地理科学进展

地理科学进展 ›› 2020, Vol. 39 ›› Issue (11): 1860-1873.doi: 10.18306/dlkxjz.2020.11.007

• 研究论文 • 上一篇    下一篇

东南沿海县域贫困度演变特征及驱动因素研究——以福建省为例

王武林1,2(), 余翠婵1, 曾献君3,4,*(), 李春强1   

  1. 1.福州大学环境与资源学院,福州 350108
    2.中国科学院地理科学与资源研究所,北京 100101
    3.华南理工大学建筑学院,广州 510641
    4.福建工程学院建筑与城乡规划学院,福州 350118
  • 收稿日期:2019-12-20 修回日期:2020-05-11 出版日期:2020-11-28 发布日期:2021-01-28
  • 通讯作者: 曾献君
  • 作者简介:王武林(1982— ),男,湖南邵阳人,博士后,副教授,硕士生导师,研究方向为区域经济、交通地理。E-mail:wangwulin421@163.com
  • 基金资助:
    国家自然科学基金项目(41701118);中国博士后科学基金项目(2018M641458);福建省软科学项目(2017R0051)

Evolution characteristics and driving factors of county poverty degree in China’s southeast coastal areas: A case study of Fujian Province

WANG Wulin1,2(), YU Cuichan1, ZENG Xianjun3,4,*(), LI Chunqiang1   

  1. 1. College of Environment and Resources, Fuzhou University, Fuzhou 350108, China
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    3. School of Architecture, South China University of Technology, Guangzhou 510641, China
    4. School of Architecture and Planning, Fujian University of Technology, Fuzhou 350118, China
  • Received:2019-12-20 Revised:2020-05-11 Online:2020-11-28 Published:2021-01-28
  • Contact: ZENG Xianjun
  • Supported by:
    National Natural Science Foundation of China(41701118);China Postdoctoral Science Foundation(2018M641458);Soft Science Project of Fujian Province(2017R0051)

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摘要：

区域贫困是各界关注和研究的热点问题,传统的贫困地区研究缺少对中国东南沿海的关注。论文以中国相对发达的东南沿海省份福建省为例,构建3个维度9个向度共30项指标的度量模型,利用多维贫困度等指数和Kohonen神经网络算法,分析2000年和2016年的县域贫困度演化特征及其驱动因素。结果表明：① 通过对广东、福建和浙江3个东南沿海省份县域发展水平的正态分布检验进行对比,发现福建省县域发展水平在东南沿海具有典型性。② 根据贫困度指数P可划分为贫困县、弱势县和一般县,贫困县、弱势县分布于福建北部和西南部的总体格局不变,且向省域边际县聚集。③ 经济维度贫困度指数、社会维度贫困指数和自然维度贫困度指数的各自变化率最多的县市区均属于缓慢恶化区,属于快速优化区的县市区数量居中,属于缓慢改善区的县市区数量少且分布散;基于多维贫困度指数PI的变化率表现为缓慢恶化区分布于福建北部、中部和西南部,缓慢改善区分布于福建中西部,快速优化区分布于福建东部沿海。④ 对2000年和2016年多维贫困度贡献率均产生较大影响的向度为医疗卫生、教育水平、居住环境、经济发展,加强基础设施、医疗卫生、教育水平等公共服务的建设供给,应作为当前福建扶贫重点关注的内容。⑤ 基于不同向度的贫困度贡献率可划分4类致贫类型：I类县市区是教育水平和基础设施向度主导的贫困类型,II类县市区是经济发展和居住环境向度主导的贫困类型,III类县市区属于人口特征和基础设施向度主导的贫困类型,IV类县市区属于医疗卫生和经济发展向度主导的贫困类型。研究结果对准确识别县域贫困和深入实施精准扶贫战略具有一定的参考价值。

关键词: 贫困度, Kohonen神经网络, 贫困县, 福建省

Abstract:

：Regional poverty is the focus of attention and research of the society, while the traditional study of poverty areas lacks attention to the southeast coast of China. Taking 64 counties in Fujian Province—a relatively developed coastal province in China's southeast coastal areas—as an example, this study constructed a measurement model of 30 indicators in three dimensions and nine vectors, and analyzed the characteristics of change and driving factors of county poverty degree in 2000 and 2016 by using the multidimensional poverty degree index (PI) and Kohonen neural network algorithm. The results show that: 1) The county development level of Fujian Province is typical among the southeast coastal provinces (Guangdong, Fujian, and Zhejiang). 2) According to the poverty degree index, the province can be divided into poor counties, disadvantaged counties, and normal counties. In general, poor counties and disadvantaged counties are found in the north and southwest, and concentrated near the peripheral areas of the province. 3) The change rates of the economic dimension, social dimension, and natural dimension of poverty indicate that most of the counties belong to the slowly deteriorating area, followed by counties with rapidly improving conditions, while the counties with slow improvement are few and scattered spatially. The change rate based on PI shows that the areas with slowly deteriorating conditions are distributed in the northern, central, and southwestern parts of Fujian Province, the areas with slowly improving conditions are distributed in western Fujian Province, while rapidly improving areas are distributed in the east coast of Fujian Province. The poverty of most poor counties are caused by economic, natural, and other factors, which have important influence on the process of poverty alleviation. 4) In 2000 and 2016, medical and health care, education level, living environment, and economic development deeply affected the contribution rate of PI. Strengthening the development and provision of public services such as infrastructure, medical and health care, and education should be the focus of current poverty alleviation efforts in Fujian Province. At the same time, the ecological environment and resource endowment also play a part in poverty alleviation. 5) There are four types of poverty factors based on contribution rate of poverty degree in different vectors: Type I is dominated by education and infrastructure, type II is dominated by economic development and living environment, type III is dominated by demographic characteristics and infrastructure, and type IV is dominated by health care and economic development. This study can be of some reference for the identification of poverty counties, and may contribute to the implementation of targeted poverty alleviation strategy.

Key words: poverty degree, Kohonen neural network, poverty county, Fujian Province