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新时期中国农村社会经济发展水平区域差异及影响因素分析 被引量:2

Analysis on the Regional Differences and Influencing Factors of Rural Social and Economic Development Level in China in the New Era
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摘要 十九大报告指出,2020年是全面建成小康社会的决胜期,而全面小康的关键在农村。但是,中国的区域发展不平衡,使得各省之间农村社会经济发展参差不齐,要想全面建成小康社会,就要了解各省农村社会经济发展的水平。本研究基于2018年中国农村统计数据,以省域为单位,通过构建指标体系,运用主成分分析法以及聚类分析法,对中国31个省域(除港澳台地区)的农村社会经济发展水平区域差异进行分析。结果表明:中国农村社会经济发展水平存在很大的区域差异,可分为5个等级,分别是:高水平地区、较高水平地区、中等水平地区、较低水平地区和低水平地区。而且影响区域差异的主要因素为农业现代化水平、产业结构、农民收入。 According to the report of the 19th National Congress of the Communist Party of China,2020 is the decisive period for building a moderately prosperous society in an all-round way,and the key to building a well-off society in an all-round way lies in rural areas.However,China's regional development is unbalanced,which makes the rural social and economic development uneven among provinces.In order to build a well-off society in an all-round way,it is necessary to understand the level of rural social and economic development of each province.Based on the statistical data of China's rural areas in 2018,this paper analyzed the regional differences of rural social and economic development level of 31 provinces in China(except Hong Kong,Macao and Taiwan)by constructing index system,using principal component analysis and cluster analysis.The results showed that there were great regional differences in the level of social and economic development in China's rural areas,which could be divided into five levels:high-level areas,high-level areas,medium-level areas,low-level areas and low-level areas.Moreover,the main factors affecting regional differences were agricultural modernization level,industrial structure and farmers'income.
作者 原卫利 陕永杰 魏绍康 苗圆 张羽 YUAN Weili;SHAN Yongjie;WEI Shaokang;MIAO Yuan;ZHANG Yu(College of Geographic Science,Shanxi Normal University,Linfen,Shanxi041000,China)
出处 《天津农业科学》 CAS 2020年第12期46-50,共5页 Tianjin Agricultural Sciences
基金 山西省哲学社会科学项目(2018B050) 山西省研究生教育改革项目(2020)。
关键词 农村社会经济发展 省域 主成分分析法 聚类分析法 rural social and economic development province principal component analysis cluster analysis
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