气候变化
温室气体
减缓气候变化
透视图(图形)
环境资源管理
计算机科学
环境科学
环境经济学
人工智能
经济
生态学
生物
作者
Lynn H. Kaack,Priya L. Donti,Emma Strubell,George Kamiya,Felix Creutzig,David Rolnick
标识
DOI:10.1038/s41558-022-01377-7
摘要
There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. However, such emissions impacts remain uncertain, owing in part to the diverse mechanisms through which they occur, posing difficulties for measurement and forecasting. Here we introduce a systematic framework for describing the effects of machine learning (ML) on GHG emissions, encompassing three categories: computing-related impacts, immediate impacts of applying ML and system-level impacts. Using this framework, we identify priorities for impact assessment and scenario analysis, and suggest policy levers for better understanding and shaping the effects of ML on climate change mitigation. The rapid growth of artificial intelligence (AI) is reshaping our society in many ways, and climate change is no exception. This Perspective presents a framework to assess how AI affects GHG emissions and proposes approaches to align the technology with climate change mitigation.
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