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 will triple the annual consumption of nearly 650 million, UN University scientists warn.
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.
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.
"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
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.
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.
"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."
Inference, efficiency, and the rebound effect
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 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.
Video generation as an emerging environmental crisis
Per-query energy varies by orders of magnitude depending on the task. A typical conversational chat query is around 200 times more energy-intensive than basic text classification. Generating a single AI image can require around 1,450 times that baseline. A single short AI-generated video can consume as much electricity as 200,000 spam classifications. Model choice, prompt length, output format and resolution all materially shape the footprint. Yet most of these decisions are taken invisibly, by product defaults the user never sees.
The report invokes the rebound effect (the Jevons Paradox), warning that as models become more efficient, they become cheaper and are used more frequently. Without explicit limits on tokens, resolution, or default output length, improvements at the per-query level are easily wiped out by sheer volume growth.
"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."
Local costs, distant benefits
The benefits and burdens of the massive global expansion of AI are highly unequal. Several site-level cases in the report show how globally distributed AI services create intense local pressures. In Ireland, data centres accounted for 21% of total metered electricity in 2023, exceeding all urban households. The national grid operator has paused new approvals around Dublin until 2028, making Ireland a concrete, documented example of what happens when AI infrastructure growth outpaces energy planning — and a preview of what other countries are heading toward.
In Querétaro, Mexico, expanding compute infrastructure is drawing on water supplies amid prolonged droughts. In Uruguay, plans for a water-intensive data centre coincided with a 2023 drought that depleted Montevideo's freshwater reserves, making tap water unsafe to drink.
Furthermore, AI infrastructure could generate up to 2.5 million tonnes of electronic waste each year by 2030, much of it processed in low-income economies with limited safeguards, while critical minerals are extracted in jurisdictions with weak environmental oversight
"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 digital divide: AI computing is 90% concentrated in two countries
While the AI infrastructure comes with environmental costs, they also have major economic, security and sovereignty advantages that encourage the wealthier countries to build more data centers. Only 32 countries in the world host AI-specialised data centres, and 90% of that capacity is concentrated in 2 countries, while more than 150 countries currently have little or no access to sovereign AI compute. The report frames this not just as an economic divide but as an environmental justice issue: excluded countries bearcritical minerals extraction and e-waste burdens while the strategic benefits flow elsewhere.
"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 governance question, not a technical one."
A roadmap for sustainability and equity
The report calls for a responsible AI ecosystem built on six principles: transparency; efficiency by design; equity and environmental justice; lifecycle responsibility; global cooperation; and sustainable use. Practical recommendations are directed at each major group of stakeholders:
Governments should integrate AI infrastructure into energy planning, water governance, and land-use permitting, and require standardized environmental footprint reporting.
Industry and AI developers should treat model selection, default outputs, and routing decisions as footprint determinants, and improve efficiency by design.
Users and deploying organizations should adopt fit-for-purpose use — selecting the lightest model and lowest-energy format that meets the task.
Data center operators and utilities should treat siting and energy procurement as environmental footprint decisions and apply cumulative impact assessment.
Investors should treat electricity, carbon, water, and land footprints as material risks in AI infrastructure portfolios.
Communities and civil society should be involved early in data center siting decisions, with enforceable transparency and grievance mechanisms.
International institutions should support harmonized measurement standards, reduce incentives for cross-border burden shifting, and build compute capacity in excluded regions.
By the numbers
945 TWh | Projected global data-centre electricity demand by 2030, almost three per cent of projected world electricity use and roughly twice France's 2025 consumption (Section 2.4). |
9.3 trillion litres | Associated water footprint of 2030 data-centre electricity, equal to the basic annual domestic water needs of 1.3 billion people in Sub-Saharan Africa (Section 2.4). |
14,500 km² | Associated land footprint of 2030 data-centre electricity, about twice the Jakarta metropolitan area, home to more than 32 million people (Section 2.4). |
80 to 90% | Estimated share of total AI energy use consumed by inference, the running of deployed models, rather than by training (Chapter 3). |
2.5 billion | Estimated daily ChatGPT prompts, translating to roughly 383 GWh of electricity a year for a single product (Section 3.3). |
1,450× | Energy demand of a typical AI-generated image relative to basic text classification (Section 3.2). |
>90% | Share of AI-specialised cloud compute concentrated in two countries, the United States and China (Section 1.5). |
2.5 million tonnes | Projected annual AI-related electronic waste by 2030, equivalent to discarding nearly 250 Eiffel Towers each year (Key Points). |
REPORT IN BRIEF
AI's environmental cost is being mismeasured. Most current assessments focus on carbon emissions from training. The report argues 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.
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.
KEY POLICY MESSAGES
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 standardised 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.
REPORT INFORMATION
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
ABOUT UNU-INWEH
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.
Article Title
Environmental Cost of AI's Energy Use: Carbon, Water and Land Footprints.
(The Conversation) — Leo XIV released his first encyclical on the 135th anniversary of Rerum Novarum, the 1891 papal document on the upheavals of the Industrial Revolution.
Pope Leo XIV attends the presentation of his first encyclical, Magnifica Humanitas, at the Vatican on May 25, 2026. (AP Photo/Alessandra Tarantino)
Nathan Schneider
June 1, 2026
(The Conversation) — With the release of his encyclical letter Magnifica Humanitas on May 25, 2026, Pope Leo XIV has signaled that he wants the church to respond to artificial intelligence much as a predecessor, Pope Leo XIII, responded to upheavals during the Industrial Revolution over a century ago.
Since the first act of his papacy – choosing his name – the current pope has repeatedly invoked the earlier Leo’s 1891 encyclical Rerum Novarum. That document, which waded into the political and economic debates of the time, denounced the excesses of the Gilded Age and pointed toward a more just social order. Now, Leo XIV has used his first major statement to the world to present a new Rerum Novarum for the age of AI.
Rerum Novarum was more than just a theological text. It helped reshape economic policy around the rights of workers, serving as a spiritual foundation for European social democracy and the 1930s New Deal programs that still undergird economic life for working Americans today. It also spurred a movement of entrepreneurs to transform the economic system from within.
Understanding its influence is key to seeing the potential of Leo XIV’s encyclical.
From guilds to cooperatives in the industrial era
In his time, Leo XIII rejected both unfettered capitalism and revolutionary socialism. He invoked the medieval guilds, in which craftspeople self-organized, and asserted the rights of industrial workers to organize as well. This was a radical statement at a time when unions often faced violent suppression from employers and police.
But in contrast to communist agitators, he didn’t want to do away with private property. He argued that to bring out the best in human beings, as creatures made in the image of God, governments should “induce as many as possible of the people to become owners.”
Pope Leo XIII.
Francesco De Federicis (1853–1908) via Wikimedia Commons
This was more of a vision than a detailed plan, but Catholics in many countries started trying to figure out what the vision meant in practice.
The English writers G.K. Chesterton and Hilaire Belloc, for instance, tried to systematize his vision in a movement they called “distributism,” which proposed policies for land redistribution and a revival of guilds. In the United States, economist and Catholic priest John A. Ryan argued in favor of cooperatives – businesses that could be co-owned by workers, consumers or small-business owners.
Ryan went on to be an important adviser for the New Deal in the United States, which used cooperatives as a powerful tool for economic development through farmer co-ops, rural electric associations and the credit union system.
The spirit of Rerum Novarum continued to spread. Starting in the 1950s, the largest network of worker cooperatives in the world, the Mondragon Corporation in Spain’s Basque region, was founded by a Catholic priest. It was a direct result of Leo XIII’s encyclical.
My own career has been in its shadow. As a media scholar and a Roman Catholic – and an advocate for efforts to build cooperative tech platforms – I sometimes think of my own work as applying Rerum Novarum to the online economy. With Magnifica Humanitas, the pope appears to be making a similar argument for the age of artificial intelligence.
A tale of 2 cities
Once again, society is going through an economic upheaval: New technologies are changing the nature of work, political systems are under strain, and wealth inequality is staggering. In Magnifica Humanitas, Leo XIV argues that an intervention akin to Rerum Novarum is needed.
Copies of Leo XIV’s first encyclical, Magnifica Humanitas, were distributed at the Vatican on May 25, 2026.
AP Photo/Alessandra Tarantino
The guiding metaphor of Magnifica Humanitas is the choice between two biblical scenes: the Tower of Babel and the rebuilding of Jerusalem under the prophet Nehemiah.
The first is a story about the dream of a city that sets out to erect a single building as high as the heavens. Babel, as Leo XIV writes in the encyclical, is a city “built on pride and the claim to self-sufficiency.” In the biblical account, the project collapses as the world’s common language is scattered into many diverse ones.
The pope contrasts this with the story of the Hebrew prophet Nehemiah, who lived in the fifth century B.C.E., when Jews were returning from exile to a ruined Jerusalem. Nehemiah organized the city’s rebuilding through a collaborative process based on shared responsibility. While united in prayer, the city’s various families and professions could each put their distinctive marks on their work.
The current AI industry, he argues, is in danger of becoming a new Tower of Babel. Just a few companies control this powerful technology supposedly poised to transform work, politics and society for everyone.
He warns that many AI leaders are enthused by ideologies that propose to trade human limits for the godlike powers of machines. Some are even cheerfully embracing a world where human labor is no longer central to the economy. Leo also fears that human choice is becoming more removed from the execution of war.
In the face of all this, the encyclical calls on people everywhere to adopt “the pressing duty to remain profoundly human” – to be neither “spectators” nor “commentators” but to take an active role by participating in what he calls “the construction sites of history.” Some already are.
Construction sites for a different kind of AI
A few large AI companies dominate the technology and decide how people can use it, but alternative models are beginning to emerge.
greenbutterfly/iStock/Getty Images Plus
It is easy to see the emerging AI industry in Babel-like terms – a few massive tech companies build the models and provide access to them on their terms. But other paths are still possible. My colleagues and I have been documenting cases that could be the germ of a different kind of AI industry – one more aligned with what the pope is calling for.
Just as during the Industrial Revolution, a more just future begins with workers resisting against the abuses of the present. From Hollywood to Nairobi, workers have been fighting for dignity as AI changes their professions. Magnifica Humanitas stresses the importance of decent jobs to a healthy society, and workers’ demands can help identify what the future of work should look like.
Other approaches begin among AI developers themselves. In Switzerland, a collaboration between government and academia has produced Apertus, a foundational model based on fully documented designs and data sources – a far cry from the opaque and at times illegal practices of leading AI companies. Some of Apertus’ developers have created a consumer cooperative, enabling users to co-own their interface with the model.
Cooperative ownership like this allows users to tune AI experiences more intentionally toward their needs. The large U.S. farmer co-op Land O’Lakes, for example, has created AI-enabled tools that provide analysis and guidance for its members based on the data that they collectively co-own. The more nascent Transkribus in Europe is co-owned by research institutions that collectively train their AI software to transcribe texts for historical research. These kinds of systems follow Leo XIV’s call to “manage data as a common or shared good.”
It is telling that even among leading AI companies such as OpenAI and Anthropic, the founders attempted to build unusual corporate governance structures to insulate their products from profit motives. Governments could encourage more appropriate ownership designs or outright require them for high-risk industries like AI.
If Rerum Novarum is any guide, the impact of Magnifica Humanitas will depend on the creative entrepreneurship and policy experiments to put it into practice – and this work has already begun.
(Nathan Schneider, Assistant Professor of Media Studies, University of Colorado Boulder. The views expressed in this commentary do not necessarily reflect those of Religion News Service.)
The Conversation religion coverage receives support through the AP’s collaboration with The Conversation US, with funding from Lilly Endowment Inc. The Conversation is solely responsible for this content.
Sen. Bernie Sanders said his new bill would “guarantee that the trillions of dollars potentially generated by AI are used to improve the lives of all of us—not simply to make the richest people in the world even richer.”
US Sen. Bernie Sanders (I-Vt.) speaks during a news conference on the impact of artificial intelligence on workers at the Hart Senate Office Building on April 16, 2026 in Washington, DC.
(Photo by Heather Diehl/Getty Images)
Jun 01, 2026
COMMON DREAMS
Sen. Bernie Sanders on Monday announced he will soon introduce legislation that would give the American public “a direct ownership stake” in the largest artificial intelligence companies in the US by establishing a sovereign wealth fund, which would ensure everyone benefits from the rapidly advancing technology.
Sanders (I-Vt.) wrote in a New York Times op-ed that his American AI Sovereign Wealth Fund Act would create the new fund by imposing a one-time, 50% tax on the stock of OpenAI, Anthropic, and other AI behemoths. The sovereign wealth fund, a government-owned investment vehicle, would both “give the public a direct role in determining the future of this technology” and “guarantee that the trillions of dollars potentially generated by AI are used to improve the lives of all of us—not simply to make the richest people in the world even richer.”
The senator emphasized that “this is not an original idea,” noting that scholars and even leading AI companies have proposed some version of a public wealth fund to broadly distribute AI-related gains. Sanders also observed that Norway and Alaska have sovereign wealth funds, and that “even President [Donald] Trump, in an executive order, has proposed establishing an American sovereign wealth fund.”
“I recognize that for the government to have a major stake in a company, particularly one for which AI is only part of its business, is complicated,” Sanders wrote. “More details—including the specific spending priorities and the mechanics of implementation—will be included in the legislation I unveil in the coming weeks.”
“But the principle is simple: When a public resource generates wealth, the public should share in that wealth. AI is being built on a public resource far more valuable than oil: the accumulated knowledge, creativity, and labor of mankind,” he continued. “The future of AI and the fate of humanity must not be decided behind closed doors in Silicon Valley. It must not be dictated by billionaires seeking to maximize their power and profit. It must be decided by workers, parents, teachers, artists, scientists, communities and the American people. It’s our future. We must decide it.”
Sanders has been among the most prominent voices expressing grave concerns about the potential for AI to turbocharge inequality and spark catastrophic unemployment. Last year, Sanders’ office released a report warning that AI could eliminate nearly 100 million US jobs over the next decade.
“Corporations are already using AI to cut jobs. Amazon, Walmart, UnitedHealth Group, JPMorgan Chase, and other companies are openly telling investors that AI will allow them to slash payrolls—even as they post tens of billions in profits and reward CEOs with pay packages of $25 million, $35 million or more,” the report said.
Sanders’ call for an AI sovereign wealth fund comes days after a pair of progressive lawmakers—Sen. Elizabeth Warren (D-Mass.) and Rep. Greg Casar (D-Texas)—separately called for new taxes on AI to fund jobs initiatives, universal healthcare, and other programs to prevent the kinds of large-scale economic displacement that experts and corporate executives say is looming.
“Taxing AI is one way we make sure the winnings from AI benefit all Americans, rather than channeling them only to the wealthy few,” Warren wrote in TIME last week. “If millions of people lose their jobs to AI, we’ll need the funds to deliver universal healthcare so those workers are not bankrupted by a visit to the doctor.”
.jpg){kind=link}
No comments:
Post a Comment