The Environmental Cost of Digital Sovereignty: Water, Energy, and Emissions Impacts of Sovereign AI Infrastructure in the Global South
For policymakers and researchers in the Global South, this paper highlights the overlooked environmental trade-offs of sovereign AI infrastructure, though the analysis is based on hypothetical scenarios rather than empirical data.
This paper analyzes the environmental costs (water, energy, carbon) of sovereign AI data centers in the Global South, finding that a 1,024-GPU cluster using evaporative cooling in the UAE would consume over 30 million liters of water annually in a high water-stress region. It identifies a sovereignty-sustainability trilemma and proposes design principles for environmentally responsible AI.
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.