Modeling the livelihood capital of rural households in drought conditions (Case study: South Suburbs, West Islamabad city)

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.