In terms of current life style, living and production conditions and hygienic and educational condition, we select 8 indices, such as annual net income of farmers per capita, annual grain yield per capita, total power...In terms of current life style, living and production conditions and hygienic and educational condition, we select 8 indices, such as annual net income of farmers per capita, annual grain yield per capita, total power of agricultural machinery per capita, dropout rate of school children and so on, to establish index system of determining the poor village in North Jiangsu. By selecting Lianqun Village in Suining County of Xuzhou City, Mawa Village in Siyang County of Suqian City, Chuanxing Village in Guanyun County of Lianyungang City, Xiaozhu Village in Hongze County of Huai'an City, Fengda Village in Xiangshui County of Yancheng City as the representative villages, after the discussion and consultation of the masses and the village cadres of all villages, we get the measuring results of weight. Through the field survey, investigation and interview in the selected regions, we get the relevant data, and then we conduct standardization processing, so as to get the index value that can comprehensively reflect the characteristics of poverty. According to the index data that have been standardized, by using participatory poverty index formula for calculation, we get the values that can explain the poverty degree of the respondents. We sequence the representative poor villages in this region according to the poverty degree from high to low, and the result is as follows: Mawa Village, ianqun Village, Chuanxing Village, Xiaozhu Village, and Fengda Village. It indicates that in terms of the operability of theory and technique, the participatory poverty index can better recognize the poor villages, so that it lays solid foundation for rationally and effectively using the limited poverty alleviation resources.展开更多
Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressi...Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressive(PVAR) model that combines the human development approach with the global indicator framework for Sustainable Development Goals(SDGs) to identify the poverty-causing and the poverty-reducing factors in China. The aim is to measure the multidimensional poverty index(MPI) of China’s provinces from 2007 to 2017, and use the exploratory spatio-temporal data analysis(ESTDA) method to reveal the characteristics of the spatio-temporal dynamics of multidimensional poverty. The results show the following:(1) The poverty-causing factors in China include the high social gross dependency ratio and crop-to-disaster ratio, and the poverty-reducing factors include the high per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 people, rate of participation in the new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita research and development(R&D) expenditure, and funding per capita for cultural undertakings.(2) From 2007 to 2017, provincial income poverty(IP), health poverty(HP), cultural poverty(CP), and multidimensional poverty have been significantly reduced in China, and the overall national poverty has dropped by 5.67% annually. there is a differentiation in poverty along different dimensions in certain provinces.(3) During the study period, the local spatial pattern of multidimensional poverty between provinces showed strong spatial dynamics, and a trend of increase from the eastern to the central and western regions was noted. The MPI among provinces exhibited a strong spatial dependence over time to form a pattern of decrease from northwestern and northeastern China to the surrounding areas.(4) The spatio-temporal networks of multidimensional poverty in adjacent provinces were mainly negatively correlated, with only 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. These results have important reference value for the implementation of China’s poverty alleviation strategy.展开更多
基金Supported by Environment and Development Innovative Experiment Project of Xuzhou Normal University (HJ201015Y)
文摘In terms of current life style, living and production conditions and hygienic and educational condition, we select 8 indices, such as annual net income of farmers per capita, annual grain yield per capita, total power of agricultural machinery per capita, dropout rate of school children and so on, to establish index system of determining the poor village in North Jiangsu. By selecting Lianqun Village in Suining County of Xuzhou City, Mawa Village in Siyang County of Suqian City, Chuanxing Village in Guanyun County of Lianyungang City, Xiaozhu Village in Hongze County of Huai'an City, Fengda Village in Xiangshui County of Yancheng City as the representative villages, after the discussion and consultation of the masses and the village cadres of all villages, we get the measuring results of weight. Through the field survey, investigation and interview in the selected regions, we get the relevant data, and then we conduct standardization processing, so as to get the index value that can comprehensively reflect the characteristics of poverty. According to the index data that have been standardized, by using participatory poverty index formula for calculation, we get the values that can explain the poverty degree of the respondents. We sequence the representative poor villages in this region according to the poverty degree from high to low, and the result is as follows: Mawa Village, ianqun Village, Chuanxing Village, Xiaozhu Village, and Fengda Village. It indicates that in terms of the operability of theory and technique, the participatory poverty index can better recognize the poor villages, so that it lays solid foundation for rationally and effectively using the limited poverty alleviation resources.
基金National Natural Science Foundation of China,No.71974070, No.41501593National Key R&D Project,No.2016YFA0602500Humanities and Social Sciences Foundation of Ministry of Education of China,No.19YJCZH068。
文摘Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressive(PVAR) model that combines the human development approach with the global indicator framework for Sustainable Development Goals(SDGs) to identify the poverty-causing and the poverty-reducing factors in China. The aim is to measure the multidimensional poverty index(MPI) of China’s provinces from 2007 to 2017, and use the exploratory spatio-temporal data analysis(ESTDA) method to reveal the characteristics of the spatio-temporal dynamics of multidimensional poverty. The results show the following:(1) The poverty-causing factors in China include the high social gross dependency ratio and crop-to-disaster ratio, and the poverty-reducing factors include the high per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 people, rate of participation in the new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita research and development(R&D) expenditure, and funding per capita for cultural undertakings.(2) From 2007 to 2017, provincial income poverty(IP), health poverty(HP), cultural poverty(CP), and multidimensional poverty have been significantly reduced in China, and the overall national poverty has dropped by 5.67% annually. there is a differentiation in poverty along different dimensions in certain provinces.(3) During the study period, the local spatial pattern of multidimensional poverty between provinces showed strong spatial dynamics, and a trend of increase from the eastern to the central and western regions was noted. The MPI among provinces exhibited a strong spatial dependence over time to form a pattern of decrease from northwestern and northeastern China to the surrounding areas.(4) The spatio-temporal networks of multidimensional poverty in adjacent provinces were mainly negatively correlated, with only 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. These results have important reference value for the implementation of China’s poverty alleviation strategy.
