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<p>Until recently, machine learning research has primarily focused on prediction accuracy, often neglecting computational efficiency and environmental impact. In this study, 10 models encompassing classical machine learning, ensemble learning, and deep learning methods were evaluated on the SMS Spam Collection dataset (5,169 messages) using the proposed Multidimensional Green AI Framework. This framework considers models in three dimensions: (i) classification performance (MCC and F1-score), (ii) operational efficiency (p95 inference latency, RAM usage, and model size), and (iii) environmental sustainability (energy consumption in Wh and carbon footprint in kg CO₂). The results reveal a clear accuracy–sustainability trade-off. Although DistilBERT achieved the highest performance (99.13% accuracy, 0.9603 MCC), its marginal gains over simpler models come at a substantial environmental and computational cost. Total pipeline time is approximately 1,720 times longer than Naive Bayes, and training carbon emissions are approximately 1,000 times higher than the most efficient models. Furthermore, CO₂ per one million inferences is 88 times greater than Logistic Regression. In contrast, classical models such as Naive Bayes and Logistic Regression demonstrated competitive performance with significantly lower resource consumption, while ensemble methods, particularly XGBoost, offered a balanced trade-off between accuracy and efficiency. These findings highlight that model selection should not rely solely on accuracy, but must also consider efficiency and environmental impact. Accordingly, this study proposes a practical and environmentally aware Green AI framework for use-specific model selection, supporting classical models for mobile/IoT environments, ensemble methods for edge computing, and deep learning approaches for cloud-based systems where higher resource consumption is acceptable.</p>

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Beyond accuracy: a multi-dimensional green AI framework for sustainable machine learning—energy, carbon, and performance trade-offs in SMS spam detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Mustafa Aksu

摘要

Until recently, machine learning research has primarily focused on prediction accuracy, often neglecting computational efficiency and environmental impact. In this study, 10 models encompassing classical machine learning, ensemble learning, and deep learning methods were evaluated on the SMS Spam Collection dataset (5,169 messages) using the proposed Multidimensional Green AI Framework. This framework considers models in three dimensions: (i) classification performance (MCC and F1-score), (ii) operational efficiency (p95 inference latency, RAM usage, and model size), and (iii) environmental sustainability (energy consumption in Wh and carbon footprint in kg CO₂). The results reveal a clear accuracy–sustainability trade-off. Although DistilBERT achieved the highest performance (99.13% accuracy, 0.9603 MCC), its marginal gains over simpler models come at a substantial environmental and computational cost. Total pipeline time is approximately 1,720 times longer than Naive Bayes, and training carbon emissions are approximately 1,000 times higher than the most efficient models. Furthermore, CO₂ per one million inferences is 88 times greater than Logistic Regression. In contrast, classical models such as Naive Bayes and Logistic Regression demonstrated competitive performance with significantly lower resource consumption, while ensemble methods, particularly XGBoost, offered a balanced trade-off between accuracy and efficiency. These findings highlight that model selection should not rely solely on accuracy, but must also consider efficiency and environmental impact. Accordingly, this study proposes a practical and environmentally aware Green AI framework for use-specific model selection, supporting classical models for mobile/IoT environments, ensemble methods for edge computing, and deep learning approaches for cloud-based systems where higher resource consumption is acceptable.