Abstract:

Exploring the poverty supervision evaluation indicator system and the dynamic characteristics of spatio-temporal interaction of poverty among regions are of great significance to the current research on sustainable poverty reduction in China. From the perspective of development geography, this paper introduces panel vector autoregressive (PVAR) model and identifies poverty-causing and poverty-reducing factors in China in combination with human development approach and global indicator framework for the SDGs, so as to measure multidimensional poverty index. Then it uses exploratory spatio-temporal data analysis (ESTDA) method to reveal the spatio-temporal interaction characteristics of multidimensional poverty. The results show that: (1) The poverty-causing factors of China's current poverty monitoring and evaluation include the crop-to-disaster ratio and social gross dependency ratio, the poverty-reducing factors include per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 persons, participation rate of new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita scientific research and experimental development (R&D) expenditure, and per capita funding for cultural undertakings. (2) From 2007 to 2017, provincial income poverty, health poverty, cultural poverty and the multidimensional poverty have been significantly improved, with the national comprehensive poverty level declining by an average of 5.67% annually, and the poverty of different dimensions in some provinces is differentiated. (3) During the study period, the local spatial pattern of multidimensional poverty between provinces had strong spatial dynamics and showed an increasing trend from the eastern region to the central and western regions; the multidimensional poverty index among provinces shows a strong spatial dependence over time, forming a pattern of decreasing from the northwestern and northeastern regions to the surrounding areas. (4) The spatio-temporal network of multidimensional poverty interaction in neighboring provinces is mainly negatively correlated with Shaanxi and Henan, Shaanxi and Ningxia, Qinghai and Gansu, Hubei and Anhui, Sichuan and Guizhou, and Hainan and Guangdong, forming spatially strong cooperative poverty reduction relationships. The research results have important reference value for the current implementation of China's strategy on targeted measures in poverty alleviation, especially the prevention of poverty-returning after 2020.

Key words: development geography, multidimensional poverty, poverty measurement, spatio-temporal interaction, collaborative poverty reduction