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.

人们对人工智能和机器学习的发展如何影响全球温室气体排放产生了浓厚的兴趣。然而，这种排放影响仍然不确定，部分原因是它们发生的机制多种多样，给测量和预测带来了困难。在这里，我们介绍了一个描述机器学习 (ML) 对温室气体排放影响的系统框架，包括三类：计算相关影响、应用 ML 的直接影响和系统级影响。使用这个框架，我们确定了影响评估和情景分析的优先事项，并提出了政策杠杆，以更好地理解和塑造机器学习对减缓气候变化的影响。