"This report is not a case against artificial intelligence, a technological transformation that is improving the lives of billions of people around the world. 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."

– Professor Kaveh Madini, director of UNU-INWEH who led the investigative team

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.

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.

"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, a co-author of the report who was recently named the 2026 Stockholm Water Prize Laureate. "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."

Efficiency improvements alone will not contain growth. The report cites the rebound effect, sometimes called the Jevons Paradox, to explain why per-query gains are typically absorbed by rising volumes. Caps on tokens, resolution and output length are needed alongside efficiency.

"If you map where data centres are getting built against where water stress is worst, you tend to see the same regions in some instances," said Dr. Mir Matin, Manager of UNU-INWEH's Geospatial, Climate and Infrastructure Analytics Programme and a co-author of the report. "And the communities living near these sites are not necessarily the ones using the AI being run there. That asymmetry is the issue. Without fixing it, we'll just be repeating older patterns, where some places carry the costs and other places capture the benefits."

"The global system building artificial intelligence must also govern it sustainably and fairly," said Professor Tshilidzi Marwala, Rector of the United Nations University and Under-Secretary-General of the United Nations. "The concentrated development of AI infrastructure in the privileged areas of the world is creating a large digital divide that poses profound challenges in the equitable development of AI. AI can certainly advance prosperity and human well-being. Whether it does so equitably is now a question of governance, not a technical one."

REPORT IN BRIEF

AI's environmental cost is being mismeasured. Most current assessments focus on carbon emissions from training. The report argues that this misses a substantial part of the picture. Every kilowatt-hour of AI electricity also carries a water footprint, from cooling and generation, and a land footprint, from infrastructure and supply chains. These three footprints can move in opposite directions, so reducing one can magnify another.

Data centres are becoming country-scale consumers of electricity, water and land. Global data-centre electricity use, estimated at 448 TWh in 2025, could reach 945 TWh by 2030. The associated water footprint is projected at 9.3 trillion litres and the associated land footprint at over 14,500 square kilometres.

Inference, not training, drives most of AI's energy use. Once a model is deployed, billions of daily user interactions consume an estimated 80 to 90 per cent of its total energy. ChatGPT alone is estimated to process around 2.5 billion prompts per day.

Per-query energy varies by orders of magnitude across tasks. A typical chat query uses around 200 times the energy of basic text classification. An AI image uses around 1,450 times. A single short AI video can match 200,000 spam classifications. Model choice and product defaults are footprint decisions.

Energy and water required to generate AI images and videos. The energy required to generate a typical AI image is enough to power a 10-watt LED bulb for 17 minutes, and the energy required for a high-complexity AI video is sufficient to run that same bulb for 42 hours. Similarly, the electricity-associated water footprint is about two tablespoons (29 mL) for a single image, but jumps to 4.1 liters for a complex video—almost equivalent to a two-day drinking water need for one person.

Efficiency improvements alone will not contain growth. The report cites the rebound effect, sometimes called the Jevons Paradox, to explain why per-query gains are typically absorbed by rising volumes. Caps on tokens, resolution and output length are needed alongside efficiency.

AI compute is geographically concentrated. Only 32 countries host AI-specialised data centres. Over 90 per cent of capacity is in two countries. More than 150 countries currently lack sovereign AI compute infrastructure.

The hardware lifecycle is the next frontier. AI infrastructure could generate up to 2.5 million tonnes of electronic waste each year by 2030. Critical minerals required for AI hardware are concentrated in regions with weaker environmental oversight, often in the Global South.

A six-principle governance framework. The report proposes a "responsible AI ecosystem" built on transparency, efficiency by design, equity and environmental justice, lifecycle responsibility, global cooperation, and sustainable use, with specific responsibilities assigned across the AI ecosystem.

Carbon-only metrics are no longer sufficient for AI. Disclosure standards for AI's environmental impact should require carbon, water and land footprints jointly, in standardized units, across both training and inference and across jurisdictions, so that regulators and investors can compare like with like.

Inference deserves the policy attention that training has received. Because operational use accounts for the majority of AI energy demand, governance should focus on product defaults, model selection and behavioural levers, not only on the largest training runs.

Siting decisions are environmental decisions. Where data centres are built, and from which grid they draw power, determines the carbon, water and land profile of the same workload. Permitting, environmental impact assessment and community consultation should reflect this reality.

Local capacity-planning needs to keep pace with global compute geography. The Irish, Mexican and Uruguayan cases described in the report show what happens when grid and water systems are asked to absorb workloads that serve users elsewhere. Transparent mitigation and benefit-sharing should accompany expansion.

Efficiency gains require demand-side guardrails. Without resource budgets, token-per-prompt limits, default low-resolution settings and similar guardrails, efficiency improvements will be absorbed by volume growth.

AI compute access is itself an equity issue. More than 150 countries currently lack sovereign AI compute. International institutions can help by supporting capacity-building, harmonising disclosure, and reducing incentives for cross-border burden-shifting.

The full value chain requires governance. Critical-mineral extraction at the upstream end and electronic waste at the downstream end are integral to AI's footprint and currently fall on communities that capture little of the benefit.

Investors and financial institutions can move first. Treating carbon, water and land footprints as material risks in due diligence on AI infrastructure portfolios is described in the report as one of the fastest available levers.

AI within planetary limits is achievable. The report's central argument is constructive. Capability and stewardship can grow together, but only with measurement, transparency, and shared responsibility across the ecosystem.

Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., Madani, K. (2026). Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints. United Nations University Institute for Water, Environment and Health (UNU-INWEH), Richmond Hill, Ontario, Canada. doi: 10.53328/INR26RMA002

Marking its 30th anniversary of operation in 2026, the United Nations University Institute for Water, Environment and Health (UNU-INWEH) is one of 13 institutions that make up the United Nations University (UNU), the academic arm of the UN. Known as 'The UN's Think Tank on Water', UNU-INWEH addresses critical water, environmental, and health challenges around the world. Through research, training, capacity development, and knowledge dissemination, the institute contributes to solving pressing global sustainability and human security issues of concern to the UN and its Member States. Headquartered in Richmond Hill, Ontario, UNU-INWEH has been hosted and supported by the Government of Canada since 1996. With a global mandate and extensive partnerships across UN entities, international organizations, and governments, UNU-INWEH operates through its UNU Hubs in Calgary, Hamburg, New York, Lund, and Pretoria, and an international network of affiliates.

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