基于数据包络分析的甘肃省城市效率评价
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摘要
城市是区域社会、经济和文化的中心,城市的发展应当以高效率为目标,只有具有高效率的城市,才能产生良好的效益。本文以甘肃省12个地级城市为研究样本,选择固定资产投资、市区土地面积、全社会用电量、全社会用水量、全部从业人员数、邮政电信业务量和R&D投入费用作为投入指标,选择市区生产总值作为产出指标,建立评价指标体系。首先,运用传统DEA模型对2008-2010年甘肃省12个城市的效率进行评价,指出各城市的DEA有效性。其次,引入交叉评价机制和虚拟决策单元,以克服传统DEA无法区分有效单元之间的优劣,并明确各城市效率提升潜力。最后,采用Malmquist指数对2004-2010年甘肃省12个地级市城市效率变化进行研究。
传统DEA评价结果表明：(1)甘肃省城市综合效率水平一般,分别为0.648、0.686和0.674,只有兰州、嘉峪关和金昌等少数城市达到了综合效率最优；(2)纯技术效率最优的城市数量要多于综合效率和规模效率最优的城市数,2008-2010年纯技术效率最优的城市比综合效率和规模效率最优的城市数分别多4个、5个和4个；(3)2008-2010年甘肃省只有兰州、嘉峪关和金昌等少数城市达到规模最优,是决定综合效率最优的主要因素。(4)从分类特征来看,河西地区综合效率最高,陇东地区最低,陇中地区则介于两者之间；专业型城市综合效率要高于综合型城市；城市综合效率和城市人口规模之间的相关性不明显。
交叉DEA评价结果表明：(1)从总体特征看,甘肃省城市效率普遍很低,仅占理想决策单元的6.0%-7.5%,城市之间的差异较为显著,兰州城市效率最高,定西城市效率最低；(2)从分类特征看,陇中地区城市效率要高于陇东地区和河西地区,专业型城市的城市效率要高于综合型城市,大城市的城市效率要高于中小城市的城市效率；(3)甘肃省目前主要处于低投入低产出阶段,城市效率具有很大的提升空间；(4)交叉评价方法较好的实现了决策单元的排序问题,有效区分DEA有效决策单元之间的优劣。
Malmquist计算结果表明：(1)2004-2010年,甘肃省城市综合效率和生产率变化指数呈现弱衰退的趋势,技术有所退步,技术应用水平和管理水平有所下降,规模有所恶化,其中,规模效率变化是影响综合效率变化和生产率变化的主要因素。(2)2004-2010年,甘肃省三大区域城市综合效率均有所下降,陇东下降最明显,陇中地区下降幅度最小；甘肃三大区域生产率均呈现出下降的趋势,其中河西地区下降幅度最小,而陇东地区下降趋势最为明显。(3)2004-2010年,不同类型城市的综合效率和生产率均呈现出下降的趋势,但是专业型城市下降幅度均要小于综合型城市。(4)2004-2010年,甘肃省大中小城市综合效率变化均呈下降趋势,其中中等城市的下降最为明显,小城市下降幅度最小；大中小城市生产率均出现下降的趋势,小城市的下降幅度要小于大中城市。
最后,结合甘肃省各城市实际情况和评价结果,从区域交流合作、城市体系构建、循环经济发展、人口合理流动、现代化进程推进、传统文化保护和信息化建设等方面提出相应的建议,以促进甘肃省各城市效率的提高。
As an important province of the less developed regions in China, the urbanization process of Gansu Province continues to accelerate, the urbanization rate continuously improve, however, the urban efficiency did not attract enough attention. It's significant to improve the urban competitiveness and sustainable development of less developed regions by increasing the urban efficiency. The data envelopment analysis (DEA) is the effective tool to measure urban efficiency, but the traditional DEA can not distinguish the difference among the effective units and define the urban efficiency growth potential. The aggressive cross-evaluation mechanism is introduced along with virtual decision making units (DMU) to overcome the shortcoming of the traditional DEA. In this paper, with land, capital, labor, technology, information, water and electricity consumption being the input indicators, and GDP the output indicator, the traditional DEA, the DEA-Cross Model and the Malmquist Model are employed to analyze the urban efficiency of12prefecture-level cities in Gansu Province based on the panel dataset of urban social economy from2004to2010.
The result of the traditional DEA showed that the urban efficiency of Gansu Province is relatively low, only three cities of Gansu Province maintain DEA effective during study period, such as Lanzhou, Jinchang, Jiayuguan, accounting for25%of the total cities. The number of cities which pure technical efficiency is optimal is more than the number of cities with the best overall efficiency and scale efficiency. During2008-2010, only three cities of Gansu Province maintain scale effective, such as Lanzhou, Jinchang, Jiayuguan. The scale efficiency is the main factor that determines the urban efficiency. From the classification features, the urban efficiency of Hexi area is the highest, Longdong area lowest, Longzhong area between. The urban efficiency of professional city is higher than integrated city. The correlation between urban efficiency and the size of the urban population is not obvious.
The results of the DEA-Cross Model showed that the urban efficiency in Gansu Province is low (between0.060and0.075) from2008to2010, less than7.5%of the ideal DMU. There is a significant differences between cities in Gansu Province, Lanzhou maximum, Dingxi minimum. From the perspective of spatial distribution, cities in the Longzhong Region have higher urban efficiency than those in the Longdong Region and in Hexi Region. From the perspective of city type, urban efficiency of industrial cities is higher than that of non-mining cities. From the perspective of city scale, urban efficiency of big cities is higher than that of small and medium-sized cities. The clustering results showed that Lanzhou, Jinchang and Jiayuguan belong to the "high input and high output" type, Dingxi and Longnan belong to the "high input and low output" type, and the remaining cities belong to the" low input and low output" type.
The analysis of urban efficiency changes shows that the changes in technical, urban efficiency, productivity change, pure technical efficiency and scale efficiency had a slight decrease. Both the comprehensive efficiency change index and productivity change index decreased, indicating that the urban efficiency did not improve, and the tendency of productivity decline was obvious. The change of scale efficiency is the major determining factor of the change of comprehensive efficiency and productivity index. In terms of the classification of urban efficiency changes, the urban efficiency declined in each of the three regions, among which the Longdong region declined most and the Hexi region came next. The changes of comprehensive efficiency in comprehensive cities are bigger than those of professional cities during this period. The productivity of comprehensive cities showed a downward tendency, and the same to professional cities. The productivity of comprehensive cities decreased because of scale efficiency declined, and the technical backwards to productivity decreased of professional cities.The urban efficiency declined in cities of different scales, with greater decline in medium-sized cities than in small and big cities during this period. The productivity decreased more in big cities and medium-sized cities because of the declining scale of production.
On the basis of the present study, some suggestions regarding improving urban efficiency were given in the current situation of urban construction and management, including strengthening regional exchanges, urban system construction, development of circular economy, rational flow of population, promoting the process of urban modernization, the protection of traditional culture and development information technology.The research would provide decision-makers and governor with some meaningful references for promoting urban resources utilization and urban sustainable development.
传统DEA评价结果表明：(1)甘肃省城市综合效率水平一般,分别为0.648、0.686和0.674,只有兰州、嘉峪关和金昌等少数城市达到了综合效率最优；(2)纯技术效率最优的城市数量要多于综合效率和规模效率最优的城市数,2008-2010年纯技术效率最优的城市比综合效率和规模效率最优的城市数分别多4个、5个和4个；(3)2008-2010年甘肃省只有兰州、嘉峪关和金昌等少数城市达到规模最优,是决定综合效率最优的主要因素。(4)从分类特征来看,河西地区综合效率最高,陇东地区最低,陇中地区则介于两者之间；专业型城市综合效率要高于综合型城市；城市综合效率和城市人口规模之间的相关性不明显。
交叉DEA评价结果表明：(1)从总体特征看,甘肃省城市效率普遍很低,仅占理想决策单元的6.0%-7.5%,城市之间的差异较为显著,兰州城市效率最高,定西城市效率最低；(2)从分类特征看,陇中地区城市效率要高于陇东地区和河西地区,专业型城市的城市效率要高于综合型城市,大城市的城市效率要高于中小城市的城市效率；(3)甘肃省目前主要处于低投入低产出阶段,城市效率具有很大的提升空间；(4)交叉评价方法较好的实现了决策单元的排序问题,有效区分DEA有效决策单元之间的优劣。
Malmquist计算结果表明：(1)2004-2010年,甘肃省城市综合效率和生产率变化指数呈现弱衰退的趋势,技术有所退步,技术应用水平和管理水平有所下降,规模有所恶化,其中,规模效率变化是影响综合效率变化和生产率变化的主要因素。(2)2004-2010年,甘肃省三大区域城市综合效率均有所下降,陇东下降最明显,陇中地区下降幅度最小；甘肃三大区域生产率均呈现出下降的趋势,其中河西地区下降幅度最小,而陇东地区下降趋势最为明显。(3)2004-2010年,不同类型城市的综合效率和生产率均呈现出下降的趋势,但是专业型城市下降幅度均要小于综合型城市。(4)2004-2010年,甘肃省大中小城市综合效率变化均呈下降趋势,其中中等城市的下降最为明显,小城市下降幅度最小；大中小城市生产率均出现下降的趋势,小城市的下降幅度要小于大中城市。
最后,结合甘肃省各城市实际情况和评价结果,从区域交流合作、城市体系构建、循环经济发展、人口合理流动、现代化进程推进、传统文化保护和信息化建设等方面提出相应的建议,以促进甘肃省各城市效率的提高。
As an important province of the less developed regions in China, the urbanization process of Gansu Province continues to accelerate, the urbanization rate continuously improve, however, the urban efficiency did not attract enough attention. It's significant to improve the urban competitiveness and sustainable development of less developed regions by increasing the urban efficiency. The data envelopment analysis (DEA) is the effective tool to measure urban efficiency, but the traditional DEA can not distinguish the difference among the effective units and define the urban efficiency growth potential. The aggressive cross-evaluation mechanism is introduced along with virtual decision making units (DMU) to overcome the shortcoming of the traditional DEA. In this paper, with land, capital, labor, technology, information, water and electricity consumption being the input indicators, and GDP the output indicator, the traditional DEA, the DEA-Cross Model and the Malmquist Model are employed to analyze the urban efficiency of12prefecture-level cities in Gansu Province based on the panel dataset of urban social economy from2004to2010.
The result of the traditional DEA showed that the urban efficiency of Gansu Province is relatively low, only three cities of Gansu Province maintain DEA effective during study period, such as Lanzhou, Jinchang, Jiayuguan, accounting for25%of the total cities. The number of cities which pure technical efficiency is optimal is more than the number of cities with the best overall efficiency and scale efficiency. During2008-2010, only three cities of Gansu Province maintain scale effective, such as Lanzhou, Jinchang, Jiayuguan. The scale efficiency is the main factor that determines the urban efficiency. From the classification features, the urban efficiency of Hexi area is the highest, Longdong area lowest, Longzhong area between. The urban efficiency of professional city is higher than integrated city. The correlation between urban efficiency and the size of the urban population is not obvious.
The results of the DEA-Cross Model showed that the urban efficiency in Gansu Province is low (between0.060and0.075) from2008to2010, less than7.5%of the ideal DMU. There is a significant differences between cities in Gansu Province, Lanzhou maximum, Dingxi minimum. From the perspective of spatial distribution, cities in the Longzhong Region have higher urban efficiency than those in the Longdong Region and in Hexi Region. From the perspective of city type, urban efficiency of industrial cities is higher than that of non-mining cities. From the perspective of city scale, urban efficiency of big cities is higher than that of small and medium-sized cities. The clustering results showed that Lanzhou, Jinchang and Jiayuguan belong to the "high input and high output" type, Dingxi and Longnan belong to the "high input and low output" type, and the remaining cities belong to the" low input and low output" type.
The analysis of urban efficiency changes shows that the changes in technical, urban efficiency, productivity change, pure technical efficiency and scale efficiency had a slight decrease. Both the comprehensive efficiency change index and productivity change index decreased, indicating that the urban efficiency did not improve, and the tendency of productivity decline was obvious. The change of scale efficiency is the major determining factor of the change of comprehensive efficiency and productivity index. In terms of the classification of urban efficiency changes, the urban efficiency declined in each of the three regions, among which the Longdong region declined most and the Hexi region came next. The changes of comprehensive efficiency in comprehensive cities are bigger than those of professional cities during this period. The productivity of comprehensive cities showed a downward tendency, and the same to professional cities. The productivity of comprehensive cities decreased because of scale efficiency declined, and the technical backwards to productivity decreased of professional cities.The urban efficiency declined in cities of different scales, with greater decline in medium-sized cities than in small and big cities during this period. The productivity decreased more in big cities and medium-sized cities because of the declining scale of production.
On the basis of the present study, some suggestions regarding improving urban efficiency were given in the current situation of urban construction and management, including strengthening regional exchanges, urban system construction, development of circular economy, rational flow of population, promoting the process of urban modernization, the protection of traditional culture and development information technology.The research would provide decision-makers and governor with some meaningful references for promoting urban resources utilization and urban sustainable development.
引文
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