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Green and Sustainable AI

Guest Editors:
Paolo Trunfio: University of Calabria, Italy
Loris Belcastro: University of Calabria, Italy
Themis Palpanas: University Paris Cité, France

Submission Status: Closed | Submission Deadline: Closed


This collection is no longer accepting submissions.


Journal of Big Data is calling for submissions to our Collection on Green and Sustainable AI. 

Green and Sustainable AI refers to the use of artificial intelligence (AI) and machine learning (ML) technologies to address environmental issues and promote sustainability, which can be considered as a two-sided research area. From one side, green AI focuses on the development of AI and ML algorithms and systems to improve resource efficiency and develop cleaner technologies. This includes many important applications such as using AI to optimize energy consumption in building and factories, improve efficiency and reliability of renewable energy sources, optimize supply chains, manage traffic flow in large cities, and reduce waste in farming. From the other side, sustainable AI refers to the design and implementation of AI and ML algorithms and systems that are themselves environmentally sustainable, for example, by using hardware with low computational, storage, and power needs. This is important not only to minimize carbon footprint during AI operations, but also to enable the execution of AI/ML algorithms on resource-constrained edge devices or in edge-cloud computing environments. 

This special issue seeks submissions from academia, industry and governmental research labs presenting novel research on all theoretical and practical aspects related to both sides of green and sustainable AI, with major focus on significant Big Data aspects (e.g., efficient partitioning, distributed processing and analysis) that should be considered to address sustainability of AI and ML applications.

  1. In this paper, a framework based on a sparse Mixture of Experts (MoE) architecture is proposed for the federated learning and application of a distributed classification model in domains (like cybersecurity an...

    Authors: Francesco Folino, Gianluigi Folino, Francesco Sergio Pisani, Luigi Pontieri and Pietro Sabatino
    Citation: Journal of Big Data 2024 11:77
    Content type: Case Study Published on:

  3. This investigation delves into Green AI and Sustainable AI literature through a dual-analytical approach, combining thematic analysis with BERTopic modeling to reveal both broad thematic clusters and nuanced e...

    Authors: Raghu Raman, Debidutta Pattnaik, Hiran H. Lathabai, Chandan Kumar, Kannan Govindan and Prema Nedungadi
    Citation: Journal of Big Data 2024 11:55
    Content type: Research Published on:

About the collection

This special issue calls for original manuscripts describing the latest research on the following topics:

• Modelling resource requirements of ML algorithms and AI systems
• Data partitioning strategies for energy-efficient AI and ML
• Parallel and distributed ML algorithms for energy-efficient AI
• Energy-aware strategies to support AI and ML on limited-resource devices
• AI and ML solutions for energy management in edge, cloud and edge-cloud systems
• Energy-aware Big data management techniques for IoT devices and networks
• Energy-saving task scheduling strategies for ML applications
• Sustainable AI applications in medicine, environmental sciences, waste management, and energy management

Image credit: © Jenny Sturm / Stock.adobe.com

Submission Guidelines

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This Collection welcomes submission of Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system. During the submission process, under the section additional information, you will be asked whether you are submitting to a Collection, please select "Green and Sustainable AI" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.