{"id":12655,"date":"2026-06-05T10:40:21","date_gmt":"2026-06-05T14:40:21","guid":{"rendered":"https:\/\/sdgnews.com\/?p=12655"},"modified":"2026-06-05T10:40:24","modified_gmt":"2026-06-05T14:40:24","slug":"ais-hidden-environmental-bill-new-un-report-warns-of-surging-water-land-and-carbon-costs","status":"publish","type":"post","link":"https:\/\/sdgnews.com\/ru\/ais-hidden-environmental-bill-new-un-report-warns-of-surging-water-land-and-carbon-costs\/","title":{"rendered":"AI\u2019s Hidden Environmental Bill: New UN Report Warns of Surging Water, Land and Carbon Costs"},"content":{"rendered":"

Artificial intelligence is often described as a digital revolution. But a major new report released this week by the United Nations University Institute for Water, Environment and Health (UNU-INWEH) argues that AI is increasingly becoming a physical one\u2014with profound implications for energy systems, water resources, land use, and climate goals worldwide.<\/p>\n\n\n\n

The report, Environmental Cost of AI\u2019s Energy Use: Carbon, Water and Land Footprints<\/em>, arrives at a pivotal moment as governments and companies race to build the infrastructure needed to power the AI economy. Its central message is clear: the environmental impact of AI extends far beyond carbon emissions and must be understood through a broader lens that includes water consumption, land occupation, and resource extraction.<\/p>\n\n\n\n

The Scale of the AI Boom<\/h3>\n\n\n\n

According to the report, data centers consumed approximately 448 terawatt-hours (TWh) of electricity in 2025, more than the annual electricity use of most countries on Earth. By 2030, that figure could rise to 945 TWh, effectively doubling in just five years as AI adoption accelerates across industries.<\/p>\n\n\n\n

If current trends continue, data centers would account for nearly 3% of global electricity consumption by 2030, placing them among the world’s largest energy consumers. Researchers estimate that AI’s share of data-center electricity demand could rise from roughly 20% today to 40% by the end of the decade.<\/p>\n\n\n\n

The report highlights a commonly overlooked reality: while much public attention focuses on training large AI models, the majority of future energy demand will come from inference\u2014the billions of daily interactions people have with AI systems after they are deployed.<\/p>\n\n\n\n

AI\u2019s Water Footprint Could Rival 1.3 Billion People<\/h3>\n\n\n\n

Perhaps the most striking finding involves water.<\/p>\n\n\n\n

The UNU researchers estimate that by 2030, AI-related infrastructure could require 9.3 trillion liters of water annually, primarily for cooling data centers and generating electricity. That amount is roughly equivalent to the annual domestic water needs of 1.3 billion people in sub-Saharan Africa.<\/p>\n\n\n\n

Water demand has emerged as one of the most underappreciated consequences of AI growth. While AI appears intangible to users, the servers supporting large language models require extensive cooling systems, often drawing freshwater resources from regions already facing water stress.<\/p>\n\n\n\n

The report warns that without careful planning, AI expansion could intensify competition for water between data centers, agriculture, industry, and local communities.<\/p>\n\n\n\n

Carbon Emissions Comparable to Major Economies<\/h3>\n\n\n\n

The climate implications are equally significant.<\/p>\n\n\n\n

Researchers project that AI-related electricity consumption could generate as much as 399 million metric tons of carbon dioxide equivalent annually by 2030, roughly comparable to the annual emissions of the United Kingdom.<\/p>\n\n\n\n

Even today, the electricity consumed by data centers produces emissions comparable to those of entire nations. In 2025 alone, data-center operations generated approximately 208 million tons of CO\u2082, roughly matching the annual emissions of Argentina.<\/p>\n\n\n\n

The report notes that these impacts vary dramatically depending on where AI infrastructure is located and what energy sources power it. Data centers operating on renewable energy grids can have substantially lower footprints than those relying on fossil fuels.<\/p>\n\n\n\n

The Land Footprint Nobody Talks About<\/h3>\n\n\n\n

The environmental conversation around AI has largely focused on electricity and emissions. The UNU report expands the discussion further by examining land use.<\/p>\n\n\n\n

Researchers estimate that by 2030, AI infrastructure and the energy systems required to support it could occupy more than 14,500 square kilometers of land\u2014an area roughly twice the size of metropolitan Jakarta or larger than Northern Ireland.<\/p>\n\n\n\n

This footprint includes not only data centers themselves but also the power generation, transmission infrastructure, and supply chains required to sustain AI operations at scale.<\/p>\n\n\n\n

As countries compete to attract AI investment, questions about land use, permitting, biodiversity impacts, and community acceptance are becoming increasingly important policy considerations.<\/p>\n\n\n\n

A New Sustainability Challenge<\/h3>\n\n\n\n

The report arrives amid growing international debate over how to balance AI-driven innovation with environmental sustainability.<\/p>\n\n\n\n

This week, the European Union cited the report while advancing proposals for new energy-efficiency standards and sustainability disclosure requirements for data centers. Policymakers are increasingly concerned that unchecked growth in AI infrastructure could complicate efforts to meet climate and energy-transition targets.<\/p>\n\n\n\n

UNU researchers argue that AI governance frameworks should move beyond carbon accounting alone and incorporate standardized reporting for water, land, and broader environmental impacts. The report calls for greater transparency from technology companies and closer integration of AI planning into national energy, water, and land-use strategies.<\/p>\n\n\n\n

The Defining Infrastructure Question of the AI Era<\/h3>\n\n\n\n

The findings underscore a growing reality: AI is no longer simply a software story.<\/p>\n\n\n\n

Behind every chatbot interaction, image generation request, or AI-powered search lies a rapidly expanding network of data centers, power plants, cooling systems, water withdrawals, and supply chains. As governments and companies invest hundreds of billions of dollars into the next generation of AI infrastructure, the question is no longer whether AI will reshape the global economy\u2014it is whether the world can scale the technology in a way that remains compatible with climate goals, water security, and sustainable development.<\/p>\n\n\n\n

The new UNU-INWEH report suggests that answering that question may become one of the defining policy challenges of the AI era.<\/p>\n\n\n\n

Read the full report: Environmental Cost of AI\u2019s Energy Use: Carbon, Water and Land Footprints<\/a><\/strong><\/span><\/em><\/p>\n\n\n\n

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