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我国实施精准扶贫的区域模式与可持续途径 被引量:147

Regional and Sustainable Approach for Target-Poverty Alleviation and Development of China
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摘要 目前我国仍有超过7 000万的贫困人口,且区域分布广、贫困程度深。促进贫困地区的转型发展、贫困群体的生计改善,直接关系到2020年我国全面建成小康社会宏伟目标的实现。文章基于我国基本地理国情,深入探讨农村贫困问题的地域生成机制,梳理精准扶贫的区域模式,提出加快精准脱贫的可持续途径。研究认为,我国农村贫困发生机制和减贫的基本模式具有明显的区域性;"十三五"期间,精准扶贫应着眼于区域转型发展过程来着力破解贫困问题;需进一步完善精准扶贫的区域政策体系、产业政策体系、土地政策体系,着力建立和完善以区域发展助推贫困农户脱贫解困的传导机制。 At present, there are still more than 70 million impoverished people in China. The transformation development of poverty-stricken regions and the livelihoods improvement of impoverished rural households are directly related to comprehensive realization the grand goal of building moderately prosperous society in 2020. Based on the analysis of basic geographical conditions, this paper probes the geographical generative mechanism of poverty, and explores the regional model and sustainability approach of targeted poverty alleviation and development. This research suggests poverty generative mechanism and poverty alleviation models are obviously regional particularity, and poverty should be alleviated in the progress of regional transformation development during the 13th Five Year Plan. This paper also suggests that in order to achieve the goals of targeted poverty alleviation and development, regional policy system, industrial policy system, and land policy system should be further improved, and transmission mechanism between regional development and poverty alleviation should be established and improved as well.
出处 《中国科学院院刊》 CSCD 2016年第3期279-288,共10页 Bulletin of Chinese Academy of Sciences
基金 "实施精准扶贫 精准脱贫"国务院重大政策措施落实情况第三方评估项目(2015) 国家自然科学基金重点项目(41130748) 面上项目(41571166)
关键词 精准扶贫 区域模式 可持续途径 区域发展 targeted-poverty alleviation and development, regional model, sustainable approach, regional development
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  • 1朱建平,陈民恳.面板数据的聚类分析及其应用[J].统计研究,2007,24(4):11-14. 被引量:100
  • 2MacQueen J. Some methods for classification and analysis of multivariate observations: Proc. 5th Berkeley Symp. Mathematical Statist. Probability, 1967.
  • 3Tukey J. Exploratory Data Analysis. Addison-Wesley, 1977.
  • 4Jain A K, Murty M N, Flynn P J. Data clustering: a review. ACM Comput. Surv. , 1999,31(3) :264 -323.
  • 5Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 1998,2 ( 3 ) : 283 - 304.
  • 6Huang Z, Ng M K. A fuzzy k -modes algorithm for clustering categorical data. Fuzzy Systems, IEEE Transactions on, 1999,7(4) :446 -452.
  • 7Huang Z. A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. : Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD'97) , 1997.
  • 8Chaturvedi A, Green P E, Caroll J D. K-modes clustering. Journal of C:ssification, 2001,18( 1 ) :35 -55.
  • 9Ding C, He X. K-nearest-neighbor consistency in data clustering: incorporating local information into global opti- mization: Proceedings of the 2004 ACM symposium on Applied computing, 2004.
  • 10Chuang K, Tzeng H, Chen S, et al. Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics, 2006,30( 1 ) :9 - 15.

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