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中国县域农村贫困化分异机制的地理探测与优化决策
引用本文:刘彦随,李进涛.中国县域农村贫困化分异机制的地理探测与优化决策[J].地理学报,2017,72(1):161-173.
作者姓名:刘彦随  李进涛
作者单位:1. 北京师范大学地理科学学部,北京 1008752. 中国科学院精准扶贫评估研究中心,北京1001013. 中国科学院地理科学与资源研究所,北京 100101
基金项目:国家社会科学基金重大项目(15ZDA021);国家自然科学基金重点项目(41130748)
摘    要:农村贫困化是长期以来备受世界各国关注的焦点问题,消除贫困,实现共同富裕是中国全面建设小康社会的重大任务,科学揭示农村贫困化地域分异机制,成为实施国家精准扶贫战略的重要课题。论文以河北省阜平县为典型案例,运用地理探测器、多元线性回归等模型方法,诊断出县域农村贫困化分异的主导因素,揭示了农村贫困化分异的动力机制,提出了不同贫困化地域类型的扶贫政策与模式。结果表明:① 影响农村贫困化分异的主导因素包括地面坡度、人均耕地资源、到主要干道距离、到县城中心距离等,各因素对贫困发生率分异的决定力分别为0.14、0.15、0.15、0.17;② 不同类型区域农村贫困化的分异机制存在明显差异,可归纳为自然环境约束型、资源丰度约束型、交通区位约束型、经济区位约束型等四大类型;③ 根据阜平县各乡镇核心主导因素,进一步划分出单因素、双因素和多因素影响区域,县域整体呈现出以横向中心为双因素影响区,两侧为单因素与多因素并存的多极核心主导因素影响的农村贫困发生分异区;④ 不同驱动机制下的县域扶贫开发亟需因地制宜、尊重科学、讲求实效,有序推进精准扶贫与城乡发展一体化战略。

关 键 词:农村贫困化  地域分异  主导因素  地理探测器  精准扶贫  阜平县  
收稿时间:2016-10-18
修稿时间:2017-01-06

Geographic detection and optimizing decision of the differentiation mechanism of rural poverty in China
Yansui LIU,Jintao LI.Geographic detection and optimizing decision of the differentiation mechanism of rural poverty in China[J].Acta Geographica Sinica,2017,72(1):161-173.
Authors:Yansui LIU  Jintao LI
Institution:1.Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China2. Center for Assessment and Research on Targeted Poverty Alleviation, CAS, Beijing 100101, China3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101
Abstract:Rural poverty has long aroused attention from countries around the world, and eliminating poverty and achieving realize common prosperity is an important mission to build the well-off society in an all-round way. Scientifically revealing the regional differentiation mechanism of rural poverty has become an important issue of implementation of national poverty alleviation strategy. This paper, taking Fuping County of Hebei Province as a typical case, diagnoses the dominant factors of differentiation of rural poverty and reveals the dynamic mechanism of rural poverty differentiation by using the Geodetector model and multiple linear regressions, and puts forward the poverty alleviation policies and models for different poverty regions. The result shows that the dominant factors affecting rural poverty differentiation include slope, elevation, per capita arable land resources, distance to the main roads and distance to the center of county, and their power determinant value to poverty incidence differentiation are 0.14, 0.15, 0.15, and 0.17. These factors affect the occurrence of poverty from different aspects and their dynamic mechanism is also different. Among various factors, the slope and per capita arable land resources affect the structure and mode of agricultural production, while distance to the main roads and distance to the center of county have influence on the relationship between the interior and exterior of the region. There are significant differences in the four types identified of regional rural poverty, namely, environment constrained region mainly affected by slope (seven towns), resource oriented region mainly affected by per capita arable land (seven towns), area dominated by traffic location affected by distance to the main roads (three towns), and economic development leading area mainly affected by distance to the center of county (four towns). Then, Fuping County is divided into single core, dual core and multi-core area according to the number of core elements of the township. The county has shown a multi differentiation of rural poverty with a horizontal center of dual core area, and both sides have a single core and multi-core, which are affected by different dominant factors. Finally, this paper suggests that policy of targeted poverty alleviation should take science and technology as the foundation and form innovation of targeted poverty alleviation according to the core dominant factors of the differentiation mechanism of rural poverty. The county's poverty alleviation and development under different driving mechanisms need orderly promotion of poverty alleviation and integration of urban and rural development strategy with adjusting measures to local conditions, respecting for science, and stressing practical results.
Keywords:rural poverty  regional differentiation  leading factors  geographical detector  targeted poverty alleviation  Fuping County  
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