AI-Enabled Sustainable Landscape Design: A Decision-Support Framework Based on “Generative-Critical” Multi-Agent

Under the dual pressures of global climate change and accelerating urbanization, landscape design has been tasked with the critical mission of enhancing urban environmental resilience and ecological livability. However, conventional design practices often struggle to efficiently integrate complex sustainability norms with aesthetic creativity, leading to a disconnect between form and function. To address this issue, this study proposes and validates an AI-enabled sustainability decision-support framework. The framework is based on a “Generative-Critical” multi-agent workflow that enables “Self-Correcting” iterative optimization of design schemes through a built-in expert knowledge base and a quantitative scorecard. The framework’s effectiveness was validated through a cultural park case study and a blind evaluation by 10 experts. It guided a design from an initial concept with only aesthetic forms and lacking effective stormwater management, to an ecologically integrated scheme that strategically incorporated bioretention ponds at key nodes and converted hard plazas into permeable pavements. This transformation significantly elevated the scheme’s sustainability score from 59.3 to 88.0 (p < 0.001), while the framework itself achieved a high system usability scale (SUS) score of 85.5. These results confirm that the proposed “Generative-Critical” mechanism can effectively guide AIGC to adhere to ecological-technical norms and constraints while pursuing aesthetic innovation, thereby achieving a scientific integration of aesthetic form and ecological function at the early conceptual design stage. This study offers a scalable methodology for AI-assisted sustainable design and provides a novel intelligent tool for creating resilient urban landscapes that possess both environmental performance and aesthetic value.