[论文解读] Towards A Comprehensive Assessment of AI's Environmental Impact

Srija Chakraborty|arXiv (Cornell University)|May 22, 2024

Air Quality Monitoring and Forecasting被引用 5

一句话总结

该论文提出一个基于卫星数据的框架来监测AI数据中心的环境足迹，展示了一个 Northern Virginia 案例研究并概述数据缺口和政策建议。

ABSTRACT

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.

研究动机与目标

• 推动在 AI 生命周期全阶段实现透明的、基于空间分解的环境足迹监测。
• 开发利用开放的地球观测数据来量化数据中心周围的环境变化的方法。
• 以 Northern Virginia 案例研究来演示该方法，并讨论扩展到全球站点的可扩展性。
• 识别数据缺口和促进全面 AI 环境影响评估的政策步骤。

提出的方法

• 利用全球开放的地球观测数据来量化数据中心站点随时间的变化。
• 利用 ElectricityMaps 数据估算关键 AI 服务商地点周围的碳强度和清洁能源获取情况。
• 使用遥感派生指标（NDVI、夜间灯光、UV 气溶胶指数）来监测数据中心周围的土地利用、能源使用指标和空气质量。
• 应用 Landsat-8 NDVI 谐波模型来创建十年尺度的植被时间序列并检测植被下降。
• 分析夜间灯光（VIIRS/NASA Black Marble）以推断城市基础设施和能源消耗的变化。
• 讨论将大气组分数据（TROPOMI UV 气溶胶指数）整合以评估空气质量影响。

实验结果

研究问题

• RQ1 在 AI 数据中心站点周围随时间发生了哪些环境变化，我们如何利用开放的 EO 数据对其进行监测？
• RQ2 我们如何为数据中心区域推断电力来源、碳强度和与水相关的影响？
• RQ3 为实现对 AI 环境足迹的透明全球监测，需要哪些数据缺口和政策行动？

主要发现

• 在案例研究区，数据中心区域通过 NDVI 下降和夜间灯光增加显示出可观测的环境变化。
• 在 Arcola, VA 区域十年间，NTL 增加约 ~10x，显示出更高的电力使用和城市扩张。
• TROPOMI UV 气溶胶指数趋势表明与数据中心区域活动相关的大气组成变化。
• 开放的 EO 数据集可以扩展到其他站点，实现全球监测并为可持续规划与政策提供信息。