数字主权的环境成本：全球南方主权人工智能基础设施的水、能源和排放影响
The Environmental Cost of Digital Sovereignty: Water, Energy, and Emissions Impacts of Sovereign AI Infrastructure in the Global South
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中文总结 AI 辅助
研究全球南方主权人工智能基础设施的环境成本，通过对四个案例分析，模拟其水、能源消耗及碳排放，发现存在主权-可持续性困境，进而提出对环境负责的主权人工智能设计原则。
中文摘要 AI 辅助
主权人工智能已成为全球南方的战略重点，2024年至2026年间各国政府承诺投入超2000亿美元。然而，计算主权所需的物理基础设施，尤其是数据中心，带来了水、能源和碳成本，对最无力承担的国家影响最大。本文对阿联酋、孟加拉国、印度和非洲（以肯尼亚为重点）的四个案例进行了比较环境压力分析。利用公开的水压力数据、电网碳强度因子和GPU功率规格，我们模拟了多种冷却技术场景下假设的主权人工智能部署的用水量、能源需求和碳排放。我们发现，在水压力被归类为“极高”的阿联酋，一个使用蒸发冷却的1024-GPU集群每年将消耗超过3000万升水。在孟加拉国，主权人工智能政策文件呼吁集中采购GPU，但没有解决在一个平均每年超过五分之一土地被洪水淹没且电网难以提供可靠供应的国家，数据中心应建在哪里的问题。我们识别出一个主权-可持续性困境，即没有一个国家能同时最大化人工智能主权、最小化环境影响并为公民维持可承受的资源获取。我们提出了对环境负责的主权人工智能的设计原则，包括强制性的用水效率报告、气候脆弱性选址评估，以及在资源受限环境中优先选择节俭的小语言模型而非前沿预训练模型。
英文摘要
Sovereign AI has become a strategic priority across the Global South, with over \$200 billion in state-led commitments announced between 2024 and 2026. Yet the physical infrastructure that compute sovereignty demands, above all data centers, imposes water, energy, and carbon costs that fall hardest on countries least equipped to absorb them. This paper presents a comparative environmental stress analysis across four cases: the United Arab Emirates, Bangladesh, India, and Africa (with a focus on Kenya). Using publicly available water stress data, grid carbon intensity factors, and GPU power specifications, we model the water consumption, energy demand, and carbon emissions of hypothetical sovereign AI deployments under multiple cooling technology scenarios. We find that a 1,024-GPU cluster using evaporative cooling in the UAE would consume over 30 million liters of water annually in a country classified as ``extremely high'' water stress. In Bangladesh, sovereign AI policy documents call for centralized GPU procurement but do not address where to site data centers in a country where more than a fifth of the land floods in an average year and the power grid struggles to deliver reliable supply. We identify a sovereignty-sustainability trilemma in which no country can simultaneously maximize AI sovereignty, minimize environmental impact, and maintain affordable resource access for citizens. We propose design principles for environmentally responsible sovereign AI, including mandatory water usage effectiveness reporting, climate-vulnerability siting assessments, and a preference for frugal small language models over frontier pre-training in resource-constrained settings.
