Generative AI's environmental footprint poses difficult tradeoffs for agrifood systems in low- and middle-income countries
The generative artificial intelligence (gen AI) revolution is not just digital - it is also physical. Data center complexes are expanding globally to meet rapidly increasing computing and data storage demands of generative AI (Figure 1). The extent of AI's immense energy requirements is still largel...
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| Formato: | Blog Post |
| Lenguaje: | Inglés |
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International Food Policy Research Institute
2025
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| Acceso en línea: | https://hdl.handle.net/10568/178202 |
| _version_ | 1855516265340207104 |
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| author | Margolies, Amy |
| author_browse | Margolies, Amy |
| author_facet | Margolies, Amy |
| author_sort | Margolies, Amy |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The generative artificial intelligence (gen AI) revolution is not just digital - it is also physical. Data center complexes are expanding globally to meet rapidly increasing computing and data storage demands of generative AI (Figure 1). The extent of AI's immense energy requirements is still largely unknown, but estimates indicate that by 2026, data centers could consume over 1,000 terawatt-hours (TWh) of electricity - more than doubling since 2022 and equivalent to Japan's total power usage. |
| format | Blog Post |
| id | CGSpace178202 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1782022025-11-25T20:14:24Z Generative AI's environmental footprint poses difficult tradeoffs for agrifood systems in low- and middle-income countries Margolies, Amy agrifood systems artificial intelligence ecological footprint environmental impact climate change natural resources The generative artificial intelligence (gen AI) revolution is not just digital - it is also physical. Data center complexes are expanding globally to meet rapidly increasing computing and data storage demands of generative AI (Figure 1). The extent of AI's immense energy requirements is still largely unknown, but estimates indicate that by 2026, data centers could consume over 1,000 terawatt-hours (TWh) of electricity - more than doubling since 2022 and equivalent to Japan's total power usage. 2025-10-24 2025-11-25T19:51:16Z 2025-11-25T19:51:16Z Blog Post https://hdl.handle.net/10568/178202 en Open Access International Food Policy Research Institute Margolies, Amy. 2025. Generative AI's environmental footprint poses difficult tradeoffs for agrifood systems in low- and middle-income countries. IFPRI Blog Post. https://www.ifpri.org/blog/generative-ais-environmental-footprint-poses-difficult-tradeoffs-for-agrifood-systems-in-low-and-middle-income-countries/ |
| spellingShingle | agrifood systems artificial intelligence ecological footprint environmental impact climate change natural resources Margolies, Amy Generative AI's environmental footprint poses difficult tradeoffs for agrifood systems in low- and middle-income countries |
| title | Generative AI's environmental footprint poses difficult tradeoffs for agrifood systems in low- and middle-income countries |
| title_full | Generative AI's environmental footprint poses difficult tradeoffs for agrifood systems in low- and middle-income countries |
| title_fullStr | Generative AI's environmental footprint poses difficult tradeoffs for agrifood systems in low- and middle-income countries |
| title_full_unstemmed | Generative AI's environmental footprint poses difficult tradeoffs for agrifood systems in low- and middle-income countries |
| title_short | Generative AI's environmental footprint poses difficult tradeoffs for agrifood systems in low- and middle-income countries |
| title_sort | generative ai s environmental footprint poses difficult tradeoffs for agrifood systems in low and middle income countries |
| topic | agrifood systems artificial intelligence ecological footprint environmental impact climate change natural resources |
| url | https://hdl.handle.net/10568/178202 |
| work_keys_str_mv | AT margoliesamy generativeaisenvironmentalfootprintposesdifficulttradeoffsforagrifoodsystemsinlowandmiddleincomecountries |