Rural Planning
ahmad hajarian
Abstract
The research method in the present research is descriptive-analytical and its type is basic in terms of purpose and documentary and field methods are used to collect information. The statistical population of this research consists of all the rural areas of Jiroft city. According to the statistics provided ...
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The research method in the present research is descriptive-analytical and its type is basic in terms of purpose and documentary and field methods are used to collect information. The statistical population of this research consists of all the rural areas of Jiroft city. According to the statistics provided by Iran Statistics Center in 2015, Jiroft city has a population of 308,858 people and 92,937 households. Of these, 153,153 people with 46,543 households live in urban areas and 155,698 people with 46,392 households live in rural areas of the city. Also, in this research, cluster sampling method (multi-stage) was used. For this purpose, in the first stage, among the 4 districts of Jiroft city, among the 14 villages of this city according to the census of 2015, 11 villages were selected as a cluster sample, and then a number of villages were randomly selected from each cluster, and in total, the desired samples were from The level of 11 villages was collected. According to the 2015 census, this city has 30 villages with more than 1000 inhabitants (Jabalbarz 2 villages, Markazi 21 villages, Ismaili 7 villages).
In the following, to investigate the effect of distributive justice and procedural justice on the "infrastructure of the creative village", while confirming the positive and significant correlation of these two variables with Pearson's correlation test, the results of structural equation modeling with the Partial Least Squares (PLS) approach show that The dimension of distributive justice, taking into account the direct and indirect effects, has had a greater impact on the infrastructure of the creative village in the sample settlements with a coefficient of 0.755. In general, according to the coefficient of determination (R2) for the variable of creative village infrastructure (0.969),
Rural Planning
ahmad hajarian
Abstract
In this research, using contextual analysis method, the contexts of rural home business development were identified. The information was collected based on semi-structured interviews and a study of sources and documents. Findings show that out of 126 open codes, 34 basic concepts were identified that ...
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In this research, using contextual analysis method, the contexts of rural home business development were identified. The information was collected based on semi-structured interviews and a study of sources and documents. Findings show that out of 126 open codes, 34 basic concepts were identified that can be divided into 11 categories of organizing themes. . These concepts are divided into 11 categories including: motivational, structural and infrastructural factors, marketing, policy-making, educational and extension, management, partnership, financial and credit resources, information system, attitude and support as organizing themes of the research subject. Were. The results of the analysis indicate that the participation of members, empowerment of stakeholders, change of attitude of villagers, improvement of credit and financial resources of villagers, improvement of infrastructure and comprehensive policy for the development of home-based businesses should be considered.Employment and unemployment, which are very important issues in rural areas, can be solved in general through home-based jobs and their development.
Rural Planning
ahmad hajarian
Abstract
Corona virus is one of the infectious and infectious diseases of the 21st century that has spread from China to the world since the end of December 2019 and has had many effects and consequences on the social structure of various regions, including rural areas. Rural areas, especially in developing countries, ...
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Corona virus is one of the infectious and infectious diseases of the 21st century that has spread from China to the world since the end of December 2019 and has had many effects and consequences on the social structure of various regions, including rural areas. Rural areas, especially in developing countries, are less prepared to deal with the direct and indirect impact of this crisis. Therefore, the purpose of this research is to study the future of rural social indicators.The results of the first stage were identified using Mick Mac software to identify 5 key factors (family health, anxiety and depression, family food security, family isolation, religious ceremonies and happiness) among 15 factors. These factors were used as the main basis in the following script writing. In the second stage, 15 possible situations were defined for 5 factors. With the analysis performed by Scenario Wizard software, 3 strong scenarios, 20 plausible scenarios based on 15 possible situations related to 5 key drivers were extracted. Also, the results of the study showed that the strongest scenario is that in the post-corona period and the effects it has on social indicators, due to the prevalence of corona, the health of rural households decreases. Anxiety and depression increase. The food security of rural households is also affected by the corona virus, but the isolation of the family before and after the corona does not change. People do not attend ceremonies due to fear before the corona.
Rural Planning
Ahmad Hajarian
Abstract
IntroductionSustainable livelihood is one of the most important approaches in the field of climate management, especially drought. Sustainable livelihood is defined as the ability of a social unit to upgrade its assets and capacities in the face of pressures over time. The goal of the sustainable livelihood ...
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IntroductionSustainable livelihood is one of the most important approaches in the field of climate management, especially drought. Sustainable livelihood is defined as the ability of a social unit to upgrade its assets and capacities in the face of pressures over time. The goal of the sustainable livelihood approach is to increase the ability to face change and unpredictable problems, improve justice and increase sustainability by reducing tensions by providing secure networks. Achieving sustainable rural livelihoods is not possible without considering the livelihood capital in rural areas. Given that today, especially in developing societies, the study of household livelihood in terms of rural development is of particular importance, to conduct studies that analyze the livelihoods of rural residents, especially in terms of sustainability and in the face of vulnerable factors such as drought, it is essential. Therefore, the present study was conducted with the aim of measuring the level of livelihood stability of rural households in Hoome Jonobi city in drought conditions in order to understand the sustainability of their livelihood status. Data and Method This research is a survey in terms of practical purpose and in terms of how to collect data. The statistical population of the study was the heads of rural households in Selseleh city (N = 2894) that using Krejcie-Morgan table, the statistical sample size of 339 people was calculated. In order to obtain samples in this study, multi-stage sampling method was used. The analytical framework used in this study was a sustainable livelihood framework. Stability radar method was used to calculate the level of stability. The results showed that among the five livelihood capitals, four human, natural, social and financial capitals are in terms of stability in terms of stability and physical capital is in a position of potential stability. The questionnaire was the main research tool whose content validity was approved by experts and professors. In order to evaluate the reliability of the research tool, 30 out-of-sample questionnaires were completed and the alpha-Cronbach value for its different sections was obtained from 0.762 to 0.862. Results and Discussion The strength of the relationship between the factor (hidden variable) and the observable variable is shown by factor loading. Factor load is a value between zero and one. If the factor load is less than 0.3, the relationship is considered weak and it is ignored. A factor load between 0.3 and 0.6 is acceptable, and if it is greater than 0.6, it is very desirable. It can be seen that all observed variables had positive and significant regression effect coefficients with their scales and the magnitude of these coefficients is relatively high for all cases, all factor loadings at the 0.01 level. They are meaningful. As can be seen, in this table, the significance level for factor loadings or standard regression coefficients of the four observed variables is not reported. This is due to the fact that these variables are respectively considered as reference variables or representative variables for four human, physical, social and financial variables, so that these hidden variables are without scale and, in other words, without their root and unit of measurement. be resolved That is why the initial path diagrams on the arrows corresponding to the paths between these observed variables with the corresponding hidden variable are considered as values of 1. The AVE measure represents the average variance shared between each construct with its indicators. In simpler terms, AVE (Average Variance Extracted) is used for convergence validity and shows the high correlation of indicators of one structure compared to the correlation of indicators of other structures. The value of this coefficient varies from zero to one, and values higher than 0.5 are accepted. Convergent validity or average extracted variance (AVE) for the human capital index is 766/. , the natural capital index was 0.711, the social capital index was 0.799 and the financial capital index was 0.526. Also, the value of the structural reliability coefficient or composite reliability (CR) is variable from zero to one, and values higher than 0.7 are accepted. , which for the human capital index is 755/. , the natural capital index was 0.737, the social capital index was 0.802, and the financial capital index was 0.514, which shows the appropriateness of these subscales. ConclusionIn order to analyze a sustainable livelihood in a geographical area, we need to examine the livelihood resources available to the residents of the settlements in that area. The present study was conducted with the aim of modeling the livelihood capital of rural households in drought conditions in the rural areas of the southern suburbs and reached the following results.The stability of livelihood capitals of rural households in the study area showed that human capital explains 0.64% of the variance. Also, for natural capitals with a score of 0.45, social capitals with a sustainability score of 0.23 and finally financial capitals with a score of 0.37, this shows the intensity of the relationship between the variable of livelihood capitals and the following It has its own indicators.
