With the ever-growing adoption of AI-based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this paper, we present a systematic review of the Green AI literature. From the analysis of 98 primary studies, different patterns emerge. The topic experienced a considerable growth from 2020 onward. Most studies consider monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models. A mix of position papers, observational studies, and solution papers are present. Most papers focus on the training phase, are algorithm-agnostic or study neural networks, and use image data. Laboratory experiments are the most common research strategy. Reported Green AI energy savings go up to 115%, with savings over 50% being rather common. Industrial parties are involved in Green AI studies, albeit most target academic readers. Green AI tool provisioning is scarce. As a conclusion, the Green AI research field results to have reached a considerable level of maturity. Therefore, from this review emerges that the time is suitable to adopt other Green AI research strategies, and port the numerous promising academic results to industrial practice.

翻译：随着人工智能系统的日益普及，AI的碳足迹已不可忽视。因此，AI研究人员和实践者被敦促对其设计和使用的人工智能模型产生的碳排放负责。这促使近年来出现了一系列关注AI环境可持续性的研究，这一领域被称为绿色人工智能。尽管人们对这一主题的兴趣迅速增长，但迄今仍缺乏对绿色人工智能研究的全面概述。为填补这一空白，本文对绿色人工智能文献进行了系统综述。通过对98项主要研究的分析，呈现出不同的模式。该主题自2020年起经历了显著增长。大多数研究关注监测AI模型足迹、调整超参数以提升模型可持续性或进行模型基准测试。其中包含立场论文、观察性研究和解决方案论文。多数论文聚焦于训练阶段，采用算法无关方法或研究神经网络，并使用图像数据。实验室实验是最常见的研究策略。报告的绿色人工智能节能效果高达115%，超过50%的节能较为常见。工业界参与了绿色人工智能研究，但大多数研究仍面向学术读者。绿色人工智能工具供给较为匮乏。结论表明，绿色人工智能研究领域已达到相当成熟度。因此，本综述表明当前正是采用其他绿色人工智能研究策略，并将众多有前景的学术成果转化为工业实践的适当时机。