Low-carbon perspective-based multi-objective optimization of land use using improved NSGA-Ⅲ in mega cities: A case...

Rapid urbanization in mega-cities has created an urgent need to refine land resource strategies, particularly to support low-carbon goals. This process involves navigating the complex tension between economic growth, environmental protection, and social welfare. To tackle these challenges, this study proposed a low-carbon perspective-based multi-objective land use optimization framework using an improved Non-dominated Sorting Genetic Algorithm III (NSGA-III), specifically enhanced with a hybrid mutation operator and tailored initialization strategies. Focusing on the Nanshan District of Shenzhen, a representative mega-city core, six development scenarios were constructed to explore the trade-offs between maximizing economic benefits and land use compatibility, and minimizing carbon emissions, resident travel distances, and conversion costs. The results demonstrated the robustness and practical effectiveness of the proposed framework. For instance, the low-carbon priority scenario achieved a 4.36 % reduction in carbon emissions in contrast to the baseline scenario, while concurrently enhancing metrics related to economy and accessibility. In addition, this research also provided a portfolio of Pareto-optimal land use plans under various scenarios in the study area, offering valuable insights for sustainable urban planning and policy-making in complex mega-city environments.