自然资源学报 ›› 2022, Vol. 37 ›› Issue (2): 440-458.doi: 10.31497/zrzyxb.20220212

• 新时期自然资源利用与管理 • 上一篇    下一篇

贫困退出背景下返贫脆弱性评价——融合区域与个体的新视角

严小燕1,2(), 祁新华1,2(), 潘颖1,2, 李亚桐1,2   

  1. 1. 福建师范大学地理科学学院,福州 350007
    2. 福建师范大学地理研究所,福州 350007
  • 收稿日期:2020-11-16 修回日期:2021-02-08 出版日期:2022-02-28 发布日期:2022-02-16
  • 通讯作者: 祁新华(1974-),男,福建莆田人,博士,教授,博士生导师,主要从事贫困地理、人文地理学与生态学教学与交叉研究。E-mail: fjqxh74@163.com
  • 作者简介:严小燕(1992- ),女,江西赣州人,博士研究生,主要从事经济地理与区域发展研究。E-mail: yxynini@sina.com
  • 基金资助:
    国家社会科学基金项目(18BJL126)

Vulnerability assessment of return-to-poverty under poverty elimination in China: A new integrated regional and individual perspective

YAN Xiao-yan1,2(), QI Xin-hua1,2(), PAN Ying1,2, LI Ya-tong1,2   

  1. 1. School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China
    2. Institute of Geography, Fujian Normal University, Fuzhou 350007, China
  • Received:2020-11-16 Revised:2021-02-08 Online:2022-02-28 Published:2022-02-16

摘要:

精准识别返贫脆弱性,预防和化解返贫风险是“后扶贫时代”的工作重点。基于区域与个体尺度融合的新视角,运用BP神经网络法、熵值法和偏相关分析法对六盘山、秦巴山和大别山三大集中连片特困区进行返贫脆弱性评价与影响因素分析。研究发现:(1)三大集中连片特困区返贫脆弱度大致呈现由西向东递减的空间格局;(2)三个典型县区域和个体返贫脆弱性评价结果均显示古浪县>新县>栾川县;(3)高返贫风险县域中,高生态暴露度特征最为显著,而高返贫风险家庭中,生计动力不足特征最为明显;(4)区域返贫脆弱性主导因子为自然环境禀赋和经济发展水平,个体返贫脆弱性主导因子则为家庭劳动力综合素质、家庭收入、生计来源多样性、家庭成员健康状况和婚姻成本等。

关键词: 返贫脆弱性, 区域与个体, BP神经网络, 集中连片特困区

Abstract:

Identifying the vulnerability to return-to-poverty (or re-poverty), and comprehensively preventing and resolving the risk of re-poverty are the key points of poverty alleviation in the "post-poverty alleviation era". From the perspective of the integration of regional and individual scales, a comprehensive analysis framework is constructed. BP neural network method, entropy method and partial correlation analysis method are adapted to evaluate the vulnerability of re-poverty in three contiguous destitute areas of Liupan Mountains, Qinba Mountains and Dabie Mountains, as well as the influencing factors. Firstly, we find the vulnerability to re-poverty in the three contiguous destitute areas shows a spatial pattern of decreasing from west to east. Secondly, according to the classification order of the EEI, WSI, EAI and RVRI indexes, three typical counties of Gulang, Luanchuan and Xinxian show the characteristics of "high-high-low-high", "high-low-high-low" and "medium low-medium low-high-low", respectively. According to the classification order of LBI, LMI, LOI and IVRI indexes, however, the characteristics of these counties are "medium low-medium low-medium high-high", "medium high-high-medium low-low" and "medium high-medium low-medium low-medium low", respectively. Therefore, the evaluation results of both regional and individual vulnerability to re-poverty show an order of Gulang > Xinxian > Luanchuan. To be specific, Gulang is characterized by high vulnerability of re-poverty from both regional and individual perspectives. Xinxian has the advantage of the lowest ecological exposure, while the main problem is the low livelihood motivation. Although Luanchuan is relatively stable in poverty alleviation, the high ecological exposure is a major potential danger. Thirdly, for counties with high risk of re-poverty, high ecological exposure is the most significant characteristic, while for households with high risk, the most significant characteristic is insufficient livelihood motivation. The last finding shows that the dominant factors of regional vulnerability to re-poverty are natural environment endowment and economic development level, while the dominant factors of individual vulnerability to re-poverty are comprehensive quality of family labor force, family income, diversity of livelihood sources, health conditions of family members and marriage cost.

Key words: vulnerability to re-poverty, regional and individual perspectives, BP neural network, contiguous destitute areas