An Empirical Analysis of the Relationship Between Learning City Indicators and Environmental Sustainability within the Framework of Smart City Development (Case Study: Ardabil)

Zavarzadeh Moghadam, Farid; Basiri, Mostafa; Saghafi Asl, َArash

doi:10.48306/juem.2025.534387.1084

An Empirical Analysis of the Relationship Between Learning City Indicators and Environmental Sustainability within the Framework of Smart City Development (Case Study: Ardabil)

Authors

Farid Zavarzadeh Moghadam ¹

Mostafa Basiri ²

َArash Saghafi Asl ³

¹ Department of Urban Planning, Tab,. C,, Islamic Azad University ,Tabriz, Iran.

² Department of Urbanism and Architecture,, Ilk., C,, Islamic Azad University, Ilkhchi, Iran.

³ Department of Urban Planning, Tab, C,. Islamic Azad University ,Tabriz, Iran.

https://doi.org/10.48306/juem.2025.534387.1084

Abstract

Abstract:
In recent decades, the integration of the concepts of learning cities and smart cities has emerged as an innovative approach for achieving environmental sustainability in urban governance. However, empirical evidence from urban contexts Iran-particularly in medium-sized metropolitan areas such as Ardabil—reveals functional gap in leveraging urban learning capacities and smart technologies to improve environmental quality. This study aims empirically examine the relationship between learning city indicators and environmental sustainability within the framework of smart city development. The research is quantitative and applied in nature. The statistical population consists of subject-related experts and professionals residing in Ardabil. The sample size, determined based on the number of measurable items, was 273 individuals. Data collection was conducted using a researcher-designed questionnaire, the validity (face, convergent, and discriminant) and reliability of which were confirmed through SmartPLS software. The results of confirmatory factor analysis and path analysis indicated that learning city indicators have a positive and significant effect smart city development, which in turn positively influences environmental sustainability. The highest path coefficient was between learning cities and smart city development (0.78), followed by smart city development and environmental sustainability (0.73). The Q² values for endogenous variables were greater than zero, indicating the model’s predictive power, and low VIF values confirmed the absence of multicollinearity. Indicators such as environmental education, lifelong learning, and data-driven governance showed the highest factor loadings. Based on these findings, strengthening educational infrastructure, implementing environmental awareness programs, and expanding smart services in Ardabil can meaningfully contribute to enhancing environmental sustainability.

Keywords

Learning City

Environmental Sustainability

Smart City Development

Urban Learning

Ardabil

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