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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
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- TRV-2026-2613raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
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- 361d1d26bdc8…check it
Abstract 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) operation…
54Gain side
How the two sides scoreProblem scores higher
61Problem side
The gain
In a test of 10 models on 5,169 text messages, Logistic Regression and Naive Bayes came close to DistilBERT's accuracy (MCC 0.9219 and 0.9250 against 0.9603) while using far less energy and carbon.
The problem
The same test found DistilBERT's edge over Logistic Regression came with 88 times more CO₂ per million predictions, and its training emissions ran about 1,000 times higher than the most efficient models.
A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.
What the scores mean
G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.
P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.
What this doesn’t fix
Only one English-language dataset was analysed, no tests against adversarial spam were run, and the CodeCarbon energy figures reflect the single hardware setup used.
Sources
1 on file- Computingdoi:10.1007/s00607-026-01706-0
Cite this record
v1Truvace Impact Record TRV-2026-2613, v1: “Beyond accuracy: a multi-dimensional green AI framework for sustainable machine learning—energy, carbon, and performance trade-offs in SMS spam detection.” Truvace, 2026-11-12. /record/TRV-2026-2613 (accessed on the day you copy it). sha256 361d1d26bdc892bd…
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