Spatial effects of regional equilibrium on regional resilience based on land management approach (Case study: Guilan province)

Resilience, which is defined by less vulnerability and more flexibility, faster and more responsive to conditions, and less vulnerability, goes deeper into conceptual areas. Considering the applicability of the objectives of the present paper, explaining the spatial effects of regional equilibrium on regional resilience is the aim of this research. With this description, the strategic question is, to what extent has regional equilibrium been effective in regional resilience? A deductive strategy based on quantitative methodology was used to answer the research question. In this regard, the study’s statistical population, a total of 30 experts and scientific-executive elites working in scientific societies and development institutions, was chosen as a sample using a stratified and systematic sampling method and based on sampling logic. The data collection was done based on a field method, using a researcher-made questionnaire, a data analysis tool, and an inferential method based on the fit indices of the measurement model, structural model, and Smart PLS software. The results show that the indicators of the measurement model are regional equilibrium (6 indices), regional equilibrium (9 indices), and regional resilience (8 indices) with a factor load higher than 0.40, and the data confirm the model. In the study structural model, the accuracy of the relationship between the study structural/latent variables was examined based on the statistics (T-value), which was higher than 1.96%. The intensity of the relationship based on the coefficient of determination (R2) and the measure of impact size (F2) between endogenous and exogenous variables has shown that the relationship between regional equilibrium and regional resilience is confirmed with the accuracy of 0.6%, intensity, and the relationship between the regional equilibrium and regional resilience with the accuracy of 0.65%. Also, the goodness of fit (GOF) = 0.47 of model showed that the model has a strong fit, and the data confirm the study experimental model.