Optimizing open space capacity under heritage constraints in the ancient city of Suzhou: a multi-scale recursive approach

Abstract Optimizing open spaces in historic urban areas presents a unique challenge: balancing macro-level planning objectives with the preservation of irreplaceable heritage. This study introduces a multi-scale recursive optimization framework that integrates assessment, prediction, and configuration to address these conflicts. Using the ancient city of Suzhou, China (a UNESCO World Heritage site), as a case study, we applied the framework at city, community, and plot scales. Comprehensive assessment identified critical challenges, including insufficient per capita open space, uneven spatial distribution, and pronounced quality–quantity mismatches. Recursive optimization refined initial macro-level capacity targets, derived from national standards, by incorporating micro-scale heritage constraints. A potential–constraint model quantified regeneration potential, while spatial-cultural rules, such as water proximity, directed capacity allocations along river corridors. The proposed framework achieves synergy between capacity enhancement and historic character preservation. By shifting spatial governance from static blueprinting to dynamic, multi-scale optimization, our findings provide transferable methodologies and actionable strategies to support sustainable regeneration in heritage cities facing conservation–development conflicts.