A new study by researchers at the UN University warns that AI is threatening the natural resources for billions of people, and that by 2030, AI’s water use will match the needs of 1.3 billion people, while its power use will triple, becoming equivalent to the water use of 650 million people.
The new report, titled Environmental Cost of AI’s Energy Use: Carbon, Water and Land Footprints, warns that global data centres powering artificial intelligence are projected to consume 945 terawatt-hours of electricity by 2030.
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 kilometres, roughly twice the Jakarta metropolitan area, home to more than 32 million people.
Researchers have previously warned about the greenhouse gas emissions of data centres, but this study says the environmental costs of AI and data centres cannot be understood through carbon emissions alone.
Professor Kaveh Madani, who led the study, says this report is not a case against artificial intelligence, but 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.”
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.
Researcher Dr Miriam Aczel says what surprised them most is how often the choices that look greenest from a carbon perspective end up worse for water or for land.
“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.”
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However, the report finds that this framing is outdated. Once a model is deployed, 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 383 GWh of electricity per year for a single product.
Offsetting associated carbon emissions would require 2.6 million tree seedlings grown for 10 years, enough trees to cover a land area the size of Manhattan. The water footprint is equivalent to the minimum annual domestic water needs of roughly 500,000 people in Sub-Saharan Africa, and the land footprint is equal to over 800 football fields.
The authors found that video generation is now emerging as an environmental crisis, finding that a single short AI-generated video can consume as much electricity as 200,000 spam classifications.
“A lot of people think that the environmental footprint of AI reduces as technology improves and processes become more efficient. But that is only a partial picture of the overall problem,” said Professor Madani.
“More efficient and affordable AI and energy mean more consumption of AI, making the overall footprint far bigger than what we save through efficiency gains.”
Read the full report HERE.
