Introduction
Housing has always been a fundamental issue in rural areas. It is the most important element of the physical structure of villages, playing a key role in shaping the spatial-physical structure and construction identity of rural communities. Housing reflects how people interact with their surroundings and is shaped by the specific historical, cultural, and socio-economic conditions of each region. It represents the various economic activities, social and cultural mindsets of rural residents, the development and use of technology, and ultimately, the income and livelihoods of the population. Rural housing naturally embodies the way of life of different groups, with its patterns and functions offering insight into the spatial-temporal dynamics and prevailing trends in rural life and activities. Ardakan is one of the counties in Yazd Province, located in the northern part of the province. Administratively, it comprises three districts (Markazi, Kharanaq, and Aqda), three cities (Ardakan, Ahmadabad, and Aqda), and five rural districts (Mohammadieh, Robat, Zarrin, Aqda, and Narestan). The county seat is the city of Ardakan. According to 2019 statistics, the population of Ardakan was 97,960, making it the third most populous county in Yazd. Approximately 84% of the population lives in urban areas, with the remainder residing in rural areas. The county has 478 villages, of which 143 are inhabited and 335 are uninhabited. In recent years, through the provision of bank loans, there has been significant improvement both in terms of quantity and in the qualitative-technical aspects (such as structural reinforcement) towards sustainable development. This study aims to evaluate the impacts of rural housing improvement on the rural structure, including physical, economic, social, cultural, and environmental aspects.

Methodology
The research method used in this study is descriptive-analytical, and the data was collected through surveys. The reliability of the data was measured using Cronbach's alpha coefficient. The statistical population consists of residents of rural areas (6,378 people) where the rural housing improvement plan has been implemented. A simple random sampling method was employed. To determine the sample size, Bartlett and colleagues' table was used, resulting in a selection of 209 participants as the sample population. Data analysis was conducted using descriptive statistics, correlation techniques, and multiple regression analysis.

Findings
The index of policies and programs for rural housing improvement has been analyzed based on 6 indicators. The overall mean score is 3.09, with a standard deviation of 0.728. Among the components of the index for housing improvement policies and programs, the highest average is related to the indicator of attention to rural cultural and architectural patterns (3.67), while the lowest average is related to the indicator of attention to energy consumption patterns (heating and cooling of homes) in the housing sector (2.87).
The rural housing structure was analyzed across four dimensions as follows:
Physical Dimension: This dimension includes 5 indicators, with an average score of 2.83. The scores range from a minimum of 2.61 for the indicator related to essential attention to water and electricity in housing to a maximum of 3.08 for the indicator concerning the selection of residential land with consideration of natural hazards (earthquakes and floods).
Economic Dimension: The 5 indicators selected for the economic dimension have an average rank of 2.74. Among these indicators, the highest average is for the indicator related to job creation for residents as labor in the construction process (average 3.04, standard deviation 0.979), while the lowest average is for the indicator related to access to financial and credit facilities for housing construction (e.g., obtaining loans) (average 2.57, standard deviation 1.03).
Environmental Dimension: For the environmental dimension, 5 indicators were used. The average rank for this dimension is 2.51. Among the indicators, the one concerning attention to productive spaces (e.g., weaving workshops, cooperatives) in housing construction has the lowest average, while the indicator related to changes in land use and encroachment on agricultural land for housing has the highest average.
Social Dimension: The average rank for the social dimension is 2.76. Among the 5 selected indicators, the highest average is for the indicator of life expectancy in rural areas (average 3.16, standard deviation 1.02), while the lowest average is for the indicator related to increasing motivation for staying in rural areas, thereby reducing rural-to-urban migration (average 2.39, standard deviation).

Discussion and Conclusion
Today, housing is recognized as one of the fundamental challenges impacting rural structures. Housing is important from various dimensions, including physical, environmental, social, cultural, and economic aspects, and the effects it has on the life of rural communities. Undoubtedly, any change or development in the world quickly influences society, and one such development is the change in the type of housing. Since structures in any community serve different functions, these changes in housing structures must align with the economic, social, cultural, and environmental needs of rural communities. According to the study, among the dimensions of rural structure, the physical dimension has relatively better conditions. Pearson correlation tests revealed a significant relationship between housing improvement policies and programs and the dimensions of rural structure. This means that enhancing the quality of these policies and programs will lead to tangible improvements in the rural housing structure. The impact of the examined dimensions on the policies and programs for housing improvement is not uniform or unidirectional. There is a direct relationship between all physical, economic, environmental, and social dimensions of rural structure and the policies and programs for improvement. Among these dimensions, the physical dimension has a higher explanatory power and impact coefficient of 0.514 compared to the others in predicting policies and programs for improvement. The economic dimension ranks second with an impact coefficient of 0.160, showing greater explanatory power than other dimensions.
The findings of this research are consistent with the study by Mikaniki et al. (2017) in the economic and physical dimensions, while differing in the social and environmental dimensions. Additionally, the results align with the research by Sartipipour et al. (2019) in the physical dimension. The findings also resemble the study by Giannetti et al. (2018) regarding the use of modern technologies in construction and attention to the heating and cooling of rural housing.