地理学报 ›› 2015, Vol. 70 ›› Issue (6): 993-1007.doi: 10.11821/dlxb201506012

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中国农村多维贫困地理识别及类型划分

刘艳华1,2(), 徐勇2()   

  1. 1. 浙江财经大学经济与国际贸易学院,杭州 310018
    2. 中国科学院地理科学与资源研究所,北京 100101
  • 收稿日期:2014-08-07 修回日期:2015-01-31 出版日期:2015-06-20 发布日期:2015-07-16
  • 作者简介:

    作者简介:刘艳华(1984-), 女, 河南辉县人, 博士, 讲师, 主要从事农村贫困、区域经济和可持续发展等研究。E-mail:yhliu2014@zufe.edu.cn

  • 基金资助:
    国家自然科学基金项目(41171449);中国科学院知识创新重点部署项目(KZZD-EW-06)

Geographical identification and classification of multi-dimensional poverty in rural China

Yanhua LIU1,2(), Yong XU2()   

  1. 1. School of Economics and International Trade, Zhejiang University of Finance and Economics, Hangzhou 310018, China
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2014-08-07 Revised:2015-01-31 Online:2015-06-20 Published:2015-07-16
  • Supported by:
    National Natural Science Foundation of China, No.41171449;Knowledge Innovation Project of the Chinese Academy of Sciences, No.KZZD-EW-06

摘要:

发展多维贫困度量方法和提高贫困识别精准度是近年国际贫困研究中的热点领域,也是中国未来提高农村扶贫实践质量和效率所面临的关键问题。本文借鉴国际上关于脆弱性—可持续生计框架模型在贫困研究中的学术思想,通过建立农村多维贫困测度指标体系和地理识别方法,对中国农村开展了县域尺度的贫困地理识别,并与单维度收入贫困以及国家最新认定的扶贫开发重点县进行了对比分析,最后对识别的多维贫困县按扶贫措施相似性进行了类型划分。研究结果表明:655个县级单元被识别为多维度贫困县,涉及农村人口1.41亿人;空间分布集中连片特征显著,青藏高原及其周边的南疆三地州、黄土高原西部、滇西—川西高山峡谷区为最大的连片贫困区;有71.79%的国家重点贫困县与识别结果重叠,与国家重点贫困县对比,识别的多维贫困县在各单维度和综合维度都处于更劣势水平;多维贫困县被划分为金融资本缺乏型、人力资本缺乏型、基础建设缺乏型、金融基建兼缺型、人力基建兼缺型、生计途径缺乏型、生存条件缺乏型和发展条件缺乏型8种类型。

关键词: 多维贫困, 地理识别, 脆弱性, 可持续生计, 中国农村

Abstract:

Developing methods for measuring multi-dimensional poverty and improving the accuracy of poverty identification have been the hot topics in international poverty research for decades. In light of the academic thoughts of the vulnerability and sustainable livelihood analysis framework, this paper establishes an index system and a method for geographical identification of multi-dimensional poverty, and carries out a county-level identification in rural China. Furthermore, this study makes a comparison between the identification result, income poverty and the latest designated poor regions by the Chinese government. At last, the identified multi-dimensional poor counties are classified by the similarity of poverty reduction measures. The results show that: (1) Taking the vulnerability and sustainable livelihood analysis framework proposed by DFID as theoretical basis, we build an index system of multi-dimensional poverty identification to reflect the farmers' livelihoods that multiple factors work on. It is feasible to develop a composite Multi-dimensional Development Index (MDI) for the integrated method of geographical identification of multi-dimensional poverty in rural China. (2) A total of 655 counties are identified as multi-dimensional poor counties. They are concentrated and jointly distributed in space, in which the Tibetan Plateau and its neighboring areas of three prefectures in southern Xinjiang, western Loess Plateau, mountainous and gully areas in western Yunnan and Sichuan, are suffering greatly from poverty. Besides, poor counties are mainly in Wumeng-Daliang mountainous areas, Yunnan-Guizhou-Guangxi rocky desertification areas, border mountainous areas in Yunnan, Wuling mountainous areas, Qinling-Daba mountainous areas, Shanxi-Shaanxi gully areas and Yanshan-Taihang mountainous areas. (3) In comparison to the latest designated poor counties, this paper targets at poor counties with more disadvantages at both single and multiple dimensions. Some 71.79% of designated poor counties overlap with identified poor counties. By contrast, the majority of the designated poor counties located in mountainous areas of central or eastern China do not belong to identified poor counties because of much less disadvantage/deprivation dimensions. However, the identified poor counties, which are mainly distributed in marginal areas of plateau or mountainous areas in western China, and suffering from multiple dimensions of disadvantages and deprivations, are not included in the designated poor counties. (4) According to the disadvantage/deprivation situation of different dimensions, multi-dimensional poor counties are classified into eight types, i.e., lack of financial capital, lack of human capital, lack of infrastructure, lack of both financial capital and infrastructure, lack of both human capital and infrastructure, lack of means/strategies of livelihoods, lack of living condition, and lack of development condition.

Key words: multi-dimensional poverty, geographical identification, vulnerability, sustainable livelihoods, rural China