Artificial Intelligence for Sustainable Architectural Design: Global Trends, Gaps, and Future Directions
Artificial Intelligence for Sustainable Architectural Design (AI4SAD) has emerged as a transformative force addressing global pressures such as resource depletion, climate vulnerabilities, and widening urban health disparities. As architecture shifts from experience-based to intelligence-driven methods, AI provides a powerful pathway toward evidence-based, optimized, and environmentally conscious design. This study synthesizes insights from 408 global publications to examine how AI contributes to sustainability goals across different stages of architectural design, revealing both the potential and challenges associated with its adoption.
Global Spatiotemporal Landscape of AI4SAD
A systematic review of existing literature highlights a rapidly expanding global research footprint, with notable growth over the past decade. The distribution of studies demonstrates significant geographical clustering, where technologically advanced regions contribute heavily to algorithmic development, while emerging economies focus on applied sustainability outcomes. The temporal evolution shows a steady transition from foundational AI applications toward sophisticated, context-aware design tools that support environmental performance and resource efficiency.
AI Applications Across Design Stages and Sustainability Priorities
AI4SAD research spans the entire design pipeline—from early conceptual modeling to post-occupancy performance analysis. Machine learning, generative design, and optimization algorithms assist in energy simulation, material selection, thermal comfort analysis, lighting optimization, and life-cycle assessment. Across this spectrum, AI contributes to core sustainability objectives including reduced carbon emissions, enhanced resource management, and improved human-centered environmental quality. However, deployment varies widely, with certain design stages receiving far greater algorithmic attention than others.
Transparency Challenges and Reproducibility Gaps
Despite the accelerated growth of AI4SAD, a major transparency deficit persists. Only a minority of studies report essential details such as dataset characteristics, model parameters, training protocols, or measurable performance gains. This lack of disclosure limits reproducibility, restricts methodological comparison, and inhibits cross-contextual transfer of findings. The resulting knowledge fragmentation hinders the cumulative progress required to establish reliable AI-driven sustainability practices in architecture.
Evolution Toward Foundation Models in Architectural AI
Analysis of current research trends indicates a clear progression from narrow, task-specific models (level 2) toward more generalizable and scalable foundation models (level 3). Foundation models have the potential to unify data, automate multi-objective optimization, and enable adaptive design insights across diverse climatic and cultural settings. This evolution marks a significant shift in how AI systems are conceptualized for architectural applications, moving toward models capable of supporting holistic and autonomous sustainability decision-making.
Roadmap for Advancing Responsible and Interpretable AI4SAD
To ensure meaningful progress, this study proposes a forward-looking roadmap centered on generalizability, autonomy, and interpretability. Future research must prioritize transparent data governance, open model reporting, and cross-scenario adaptability. Enhancing interpretability will strengthen user trust, ensuring architects and planners can reliably integrate AI insights into sustainable design workflows. Ultimately, this roadmap supports a responsible and scientifically grounded evolution of AI4SAD, enabling architecture to meet global sustainability challenges with intelligence and accountability.
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