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

迈向可持续人工智能：深度学习模型碳足迹的全面综述与对比分析

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.

摘要： 人工智能（AI）和机器学习（ML）已成为支持和自动化复杂人类任务的强大工具。尽管它们带来了诸多益处，但由于其高能耗及相关的碳排放，人们对其环境影响的关注日益增加。随着大规模模型（尤其是深度学习架构）部署的不断增加，这种担忧显得尤为重要；这些模型虽然提供了先进的预测能力，但也需要大量的计算资源。

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.

本文对旨在减少人工智能模型环境影响的绿色人工智能（Green AI）、绿色深度学习（Green DL）及优化技术的研究进行了系统性综述。此外，我们还审查并比较了几种用于估算人工智能算法所产生排放量的碳测量工具。

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.

为了补充综述内容，我们使用基于 CPU 的实验环境进行了实证评估，其中实现了六种用于多标签分类任务的深度学习模型。其目标是量化并比较它们的总碳排放量，并确定深度学习生命周期中的哪些阶段对总碳足迹的贡献最为显著。

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.

结果表明，训练阶段是排放的主要来源。此外，研究发现，架构复杂性的增加并不一定能带来成比例的精度提升，这凸显了在预测性能与环境成本之间进行仔细权衡的重要性。这些结果进一步强调了将可持续性考量纳入模型选择和人工智能系统设计的必要性。