Artificial Intelligence, machine learning (AI/ML) has allowed exploring solutions for a variety of environmental and climate questions ranging from natural disasters, greenhouse gas emission, monitoring biodiversity, agriculture, to weather and climate modeling, enabling progress towards climate change mitigation. However, the intersection of AI/ML and environment is not always positive. The recent surge of interest in ML, made possible by processing very large volumes of data, fueled by access to massive compute power, has sparked a trend towards large-scale adoption of AI/ML. This interest places tremendous pressure on natural resources, that are often overlooked and under-reported. There is a need for a framework that monitors the environmental impact and degradation from AI/ML throughout its lifecycle for informing policymakers, stakeholders to adequately implement standards and policies and track the policy outcome over time. For these policies to be effective, AI's environmental impact needs to be monitored in a spatially-disaggregated, timely manner across the globe at the key activity sites. This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations. We present a case study around Northern Virginia, United States that hosts a growing number of datacenters and observe changes in multiple satellite-based environmental metrics. We then discuss the steps to expand this methodology for comprehensive assessment of AI's environmental impact across the planet. We also identify data gaps and formulate recommendations for improving the understanding and monitoring AI-induced changes to the environment and climate.

翻译：人工智能与机器学习（AI/ML）技术为探索解决各类环境与气候问题提供了可能，其应用范围涵盖自然灾害应对、温室气体排放监测、生物多样性保护、农业生产优化，以及天气与气候建模等领域，有力推动了气候变化减缓进程。然而，AI/ML与环境的交汇并非总是积极的。当前机器学习研究热潮的兴起，得益于海量数据处理能力与强大计算资源的支持，正推动AI/ML技术走向大规模应用。这种趋势对自然资源造成了巨大压力，而相关影响常被忽视且未得到充分披露。我们亟需建立一套能够监测AI/ML全生命周期环境影响的评估框架，为政策制定者与利益相关方提供决策依据，以有效实施相关标准政策并持续追踪政策成效。为确保政策有效性，需在全球关键活动区域对AI的环境影响进行高时空分辨率的动态监测。本研究提出一种创新方法，通过整合公开能源数据与全球卫星观测资料，追踪数据中心周边与AI多维环境影响相关的环境变量。我们以美国弗吉尼亚州北部地区为案例开展研究——该区域正聚集越来越多的数据中心——并通过多源卫星遥感指标观测环境变化。进而探讨如何将本方法扩展至全球尺度，实现对AI环境影响的系统性评估。最后，我们指出现有数据缺口，并提出加强理解与监测AI引发环境气候变化的可行性建议。