Structural modeling and measuring the dimensions of urban development strategy with a sustainable development approach Case study: Kashan city

The breakdown of the urban system and the particular problems of modern urban life have highlighted the need for comprehensive attention to strategies and beneficial solutions to optimize the lives of urban dwellers. In the meantime, having a stable and ideal city is perhaps the most important human need today. Urban Development Strategy A new approach has been adopted by the World Bank and the Coalition of Cities in order to sustainably develop the city, reduce poverty, and improve human societies. This view is based on the four indicators of good governance, banking, competitiveness and viability. The present article tries to evaluate the above indicators in Kashan city and to measure the adaptability of this city with CDS principles. The present study is descriptive-analytical and has an applied nature. The sample size was estimated to be 160 questionnaires using Sample Power software. Classification and evaluation of indicators has been done using a range of five Likert options and the time domain of data preparation can be considered as related to the year 1398. The required maps have also been prepared using ArcGIS software. To analyze the descriptive and inferential analysis of data, T-Test statistical test was used in SPSS software and to explain and model the effects, structural equation modeling was used in AMOS software. The results of the single-sample T test showed that the average indicators of good governance, competitiveness, banking, and viability were 2.26, 2.58, 2.91, 2.65, respectively, and were far from the standard value and weak. Are estimated. Analysis of the findings from structural equation modeling indicates that the regression weight of the RMSEA index is equal to (059 /), which indicates a good fit of the model. Also, the analysis of the results of the modeling showed that among the measured dimensions, the viability dimension has the highest factor load with regression weight (0.98) and error value (0.000) P Value =.