A Systematic Review of Green AI
Roberto Verdecchia, June Sallou, Lu\'is Cruz

TL;DR
This systematic review analyzes 98 studies on Green AI, highlighting recent growth, common research themes, and promising energy savings, emphasizing the field's maturity and need for industrial application.
Contribution
It provides a comprehensive overview of Green AI research, identifying key patterns, research strategies, and gaps, and suggests directions for future industrial adoption.
Findings
Green AI research has grown significantly since 2020.
Most studies focus on monitoring, tuning, and benchmarking AI models.
Reported energy savings reach up to 115%, with many over 50%.
Abstract
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,…
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Taxonomy
TopicsGreen IT and Sustainability · Air Quality Monitoring and Forecasting · Smart Cities and Technologies
