The field of machine learning is moving towards more sustainable and energy-efficient solutions. Researchers are exploring innovative algorithms and techniques to reduce the environmental footprint of AI systems, such as green online learning, dynamic model selection, and energy-efficient deep neural network training. These approaches aim to minimize the computational resources and energy required for training and deploying AI models, while maintaining or even improving their predictive performance. Notably, some studies have achieved significant energy savings and reduced carbon footprint without compromising accuracy requirements. Some noteworthy papers in this area include: Lift What You Can: Green Online Learning with Heterogeneous Ensembles, which proposes a novel approach to enable green online learning with heterogeneous ensembles. Beyond Backpropagation: Exploring Innovative Algorithms for Energy-Efficient Deep Neural Network Training, which investigates three backpropagation-free training methods and demonstrates their potential for energy-efficient deep learning. Choosing to Be Green: Advancing Green AI via Dynamic Model Selection, which presents an approach to reduce the environmental footprint of AI by dynamically selecting the most sustainable model while minimizing potential accuracy loss.