地理学报 ›› 2018, Vol. 73 ›› Issue (10): 1850-1864.doi: 10.11821/dlxb201810003

• 土地整治与乡村振兴 • 上一篇    下一篇

黄土高原村域多维贫困空间异质性研究——以宁夏彭阳县为例

文琦1,2(),施琳娜1,2,马彩虹1,2(),王永生3   

  1. 1. 宁夏大学资源环境学院,银川 750021
    2. 宁夏(中阿)旱区资源评价与环境调控重点实验室,银川 750021
    3. 中国科学院地理科学与资源研究所,北京 100101
  • 收稿日期:2017-10-09 出版日期:2018-10-25 发布日期:2018-10-25
  • 基金资助:
    国家自然科学基金项目(41661042, 41761034);宁夏重点研发计划软科学项目(NZY201604)

Spatial heterogeneity of multidimensional poverty at the village level: Loess Plateau

WEN Qi1,2(),SHI Linna1,2,MA Caihong1,2(),WANG Yongsheng3   

  1. 1. School of Resources and Environment, Ningxia University, Yinchuan 750021, China
    2. Key Laboratory (China-Arab) of Resource Evaluation and Environmental Regulation of Arid Region in Ningxia, Yinchuan 750021, China
    3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2017-10-09 Online:2018-10-25 Published:2018-10-25
  • Supported by:
    National Natural Science Foundation of China, No.41661042, No.41761034;The Key R&D Program of Soft Science Project Foundation of Ningxia Hui Autonomous Region, No.NZY201604

摘要:

黄土高原属于生态环境脆弱与农村经济贫困的复合区域,研究其村域多维贫困及空间异质性有助揭示乡村贫困原因及空间格局。以宁夏彭阳县为研究区,运用A-F法对村域多维贫困进行测度,并结合空间自相关、地理探测器和回归分析方法对其空间异质性进行了系统分析。结果表明:① 研究区农户多维贫困程度较深,K = 3时,多维贫困指数(MPI)为0.045,平均剥夺份额0.361,主要致贫维度是住房、健康和教育,贡献率分别为0.263、0.245、0.227,收入维度贡献率仅占0.130;② MPI空间自相关Moran's I值为0.2,即存在正相关,呈现“南北高,中部低”的格局;③ 地理探测器结果显示行政村到镇中心的距离、村平均高程、村委会到主要河流的距离是影响MPI空间异质性的主要因子,其决定力q值分别为0.552、0.396、0.326,且在最小二乘线性回归(OLSR)和分位数回归(QR)中均通过了1%的显著性检验;④ 各因子间的相互作用形成了黄土高原农户福利缺失、基础设施落后与产业发展受阻、乡镇政府职能被削弱的村域多维贫困空间分异机制。⑤ 最后提出推进新型城镇化建设,实现公共服务均等化,从根源上解决农村医疗、住房、交通设施落后等难题的建议。

关键词: 多维贫困, 空间异质性, 地理探测器, 分位数回归, 黄土高原

Abstract:

Targeted poverty alleviation is a pillar of China's new and innovative anti-poverty and development strategy. China is an agricultural country with a large but poor rural population. At the village level, poverty alleviation has the potential to affect millions of citizens. The Loess Plateau is a composite of fragile ecological environment and rural poverty communities. As a backward economy often twins with fragile ecology, studying poverty from a multidimensional perspective in tandem with spatial heterogeneity and its influencing factors can provide an effective analysis of poverty in regions with characteristics similar to those of the Loess Plateau. This study features Pengyang, which is a poverty-stricken county located on the Loess Plateau, and relies on the approach by Alkire and Foster to measure multidimensional poverty, using spatial autocorrelation, Geodetector, OLSR and quantile regression (QR) analysis. Results from our investigation show that the study area has a deep level of multidimensional poverty, with a Multidimensional Poverty Index (MPI) score of 0.045. The rates of health, housing, and education dimensions were 0.263, 0.245, and 0.227, respectively. Moran's I of MPI was 0.2, which indicates that multidimensional poverty was positively correlated with a spatial pattern of "north, south, middle; high, high, low". Using Geodetector, the mean distance between villages and the center of town, as well as the mean elevation of the villages and mean distance to a river, were found to be the main factors of spatial heterogeneity within the MPI. The q-values of them were 0.552, 0.396, and 0.326, respectively, and the result of regression analysis conforms to the Geodetector. The interaction of the above factors enabled the creation of a multidimensional poverty spatial heterogeneity mechanism at village level for the Loess Plateau, revealing a lack in welfare of farmers, the poor of infrastructure, the congenital deficiency and stalled development of industry, and weak government functioning at the township level. Our results suggest that new strategies regarding urbanization should be investigated in order to improve and ensure the quality of public services in areas with similar characteristic as the Loess Plateau. These new strategies could enable the resolution of problems from the root, such as designing public goods and services that fill gaps in current health care and housing options, as well as the operation of transport facilities at the village level. This could help to mitigate welfare loss of farmers as well as reduce the negative impact of health, housing, education, and other dimensions of poverty.

Key words: multidimensional poverty, spatial heterogeneity, Geodetector, quantile regression, Loess Plateau