题目：Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA（美国可持续人工智能服务器的环境影响与净零路径）
作者：Tianqi Xiao, Francesco Fuso Nerini, H. Damon Matthews, Massimo Tavoni and Fengqi You
摘要：The rapidly increasing demand for generative artificial intelligence (AI) models requires extensive server installation with sustainability implications in terms of the compound energy–water–climate impacts. Here we show that the deployment of AI servers across the United States could generate an annual water footprint ranging from 731 to 1,125 million m3 and additional annual carbon emissions from 24 to 44 Mt CO2-equivalent between 2024 and 2030, depending on the scale of expansion. Other factors, such as industry efficiency initiatives, grid decarbonization rates and the spatial distribution of server locations within the United States, drive deep uncertainties in the estimated water and carbon footprints. We show that the AI server industry is unlikely to meet its net-zero aspirations by 2030 without substantial reliance on highly uncertain carbon offset and water restoration mechanisms. Although best practices may reduce emissions and water footprints by up to 73% and 86%, respectively, their effectiveness is constrained by current energy infrastructure limitations. These findings underscore the urgency of accelerating the energy transition and point to the need for AI companies to harness the clean energy potential of Midwestern states. Coordinating efforts of private actors and regulatory interventions would ensure the competitive and sustainable development of the AI sector.
生成式人工智能（AI）模型需求的激增要求大规模安装服务器，这在能源-水-气候的复合影响方面对可持续性产生了深远意义。本研究表明，2024 年至 2030 年间，全美范围内 AI 服务器的部署可能产生 7.31 亿至 11.25 亿 m³ 的年水足迹，以及 2400 万至 4400 万吨（Mt）的年新增二氧化碳当量（CO₂-equivalent）排放，具体取决于扩张规模。行业效率倡议、电网脱碳速率以及美国境内服务器的地理分布等因素，为预估的水足迹和碳足迹带来了巨大的不确定性。我们指出，如果不大量依赖高度不确定的碳抵消和水修复机制，AI 服务器行业不太可能在 2030 年实现其净零愿景。尽管通过最佳实践可以将排放量和水足迹分别降低 73% 和 86%，但其有效性受限于当前的能源基础设施。这些发现强调了加速能源转型的紧迫性，并指出 AI 公司需要挖掘美国中西部地区清洁能源的潜力。通过私人参与者与监管干预的协同努力，方能确保 AI 行业在具备竞争力的同时实现可持续发展。
原文： Xiao T , Nerini F F , Matthews H D ,et al.Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA[J].Nature Sustainability, 2025.
链接：https://link.springer.com/article/10.1038/s41893-025-01681-y
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