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

• URL: http://arxiv.org/abs/2506.12262v1
• Date: Fri, 28 Mar 2025 16:09:43 GMT
• Title: Energy-Efficient Green AI Architectures for Circular Economies Through Multi-Layered Sustainable Resource Optimization Framework
• Abstract summary: 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.<n>We tested the framework on real-world datasets from lithium-ion battery recycling and urban waste management systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design [71.32556809242666]
  The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains.<n>It has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.<n>This survey aims to offer a holistic roadmap toward sustainable, scalable, and socially responsible development of large models.
  arXiv  Detail & Related papers  (2026-07-10T04:02:22Z)
• Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation [2.2165845825140695]
  This paper presents the first systematic empirical study investigating how different prompt engineering strategies impact code generation accuracy alongside sustainability factors.<n>We evaluate six prominent prompting strategies across 11 open-source models (ranging from 1B to 34B parameters) using the HumanEval+ and MBPP+ benchmarks.<n>Our findings indicate that Chain-of-Thought, being a simpler prompting technique, can provide a near-optimal balance between reasoning capability and energy efficiency.
  arXiv  Detail & Related papers  (2026-04-03T06:37:51Z)
• Perspective: Towards sustainable exploration of chemical spaces with machine learning [49.29528647472741]
  We argue that sustainable progress will rely on open data and models, reusable datasets, and domain-specific AI systems.<n>We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning.
  arXiv  Detail & Related papers  (2026-03-31T13:29:20Z)
• EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents [0.14323566945483493]
  EcoThink reconciles high-performance AI intelligence with environmental responsibility.<n>It reduces inference energy by 40.4% on average without statistically significant performance loss.<n>By mitigating algorithmic waste, EcoThink offers a scalable path toward a sustainable, inclusive, and energy-efficient generative AI Agent.
  arXiv  Detail & Related papers  (2026-03-26T14:37:46Z)
• A Multi-Objective Optimization Approach for Sustainable AI-Driven Entrepreneurship in Resilient Economies [0.0]
  This research introduces the EcoAI-Resilience framework, a multi-objective optimization approach designed to maximize the sustainability benefits of AI deployment.<n>The framework addresses three critical objectives through mathematical optimization: sustainability impact, economic resilience enhancement, and environmental cost minimization.<n>The framework successfully identifies optimal AI deployment strategies featuring 100% renewable energy integration, 80% efficiency improvement targets, and optimal investment levels of $202.48 per capita.
  arXiv  Detail & Related papers  (2026-03-09T17:54:32Z)
• HybridSOMSpikeNet: A Deep Model with Differentiable Soft Self-Organizing Maps and Spiking Dynamics for Waste Classification [0.7734726150561086]
  Misclassification of recyclable materials contributes to landfill accumulation, inefficient recycling, and increased greenhouse gas emissions.<n>This study introduces HybridSOMSpikeNet, a hybrid deep learning framework that integrates convolutional feature extraction, differentiable self-organization, and spiking-inspired temporal processing.
  arXiv  Detail & Related papers  (2025-10-23T15:47:09Z)
• Improving AI Efficiency in Data Centres by Power Dynamic Response [74.12165648170894]
  The steady growth of artificial intelligence (AI) has accelerated in the recent years, facilitated by the development of sophisticated models.<n> Ensuring robust and reliable power infrastructures is fundamental to take advantage of the full potential of AI.<n>However, AI data centres are extremely hungry for power, putting the problem of their power management in the spotlight.
  arXiv  Detail & Related papers  (2025-10-13T08:08:21Z)
• Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
  6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
  arXiv  Detail & Related papers  (2025-05-06T06:46:19Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [59.52058740470727]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Explainable AI for building energy retrofitting under data scarcity [40.14307808809578]
  This study presents an Artificial Intelligence (AI) and Machine Learning (ML)-based framework to recommend energy efficiency measures for residential buildings.<n>Using Latvia as a case study, the methodology addresses challenges associated with limited datasets, class imbalance and data scarcity.<n>The evaluation of the approach shows that it notably overcomes data limitations, achieving improvements up to 54% in precision, recall and F1 score.
  arXiv  Detail & Related papers  (2025-04-08T14:00:08Z)
• Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights [2.1249213103048414]
  The rapid adoption of large language models (LLMs) has led to significant energy consumption and carbon emissions.<n>This paper explores the integration of energy-efficient optimization techniques in the deployment of LLMs to address these concerns.
  arXiv  Detail & Related papers  (2025-04-07T21:56:59Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges [0.7889270818022226]
  Large Language Models (LLMs) have transformed numerous domains by providing advanced capabilities in natural language understanding, generation, and reasoning.<n>Despite their groundbreaking applications across industries such as research, healthcare, and creative media, their rapid adoption raises critical concerns regarding sustainability.<n>This survey paper examines the environmental, economic, and computational challenges associated with LLMs, focusing on energy consumption, carbon emissions, and resource utilization in data centers.
  arXiv  Detail & Related papers  (2024-12-06T05:20:04Z)
• AI-Driven Approaches for Optimizing Power Consumption: A Comprehensive Survey [0.0]
  Reduced environmental effect, lower operating costs, and a stable and sustainable energy supply are the main reasons why power optimization is important. Power optimization and artificial intelligence (AI) integration are essential to changing the way energy is produced, used, and distributed. Real-time monitoring and analysis of power usage trends is made possible by AI-driven algorithms and predictive analytics.
  arXiv  Detail & Related papers  (2024-06-22T04:42:37Z)
• Green AI in Action: Strategic Model Selection for Ensembles in Production [2.464194460689648]
  Ensemble learning, combining predictions from multiple models to form a single prediction, intensifies this problem due to cumulative energy consumption. This paper presents a novel approach to model selection that addresses the challenge of balancing the accuracy of AI models with their energy consumption in a live AI ensemble system.
  arXiv  Detail & Related papers  (2024-05-21T18:57:43Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach [48.18355658448509]
  Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption. Scheduling training jobs among geographically distributed cloud data centers unveils the opportunity to optimize the usage of computing capacity powered by inexpensive and low-carbon energy. We propose an algorithm based on multi-agent reinforcement learning and actor-critic methods to learn the optimal collaborative scheduling strategy through interacting with a cloud system built with real-life workload patterns, energy prices, and carbon intensities.
  arXiv  Detail & Related papers  (2023-04-17T02:12:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.