中国城市群是中国未来经济发展格局中最具活力和潜力的核心地区,是中国主体功能区划中的重点开发区和优化开发区,在全国生产力布局格局中起着战略支撑点、增长极点和核心节点的作用。但城市群高密度的聚集导致高强度的相互作用,在拉动城市群高速度成长的同时,造成了高风险的生态环境威胁。如何客观评价城市群高密度集聚的效果?基于这一问题,本文从投入产出效率视角,构建城市群投入产出效率指标体系,采用CRS 模型、VRS 模型和Bootstrap-DEA 方法,综合测算了中国城市群投入产出效率、变化趋势及空间分异特征。结果表明,中国城市群投入产出效率总体较低且呈下降趋势,2002 年、2007 年中国城市群投入产出综合效率为0.853 和0.820,分别达到最优水平的85%和82%,平均综合效率下降了0.033；基于Bootstrap-DEA 纠偏后的中国城市群投入产出效率更低,但更可靠有效；城市群投入产出综合效率、纯技术效率和规模效率总体表现为东部高于中部,中部高于西部的区域空间格局,呈现出与中国东、中、西区域经济发展格局相似的特征；2002-2007 年中国城市群规模效率指数微弱上升,全要素生产率指数、综合效率指数、技术效率变化指数以及纯技术效率指数下降趋势显著。该研究旨在为评估我国城市群高密度集聚的效果提供定量的测算依据,进而为提高中国城市群的投入产出效率与空间集聚效率奠定科学的决策基础。
Urban agglomerations in China which perform a vital role in distribution of productive forces are the most dynamic and potential core area in future economic development, and are the key and optimized development districts in the division of main-function zones. However, while driving up rapid economic growth of urban agglomerations, high-intensity interaction caused by high-density aggregation also contributed to high-risk threats to natural environment. How do we assess the effect of high-density urban agglomerations? Accordingly, from the perspective of input and output efficiency, this paper established input and output efficiency indicator system of urban agglomerations, using CRS model, VRS model and Bootstrap-DEA, and measured the changing trend and spatial differentiation of input and output efficiency of urban agglomerations in China comprehensively. Results showed that input and output efficiency of urban agglomerations in China is low and slipping. In 2002 and 2007, comprehensive input and output efficiency of urban agglomerations in China was respectively 0.853 and 0.820, which dropped by an average of 0.033. Similarly, technical and scale efficiency of urban agglomerations in China is low and slipping; Input and output efficiency of urban agglomerations in China modified by Bootstrap-DEA model is lower but more reliable and effective. Input and output efficiency of urban agglomerations decreases gradually from the eastern region to the central and western regions of China. In 2002 and 2007, comprehensive input and output efficiency, technical efficiency and scale efficiency of urban agglomerations in eastern and central regions were higher than those in the western region, which was correlated with the regional economic development pattern in China. Otherwise, technical and production efficiency of urban agglomerations also decreases gradually from the eastern to the central and western regions. This study aims to provide a quantitative basis for assessing the effect of high-density urban agglomerations in China, and further lay a solid foundation for decision-making of improving input and output and spatial agglomeration efficiency of urban agglomerations in China.
[1] Fang Chuanglin, Song Jitao, Lin Xueqin. Sustainable Development Theory and Practice of Urban Agglomerations in China.Beijing: Science Press, 2010: 27-46. [方创琳, 宋吉涛, 蔺雪芹. 中国城市群可持续发展理论与实践. 北京: 科学出版社,2010: 27-46.]
[2] Fang Chuanglin, Song Jitao, Zhang Qiang et al. The formation, development and spatial heterogeneity patterns for thestructures system of urban agglomerations in China. Acta Geographica Sinica, 2005, 60(5): 827-840. [方创琳, 宋吉涛, 张蔷等. 中国城市群结构体系的组成与空间分异格局. 地理学报, 2005, 60(5): 827-840.]
[3] Rolf Fare, Shawna Grosskopf, Mary Norris et al. Productivity growth, technical progress, and efficiency change inindustrialized countries. The American Economic Review, 1994, 84(1): 66-83.
[4] Ila M Semenick Alam, Robin C Sickles. The relationship between stock market returns and technical efficiencyinnovations: Evidence from the US airline industry. Journal of Productivity Analysis, 1998, 9(1): 35-51.
[5] Daniel J. Graham. Productivity and efficiency in urban railways: Parametric and non-parametric estimates. TransportationResearch. Part E: Logistics and Transportation Review, 2008, 44(1): 84-99.
[6] Lilyan E Fulginiti, Richard K Perrin. Agricultural productivity in developing countries. Agricultural Economics, 1998, 19:45-51.
[7] Bar?? K Y?rük, Osman Zaim. Productivity growth in OECD countries: A comparison with Malmquist Indices. Journal ofComparative Economics, 2005, 33(2): 401-420.
[8] Goran Bergendahl, Ted Lindblom. Evaluating the performance of Swedish savings banks according to service efficiency.European Journal of Operational Research, 2008, 185(3): 1663-1673.
[9] Fan Hua. Effectiveness research of urban economic development in Yangtze River Delta. Development Research, 2005, 60(4): 60-63. [樊华. 长江三角洲各城市经济发展有效性研究. 开发研究, 2005, (4): 60-63.]
[10] Gu Lixian, Xiao Jinsong. Economic efficiency of resources-based cities in China. Arid Land Geography, 2009, 32(4):624-630. [古丽鲜, 肖劲松. 中国资源型城市经济发展效率评析. 干旱区地理, 2009, 32(4): 624-630.]
[11] Li Xun, Xu Xianxiang, Chen Haohui. Temporal and spatial changes of urban efficiency in the 1990s. Acta GeographicaSinica, 2005, 60(4): 615-625. [李郇, 徐现祥, 陈浩辉. 20 世纪90 年代中国城市效率的时空变化. 地理学报, 2005, 60(4): 615-625.]
[12] Guo Tengyun, Xu Yong, Wang Zhiqiang. The analyses of metropolitan efficiencies and their changes in China based onDEA and Malmquist Index models. Acta Geographica Sinica, 2009, 64(4): 408-416. [郭腾云, 徐勇, 王志强. 基于DEA的中国特大城市资源效率及其变化. 地理学报, 2009, 64(4): 408-416.]
[13] Jin Xiangyu. Total factor productivity of cities in China: 1990-2003. Shanghai Journal of Economics, 2006, (7): 14-23. [金相郁. 中国城市全要素生产率研究: 1990-2003. 上海经济研究, 2006, (7): 14-23.]
[14] Yang Kaizhong, Xie Xie. The analysis about DEA-efficiency of China cities' input-output. Geography and TerritorialResearch, 2002, 18(3): 45-47. [杨开忠, 谢燮. 中国城市投入产出有效性的数据包络分析. 地理学与国土研究, 2002, 18(3): 45-47.]
[15] Guo Haitao, Yu Linlin, Li Jingtao. Assessment on efficiency of resources cities with data envelopment analysis. ChinaMining Magazine, 2007, 16(6): 5-9. [郭海涛, 于琳琳, 李经涛. 我国资源型城市效率的DEA方法评价. 中国矿业, 2007,16(6): 5-9.]
[16] Gao Weiyu. Production efficiencies comparative analysis of metropolis in China. Shanghai Journal of Economics, 2008,11: 3-10. [高炜宇. 国内大城市生产效率的对比分析. 上海经济研究, 2008, (11): 3-10.]
[17] Simar L, Wilson P. Statistical inference in nonparametric frontier models. Journal of Productivity Analysis, 2000, 13(1):49-78.
[18] Sten Malmquist. Index numbers and indifference surfaces. Trabajos de Estadísticay de Investigación Operativa, 1953, 4(2): 210-242.
[19] Wang Yahua, Wu Fan, Wang Zheng. Productivity in China's transportation sector: A Malmquist Index and Bootstrapapproach (1980-2005). China Economic Quarterly, 2008, 7(3): 891-913. [王亚华, 吴凡, 王争. 交通行业生产率变动的Bootstrap-Malmquist指数分析(1980-2005). 经济学(季刊), 2008, 7(3): 891-913.]
[20] Fare R, Grosskopf S. Malmquist productivity indexes and Fisher ideal indexes. The Economic Journal, 1992, 102(1):158-160.
[21] Zhang Junrong, Guo Yaohuang. Relationships between evaluation indicators with DEA effectivity. SystemsEngineering-Theory Methodology Applications, 2004, 13(6): 520-523. [张俊容, 郭耀煌. 评价指标与DEA有效的关系.系统工程理论方法应用, 2004, 13(6): 520-523.]
[22] Hu Tongyu, Zhang Wangqiao. Factor analysis of information development of urban agglomerations. China Science andTechnology Information, 2008, (4): 178-179. [胡同宇, 张旺俏. 城市群信息化发展的要素分析. 中国科技信息, 2008,(4): 178-179.]
[23] Wei Quanling. Effective methods on evaluating relative efficiency (DEA). Chinese Science Bulletin, 2000, 45(17):1793-1807. [魏权龄. 数据包络分析(DEA). 科学通报, 2000, 45(17): 1793-1807.]
[24] Charnes A, Cooper W W, Rhodes E. Measuring the efficiency of decision making units. European Journal of OperationResearch, 1978, 2: 429-444.
[25] Kniep A, Simar L, Wilson P. Asymptotics for DEA estimators in nonparametric frontier models. IAP Technical Report,0323, 2003, 27-29.
[26] Zeng Chong , Wan Jianping. Interval estimate for VaR based on bootstrap method. Mathematics in Economics, 2009, 26(1): 58-63. [曾翀, 万建平. 基于Bootstrap方法VaR区间估计. 经济数学, 2009, 26(1): 58-63.]
[27] Hall P. On the number of bootstrap simulations required to construct a confidence interval. Ann. Statistics, 1986, 14:1453-1462.
[28] Simar L, Wilson P. Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models.Management Science, 1998, 44(1): 49-61.
