Identification and poverty alleviation pathways of multidimensional poverty and relative poverty at county level in China

被引:0
作者
Xu L. [1 ]
Deng X. [2 ]
Jiang Q. [1 ,2 ,3 ]
Ma F. [1 ]
机构
[1] School of Soil and Water Conservation, Beijing Forestry University, Beijing
[2] Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing
[3] Key Laboratory of Soil and Water Conservation and Desertification Prevention, Beijing Forestry University, Beijing
来源
Dili Xuebao/Acta Geographica Sinica | 2021年 / 76卷 / 06期
基金
中国国家自然科学基金;
关键词
Coupling coordination model; Multidimensional poverty; Night light index; Relative poverty; Targeted poverty alleviation;
D O I
10.11821/dlxb202106010
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
China has secured a comprehensive victory in its fight against poverty. After 2020, the focus of China's battle against poverty will shift from relative poverty to absolute poverty, and from poverty in terms of income to that in other dimensions. This study applies the county as the basic unit and 31 provinces (autonomous regions/municipalities) of China as the study area. It identifies poverty levels in each county by the average night light index and the county multidimensional development index. Using the multidimensional relative poverty identification method based on the sustainable models, we analyzed the current situation of China's poverty from two aspects-multidimensional poverty and relative poverty. Finally, we explore the poverty alleviation pathways in four aspects, namely, education poverty alleviation, agricultural poverty alleviation, industrial poverty alleviation, and tourism poverty alleviation. The results revealed that nearly 60% of counties in China were primarily in multidimensional relative poverty, most of which were classified as multidimensional relatively light poverty counties. According to the average night light index and the county multidimensional development index, the numbers of poverty counties in China were 602 and 611, respectively; as of 2018, the proportions of national poverty-stricken counties accounted for 63% and 79%, respectively. The result implied that the county multidimensional development index had a more comprehensive poverty identification mechanism. Moreover, the multidimensional poverty counties were concentrated in Gansu, Sichuan, and Yunnan. Meanwhile, the development of Jilin, Liaoning, and Heilongjiang should not be overlooked. From the viewpoint of pathways, 414, 172, 442, and 298 poverty counties were suitable to industrial poverty alleviation, education poverty alleviation, tourism poverty alleviation, and agricultural poverty alleviation, respectively. Some 61% of counties had more poverty-causing factors, implying that multidimensional poverty alleviation is suitable in most of the poverty-stricken counties. These conclusions can provide a crucial scientific basis for ensuring sustainable poverty alleviation. © 2021, Science Press. All right reserved.
引用
收藏
页码:1455 / 1470
页数:15
相关论文
共 30 条
[1]  
Huang Chengwei, The CPC's governance capability in poverty alleviation, China Leadership Science, 2020, 3, pp. 23-27
[2]  
Wang Shufang, Zhou Wei, Research status and development trend of poverty alleviation in deeply impoverished areas, Rural Economy and Science, 31, 7, pp. 180-182, (2020)
[3]  
Chen Zhigang, Bi Jieying, Wu Guobao, Et al., Post-2020 urban integrative poverty reduction strategy: Development status, evolution, new vision and key areas, Chinese Rural Economy, 1, pp. 2-16, (2019)
[4]  
Yang Guotao, Wang Guangjin, Estimation and simulation of China's rural poverty: 1995-2003, China Population, Resources and Environment, 15, 6, pp. 30-34, (2005)
[5]  
Liu Yansui, Zhou Yang, Liu Jilai, Regional differentiation characteristics of rural poverty and targeted poverty alleviation strategy in China, Bulletin of Chinese Academy of Sciences, 31, 3, pp. 269-278, (2016)
[6]  
Zeng Yongming, Zhang Guo, Spatial simulating in regional rural poverty based on GIS and BP neural network: A new appraisement method on regional rural poverty, Geography and Geo-Information Science, 27, 2, pp. 70-75, (2011)
[7]  
Zhao X Z, Yu B L, Liu Y, Et al., Estimation of poverty using random forest regression with multi-source data: A case study in Bangladesh, Remote Sensing, 11, 4, (2019)
[8]  
Wu Peng, Li Tongsheng, Li Weimin, Spatial differentiation and influencing factors analysis of rural poverty at county scale: A case study of Shanyang county in Shaanxi Province, China, Geographical Research, 37, 3, pp. 593-606, (2018)
[9]  
Luo Qing, Fan Xinsheng, Gao Genghe, Et al., Spatial distribution of poverty village and influencing factors in Qinba Mountains, Economic Geography, 36, 4, pp. 126-132, (2016)
[10]  
Okwi P O, Ndeng' E G, Kristjanson P, Et al., Spatial determinants of poverty in rural Kenya, PNAS, 104, 43, pp. 16769-16774, (2007)