我国中部六省产业集聚与扩散的空间计量
Analysis of industrial agglomeration and diffusion with spatial econometrics for six provinces in Central China
查看参考文献23篇
文摘
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"中部崛起"战略给中部六省各地区的大发展带来了新的机遇。进入十二五时期,中部地区有必要总结之前发展过程中的经验,从产业结构调整和升级着手,掌握产业更新规律,尽可能规避经济发展中的不利因素,实现产业结构升级承托的区域经济跨越式可持续发展。本文收集整理了我国中部六省共88个地市2000年至2008年第二产业数据,采用LISA统计分析空间聚集和扩散模式,结合Moran’s I和Geary’s C方法,对这88个地市产业发展所处的状态进行综合判断,并结合马尔科夫链模拟,找到各个状态之间转移的概率,预测其未来发展状态。实证表明,在内外环境没有显著变化的情况下,经过10年的快速发展之后,中部六省各地市的第二产业水平会达到相对均衡状态。 |
其他语种文摘
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With the strategy of "The Rise of Central China",the six provinces in Central China are offered golden opportunities for their great development.Accordingly,during the period of the 12th Five-Year Plan(2011-2015),it is vital for them to sum up experience of precedent development concerning adjusting and upgrading industrial structure,master the law of industrial updates,and reduce the unfavorable factors in economic development to a minimum,based on which sustainable development of regional economy by leaps and bounds can be realized.First,data of the secondary industry in the period of 2000-2008 for 88 prefectures and cities in Central China are collected and LISA is used to analyze spatial aggregation and diffusion,the result of which in combination with Moran’s I and Geary’s C methods provides a comprehensive assessment for the industrial development in the 88 prefectures and cities.Then integrated with Markov chain simulation,the probabilities of transferring among various states are calculated to predict industrial development in the future.Finally,the empirical results indicate that,with 10 years of rapid development,the secondary industry of the six provinces will reach the level of relative equilibrium on the condition that there will be no significant change in the environment inside and outside the whole region. |
来源
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地理研究
,2012,31(3):534-542 【核心库】
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关键词
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中部六省
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产业集聚与扩散
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LISA分析
;
马尔科夫链模拟
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地址
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1.
安徽财经大学统计与应用数学学院, 蚌埠, 233030
2.
南开大学经济学院数量经济研究所, 天津, 300071
3.
安徽财经大学经济学院, 蚌埠, 233030
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语种
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中文 |
文献类型
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研究性论文 |
ISSN
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1000-0585 |
学科
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社会科学总论 |
基金
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国家自然科学基金
;
国家社会科学基金青年项目
;
安徽省高校省级自然科学研究重点项目
;
安徽财经大学研究生创新基金
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文献收藏号
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CSCD:4490592
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