🤖 AI Summary
This study addresses the substantial carbon emissions associated with the high energy consumption of deep learning models, calling for systematic evaluation and mitigation of their environmental impact. Combining a systematic literature review with empirical analysis, it presents the first multi-label classification experiments on six deep learning models conducted on CPU platforms, quantifying their full lifecycle carbon footprints and benchmarking widely used carbon accounting tools. The findings reveal that the training phase dominates emissions and that model complexity exhibits a nonlinear relationship with accuracy gains—increased architectural sophistication does not consistently yield proportional performance improvements. These results provide empirical guidance for model selection that balances predictive accuracy with environmental sustainability, thereby advancing the paradigm of green AI design.
📝 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.