地理学报 ›› 2020, Vol. 75 ›› Issue (8): 1633-1646.doi: 10.11821/dlxb202008006

• 区域与乡村发展 • 上一篇    下一篇

发展地理学视角下中国多维贫困测度及时空交互特征

金贵1,2(), 邓祥征2, 董寅3, 吴锋2   

  1. 1.中国地质大学(武汉)经济管理学院,武汉 430074
    2.中国科学院地理科学与资源研究所,北京 100101
    3.中国地质大学(武汉)公共管理学院,武汉 430074
  • 收稿日期:2019-11-06 修回日期:2020-04-10 出版日期:2020-08-25 发布日期:2020-10-25
  • 作者简介:金贵(1986-), 男, 江苏邳州人, 博士, 教授, 博导, 主要从事国土资源评价与国土空间优化利用研究。E-mail:jingui@igsnrr.ac.cn
  • 基金资助:
    国家自然科学基金项目(71974070);国家自然科学基金项目(41501593);国家重点研发计划(2016YFA0602500);教育部人文社会科学研究基金项目(19YJCZH068)

China's multidimensional poverty measurement and its spatiotemporal interaction characteristics in the perspective of development geography

JIN Gui1,2(), DENG Xiangzheng2, DONG Yin3, WU Feng2   

  1. 1. School of Economics and Management, China University of Geosciences, Wuhan 430074, China
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    3. School of Public Administration, China University of Geosciences, Wuhan 430074, China
  • Received:2019-11-06 Revised:2020-04-10 Online:2020-08-25 Published:2020-10-25
  • Supported by:
    National Natural Science Foundation of China(71974070);National Natural Science Foundation of China(41501593);National Key R&D Project(2016YFA0602500);Humanities and Social Sciences Foundation of Ministry of Education of China(19YJCZH068)

摘要:

探索贫困监测评估指标体系及区域间贫困时空交互动态特征对当前中国可持续减贫研究具有重要意义。基于发展地理学视角,引入面板向量自回归(PVAR)模型并结合人类发展分析路径与SDGs全球指标框架识别影响中国贫困的致贫和减贫因素,以此测度多维贫困指数,进而采用探索性时空数据分析(ESTDA)方法揭示多维贫困的时空交互特征。结果表明:① 中国当前贫困监测评估的致贫因子包括农作物受灾比和社会总抚养比,减贫因子则涉及人均GDP、人均社会保障支出、人均公共卫生支出、每万人医院数、新型农村合作医疗参合率、植被覆盖率、人均教育支出、高校数量、人均科学研究与试验发展支出、人均文化事业经费。② 2007—2017年中国省域收入贫困、健康贫困、文化贫困及多维贫困状况得到显著改善,全国综合贫困程度年均下降5.67%,部分省域的不同维度贫困内部出现差异化。③ 研究期内省域间多维贫困局域空间格局表现为较强的空间动态性,并呈现由东部向中、西部增大的变化态势;省域间多维贫困指数随时间演变呈现强的空间依赖关系,形成以西北和东北为高值区向四周递减的变化格局。④ 邻接省域多维贫困交互的时空网络以负向关联为主,仅有陕西与河南、陕西与宁夏、青海与甘肃、湖北与安徽、四川与贵州、海南与广东形成空间上较强的减贫协同关系。研究成果对当前中国精准扶贫战略实施尤其是2020年后预防返贫具有重要的参考价值。

关键词: 发展地理学, 多维贫困, 贫困测度, 时空交互, 协同减贫

Abstract:

Exploring the poverty supervision evaluation indicator system and the dynamic characteristics of spatio-temporal interaction of poverty among regions are of great significance to the current research on sustainable poverty reduction in China. From the perspective of development geography, this paper introduces panel vector autoregressive (PVAR) model and identifies poverty-causing and poverty-reducing factors in China in combination with human development approach and global indicator framework for the SDGs, so as to measure multidimensional poverty index. Then it uses exploratory spatio-temporal data analysis (ESTDA) method to reveal the spatio-temporal interaction characteristics of multidimensional poverty. The results show that: (1) The poverty-causing factors of China's current poverty monitoring and evaluation include the crop-to-disaster ratio and social gross dependency ratio, the poverty-reducing factors include per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 persons, participation rate of new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita scientific research and experimental development (R&D) expenditure, and per capita funding for cultural undertakings. (2) From 2007 to 2017, provincial income poverty, health poverty, cultural poverty and the multidimensional poverty have been significantly improved, with the national comprehensive poverty level declining by an average of 5.67% annually, and the poverty of different dimensions in some provinces is differentiated. (3) During the study period, the local spatial pattern of multidimensional poverty between provinces had strong spatial dynamics and showed an increasing trend from the eastern region to the central and western regions; the multidimensional poverty index among provinces shows a strong spatial dependence over time, forming a pattern of decreasing from the northwestern and northeastern regions to the surrounding areas. (4) The spatio-temporal network of multidimensional poverty interaction in neighboring provinces is mainly negatively correlated with Shaanxi and Henan, Shaanxi and Ningxia, Qinghai and Gansu, Hubei and Anhui, Sichuan and Guizhou, and Hainan and Guangdong, forming spatially strong cooperative poverty reduction relationships. The research results have important reference value for the current implementation of China's strategy on targeted measures in poverty alleviation, especially the prevention of poverty-returning after 2020.

Key words: development geography, multidimensional poverty, poverty measurement, spatio-temporal interaction, collaborative poverty reduction