发表机构
Royal Military College of Canada; Concordia University; École Nationale des Sciences de l’Informatique（加拿大皇家军事学院; 康考迪亚大学; 国家计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述绿色AI等相关研究，考察比较碳测量工具，通过CPU实验评估6个DL模型的碳足迹，发现训练阶段是主要排放源，架构复杂度提升未对应成比例准确率提升，强调需平衡性能与环境成本。
AI 中文摘要
人工智能（AI）与机器学习（ML）已成为支持和自动化复杂人类任务的强大工具。尽管它们带来诸多益处，但其环境影响正受到越来越多关注，主要源于高能源需求及相关碳排放。鉴于大规模模型（尤其是提供先进预测能力但需大量计算资源的深度学习（DL）架构）的部署日益增加，这一担忧尤为突出。本文对绿色AI、绿色DL及旨在降低AI模型环境影响的优化技术相关研究进行了系统综述。此外，我们还对多种用于估算AI算法碳排放的碳测量工具进行了考察与比较。为补充该综述，我们采用基于CPU的实验设置开展了实证评估，其中实现了6个DL模型以执行多标签分类任务，目标是量化并比较它们的总碳排放量，确定DL生命周期的哪些阶段对总碳足迹贡献最大。结果显示，训练阶段是主要排放源。此外，研究发现架构复杂度提升并未系统地转化为成比例的准确率提升，凸显了谨慎平衡预测性能与环境成本的重要性。这些结果强化了将可持续性考量纳入模型选择与AI系统设计的必要性。
英文摘要
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.
CommentsPublished in Applied Intelligence. DOI: https://doi.org/10.1007/s10489-026-07208-y
Journal refApplied Intelligence, vol. 56, Article 173, 2026
DOI：10.1007/s10489-026-07208-y
