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原文摘要

The continuous expansion of digital infrastructure, especially data centers, has imposed unprecedented, geographically concentrated stress on existing power grids. However, rigorous causal evidence quantifying how these facilities reshape local power-infrastructure supply remains limited. This study builds a comprehensive county-level US panel dataset spanning 2010-2024, uniquely integrating individual data center building footprints and power-plant additions. To mitigate endogeneity and high-dimensional confounding, this study deploys Double Machine Learning with five-fold cross-fitting, conditioning on county and year fixed effects alongside regional socioeconomic covariates. Baseline estimation identifies a significant positive causal effect robust across multiple rigorous specifications. The result holds following 1%-99% winsorization that downweights mega-clusters including Loudoun County: growth in data-center floor area drives meaningful increases in nearby power-generation capacity (ATE=1.982, P=0.000, CI: [1.25, 2.72]). Outcomes remain statistically significant using a one-year lagged treatment to address reverse causality, and are stable under state-level fixed-effect alternatives. An event-study framework augmented with historical agglomeration controls validates clear parallel trends and detects a sharp causal spike at t = 3, matching typical utility-scale grid-construction lead times. A placebo test further verifies these causal estimates cannot emerge from random chance. Substantial regional heterogeneity emerges: strong positive impacts concentrate in the West, while significant negative displacement-related effects appear within resource-constrained Northeastern counties. These results deliver timely insights for urban planners and energy regulators, stressing the importance of forward-looking grid-capacity investment to support ongoing digital transformation.