Energy-efficient green AI architectures for circular economies through multi-layered sustainable resource optimization framework

Abstract

This research paper proposes a new type of energy-efficient Green AI architecture to support circular economies and address the contemporary challenge of sustainable resource consumption in modern systems. We propose a multi-layered framework and meta-architecture that integrates state-of-the-art machine learning algorithms, energy-conscious computational models, and optimization techniques to facilitate resource reuse, waste reduction, and sustainable production decision-making. We tested the framework on real-world datasets from lithium–ion battery recycling and urban waste management systems, showing its practical applicability. Notably, the key findings of this research paper study indicated a 25% reduction in energy consumption during workflows compared to traditional methods and an 18% improvement in resource recovery efficiency. Quantitative optimization was based on mathematical models (e.g., mixed-integer linear programming and lifecycle assessments). Moreover, AI algorithms improved classification accuracy on urban waste by 20%, and optimized logistics reduced transportation emissions by 30%. Began with graphical analyses and visualised the results of the developed framework, which illustrates the framework’s impact on energy efficiency and sustainability, as reflected in the simulation. This paper combines the principles of Green AI with practical insights into how such architecture models contribute to circular economies, presenting a fully scalable and scientifically rooted solution path aligned with applicable UN Sustainable Development Goals worldwide. Such results open up avenues for incorporating newly developed AI technologies into sustainable management strategies, which could help safeguard local natural capital amid technological progress.