# Energy-Efficient Green AI Architectures for Circular Economies Through Multi-Layered Sustainable Resource Optimization Framework

arXiv: https://arxiv.org/abs/2506.12262 · HTML: https://mlsanity.com/paper/2506.12262/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI](https://mlsanity.com/paper/2303.13972/) (2023)
- [Towards Green AI: Current status and future research](https://mlsanity.com/paper/2407.10237/) (2024)

## Built on by (most cited first, 1 of 1)

- [Mapping the Landscape of Artificial Intelligence in Life Cycle Assessment Using Large Language Models](https://mlsanity.com/paper/2602.22500/) (2026)

## Abstract (arXiv)

In this research paper, we propose 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 introduce 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 decision-making for resource reuse, waste reduction, and sustainable production.We tested the framework on real-world datasets from lithium-ion battery recycling and urban waste management systems, demonstrating its practical applicability. Notably, the key findings of this study indicate a 25 percent reduction in energy consumption during workflows compared to traditional methods and an 18 percent improvement in resource recovery efficiency. Quantitative optimization was based on mathematical models such as mixed-integer linear programming and lifecycle assessments. Moreover, AI algorithms improved classification accuracy on urban waste by 20 percent, while optimized logistics reduced transportation emissions by 30 percent. We present graphical analyses and visualizations of the developed framework, illustrating its impact on energy efficiency and sustainability as reflected in the simulation results. This paper combines the principles of Green AI with practical insights into how such architectural models contribute to circular economies, presenting a fully scalable and scientifically rooted solution aligned with applicable UN Sustainability Goals worldwide. These results open avenues for incorporating newly developed AI technologies into sustainable management strategies, potentially safeguarding local natural capital while advancing technological progress.