مطالعات جغرافیایی مناطق خشک

مطالعات جغرافیایی مناطق خشک

مدل‌سازی اثرات هوشمندسازی بر تاب‌آوری اقتصادی خانوارهای روستایی در مواجهه با تغییرات اقلیمی (مورد مطالعه: شهرستان‌های فردوس، بشرویه و سرایان)

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه جغرافیای انسانی و آمایش، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران
2 گروه جغرافیا و برنامه ریزی روستایی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران
چکیده
هدف: امروزه هوشمندسازی روستاها در سطح بین‌المللی به عنوان یک برنامه توسعه روستایی برای بهینه‌سازی توانایی‌های روستایی و افزایش استفاده از فن‌آوری اطلاعات و ارتباطات در جهت دستیابی به رفاه بهتر جامعه روستای ظاهر شده ‌است. از این رو این هدف پژوهش حاضر به بررسی اثرات هوشمندسازی روستاها بر تاب‌آوری اقتصادی خانوارهای روستایی در مواجهه با تغیرات اقلیمی می‌پردازد.
روش و داده: پژوهش حاضر از نوع کمی بوده و به روش توصیفی - تحلیلی انجام شده ‌است. جامعه آماری تحقیق خانوارهای روستاهای سه شهرستان (فردوس، بشرویه و سرایان) است که دارای ۵۶۸۳ خانوار هست. برای محاسبه حجم نمونه در بین خانوارها با استفاده از فرمول کوکران تعداد ۳۰۱ نمونه انتخاب گردید. جهت تجزیه و تحلیل داده‌ها در این پژوهش از آزمون همبستگی و رگرسیون گام به گام در نرم‌افزار SPSS انجام شده است. علاوه بر این، از مدل معادلات ساختاری (SEM)  با استفاده از روش حداقل مربعات جزئی از نرم‌افزار (‏SMART PLS 4) برای بررسی اثرگذاری متغیرها ‏استفاده شد.
یافته‌ها: نتایج این پژوهش نشان داد که هوشمندسازی روستاها با مقدار T (۱۸/۹۵۸) و مقدار ضریب مسیر (۰/۷۴۱) بر تاب‌آوری اقتصادی خانوارهای روستایی در مواجهه بر تغییرات اقلیمی تأثیر مثبتی دارند. همچنین نتایج حاصل از رگرسیون خطی چند متغیره به‌صورت گام به گام نشان داد که متغیر محیط هوشمند با (۰/۳۷۳) بیشترین تأثیر و اتصال هوشمند با (۰/۱۲۵) کمترین تأثیر را بر تاب‌آوری اقتصادی روستاها در مواجهه با تغییرات اقلیمی داشته ‌است.
نتیجه‌گیری: با ارتقاء فناوری‌های هوشمند روستاها قادر به بهره‌برداری بهتر از منابع موجود و افزایش بهره‌وری در فعالیت‌های اقتصادی خواهند بود. این شامل بهبود سیستم‌های آبیاری، کنترل هوشمند انرژی، مدیریت کشاورزی و پایش زمین‌های کشاورزی است.
نوآوری، کاربرد نتایج: نوآوری پژوهش حاضر این است که این پژوهش تمامی متغیرهای مرتبط با هوشمندسازی و تاب آوری اقتصادی در مواجهه با تغییرات اقلیمی را به صورت یک جا مورد بررسی قرار داده است در حالی که مطالعات پیشین به طور معمول بر روی جنبه‌های محدودتری از این موضوع تمرکز داشته‌اند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Modelling the Effects of Intelligentization on the Economic Resilience of Rural Households in the Face of Climate Change (Case study: Ferdows, Boshrouyeh & Sarayan Counties)

نویسندگان English

Aliakbar Anabestani 1
Nabiollah Taheri 2
Pegah Moridsadat 1
1 Department of Human Geography and Spatial Planning, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
2 Department of Geography and Rural Planning, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
چکیده English

Aim: Nowadays, the smartening of villages has appeared at the international level as a rural development program to optimize rural abilities and increase the use of information and communication technology to achieve better welfare of the rural community. Therefore, this research aims to investigate the effects of village smartening on the economic resilience of rural households in the face of climate change.
Material & Method: The statistical population of the research is the households of the villages of three cities (Ferdows, Beshroieh and Sarayan), which have 5683 households. To calculate the sample size among households, 301 samples were selected using Cochran's formula. For data analysis in this research, correlation and stepwise regression tests were conducted using SPSS software. Additionally, the Structural Equation Modelling (SEM) approach was utilized with the Partial Least Squares method in the SMART PLS 4 software to examine the effects of the variables.
Finding: The results of this research showed that the smartness of villages, with the value of T (18.958) and the value of the path coefficient (0.741), has a positive effect on the economic resilience of rural households in the face of climate change. Also, the results of multivariable linear regression showed step by step that the smart environment variable (0.373) had the most effect, and the smart connection (0.125) had the least effect on the economic resilience of villages in the face of climate change.
Conclusion: By upgrading smart technologies, villages will be able to make better use of available resources and increase productivity in economic activities. This includes improving irrigation systems, intelligent energy control, agricultural management and agricultural land monitoring.
Innovation: The innovation of the current research is that this research has examined all variables related to economic intelligence and resilience in the face of climate change, while previous studies have usually focused on more limited aspects of this issue.
 

کلیدواژه‌ها English

Intelligentization
Economic resilience
Structural equation
Rural households
South Khorasan

Extended Abstract

  1. Introduction

Rural regions are rich in human, economic, social, and environmental aspects and are considered the foundation and main criteria for national development. They play a crucial role in food and raw material supply, strengthening the national economy, creating job opportunities, and preserving landscapes. They are also the backbone of the economy and contribute significantly to Gross Domestic Product (GDP). In this context, many approaches and strategies for rural development and sustainable rural development have been proposed. However, they generally do not align with the broad transformations of the modern technological era, complex global structural and functional changes, as well as local differences, environmental diversity, and uniqueness, each requiring specific planning. This necessitates new models based on knowledge and technology, referred to as "smart villages" or "smart ruralization." The goal of smart ruralization is to sustain these areas without altering the fundamental lifestyle of rural residents. Therefore, considering the discussions above, it is essential to explore smart ruralization and economic resilience in rural areas. This can be achieved by analyzing the application of smart growth and its influential indicators in these regions to utilize the potential of the smart growth approach in rural areas. The present research seeks to answer the question: To what extent does smart ruralization impact the economic resilience of rural populations in the face of climate change in the studied region?

  1. Materials and methods

The present study is quantitative, applied in terms of its objective, and descriptive and analytical in terms of its methodology. In conducting this research, data were collected using two methods as documentary (gathering information from books, journals, statistical yearbooks, maps, and websites) and fieldwork (utilizing questionnaires). The validity of the research tools was confirmed by obtaining feedback from university professors and making the necessary revisions in several stages. The overall reliability of the research questionnaire, determined using Cronbach's alpha, was found to be 0.940, indicating a high level of reliability, and was calculated separately for each indicator in The research indicators were measured using Likert scale ranking options, ranging from 1 (very low) to 5 (very high). The statistical methods employed in this research include descriptive statistics (mean and standard deviation) and inferential statistics (using correlation and regression analysis) conducted through SPSS software. Additionally, structural equation modelling (SEM) was performed using the SMART PLS4 software.

  1. Results and Discussion

Given the non-normality of the data, the non-parametric Kendall's Tau-b test was used. The statistical test results showed that all smart ruralization indices have a positive and significant correlation with economic resilience, indicating a direct and meaningful relationship between these dimensions. Specifically, indices such as smart agriculture and smart environment have a stronger impact on economic resilience, with higher correlation coefficients (0.477 and 0.455), while other dimensions like smart tourism and smart health show weaker effects, with lower coefficients (0.269 and 0.297). The significance level of all values (0.000) also confirms that these relationships are statistically significant. Therefore, improving and developing various aspects of smart ruralization can effectively enhance economic resilience. Furthermore, stepwise multiple linear regression was used to examine the effects of smart ruralization indices on economic resilience. In the stepwise regression model, the independent variables explain the impact of smart ruralization on economic resilience. In Model 1, after the inclusion of the smart environment variable, this model could predict 45.3% of the changes in economic resilience. Subsequently, Models 2 and 3 show that after the addition of the smart governance and smart agriculture variables, the explained variance increased by approximately 55.5% and 58.7%, respectively. Afterwards, in the fourth and fifth models, the inclusion of two more variables, smart education and smart connectivity, further increased the explained variance to approximately 59.7% and 60.6%, respectively. The final model's effect coefficients for the independent variables indicate that among the available variables, the smart environment variable had the highest impact (0.373), while the smart connectivity variable had the least impact (0.125) on economic resilience. Based on the results obtained from structural equations, smart ruralization (independent variable) impacts economic resilience (dependent variable), with a T-value of 18.958 and a path coefficient of 0.711, indicating a positive and significant relationship between smart ruralization and the economic resilience of rural households. Therefore, the main hypothesis of the research is confirmed, as the T-statistic is greater than 1.96, and its significance level is p = 0.000.

  1. Conclusion

The statistical test results showed a positive and significant relationship between the variables of smart ruralization and economic resilience. In Model 1, after the inclusion of the smart environment variable, the model was able to predict 45.3% of the changes in economic resilience. Subsequently, Models 2 and 3 indicate that the addition of smart governance and smart agriculture variables increased the explained variance to approximately 55.5% and 58.7%, respectively. After that, in the fourth and fifth models, the inclusion of smart education and smart connectivity variables further increased the explained variance to approximately 59.7% and 60.6%, respectively, as shown in the effect coefficients of the independent variables in the final model are presented in Among the existing variables, the smart environment variable had the greatest impact (0.373), while the smart connectivity variable had the least impact (0.125) on economic resilience. Subsequently, to examine the impact of smart ruralization on the economic resilience of rural areas based on the conceptual model of the research and to test the hypothesis, Structural Equation Modelling (SEM) was performed using the Partial Least Squares (PLS) method in SMART-PLS 4 software, which confirmed the main hypothesis of the research. The findings indicate that strengthening the smart ruralization variables in the studied villages leads to enhanced economic resilience of rural households in coping with climate change.

  1. Acknowledgement & Funding
  • The authors are thankful to all interview participants for supporting this research.
  • The manuscript did not receive a grant from any organization
  1. Conflict of Interest

The authors are thankful to all interview participants for supporting this research.

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