Integrating Green Standards and AI Tools for Sustainability Assessment of Tall Buildings

Abstract

The urgent need to address climate change has accelerated the development of sustainable high-rise structures
worldwide. This review critically analyzes the integration of green building standards with artificial intelligence
(AI) modeling techniques for sustainability assessment of high-rise buildings. The paper examines how major
certification frameworks—LEED, BREEAM, and Green Star—are being enhanced through machine learning
algorithms, neural networks, and evolutionary computing to optimize building performance. Recent advances
show promising results in energy consumption prediction, embodied carbon reduction, and lifecycle assessment
automation. However, challenges remain in data standardization, model transparency, and addressing regional
climate variations. The integration of AI with building information modeling (BIM) emerges as particularly
effective for holistic sustainability evaluation, while deep learning algorithms demonstrate superior capability in
complex performance prediction compared to traditional methods. This research highlights the transformative
potential of AI-enhanced assessment tools in achieving net-zero carbon objectives for the high-rise built
environment, while identifying critical research gaps requiring future investigation.