Environmental Variables, Employment Rates and Internal Migration in Iran; the Importance of Job Creation in the Proportional Distribution of the Population Based on Environmental Resources

Introduction
Environmental changes and environmental variables have influenced migration worldwide. Despite extensive studies on the relationship between environmental variables and migration, there are contradictory findings, partly due to the complexity of the migration phenomenon. On the other hand, the relationship between migration and environmental variables is also complex and multifaceted. Migration affects population distribution and, through this, can influence the distribution of resources across different regions, creating changes in national and regional planning in the domain of resources. Iran has experienced extensive internal migration in recent decades. Considering the importance of environmental variables in migration decisions and taking into account the role of internal migration in exacerbating environmental crises and the provision of biological resources, this study examines the relationship between environmental variables and the spatial pattern of internal migration in Iran. Furthermore, given the effect of the employment variable on internal migration in Iran, this study aims to compare the relationship between environmental variables and the employment rate with the spatial pattern of internal migration.
Methodology
This research adopts a quantitative method and employs secondary data analysis. ArcGIS and GeoDa software were employed in this study. The variables examined in this research are net migration, employment rate, precipitation, land surface temperature, vegetation cover, and particulate matter air pollution. Data from the 2016 census at the national level were used to calculate net migration and employment rate. Various satellite data were utilized to calculate environmental variables. Global spatial autocorrelation tests and local Moran's spatial autocorrelation tests were employed to analyze the data. Finally, the spatial autocorrelation between the two variables of net migration and environmental variables, as well as the employment rate, was examined using the bivariate local Moran's test.
Findings
The spatial analysis of environmental variables revealed that precipitation and vegetation cover are more pronounced in the western areas and the northern strip, while lower values are observed in the central, semi-eastern, and western parts of the region. In contrast, for the land surface temperature variable, the western and northern belt regions have the lowest temperatures, while the central, eastern, and southern regions have the highest temperatures. The spatial distribution of particulate matter air pollution indicates that the counties of Sistan and Baluchestan, South Khorasan, Khuzestan, and Hormozgan provinces are the most polluted counties in Iran, while counties in the northern and northwestern belt have the lowest levels of particulate matter air pollution. Counties in the western, southeastern, and northeastern halves of Iran recorded the highest negative net migration, while counties in the center and north had the highest positive net migration, indicating that these regions are the most migrant-attracting areas in Iran. For the employment rate variable, counties in the western half of Iran had the lowest rates, while counties in central part, Bushehr in the south, and some counties of West Azerbaijan province in the northwest had the highest employment rates.
The results of global spatial autocorrelation indicated that all six research variables have significant spatial autocorrelation, and the distribution of research variables across Iran exhibits spatial clustering. The examination of spatial clustering maps in this study revealed that the main spatial pattern of internal migration in Iran is migration from high-precipitation, high-vegetation-cover areas toward low-precipitation, low-vegetation-cover areas. The spatial clustering obtained regarding the relationship between precipitation rate and employment rate with net migration indicates that the high-precipitation counties from which out-migration occurs are counties with low employment rates. The calculation of the bivariate Moran's test revealed that the spatial autocorrelation between the two variables of employment rate and net migration (0.179) is considerably higher than the autocorrelation between environmental variables and net migration.
Conclusion
Comparing the relationship between environmental variables - particularly precipitation and vegetation cover - and the employment rate with net migration in Iran reveals the importance of employment status in the internal migration pattern in Iran, which has led some residents of areas with favorable environmental conditions to migrate toward regions with high employment rates but lower precipitation and vegetation cover and higher temperatures. However, the study also revealed a different pattern. It indicated that some counties in the north of Iran— particularly a number of counties in Gilan and Mazandaran provinces — are migrant-attracting despite having low employment rates. This pattern can be explained by the existence of a spatial migration pattern among individuals of retirement age and at the end of their working lives, which leads to migration toward areas with favorable environmental characteristics, even though these areas do not have high employment rates.
The migration of a portion of the population from the high-precipitation western regions to low-precipitation, densely populated areas has placed greater pressure on the use of biological resources and exacerbated water resource supply crises in Iran. Creating job opportunities and relocating some industries to migrant-sending western regions of Iran — thereby reducing the volume of migration toward the densely populated, low-precipitation central regions — could be among the measures for redistributing the population based on biological resources, particularly water resources.