地理学报 ›› 2021, Vol. 76 ›› Issue (6): 1455-1470.doi: 10.11821/dlxb202106010

• 乡村发展与聚落研究 • 上一篇    下一篇

中国县域多维贫困与相对贫困识别及扶贫路径研究

徐藜丹1(), 邓祥征2, 姜群鸥1,2,3(), 马丰魁1   

  1. 1. 北京林业大学水土保持学院,北京 100038
    2. 中国科学院地理科学与资源研究所,北京 100101
    3. 北京林业大学水土保持与荒漠化防治教育部重点实验室,北京 100083
  • 收稿日期:2020-07-27 修回日期:2021-04-30 出版日期:2021-06-25 发布日期:2021-08-25
  • 通讯作者: 姜群鸥(1981-), 女, 博士, 副教授, 研究方向为人地关系研究。E-mail: jiangqo.dls@163.com
  • 作者简介:徐藜丹(1997-), 女, 吉林人, 博士生, 研究方向为3S技术应用。E-mail: xlidan@bjfu.edu.cn
  • 基金资助:
    中国科学院战略性先导科技专项(XDA23070400);国家自然科学基金项目(41901234);国家自然科学基金项目(51909052)

Identification and poverty alleviation pathways of multidimensional poverty and relative poverty at county level in China

XU Lidan1(), DENG Xiangzheng2, JIANG Qun'ou1,2,3(), MA Fengkui1   

  1. 1. School of Soil and Water Conservation, Beijing Forestry University, Beijing 100038, China
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    3. Key Laboratory of Soil and Water Conservation and Desertification Prevention, Beijing Forestry University, Beijing 100083, China
  • Received:2020-07-27 Revised:2021-04-30 Published:2021-06-25 Online:2021-08-25
  • Supported by:
    Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23070400);National Natural Science Foundation of China(41901234);National Natural Science Foundation of China(51909052)

摘要:

当前,中国脱贫攻坚任务步入了由绝对贫困转向相对贫困、收入贫困转向多维贫困的新阶段。本文以中国31个省(直辖市)县域为研究对象,采用基于夜间灯光数据的平均夜间灯光指数以及基于脆弱性可持续生计框架的县域多维发展指数和多维相对贫困识别方法,从多维贫困和相对贫困两个层面对中国多维贫困现状进行分析,并基于以上研究筛选出的贫困县,采用耦合协调模型从产业扶贫、教育扶贫、旅游扶贫和农业扶贫分析适宜县域的扶贫路径。结果表明:中国约60%县域处于多维相对贫困状态,其中47%为多维相对轻度贫困县;基于平均夜间灯光指数和县域多维发展指数分别识别出602个和611个多维贫困县,分别包含了63%和79%的国家级贫困县(截至2018年),这表明县域多维发展指数识贫机理更为精准。多维贫困县集中在甘肃、四川和云南等地;而在筛选的贫困县中,适宜产业扶贫、教育扶贫、旅游扶贫和农业扶贫的县域分别有414个、172个、442个和298个,且在4种扶贫方式中,约61%县域适宜采用多种扶贫路径共同扶贫。研究结论将为确保中国扶贫的可持续性提供重要的科学依据。

关键词: 夜间灯光指数, 多维贫困, 相对贫困, 耦合协调模型, 精准扶贫

Abstract:

China has secured a comprehensive victory in its fight against poverty. After 2020, the focus of China's battle against poverty will shift from relative poverty to absolute poverty, and from poverty in terms of income to that in other dimensions. This study applies the county as the basic unit and 31 provinces (autonomous regions/municipalities) of China as the study area. It identifies poverty levels in each county by the average night light index and the county multidimensional development index. Using the multidimensional relative poverty identification method based on the sustainable models, we analyzed the current situation of China's poverty from two aspects—multidimensional poverty and relative poverty. Finally, we explore the poverty alleviation pathways in four aspects, namely, education poverty alleviation, agricultural poverty alleviation, industrial poverty alleviation, and tourism poverty alleviation. The results revealed that nearly 60% of counties in China were primarily in multidimensional relative poverty, most of which were classified as multidimensional relatively light poverty counties. According to the average night light index and the county multidimensional development index, the numbers of poverty counties in China were 602 and 611, respectively; as of 2018, the proportions of national poverty-stricken counties accounted for 63% and 79%, respectively. The result implied that the county multidimensional development index had a more comprehensive poverty identification mechanism. Moreover, the multidimensional poverty counties were concentrated in Gansu, Sichuan, and Yunnan. Meanwhile, the development of Jilin, Liaoning, and Heilongjiang should not be overlooked. From the viewpoint of pathways, 414, 172, 442, and 298 poverty counties were suitable to industrial poverty alleviation, education poverty alleviation, tourism poverty alleviation, and agricultural poverty alleviation, respectively. Some 61% of counties had more poverty-causing factors, implying that multidimensional poverty alleviation is suitable in most of the poverty-stricken counties. These conclusions can provide a crucial scientific basis for ensuring sustainable poverty alleviation.

Key words: night light index, multidimensional poverty, relative poverty, coupling coordination model, targeted poverty alleviation