Abstract
There are many factors affecting poverty, among which education is an important one. Firstly, from the perspective of digital statistics, this research quantitatively analyzes the correlation between average education years (AEY) and Gross Domestic Product per capita (GDP/C), and finds that there is a significant positive correlation between AEY and GDP/C in provinces of China. Furthermore, from the perspective of spatial distribution and geostatistics, this research analyzes the correlation between AEY and the distribution of poor counties, revealing the inherent connection between education and poverty. Based on the data processing of nighttime light remote sensing images, this research adopts the machine learning method of random forest to extract the distribution status of spatio-temporal sequences for poor counties. Through the analysis, it is found that poor counties are characterized by centralized distribution and spatial autocorrelation spatially, and the number of poor counties decreases year by year in temporal evolution. On this basis, we analyze the correlation between education levels and the distribution of poor counties. It is found that, on the spatial scale, AEY in poor counties is relatively low, while AEY in non-poor counties is relatively high, showing a significant negative correlation between the two. On the temporal scale, the number of poor counties gradually decreased from 2000 to 2010, and at the same time, the education levels of poor counties also gradually improved. Finally, from the perspective of improving education levels to promote poverty elimination, we analyze the main factors affecting education using Principal Component Analysis (PCA) and other methods and obtain a regression model. This research proposes the Linear and Residual Integration Model (LRIM) to more accurately predict AEY in each province in 2020 based on historical data, and identifies the regions with low AEY as key regions for targeted poverty alleviation through education (TPAE) in the future. This research provides a decision-making basis to achieve TPAE means, helping to achieve the victory of the national education poverty elimination battle.
Highlights
Li and Ye [4] believed that targeted poverty alleviation (TPA) is to carry out precision poverty alleviation in different poor regions according to scientific standards, and introduced a dynamic development mechanism for poverty alleviation according to the local actual condition
We further explore the influencing factors of education levels and adopt the Principal Component Analysis (PCA) method to extract the main factors to obtain the regression model
Education levels are divided into five levels: illiteracy, elementary school education level (EEL), junior high school, high school education level (JHEL) and technical secondary school education level (HEL), university and above education level (UEL)
Summary
The Chinese government innovatively proposed a strategy for targeted poverty alleviation (TPA) and poverty elimination [1,2] in 2013 and implemented a major policy of TPA in 2015. TPA is a poverty alleviation method that accurately identifies, assists and manages poverty alleviation objects based on the actual condition of different poor regions and different poor peasant households [3]. There are different understandings for the definition of TPA. Li and Ye [4] believed that TPA is to carry out precision poverty alleviation in different poor regions according to scientific standards, and introduced a dynamic development mechanism for poverty alleviation according to the local actual condition
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