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<p>The rapid evolution of globalization and the knowledge economy underscores the strategic importance of learning city development for sustainable urban growth. This study investigates Yazd City, Iran a UNESCO-listed heritage city with a distinct economic profile and arid climate by assessing the integration of economic indicators into learning city frameworks. A mixed-methods approach was employed, incorporating the Kolmogorov–Smirnov test to assess data normality, multiple linear regression analysis to establish causal relationships, and Kendall’s W test to rank economic performance across the city’s eight urban districts. Stratified random sampling and ethical protocols were observed in data collection. The results reveal a statistically significant correlation (R<sup>2</sup> = 0.91) between economic stability and learning city attributes. District prioritization identified Area 7 as the most developed marked by commercial vibrancy and accessible education while Area 5 ranked lowest due to infrastructural degradation and limited policy support. These spatial disparities highlight the need for targeted, district-specific economic interventions to promote balanced learning city development. By offering an innovative, data-driven prioritization model tailored to Yazd’s socio-economic context, the study contributes to learning city theory and provides actionable guidance for policymakers and urban planners. The framework also holds relevance for other cities confronting the dual challenge of economic and educational integration.</p>

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Economic indicator assessment and prioritization in Yazd City: a study of learning city development in Iran

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • Aimal Formolly,
  • Mohammad Hossain Saraei,
  • Shahabadin Hajforoush

摘要

The rapid evolution of globalization and the knowledge economy underscores the strategic importance of learning city development for sustainable urban growth. This study investigates Yazd City, Iran a UNESCO-listed heritage city with a distinct economic profile and arid climate by assessing the integration of economic indicators into learning city frameworks. A mixed-methods approach was employed, incorporating the Kolmogorov–Smirnov test to assess data normality, multiple linear regression analysis to establish causal relationships, and Kendall’s W test to rank economic performance across the city’s eight urban districts. Stratified random sampling and ethical protocols were observed in data collection. The results reveal a statistically significant correlation (R2 = 0.91) between economic stability and learning city attributes. District prioritization identified Area 7 as the most developed marked by commercial vibrancy and accessible education while Area 5 ranked lowest due to infrastructural degradation and limited policy support. These spatial disparities highlight the need for targeted, district-specific economic interventions to promote balanced learning city development. By offering an innovative, data-driven prioritization model tailored to Yazd’s socio-economic context, the study contributes to learning city theory and provides actionable guidance for policymakers and urban planners. The framework also holds relevance for other cities confronting the dual challenge of economic and educational integration.