A systematic analysis of data center water use, methodological divergence, and policy implications

Abstract Data center water use is an increasingly important yet poorly understood component of the environmental impacts of digital infrastructure. Reported estimates of data center water use vary by several orders of magnitude, creating interpretive challenges for policymakers, utilities, and the public. This study examines how methodological choices are associated with divergence across published estimates and evaluates implications for governance and reporting. We compile and analyze 100 estimates from 33 sources, including academic studies, government reports, corporate disclosures, and industry analyses, and classify each estimate across seven analytical dimensions spanning system boundary, water metric, operational assumptions, and temporal basis. Results show that divergence is structured rather than arbitrary. Within the same sources, estimates of indirect electricity-related water use (Scope 2) are approximately 2-12× larger than direct on-site use (Scope 1). Measurement choices, particularly the distinction between consumption and withdrawal, can produce differences ranging from modest to extreme, while cooling technology assumptions and facility composition introduce additional variability even in normalized metrics. Growth scenario assumptions further widen divergence between historical and projected values. These findings demonstrate that aggregated and modeled estimates of data center water use are not directly comparable without accounting for their methodological choices. We propose a minimum reporting standard of nine disclosure elements: system boundary, water metric, electricity attribution, cooling technology, operational efficiency definition, spatial scale, temporal resolution, growth scenario basis, and data provenance. We recommend consistent disclosure of these elements so that estimates can be matched to the decisions they can inform, such as benchmarking, utility planning, and permitting.