Özet:

For studies examining education's role in China's effort to reduce poverty, an understanding of the spatial and temporal dynamics of that country's poverty is crucial. The objective of this study is to collect MPI data for all provinces in China between 2007 and 2017, and then apply exploratory Spatiotemporal data analysis (ESTDA) to better understand the spatial and temporal dynamics of poverty in China. This research uses a panel vector autoregression (PVAR) model that integrates the human development method with the global indicator framework for Sustainable Development Goals (SDGs) to analyze the goal of China's general pro-education to identify the causes and solutions of the country's widespread economic hardship. The findings indicate the following: (i) Extreme poverty in China is caused by the country's high social gross participation rate and harvest ratio and reduced by its high per capita GDP, per capita social security expenditure, per capita global health expenditure, percentage of involvement in setting up a new collaborating health care scheme, per head of population education spending, and the total amount of institutions. (ii) From "2007 to 2017, province income poverty (IP), health poverty (HP), cultural poverty (CP), and multidimensional poverty decreased in China, while national poverty fell by 5.67% yearly".(iii) During the research period, the local geographical pattern of multidimensional poverty between provinces exhibited substantial spatial dynamics and a rise from the east to the center and west. The MPI decreased from northern and northeastern China to the surrounding regions throughout time.

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