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<p>The renewal of historic districts requires a delicate balance between preserving cultural heritage and stimulating urban vitality. However, existing evaluation mechanisms often fail to systematically translate qualitative stakeholder demands into actionable technical parameters, leading to subjective or fragmented decision-making. To address this gap, this study proposes a quantitative Decision Support Model (DSM) that integrates Quality Function Deployment (QFD) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In this hybrid framework, QFD is utilized to map heterogeneous stakeholder requirements onto technical characteristics, establishing a weighted evaluation matrix that serves as the empirical input for the TOPSIS ranking algorithm. The model evaluates renewal strategies against five core criteria: technical difficulty, cost, economic benefits, potential for vitality enhancement, and market demand. Application of the model in a case study demonstrates that strategies optimizing vitality enhancement potential while minimizing technical complexity and cost are prioritized. Notably, sensitivity analysis identifies vitality enhancement and market demand as the dominant variables influencing strategic ranking. By extending the demand-translation capability of QFD into the comparative ranking structure of TOPSIS, this study contributes a robust methodology that reduces the subjectivity of traditional expert scoring. The findings advocate for a renewal paradigm that aligns policy incentives with culturally driven, functionally adaptive interventions.</p>

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Decision support model for evaluating renewal strategies to enhance the vitality of historical districts

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月8日

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  • Yanqing Xu,
  • Yi-Kai Juan

摘要

The renewal of historic districts requires a delicate balance between preserving cultural heritage and stimulating urban vitality. However, existing evaluation mechanisms often fail to systematically translate qualitative stakeholder demands into actionable technical parameters, leading to subjective or fragmented decision-making. To address this gap, this study proposes a quantitative Decision Support Model (DSM) that integrates Quality Function Deployment (QFD) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In this hybrid framework, QFD is utilized to map heterogeneous stakeholder requirements onto technical characteristics, establishing a weighted evaluation matrix that serves as the empirical input for the TOPSIS ranking algorithm. The model evaluates renewal strategies against five core criteria: technical difficulty, cost, economic benefits, potential for vitality enhancement, and market demand. Application of the model in a case study demonstrates that strategies optimizing vitality enhancement potential while minimizing technical complexity and cost are prioritized. Notably, sensitivity analysis identifies vitality enhancement and market demand as the dominant variables influencing strategic ranking. By extending the demand-translation capability of QFD into the comparative ranking structure of TOPSIS, this study contributes a robust methodology that reduces the subjectivity of traditional expert scoring. The findings advocate for a renewal paradigm that aligns policy incentives with culturally driven, functionally adaptive interventions.