超越PUE：面向数据中心环境问责的地方影响审计框架

中文摘要 AI 辅助

标准的数据中心可持续性指标，包括电能使用效率（PUE）、水使用效率（WUE）和碳使用效率（CUE），衡量设施的资源使用和排放强度，并归一化到IT能源使用量，但未直接反映当地资源稀缺性、基础设施容量或社会足迹。这一差距已具有政治后果。仅在2026年第一季度，当地反对就延迟或取消了美国约1300亿美元的项目，主要驱动力是对水资源使用、电力需求、基础设施容量和透明度的反复担忧，而非内部效率，这一数额与2025年全年总额相当[11]。我们提出了一个五类地方影响审计框架，涵盖效率、水资源管理、碳与可再生能源、法规合规性以及地方披露。该框架设计用于每季度重复评估，并可根据公共记录进行独立验证。我们利用来自伊利诺伊州三个目前处于地方政策争议中心的设施公开数据，展示了其应用，并考察了限制独立验证的数据访问障碍。我们将此框架定位为研究贡献和实用工具，供县级政策制定者在评估数据中心许可和暂停（暂停）决策时使用。

英文摘要

Standard data center sustainability metrics, including Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), and Carbon Usage Effectiveness (CUE), measure a facility's resource use and emissions intensity, normalized to IT energy use, without directly representing local resource scarcity, infrastructure capacity, or social footprint. This gap has become politically consequential. In the first quarter of 2026 alone, local opposition delayed or canceled roughly $130 billion in projects across the United States, driven overwhelmingly by recurring concerns over water use, power demand, infrastructure capacity, and transparency rather than internal efficiency, matching the total for all of 2025 [11]. We propose a five-category local impact audit framework covering efficiency, water stewardship, carbon and renewables, regulatory compliance, and local disclosure. The framework is designed for recurring quarterly assessment and independent verification against public records. We illustrate its application using publicly available data from three Illinois facilities that are currently at the center of local policy disputes, and we examine the data-access barriers that constrain independent verification. We position this framework as both a research contribution and a practical instrument for county-level policymakers evaluating data center permitting and moratorium decisions.

发表机构

• University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
• University of Toronto（多伦多大学）

机构由 AI 辅助整理，请以论文原文为准。