A systematic review of Green AI
Open accessAbstract
With the ever‐growing adoption of artificial intelligence (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 article, 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. This article is categorized under: Technologies > Machine Learning
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The significant energy footprint of training and running AI models presents a 'Sustainability Paradox'.
“The significant energy footprint of training and running AI models presents a 'Sustainability Paradox' [18,42,54,55].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [55]
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References 114 · Cited by 375
Research works printed but not linked: 5
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- Authors
- Verdecchia, R., Sallou, J., & Cruz, L.
- Affiliations
- University of Florence (IT)
- Delft University of Technology (NL)
- Corresponding author
- Verdecchia, Roberto
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2023-06-05
- Volume
- 13
- Issue
- 4
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W4379743664
- Cited by
- 375
- References
- 114
- Citations by year
- 2026: 142 · 2025: 123 · 2024: 90 · 2023: 20
- FWCI
- 6.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (publishedVersion, cc-by-nc)
- Florence Research (University of Florence) (submittedVersion, cc-by-nc)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- green AI, energy efficiency, hyperparameter optimization, model benchmarking, artificial neural networks
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