Acta Geographica Sinica ›› 2020, Vol. 75 ›› Issue (4): 753-768.doi: 10.11821/dlxb202004007

• Agriculture and Rural Geography • Previous Articles     Next Articles

Theory and measurement of regional multidimensional poverty

LI Xunhuan1,2,3, ZHOU Yang1,2,3(), CHEN Yufu1,2,3   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. Center for Assessment and Research on Targeted Poverty Alleviation, CAS, Beijing 100101, China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-05-29 Revised:2020-02-10 Online:2020-04-25 Published:2020-04-22
  • Contact: ZHOU Yang E-mail:zhouyang@igsnrr.ac.cn
  • Supported by:
    National Natural Science Foundation of China(41871183);National Natural Science Foundation of China(41601172)

Abstract:

Poverty includes regional poverty and individual poverty, both of which are featured by multidimensional concept. Regional multidimensional poverty (RMP) is a major theme and content in poverty geography. Because of harsh natural environment, vulnerable economy and inadequate public services, severely impoverished areas (SIAs) are typical and highlighted areas of RMP, which have been the biggest obstacle to poverty alleviation in China. Based on the theory of spatial poverty, this paper defines the notion of impoverished areal system (IAS) and regional multidimensional poverty (RMP), explores their internal connections and proposes the evaluation indictors and measurement method for RMP. Taking 334 severely impoverished counties as research samples, we analyze the multidimensional poverty patterns of SIAs in 2016 by BP neural network model and exploratory spatial data analysis (ESDA). Results show that: (1) RMP is an external manifestation of the coupling imbalance of "human", "environment" and "industry" in the evolution of IAS. It reveals regional disadvantages in natural environment, economic development and social welfare, corresponding to natural poverty, economic poverty and welfare poverty, respectively. (2) The most severely impoverished county, with the poorest services and infrastructure, is found in the Qinghai-Tibet Plateau. From a single dimension of regional poverty, the Welfare Poverty Index (WPI) > Economic Poverty Index (EPI) > Natural Poverty Index (NPI) in the SIAs, whose average is 2.77, 2.66 and 1.89, respectively, indicating that the lack of social welfare and public services for the poor has become the prominent problem in the SIAs. From the perspective of multidimensional poverty, the Qinghai-Tibet Plateau is a high agglomeration region or "hot spot" of RMP, which is significantly higher than other areas in terms of natural poverty, economic poverty and welfare poverty. (3) Both RMP and individual multidimensional poverty are effective measures of poverty targeting. Their matching coefficient (M) can help us to judge the poverty status of some specific areas, e.g., RMP is superior to individual multidimensional poverty in the remote areas, extremely fragile ecological environment and obvious regional disadvantage, where the matching coefficient (M) is higher. RMP can more objectively reflect the true level of geographical capitals, effectively target poor areas and identify determinant impoverishing factors.

Key words: regional multidimensional poverty, poverty geography, impoverished areal system, severely impoverished areas, regional sustainable development