Framework for Leveraging Artificial Intelligence in Monitoring Environmental Impacts of Green Buildings

The transition toward green buildings has emerged as a pivotal strategy for mitigating climate change, reducing urban environmental footprints, and improving human well-being. However, one of the persistent challenges lies in effectively monitoring the environmental impacts of green buildings across their lifecycle. Conventional assessment tools, while useful, often rely on static datasets and periodic audits, limiting the ability to capture real-time performance dynamics and adaptive responses. This proposes a framework for leveraging Artificial Intelligence (AI) in monitoring the environmental impacts of green buildings, offering a comprehensive, data-driven, and adaptive solution. The framework is structured into four interrelated layers. The input layer encompasses sensor networks, Internet of Things (IoT) devices, and external datasets to gather information on energy consumption, water use, indoor air quality, carbon emissions, and waste generation. The decision layer employs AI tools—such as machine learning models, neural networks, and predictive analytics—to detect anomalies, conduct life-cycle impact assessments, and optimize building performance against multiple sustainability criteria. The implementation layer integrates AI outputs into building management systems and policy compliance mechanisms, providing actionable insights for facility managers, developers, and regulators. Finally, the feedback layer ensures continuous monitoring, adaptive learning, and replication, enabling real-time refinement of building operations and scaling across portfolios of green buildings. Application scenarios include optimizing energy performance in commercial complexes, enhancing water efficiency in urban housing estates, tracking carbon footprints in office buildings, and reducing waste during construction and operation. By bridging technological innovation, sustainability assessment, and governance, the proposed framework highlights how AI can move green building monitoring beyond static evaluations to dynamic, responsive systems. Future directions point to the convergence of AI with digital twins, blockchain-enabled reporting, and global standardization efforts, positioning AI-enabled monitoring as central to the next generation of sustainable built environments.