中国农村多维贫困地理识别及类型划分  

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

作　　者：刘艳华[1,2] 徐勇[2] 

机构地区：[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

出　　处：《地理学报》2015年第6期993-1007,共15页Acta Geographica Sinica

基　　金：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)~~

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。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, QinlingDaba 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 o

关 键 词：多维贫困 地理识别 脆弱性 可持续生计 中国农村 

分 类 号：F323.8[经济管理—产业经济]