Danae Hernandez-Cortes, Kyle Meng, and Paige Weber “The Environmental Costs and Geography of U.S. Data Center Expansion” (May 2026) | WP-363
Abstract:
Data centers powering artificial intelligence are growing rapidly across the United States, raising concerns among policymakers and local communities about their environmental and social costs. These costs depend on where data centers locate, and strategic siting has been proposed as a way to limit them. Using a comprehensive facility-level dataset of past, operational, and announced U.S. data centers, we characterize how the environmental and community characteristics of data center locations evolve over 2010–2030. We quantify how much of the projected growth in environmental damages can be attributed to changes in data center locations versus growth in power requirements. Decomposing projected carbon emissions and monetized local air pollution damages into scale and location-driven composition effects, we find that over 2010–2025, composition changes reduced carbon and local air pollution damages by 4 and 12%, respectively. Over 2024–2030, scale accounts for approximately 97% of projected pollution growth, with composition changes contributing around 3%. We further find that data centers consistently locate in less densely populated areas, with planned facilities entering census tracts roughly five times less dense than tracts without data centers. Contrary to patterns documented for other disamenities, data centers are not systematically located in lower-income, higher-poverty, or higher nonwhite-share communities. Our results imply that policies nudging the location of data centers would do little to mitigate their aggregate environmental footprint, which is governed instead by the scale of buildout, and that permitting and community negotiations over siting will increasingly concentrate in the nation’s least population dense communities.