地理学报 ›› 2020, Vol. 75 ›› Issue (4): 753-768.doi: 10.11821/dlxb202004007

• 农业与乡村地理 • 上一篇    下一篇

区域多维贫困测量的理论与方法

李寻欢1,2,3, 周扬1,2,3(), 陈玉福1,2,3   

  1. 1. 中国科学院地理科学与资源研究所 中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
    2. 中国科学院精准扶贫评估研究中心,北京 100101
    3. 中国科学院大学,北京 100049
  • 收稿日期:2019-05-29 修回日期:2020-02-10 出版日期:2020-04-25 发布日期:2020-04-22
  • 通讯作者: 周扬 E-mail:zhouyang@igsnrr.ac.cn
  • 作者简介:李寻欢(1998-), 男, 重庆彭水人, 硕士生, 主要从事农村减贫与发展研究。E-mail: lixh.19s@igsnrr.ac.cn
  • 基金资助:
    国家自然科学基金项目(41871183);国家自然科学基金项目(41601172)

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)

摘要:

贫困包括区域贫困和个体贫困,两者均具有多维属性。区域多维贫困是贫困地理学研究的重要内容,深度贫困地区是区域多维贫困的集中表现,是当前和未来脱贫攻坚的贫中之贫、困中之困、坚中之坚。本文以空间贫困理论为基础,界定了贫困地域系统和区域多维贫困的概念,并探究了区域多维贫困测度指标体系与评估方法。据此,以中国334个深度贫困县为研究对象,运用BP神经网络模型和ESDA技术刻画了中国深度贫困地区的多维贫困空间格局。结果表明:① 区域多维贫困是贫困地域系统发展演化过程中“人”“地”“业”三个核心要素耦合失调的一种外在表现形式,是特定地域在生态环境劣势、经济劣势、社会福利劣势上的综合表现,包括生态贫困、经济贫困和福利贫困3个维度。② 深度贫困地区最“深”的地方在青藏高原,最“深”的短板在公共服务和基础设施。从单一维度的贫困指数来看,深度贫困地区的福利贫困指数(WPI)>经济贫困指数(EPI)>生态贫困指数(NPI),三者平均值分别为2.77、2.66、1.89,贫困人口社会福利供给不足和公共服务短缺是深度贫困地区最突出的问题;从多维贫困指数来看,青藏高原是区域多维贫困指数(RMP)的高集聚区或热点区,生态贫困、经济贫困和福利贫困程度均显著高于其他地区。③ 区域多维贫困指数能较好地揭示特定区域的贫困状况。在地理位置偏远、生态环境极其脆弱、区域劣势突出的地区,区域贫困和个体贫困在空间上高度重叠。

关键词: 区域多维贫困, 贫困地理学, 贫困地域系统, 深度贫困地区, 区域可持续发展

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