Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint

• URL: http://arxiv.org/abs/2608.09998v1
• Date: Fri, 07 Aug 2026 13:52:38 GMT
• Title: Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint
• Abstract summary: Artificial Intelligence (AI) and Machine Learning (ML) have become powerful tools for supporting and automating complex human tasks.<n>Despite their benefits, growing attention has been directed toward their environmental implications.<n>This paper presents a systematic review of research on Green AI, Green DL, and optimization techniques aimed at reducing the environmental impact of AI models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have become powerful tools for supporting and automating complex human tasks. Despite their benefits, growing attention has been directed toward their environmental implications, primarily due to their high energy demands and associated carbon emissions. This concern is particularly relevant in light of the increasing deployment of large-scale models, especially Deep Learning (DL) architectures, which provide advanced predictive capabilities but require substantial computational resources. This paper presents a systematic review of research on Green AI, Green DL, and optimization techniques aimed at reducing the environmental impact of AI models. In addition, we examine and compare several carbon measurement tools for estimating emissions generated by AI algorithms. To complement the review, we conducted an empirical evaluation using a CPU-based experimental setup, in which six DL models were implemented for a multi-label classification task. The objective was to quantify and compare their overall carbon emissions and to determine which stages of the DL lifecycle contribute most significantly to the total footprint. The results show that the training phase is the primary source of emissions. Moreover, the findings reveal that increased architectural complexity does not systematically translate into proportional accuracy gains, highlighting the importance of carefully balancing predictive performance and environmental cost. These results reinforce the need to integrate sustainability considerations into model selection and AI system design.

Related papers

• A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design [71.32556809242666]
  The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains.<n>It has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.<n>This survey aims to offer a holistic roadmap toward sustainable, scalable, and socially responsible development of large models.
  arXiv  Detail & Related papers  (2026-07-10T04:02:22Z)
• AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery [49.77172027295716]
  AutoSOTA is an end-to-end automated research system that advances the latest State-Of-The-Art (SOTA) models published in top-tier AI papers.<n>It successfully discovers 105 new SOTA models that surpass the original reported methods, averaging approximately five hours per paper.
  arXiv  Detail & Related papers  (2026-04-07T07:52:01Z)
• Perspective: Towards sustainable exploration of chemical spaces with machine learning [49.29528647472741]
  We argue that sustainable progress will rely on open data and models, reusable datasets, and domain-specific AI systems.<n>We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning.
  arXiv  Detail & Related papers  (2026-03-31T13:29:20Z)
• AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models [2.7946918847372277]
  We propose AI-CARE, an evaluation tool for reporting energy consumption, and carbon emissions of machine learning models.<n>We demonstrate, through theoretical analysis and empirical validation, that carbon-aware benchmarking changes the relative ranking of models.<n>Our proposal aims to shift the research community toward transparent, multi-objective evaluation and align ML progress with global sustainability goals.
  arXiv  Detail & Related papers  (2026-02-17T21:52:48Z)
• Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation [192.53529928861818]
  Learning with high-resource data has demonstrated substantial success in artificial intelligence (AI)<n>However, the costs associated with data annotation and model training remain significant.<n>This survey employs active sampling theory to analyze the generalization error and label complexity associated with learning from low-resource data.
  arXiv  Detail & Related papers  (2025-10-10T03:15:42Z)
• Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements [2.538209532048867]
  This study systematically evaluates the reliability of static and dynamic energy estimation approaches.<n>The established estimation approaches are shown to consistently make errors of up to 40%.<n>This study establishes transparency and validates widely used tools for sustainable AI development.
  arXiv  Detail & Related papers  (2025-09-26T09:12:21Z)
• AIMeter: Measuring, Analyzing, and Visualizing Energy and Carbon Footprint of AI Workloads [7.7878942091873755]
  AIMeter is a comprehensive software toolkit for the measurement, analysis, and visualization of energy use, power draw, hardware performance, and carbon emissions across AI workloads.<n>By seamlessly integrating with existing AI frameworks, AIMeter offers standardized reports and exports fine-grained time-series data.<n>It further enables in-depth correlation analysis between hardware metrics and model performance and thus facilitates bottleneck identification and performance enhancement.
  arXiv  Detail & Related papers  (2025-06-25T15:24:45Z)
• Systematic Weight Evaluation for Pruning Large Language Models: Enhancing Performance and Sustainability [1.542607498220242]
  This research focuses on the systematic evaluation of individual weight importance throughout the training process.<n>We propose a method that effectively reduces model size without compromising performance.<n>These findings highlight the critical need for optimized AI models to ensure sustainable development.
  arXiv  Detail & Related papers  (2025-02-24T11:34:49Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI [47.130004596434816]
  In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting. The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.
  arXiv  Detail & Related papers  (2022-07-31T09:34:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.