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Performance-Based Multi-Criteria Assessment of Low-Carbon Social Housing: A Comparative Life Cycle Analysis in the Climatic Context of Sindh, Pakistan

低炭素社会住宅の性能ベース多基準評価:シンド州(パキスタン)の気候状況における比較ライフサイクル分析 (AI 翻訳)

Ali Raza Khoso, Muhammad Saleem Raza, Sajan Laghari, Sarfraz Hyder

Mehran University Research Journal of Engineering and Technology📚 査読済 / ジャーナル2026-07-01#省エネ経営インパクト: コスト削減対象セクター: construction
DOI: 10.22581/muet1982.0796
原典: https://doi.org/10.22581/muet1982.0796

🤖 gxceed AI 要約

日本語

パキスタンのシンド州における低炭素社会住宅の最適設計を、LCAと多基準意思決定(AHP/TOPSIS)を用いて評価。13種類の材料と14の工法を比較し、圧縮安定土ブロック(CSEB)とプレキャスト屋根の組み合わせが最適であることを示した。従来のレンガ構造と比較して、組込み炭素排出量を30%、建設コストを13%削減可能。専門家のコンセンサスも高く、実現可能性が確認された。

English

This study develops an optimized low-carbon housing model for Sindh, Pakistan using LCA and multi-criteria decision-making (AHP/TOPSIS). It evaluates 13 materials and 14 construction strategies, finding that compressed stabilized earth blocks (CSEBs) with precast roofing reduce embodied emissions by 30% and costs by 13% compared to conventional brick. Strong expert consensus supports feasibility.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

パキスタン国内の事例ではあるが、コスト抑制と排出削減を両立するアプローチは、日本の住宅分野(特に災害復興住宅や地方の低コスト住宅)にも応用可能性がある。LCAと多基準評価の統合手法は、日本の建設業界のサステナビリティ評価にも参考となる。

In the global GX context

While specific to Pakistan, this paper offers a replicable methodology for low-carbon housing in developing regions, combining LCA with multi-criteria decision-making. The embodied carbon reductions achieved through material substitution (CSEB) are relevant to global sustainable construction goals and UN SDGs 9, 11, 13.

👥 読者別の含意

🔬研究者:Researchers in sustainable construction and LCA can learn from the hybrid AHP-TOPSIS framework and its application to housing material selection.

🏢実務担当者:Construction firms and housing developers in hot climates can adopt CSEB and precast roofing for cost-effective, low-carbon housing.

🏛政策担当者:Policymakers in developing countries can use this evidence-based model to promote low-carbon building codes and affordable housing programs.

📄 Abstract(原文)

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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