Journal of Geographical Sciences ›› 2021, Vol. 31 ›› Issue (12): 1715-1736.doi: 10.1007/s11442-021-1919-8

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Identification and alleviation pathways of multidimensional poverty and relative poverty in counties of China

XU Lidan1(), DENG Xiangzheng2, JIANG Qun’ou1,2,3,*(), MA Fengkui1   

  1. 1. School of Soil and Water Conservation, Beijing Forestry University, Beijing 100038, China
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    3. Key Laboratory of Soil and Water Conservation and Desertification Prevention, Beijing Forestry University, Beijing 100083, China
  • Received:2021-07-20 Accepted:2021-09-28 Online:2021-12-25 Published:2022-02-25
  • Contact: JIANG Qun’ou E-mail:xlidan@bjfu.edu.cn;jiangqo.dls@163.com
  • About author:Xu Lidan (1997-), specialized in 3S technology application. E-mail: xlidan@bjfu.edu.cn
  • Supported by:
    Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23070400);National Natural Science Foundation of China(41901234);National Natural Science Foundation of China(51909052)

Abstract:

To realize efficient and sustainable poverty alleviation, this study firstly investigated the identification of multidimensional poverty and relative poverty, and then explored relevant poverty alleviation pathways. Poverty levels in 31 provinces including the autonomous regions and municipalities of China were identified at the county level using the average nighttime light index (ANLI), county multidimensional development index (CMDI), and a method combining multidimensional poverty index and relative poverty standards. Poverty alleviation pathways for poverty-stricken counties were explored from the aspects of industry, education, tourism and agriculture. The results revealed that nearly 60% of counties in China were primarily under relative poverty, most of which were corresponded to light relative poverty. In terms of ANLI and CMDI, 63% and 79% of the national poverty-stricken counties, as of 2018, could be identified, suggesting that CMDI had a higher performance for identifying poverty at the county level. In terms of poverty alleviation pathways, 414, 172, 442, and 298 poverty-stricken counties were receptive to industry poverty alleviation, education poverty alleviation, tourism poverty alleviation, and agriculture poverty alleviation, and 61% of counties had more poverty-causing factors, implying that multidimensional poverty alleviation is suitable in most of the counties.

Key words: nighttime light index, multidimensional poverty, relative poverty, coupling coordination model, targeted poverty alleviation