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According to a study by the United Nations University, the global network of data centres powering AI could consume up to 945 terawatt-hours of electricity annually by 2030. This level of consumption would be nearly three times the combined electricity use of countries such as Pakistan, Bangladesh and Nigeria, highlighting the scale of demand tied to the rapid expansion of AI technologies.<\/p>\n
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The report stresses that focusing solely on greenhouse gas emissions risks overlooking the broader environmental footprint of AI. Every unit of electricity used by data centres is also linked to water consumption for cooling and energy production, as well as land use associated with infrastructure and supply chains. By the end of the decade, AI-related water use could match the basic domestic needs of 1.3 billion people, while its land footprint could exceed 14 500 square kilometres.<\/p>\n
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A key finding is that the largest share of AI\u2019s energy demand comes not from training complex models, but from everyday usage. Routine interactions such as generating text, images or video account for roughly 80 to 90% of total energy consumption. The report notes that widely used AI systems now process billions of requests daily, with energy requirements varying significantly depending on the type of task.<\/p>\n
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Efficiency gains alone are unlikely to resolve the issue. The study highlights the \u201crebound effect\u201d, where improvements in performance and cost lead to increased usage, ultimately driving up total resource consumption rather than reducing it.<\/p>\n
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The environmental burden of AI is also unevenly distributed. While the benefits of the technology are global, the infrastructure supporting it is often concentrated in specific regions, placing pressure on local energy systems and water supplies. In some areas, data centres are already contributing significantly to national electricity demand, while expansion in water-scarce regions is raising concerns about sustainability.<\/p>\n
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In addition to resource use, the report warns of a growing electronic waste problem. AI infrastructure could generate up to 2.5 million tonnes of e-waste annually by 2030, much of which may end up in lower-income countries that lack the capacity for safe disposal. The extraction of critical minerals needed for AI hardware is also raising environmental and social concerns in producing regions.<\/p>\n
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The study further highlights a widening global imbalance in AI capacity. More than 90% of specialised computing power is concentrated in just two countries, while over 150 nations lack significant domestic infrastructure. This imbalance raises questions about equity, as some regions bear environmental costs without benefiting fully from technological advances.<\/p>\n
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Despite the scale of the challenge, researchers stress that the findings are not an argument against AI, but rather a call for more responsible development. The report outlines the need for a more comprehensive approach to measuring sustainability, one that includes energy, water and land use as well as emissions.<\/p>\n
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Governments are being urged to integrate AI infrastructure into broader energy, water and land-use planning, while companies are encouraged to design systems that minimise resource consumption. Users are also seen as part of the solution through more mindful use of AI tools.<\/p>\n
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–UN\/ChannelAfrica–<\/p>\n","protected":false},"featured_media":36330,"template":"","meta":{"_acf_changed":false,"_links_to":"","_links_to_target":""},"news-type":[78,44],"class_list":["post-36328","news","type-news","status-publish","has-post-thumbnail","hentry","news-type-featured","news-type-finance","entry"],"acf":{"short_description":"Artificial Intelligence (AI) is emerging as a major driver of environmental strain, with new research warning that its impact extends far beyond carbon emissions to include growing pressure on water, land and natural resources.","published_date":"","news_description":"Artificial Intelligence (AI) is emerging as a major driver of environmental strain, with new research warning that its impact extends far beyond carbon emissions to include growing pressure on water, land and natural resources.","form_embed":"","author":"","image_caption":""},"_links":{"self":[{"href":"https:\/\/www.channelafrica.co.za\/channelafrica\/wp-json\/wp\/v2\/news\/36328","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.channelafrica.co.za\/channelafrica\/wp-json\/wp\/v2\/news"}],"about":[{"href":"https:\/\/www.channelafrica.co.za\/channelafrica\/wp-json\/wp\/v2\/types\/news"}],"version-history":[{"count":1,"href":"https:\/\/www.channelafrica.co.za\/channelafrica\/wp-json\/wp\/v2\/news\/36328\/revisions"}],"predecessor-version":[{"id":36331,"href":"https:\/\/www.channelafrica.co.za\/channelafrica\/wp-json\/wp\/v2\/news\/36328\/revisions\/36331"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.channelafrica.co.za\/channelafrica\/wp-json\/wp\/v2\/media\/36330"}],"wp:attachment":[{"href":"https:\/\/www.channelafrica.co.za\/channelafrica\/wp-json\/wp\/v2\/media?parent=36328"}],"wp:term":[{"taxonomy":"news-type","embeddable":true,"href":"https:\/\/www.channelafrica.co.za\/channelafrica\/wp-json\/wp\/v2\/news-type?post=36328"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}