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Impact in Computics

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Towards Green AI: A Lightweight Experimental Study on Carbon-Aware Neural Network Training

Basab Nath 1 ORCID , Fatima Ahmed Mohamed 2 , Farrukh Hassan 3,4 ORCID
1 School of Computer Science and Engineering, Bennett University, Greater Noida, Uttar Pradesh 201310, India
2 Department of Information and Communication Technology, Faculty of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur 53300, Malaysia
3 School of Computing and Artificial Intelligence, Faculty of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, Selangor Darul Ehsan 47500, Malaysia
4 Sunway Centre for Electrochemical Energy and Sustainable Technology (SCEEST), School of Engineering & Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, Petaling Jaya, Selangor Darul Ehsan 47500, Malaysia
DOI: https://doi.org/10.65500/computics-2026-001
Received: 11 November 2025 | Revised: 28 December 2025 | Accepted: 7 January 2026 | Published: 23 January 2026

Abstract

Industry 5.0 places emphasis on human-centered innovation, aligning digital transformation with sustainability, resilience, and societal well-being. Smart cities serve as practical environments where advanced technologies must balance operational efficiency with environmental responsibility. Since October 2023, deep neural networks have been widely used in urban decision-making, including traffic optimization and environmental monitoring. However, the high energy demand required to train large-scale models has raised concerns regarding both environmental impact and economic cost, leading to growing interest in sustainable AI practices. This study explores Carbon-Aware Neural Network Optimization within the Green AI framework as a practical approach to reduce computational carbon emissions while maintaining predictive performance. Three complementary strategies are integrated into a unified training pipeline and monitored using the CodeCarbon framework: partial layer freezing, quantization-aware training, and adaptive early stopping. Experiments are conducted on CIFAR-10 using MobileNetV2 and on a subset of ImageNet with ResNet-50. Results show a reduction in CO₂ emissions of 52% and 49%, respectively, with only minor changes in model accuracy. The findings indicate that combining these techniques provides an effective balance between efficiency and predictive reliability, supporting the development of sustainable AI solutions for Industry 5.0 and smart city applications.

Keywords: Industry 5.0; Green AI; Energy-aware training; Neural networks; CodeCarbon; Sustainability; Quantization-aware training; Early stopping; Runtime layer freezing

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