligence, a technological transformation that is improving the lives of billions of people around the world," said Professor Kaveh Madani, Director of UNU-INWEH who led the investigation team. "It is a call for using it responsibly and addressing its unintended impacts proactively to make it sustainable and equitable. We have a narrow window to ensure that the backbone of the technological revolution of our era develops within planetary limits, and that the communities who provide the critical minerals for advancing AI and the ones that host its infrastructure and e-waste are also among those who benefit from it."

A footprint that is being mismeasured<br>The report finds that AI's environmental cost is being systematically mismeasured. Most existing assessments focus on the carbon emissions associated with training large models. Yet every kilowatt-hour of electricity used to train or run an AI system also carries a water footprint, from cooling and power generation, and a land footprint, from energy infrastructure and supply chains. These three footprints do not move in the same direction. Switching from coal to bioenergy, for example, can on average cut the carbon footprint of electricity by 70 per cent, while increasing its water footprint more than thirty-fold and its land footprint a hundred-fold. The report concludes that "low-carbon" is not automatically "low-water" or "low-land” and warns that evaluating AI sustainability through a single metric can hide trade-offs and shift environmental burdens onto regions already facing water or land stress.<br>The numbers compound rapidly at the infrastructure level. Global data centres consumed an estimated 448 terawatt-hours of electricity in 2025. If treated as a nation, they would have been the world's 11th largest electricity consumer, behind France and ahead of Saudi Arabia.<br>"What surprised us most is how often the choices that look greenest from a carbon perspective end up worse for water or for land," said Dr. Miriam Aczel , UNU-INWEH Researcher and the lead author of the report. "If we keep judging AI sustainability by carbon alone, we might think that renewables make AI infrastructure clean but that is solving one problem while creating other problems, often in places that didn't ask for it."<br>Inference, efficiency, and the rebound effect<br>Public discussion has largely focused on the energy required to train massive models. Training GPT-3 was estimated to require 1.3 gigawatt-hours (GWh) of electricity, while estimates suggest GPT-4 consumed between 50 and 70 GWh. However, the report reveals this framing is outdated. Once a model is deployed, inference—the continuous running of models to answer everyday user prompts—becomes the dominant cost, accounting for 80 to 90 per cent of total AI energy use. ChatGPT alone is estimated to process around 2.5 billion prompts per day, translating to roughly...