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资源基础型城市群时空演变规律及动力机制研究

Research on the Spatiotemporal Evolution and Dynamic Mechanism of the Urban Agglomeration Deriving from Exploitation of Natural Resource

【作者】 刘振灵

【导师】 刘海滨;

【作者基本信息】 中国矿业大学(北京) , 管理科学与工程, 2009, 博士

【摘要】 我国丰富的矿产资源是国民经济持续、快速发展的基础和保障,资源开发不仅促进了区域发展,而且推进了城市化进程。论文在对资源基础型城市群的内涵界定基础上,对我国资源基础型城市群的地理分布、社会经济状况进行了研究;构建起资源基础型城市群发展水平的测度指标体系,引入可拓元评价模型以及灰色关联模型,对我国的资源基础型城市群进行了综合测度、排序和评价;讨论了资源开发周期及对资源型城市的影响,研究了资源型城市形成和发展规律、资源产业演化规律、城市群中资源型城市演变规律、城市群及资源型城市的经济增长规律等;以辽宁中部城市群为例,从城镇规模结构、城市职能结构、经济空间结构和产业结构四个维度,研究了典型资源基础型城市群城镇体系结构的时空演变规律:最后,从多个角度选择26个相关变量,使用因素相关分析法,对资源基础型城市群的驱动因素进行识别,建立起资源基础型城市群时空演变的驱动力演进模型,并进行了模拟。

【Abstract】 It is generally believed that the abundant natural resources are foundation and guarantee for the fast and sustainable development of the national economy. The exploitation of the natural resources has not only brought about regional development in many aspects, but promoted the process of urbanization. In this article, the concept of the Urban Agglomeration deriving from Exploitation of Natural Resources Dependent (UAENR) is briefly given, then, the geographical distribution of the UAENRs in China and their social economy status is discripted according to the data collected from the Chinese Urban Statistical book. Then, in the third chapter, the extension assessment model and the gray evaluation model are introduced into the measures, ranking and evaluation of the ten UAENRs in China. Next, the natural resource exploitation life cycles and influences on the resource-dependent city is discussed and formation and evolution law of the resource-dependent city, the varying law of the resource exploring industries, urban economy growth law of the resource-dependent city and other evolution law are also summarized. Furthermore, taking the typical UAENR, the Central Urban Agglomeration in Liaoning province as example, the spatiotemporal evolution law of the urban system, size-ranking structure, urban functional structure, economic spatial structure and industrial structure, are extensively studied. Finally, by selecting26variables and using the bivariate correlation analysis, the driving factor for the UAENR is identified and input the system dynamics model for the further development of the UAENR.

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