基于DEA模型的城市建设用地利用效率时空格局演变及驱动因素

doi:10.18402/resci.2017.03.04

Abstract

Abstract:

Exploring spatial and temporal pattern evolution and driving factors of urban construction land use efficiency has important theoretical and practical significance for urban sustainable development. Here,31 provinces in China were taken as the research object to measure urban construction land use efficiency using data envelopment analysis. At the same time,ArcGIS was used to explore the spatiotemporal characteristics of urban construction land use efficiency evolution. Driving factors are discussed using grey correlation modeling. We found that from 2009-2013,provincial urban construction land use efficiency increased,but the efficiency value was not high. Spatial differences in urban construction land use efficiency were remarkable. Overall,the use efficiency of urban construction land in China shows significant spatial autocorrelation. The evolution of local spatial patterns was characterized by the following features：comprehensive efficiency of spatial pattern change was small and pure technical efficiency of the overall spatial pattern change was large. The overall spatial pattern of the scale efficiency was consistent with that of comprehensive efficiency,and the overall pattern changed little. The urbanization level,level of economic development,industrial structure,degree of marketization of land,level of science and technology and location conditions all affect urban construction land use efficiency,but the role of these factors in different research issues differed.

Key words: urban construction land, land use efficiency, spatiotemporal pattern, driving factors, China

Lixin ZHANG, Daolin ZHU, Ting DU, Baopeng XIE. Spatiotemporal pattern evolvement and driving factors of urban construction land use efficiency using data envelopment analysis[J].Resources Science, 2017, 39(3): 418-429.

Figures/Tables 5

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Table 1

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Table 2

Table 3

References 34

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地区	2009年			2013年
地区	综合效率	纯技术效率	规模效率	综合效率	纯技术效率	规模效率
北京	0.994	1.000	0.994	1.000	1.000	1.000
天津	0.991	1.000	0.991	1.000	1.000	1.000
河北	0.846	0.871	0.971	0.879	0.948	0.927
山西	0.666	0.716	0.931	0.783	0.883	0.887
内蒙古	0.541	0.770	0.702	0.590	0.699	0.844
辽宁	0.797	0.822	0.969	0.881	0.922	0.955
吉林	0.778	0.805	0.967	0.807	0.855	0.944
黑龙江	0.595	0.660	0.902	0.749	0.815	0.919
上海	1.000	1.000	1.000	1.000	1.000	1.000
江苏	1.000	1.000	1.000	1.000	1.000	1.000
浙江	0.897	0.916	0.979	0.999	1.000	0.999
安徽	0.649	0.706	0.919	0.789	0.880	0.898
福建	0.860	0.883	0.974	0.961	0.983	0.978
江西	0.632	0.694	0.911	0.795	0.888	0.895
山东	0.851	0.877	0.970	0.899	0.937	0.959
河南	0.684	0.732	0.935	0.779	0.860	0.906
湖北	0.726	0.763	0.952	0.839	0.877	0.957
湖南	0.721	0.760	0.949	0.844	0.882	0.956
广东	0.979	0.979	0.999	0.970	0.985	0.985
广西	0.539	0.650	0.829	0.658	0.749	0.878
海南	0.829	0.849	0.977	0.849	0.884	0.961
重庆	0.707	0.753	0.939	0.797	0.839	0.950
四川	0.605	0.667	0.907	0.699	0.797	0.878
贵州	0.552	0.716	0.771	0.740	0.838	0.883
云南	0.627	0.694	0.904	0.693	0.805	0.861
西藏	0.499	0.630	0.792	0.579	0.696	0.832
陕西	0.647	0.697	0.929	0.784	0.872	0.899
甘肃	0.510	0.681	0.749	0.556	0.700	0.794
青海	0.482	0.577	0.835	0.529	0.688	0.770
宁夏	0.609	0.659	0.925	0.768	0.850	0.905
新疆	0.526	0.656	0.803	0.594	0.706	0.840
均值	0.717	0.777	0.915	0.800	0.866	0.918

解释变量	定义	变量类型
城镇化水平X1	采用城镇人口比重表征/%	连续变量
经济发展水平X2	地区人均GDP/亿元	连续变量
产业结构X3	二、三产业产值占地区生产总值的比重/%	连续变量
科技水平X6	研究与发展经费投入强度	连续变量
区位条件X4	东部地区=1,中部地区=2,西部地区=3	虚拟变量
土地市场化程度X5	城市一级市场土地招拍挂地块数在总出让地块总占比/%	连续变量

影响因素	2009年		2013年
影响因素	灰色关联度	关联等级	灰色关联度	关联等级
城镇化水平	0.892 6	强	0.739 7	中
经济发展水平	0.741 3	中	0.806 7	强
产业结构	0.879 4	强	0.918 6	强
科技水平	0.729 7	中	0.876 9	强
区位条件	0.809 0	强	0.727 6	中
土地市场化程度	0.592 1	中	0.605 6	中

Spatiotemporal pattern evolvement and driving factors of urban construction land use efficiency using data envelopment analysis

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