地理学报 ›› 2017, Vol. 72 ›› Issue (3): 545-557.doi: 10.11821/dlxb201703014

• 土地利用与环境变化 • 上一篇    下一篇

县域空间贫困的地理识别研究——以宁夏泾源县为例

刘小鹏(), 李永红, 王亚娟, 郭占军, 郑芳   

  1. 宁夏大学资源环境学院,银川 750021
  • 收稿日期:2016-06-01 修回日期:2016-11-28 出版日期:2017-03-15 发布日期:2017-05-03
  • 作者简介:

    作者简介:刘小鹏(1973-), 男, 满族, 宁夏海原人, 博士, 教授, 中国地理学会会员(S110005070M), 主要研究方向为人文地理与城乡规划。E-mail: nxdxlxp@163.com

  • 基金资助:
    国家自然科学基金项目(41261021)

Geographical identification of spatial poverty at county scale

Xiaopeng LIU(), Yonghong LI, Yajuan WANG, Zhanjun GUO, Fang ZHENG   

  1. School of Resources and Environment, Ningxia University, Yinchuan 750021, China
  • Received:2016-06-01 Revised:2016-11-28 Online:2017-03-15 Published:2017-05-03
  • Supported by:
    National Natural Science Foundation of China, No.41261021

摘要:

空间贫困研究是对多维贫困理论的丰富和发展,创新空间贫困度量方法是精准配置扶贫资源和提高农村扶贫质量的关键。本文借鉴国际空间贫困研究的基本理论思想,总结出了空间贫困三维结构分析框架,构建了县域空间贫困指标体系和地理识别方法,以宁夏泾源国家扶贫开发重点县为例,开展了空间贫困地理识别。研究发现:① 不同自然地理区SPI排序为侵蚀堆积河谷平川区(均值1.571)>剥蚀构造丘陵区(均值-0.199)>侵蚀构造石山区(均值-0.334),表明侵蚀构造石山区贫困程度最大,是减贫重点区域,其中3个自然地理区空间贫困的共同特点是存在经济劣势(C4~C7、C17),主要差异是侵蚀堆积河谷平川区生态劣势(C25、C16)>经济劣势(C5)>位置劣势(C20)>政治劣势(C8);剥蚀构造丘陵区经济劣势(C5~C7、C17)>位置劣势(C18~C20)>生态劣势(C16、C23~C25)>政治劣势(C8);侵蚀构造石山区经济劣势(C4~C7、C17)>政治劣势(C8)>位置劣势(C18~C20)>生态劣势(C16、C21~C25)。② 不同民族村SPI排序为汉族村(均值1.484)>回族村(均值1.262)>回汉混居村(均值-1.033),表明县域尺度上回汉混居村是减贫重点村。不同民族村空间贫困的共同特征是由于距离最近市场远(C17)形成的经济劣势,以及人均耕地面积(C23)和农作物特别经济作物种植面积小(C24)形成的生态劣势。不同民族村空间贫困的主要差异在于汉族村人居环境差和灾损率高形成的生态劣势,以及贷款不足(C5)导致的经济劣势突出,回族村主要因文化知识素质较低(C12、C13)导致进入市场的机会成本高而形成经济劣势,回汉混居村因汉族和回族生产生活方式差异,造成扶贫资源配置困难,农户对扶贫政策满意率最低(C8),反映该类村空间贫困的政治劣势最为显著。③ 同一自然地理区域不同民族村空间贫困存在显著差异。侵蚀堆积河谷平川区汉族村、回族村和回汉混居村SPI均值分别为0.526、2.557和1.644,表明该类型区汉族村贫困程度高,经济劣势显著(C5);剥蚀构造丘陵区回族村和回汉混居村的SPI均值分别约为0.321和-1.934,表明该类型区回汉混居村贫困程度较高,经济劣势(C2、C6、C7)和生态劣势(C16)突出;侵蚀构造石山区汉族村、回族村和回汉混居村的SPI均值分别为1.031、-0.029和-0.842,表明该类型区回汉混居村贫困程度亦高,经济劣势(C5~C7、C17)、政治劣势(C8)、位置劣势(C18~C20)和生态劣势(C16、C25)并存。因此,侵蚀堆积河谷平川区的汉族村、剥蚀构造丘陵区和侵蚀构造石山区的回汉混居村是减贫重点村。

关键词: 空间贫困, 地理识别, 地理资本, 空间贫困指数, 县域

Abstract:

The innovation of spatial poverty measurement method is the key to precisely allocate poverty alleviation resources and improve the quality of rural poverty alleviation. This paper summarized the three-dimensional analysis framework of spatial poverty and constructed the spatial poverty index system at county scale and the method of geographical identification. With Jingyuan county as a study case, the geographical identification of spatial poverty was carried out. The results can be obtained as follows. (1) Spatial poverty index (SPI) in different geographical areas is listed in the order: Plain Area in Valleys Formed by Erosion and Deposition of Rivers (PAVFEDR) (mean value 1.571) > Denudation Structure in Hilly Regions (DSHR) (mean value -0.199) > Erosion Structure in Mountainous Regions (ESMR) (mean value -0.334), indicating that the highest poverty degree is in ESMR, which should be the main area of poverty alleviation. The common feature of spatial poverty in the three geographical areas is economic disadvantage (C4-C7, C17). The main differences are: for PAVFEDR, ecological disadvantage (C25, C16) > economic disadvantage (C5) > location disadvantage (C20) > political disadvantage (C8); for DSHR, economic disadvantage (C5-C7, C17) > location disadvantage > (C18-C20) > ecological disadvantage (C16, C23-C25) > political disadvantage (C8); for ESMR, economic disadvantage (C4-C7, C17) > political disadvantage (C8) > location disadvantage (C18-C20) > ecological disadvantage (C16, C21-C25). (2) The rank of SPI for different ethnic villages is as follows: Han villages (mean value 1.484) > Hui villages (mean value 1.262) > Hui-Han mixed villages (mean value 1.033), indicating that Hui-Han mixed villages at county scale should be the key villages for poverty alleviation. The common feature of spatial poverty in different ethnic villages is the economic disadvantage resulting from the long distance to the nearest market (C17), as well as the ecological disadvantage due to less arable land per capita (C23) and small crop area, especially economical crop area (C24). The main differences of spatial poverty in different ethnic villages are ecological disadvantage resulting from poor living conditions and high disaster loss rate in Han villages, as well as economic disadvantage caused by lack of loans (C5). For Hui villages, it is economic disadvantage caused mainly by low level of knowledge (C12, C13) that leads to the high opportunity cost in entering the market. In Hui-Han mixed villages the differences of production and lifestyle resulted in difficulties in allocation of poverty alleviation resources, consequently the satisfaction rate of poverty alleviation policies (C8) is the lowest for farmers, reflecting that the political disadvantage of spatial poverty is most significant. (3) There are significant differences in spatial poverty among different ethnic villages in the same physical geographical area. The mean values of SPI for Han, Hui, and Hui-Han mixed villages in PAVFEDR are 0.526, 2.557 and 1.644, respectively, which indicates that the Han villages have a high level of poverty and economic disadvantage (C5); the mean values of SPI for Hui and Hui-Han mixed villages in DSHR are 0.321 and -1.934, respectively, which indicates that the poverty level for Hui-Han mixed villages is high, and economic disadvantage (C2, C6, C7) and ecological disadvantage (C16) are both significant; the mean values of SPI in Han, Hui and Hui-Han mixed villages in ESMR are 1.031, -0.029 and -0.842, respectively, which indicates that the poverty level of Hui-Han mixed villages is high, and economic disadvantage (C5-C7, C17), political disadvantage (C8), location disadvantage (C18-C20) and ecological disadvantage (C16, C25) are all present. Therefore, the Han-villages in PAVFEDR, Hui-Han mixed villages in both DSHR and ESMR should be the key villages of poverty alleviation.

Key words: spatial poverty, geographical identification, geographical capital, spatial poverty index, county scale