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：随着基于人工智能系统的日益普及，人工智能的碳足迹已不再可忽略不计。因此，人工智能研究人员和从业者被敦促对自身设计和使用的人工智能模型的碳排放负责。近年来，这催生了针对人工智能环境可持续性的研究，即被称为"绿色AI"的领域。尽管对该主题的兴趣迅速增长，但迄今仍缺乏对绿色AI研究的全面概述。为填补这一空白，本文对绿色AI文献进行了系统性综述。通过对98项原始研究的分析，呈现出不同模式：该主题自2020年起显著增长；多数研究关注AI模型足迹监测、超参数调优以提升模型可持续性或模型基准测试；研究类型涵盖立场论文、观察性研究和解决方案论文；大多数研究侧重于训练阶段，采用算法无关方法或针对神经网络，并使用图像数据；实验室实验是最常见的研究策略；绿色AI报告的能量节省最高达115%，超过50%的节能较为普遍；工业界虽参与研究但主要面向学术读者；绿色AI工具供应匮乏。结论表明，绿色AI研究领域已达到相当成熟度，因此本综述认为当前时机适合采用其他绿色AI研究策略，并将大量有前景的学术成果转化为工业实践。