Performance-Based Multi-Criteria Assessment of Low-Carbon Social Housing: A Comparative Life Cycle Analysis in the Climatic Context of Sindh, Pakistan
DOI:
https://doi.org/10.22581/muet1982.0796Keywords:
Social Housing, Low-Cost Housing, Embodied Carbon, Life Cycle Assessment, AHP-TOPSIS, SustainabilityAbstract
The housing sector in Pakistan has a documented backlog of 10.3 million units resulting from multiple social and economic challenges. The crisis is particularly severe in Sindh Province because of extreme climatic conditions and the vulnerability of informal settlements. This situation requires a robust engineering approach to deliver housing that is both resilient and affordable. Accordingly, a paradigm shift is needed to reconcile cost sensitivity with low-carbon sustainability. This study addresses this need by developing an optimized housing model based on standardized life cycle assessment (LCA) and multi-criteria decision-making (MCDM) techniques. A hybrid decision-support tool combining the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) was developed. In total, 13 alternative materials and 14 construction strategies were evaluated against environmental, economic, and technical criteria. Qualitative insights from industry professionals were cross-validated for consistency and statistical significance using Kendall's coefficient of concordance (W), whereas the quantitative assessment comprised a cradle-to-gate embodied-carbon inventory and standardized cost modeling. The findings indicate that compressed stabilized earth blocks (CSEBs) and precast roofing form the optimal configuration for the local context. Compared with a conventional brick structure, the proposed design reduced embodied emissions by 30% and construction costs by 13%. Strong expert consensus (W = 0.82, p < 0.001) also supported the model's implementation feasibility. The study therefore presents an actionable pathway for resilient and affordable housing by integrating rigorous decision-support tools with locally measured environmental, economic, and technical performance. It also contributes to the achievement of UN Sustainable Development Goals 9, 11, and 13.
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