Integrated AI-Ecomanagement Systems: A Cross-Disciplinary Framework For Sustainable Material Optimization And Energy Governance In Smart Urban Infrastructure

Abstract

The increasing complexity of urban systems and the urgent need for environmental sustainability have driven the demand for integrated, intelligent solutions in infrastructure planning and management. This paper proposes an Integrated AI-Ecomanagement System that unifies material optimization and energy governance through the application of artificial intelligence (AI), digital twin technologies, and circular economy principles. A multi-layer architecture is developed to monitor, analyze, and optimize material flows and energy consumption within smart urban infrastructure. Using a simulation-based case study, the proposed system demonstrates a 25% reduction in material waste, 30% energy savings, and a 5200 kg/year reduction in CO₂ emissions, along with improved battery recycling efficiency from 65% to 90%. These results indicate the system’s capability to enhance environmental performance while supporting real-time decision-making. The framework aligns with the United Nations Sustainable Development Goals (SDGs) and offers practical pathways for operationalizing sustainability in urban development. This study contributes a cross-disciplinary model that not only addresses technical efficiency but also considers ecological impact and long-term resilience.