<![CDATA[Retrofitting community buildings is a key pathway toward carbon neutrality, yet most existing retrofit planning models lack adaptability to the diverse urban contexts of the Global South, where building typologies are heterogeneous and resources limited. Addressing this gap requires approaches that are both computationally efficient and context-sensitive. This study introduces a hybrid optimization framework that integrates Genetic Algorithm (GA) and Mixed-Integer Linear Programming (MILP) to tackle the multidimensional multiple-choice knapsack problem inherent in retrofit planning. The GA explores high-level system configurations, while MILP ensures precise component-level selection under budget and technical constraints. Compared to conventional single-method approaches, the hybrid GA–MILP achieves near-optimal emission reduction with reduced computation time and greater feasibility, offering a balanced trade-off between performance and scalability. Importantly, the framework demonstrates that medium-cost retrofit strategies provide the most cost-effective path to scalable carbon savings, making it highly relevant for resource-constrained urban environments. By situating retrofit planning within the realities of the Global South, this study advances methodological innovation and provides a robust decision-support tool aligned with sustainable development goals for inclusive and low-carbon urban futures.]]>
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