気候レジリエントなグリーンビルディングのための人工知能：AI駆動の持続可能性指標の統合的概念枠組み

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a structured taxonomy of AI applications in green buildings, useful for designing empirical studies on AI-driven sustainability metrics.

🏢実務担当者:Offers a framework to guide the adoption of AI tools for energy management and sustainability reporting in building operations.

🏛政策担当者:Highlights the potential of AI in achieving building decarbonization targets, informing policies that promote smart building technologies.

Introduction Climate change has emerged as one of the most pressing global challenges of the 21st century, exerting severe impacts on urban environments, infrastructure, and the health and wellbeing of populations. As buildings contribute significantly to global carbon emissions and resource consumption, the demand for environmentally sustainable solutions within the built environment has intensified. In response to these challenges, Artificial Intelligence (AI) offers new opportunities to drive performance optimization, energy efficiency, and climate resilience in architectural and urban systems. This study explores the role of AI in advancing environmental sustainability, with a particular focus on climate-resilient green buildings. The study aims to provide a holistic understanding of how AI technologies contribute to sustainable development by examining the key sustainability indicators associated with their application in green buildings. Methods A systematic literature review was conducted to synthesize recent peer-reviewed studies and industry reports on AI applications in green buildings. The reviewed literature was analyzed to identify key sustainability indicators and develop an integrated conceptual framework linking AI technologies with climate-resilient building performance. Results The review identified six principal AI-driven sustainability indicators: (1) energy efficiency and demand reduction, (2) renewable energy integration and storage optimization, (3) carbon footprint reduction and net-zero strategies, (4) water efficiency and resource management, (5) indoor environmental quality and thermal comfort, and (6) predictive maintenance and lifecycle optimization. Based on these findings, an integrated conceptual framework was developed to demonstrate how AI-enabled technologies support real-time monitoring, predictive analytics, adaptive management, and improved sustainability outcomes. Discussion The proposed framework provides a comprehensive understanding of how AI contributes to climate-resilient green buildings by integrating sustainability indicators, intelligent technologies, and resilience mechanisms within a unified analytical structure. The study offers a theoretical foundation for future empirical research and supports the integration of AI-driven sustainability strategies into climate-resilient building design and sustainable urban development.

• openalex https://doi.org/10.3389/fbuil.2026.1896030first seen 2026-08-30 04:35:19