An Analysis of the Urban Resilience Capacity of Rudsar County in Facing Natural Disasters

Document Type : Research Paper

Authors

Department of Urban Planning, Faculty of Architecture and Art, University of Guilan, Rasht, Iran.

Abstract

This study aimed to assess the resilience of Rudsar County against natural disasters, focusing on five dimensions: social, economic, institutional, infrastructural, and urban planning/management. The research employed a descriptive-analytical method, collecting data via a questionnaire distributed among 380 citizens. Data were evaluated using SPSS software and analyzed with Structural Equation Modeling (SEM). DEM maps, seismic hazard maps, and watershed maps were also used for spatial analysis and identifying natural hazards. Results revealed severe inequality among the different resilience dimensions. Based on regression analysis, the institutional dimension, with a coefficient of 0.696, was the most influential factor. The Friedman test identified the infrastructural dimension (mean rank 3.98) as the most important and the economic dimension (mean rank 2.09) as the weakest. Spearman’s correlation analysis revealed a strong, significant relationship (0.564) between the institutional and infrastructural dimensions. Conversely, the social and economic dimensions, with low means (2.91 and 2.74, respectively) and weak indicators like “voluntary participation” and “efficiency of economic measures,” were identified as key factors increasing vulnerability. The PLS-SEM results confirmed the significant impact of all dimensions and showed that the economic dimension (path coefficient 0.405) had the strongest effect size. Therefore, achieving better resilience in Rudsar County requires an integrated approach, prioritizing institutional and infrastructural strengthening first, with socio-economic empowerment as a subsequent necessity.
 
Extended Abstract
1-Introduction
Urbanization in disaster-prone areas is increasing inevitably. Natural disasters pose critical threats to human societies, particularly in developing countries like Iran, where their intensity and impacts are more pronounced. Rudsar County, located in the north of Iran along the Caspian Sea and adjacent to the Alborz mountain range, is persistently exposed to natural hazards such as floods, earthquakes, and landslides, leading to significant human and financial losses and widespread disruptions across social, economic, infrastructural, institutional, and urban planning/management dimensions. Urban resilience has emerged as a fundamental strategy in urban development and risk management, representing a city's ability to adapt, withstand, and recover from major disturbances while preserving its essential functions and ensuring the well-being of its inhabitants. Given the increasing number of disasters due to climate change, unsustainable urban development, and the lack of an integrated management system in Rudsar, this study aims to comprehensively assess the county’s resilience level against natural disasters. The primary research questions are: What is the status of urban resilience in Rudsar across social, economic, infrastructural, institutional, and urban planning/management dimensions? And what strategies can be proposed to enhance it?
 
2-Materials and Methods
This applied research employed a descriptive-analytical method with a survey approach. The statistical population consisted of citizens over 18 years old in Rudsar County. Using an unlimited population sampling formula, a sample size of 380 individuals was selected randomly. The main data collection tool was a researcher-made questionnaire based on a theoretical framework encompassing five key dimensions: social, economic, institutional, infrastructural, and urban planning/management. The content validity of the questionnaire was confirmed by experts in urban studies and crisis management, and its reliability was verified with a Cronbach’s alpha coefficient of 0.706. Data were analyzed using SPSS software and inferential statistical techniques, including Spearman’s correlation, multiple regression, and the Friedman test. To test the comprehensive conceptual model and measure causal relationships between latent and observed variables, Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach was utilized. Additionally, for spatial analysis and precise identification of natural hazards within the study area, DEM (Digital Elevation Model) maps, seismic hazard maps, and watershed maps were used within a Geographic Information System (GIS) environment, providing a deeper understanding of the geographical context of the hazards.
 
3- Results and Discussion
The findings revealed severe inequality among the different resilience dimensions. Descriptive statistics showed that the infrastructural dimension had the highest mean score (3.56), indicating it as the strongest point of resilience, particularly regarding housing resistance. The institutional dimension ranked second (mean=3.42), while the social (mean=2.91) and economic (mean=2.74) dimensions were in the weakest positions, with critical weaknesses in indicators like “voluntary participation” and “efficiency of economic measures.”
Spearman’s correlation analysis revealed a strong and significant positive relationship (0.564) between the institutional and infrastructural dimensions, highlighting their interdependence. A moderate correlation was found between urban planning/management and institutional (0.448) and infrastructural (0.359) dimensions. In contrast, the social and economic dimensions showed weak correlations with others, indicating their relative isolation or that they are influenced by other stronger factors.
Multiple regression analysis identified the institutional dimension, with a coefficient of 0.696, as the most influential factor on overall resilience, followed by the infrastructural (β=0.676) and urban planning/management (β=0.646) dimensions. The social and economic dimensions also had significant but comparatively lower impacts. The Friedman test ranked the dimensions by importance from the respondents’ perspective: infrastructural (mean rank=3.98) as the most important, followed by institutional (3.55), urban planning/management (3.09), social (2.29), and economic (2.09) as the weakest.
The PLS-SEM model results provided nuanced insights. While the Friedman test and regression highlighted institutional and infrastructural strengths, the SEM path coefficients revealed that the economic dimension had the strongest effect size (path coefficient=0.405) on overall resilience, meaning that a unit of improvement in the economic condition would lead to the greatest increase in overall resilience. This was followed by the institutional (0.331), social (0.323), and infrastructural (0.311) dimensions. Urban planning/management had the lowest direct effect (0.294). This apparent contradiction between the Friedman test (ranking economic as weakest) and SEM (showing its highest potential impact) underscores a deep gap between the county’s current vulnerable economic status and its potential pivotal role in driving resilience if properly strengthened.
 
4- Conclusion
This study confirms that urban resilience in Rudsar County is a multidimensional and dynamic phenomenon. The analysis demonstrates severe disparities among its dimensions. The institutional and infrastructural dimensions currently form the relative core of resilience and are strongly interconnected. However, the profound weakness in the social and economic dimensions, particularly in operational indicators like voluntary participation, rebuilding skills, and efficiency of economic policies, constitutes a major vulnerability. Achieving sustainable resilience in Rudsar requires an integrated and prioritized approach. The first priority should be strengthening institutional capacities (inter-agency coordination, governance) and urban planning/management (real public participation), as these provide the essential framework. The second priority must be a serious and targeted focus on economic empowerment (diversifying local income, efficient financial policies) and social capacity building (continuous education, skill development), as these dimensions, despite their current weakness, hold the greatest potential leverage for improving overall resilience according to the causal model. Simultaneously, continued investment in critical and resilient infrastructure remains fundamental. This strategic, phased approach, combining institutional reform, economic empowerment, and social engagement, is essential for transforming Rudsar County into a more resilient entity capable of facing future natural disasters.

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Main Subjects

References

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