  As the world continues to face the challenges of climate change, it is
crucial to consider the environmental impact of the technologies we use. In
this study, we investigate the performance and computational carbon emissions
of various transfer learning models for garbage classification. We examine the
MobileNet, ResNet50, ResNet101, and EfficientNetV2S and EfficientNetV2M models.
Our findings indicate that the EfficientNetV2 family achieves the highest
accuracy, recall, f1-score, and IoU values. However, the EfficientNetV2M model
requires more time and produces higher carbon emissions. ResNet50 outperforms
ResNet110 in terms of accuracy, recall, f1-score, and IoU, but it has a larger
carbon footprint. We conclude that EfficientNetV2S is the most sustainable and
accurate model with 96.41% accuracy. Our research highlights the significance
of considering the ecological impact of machine learning models in garbage
classification.
