In the past ten years artificial intelligence has encountered such dramatic progress that it is seen now as a tool of choice to solve environmental issues and in the first place greenhouse gas emissions (GHG). At the same time the deep learning community began to realize that training models with more and more parameters required a lot of energy and as a consequence GHG emissions. To our knowledge, questioning the complete environmental impacts of AI methods for environment ("AI for green"), and not only GHG, has never been addressed directly. In this article we propose to study the possible negative impact of "AI for green" 1) by reviewing first the different types of AI impacts 2) by presenting the different methodologies used to assess those impacts, in particular life cycle assessment and 3) by discussing how to assess the environmental usefulness of a general AI service.

翻译：过去十年来,人工智能经历了如此巨大的进步,现在人们把它看作是解决环境问题的首选工具,首先是温室气体的排放。与此同时,深层次的学习界开始认识到,具有越来越多的参数的培训模式需要大量的能源,并因此需要大量温室气体排放。据我们所知,对人工智能环境方法(“AI for Green”),而不仅仅是温室气体的完整环境影响的质疑从未直接涉及。在本篇文章中,我们提议通过首先审查不同种类的AI影响2 来研究“AI for Green”1 可能产生的负面影响,方法是介绍用来评估这些影响的不同方法,特别是生命周期评估和3 讨论如何评估一般AI 服务的环境效用。