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| 2000-2013年中国城市群经济绩效动态实证分析——基于DEA和Malmquist生产率指数法 | |
| 引用本文： | 黄金川,林浩曦,陈明.2000-2013年中国城市群经济绩效动态实证分析——基于DEA和Malmquist生产率指数法[J].地理科学进展,2017,36(6):685-696. |
| 作者姓名： | 黄金川 林浩曦 陈明 |
| 作者单位： | 1. 中国科学院区域可持续发展分析与模拟重点实验室,北京 100101 2. 中国科学院地理科学与资源研究所,北京 100101 3. 中国科学院大学,北京 100049 4. 中国城市规划设计研究院,北京 100044 |
| 基金项目： | 国家自然科学基金项目(41690145);国家科技支撑计划项目(2012BAJ15B01) |
| 摘 要： | 在全球化和新型城镇化大背景下,城市群是辐射面最广、潜力最大、活力最强的核心增长极,但其高速成长背后是各类要素的大量投入,客观评估城市群经济绩效对于推动中国城镇化持续健康发展意义深远。在总结既有研究的基础上,选取中国13个典型城市群为样本,以2000-2013年为研究时段,采用资本、土地、劳动、科技和经济五大要素长时间序列数据,在全要素生产率(TFP)分析框架下,通过数据包络分析方法(DEA)静态评价时间截面的城市群投入产出效率,进而运用Malmquist生产率指数法动态分析城市群TFP年际变化,探讨其时空格局和内在演化机制。研究发现：城市群经济绩效进步明显,但空间分布仍不均衡;科技进步贡献的滞后在一定程度上抵消了通过规模扩张和资源配置优化带来的正效应,只有长三角、珠三角和京津冀三大城市群初步实现依靠科技进步促进发展,表明各城市群所处发展阶段和动力机制差异显著。据此,提出针对不同地区、不同发展阶段城市群的发展策略。 |
| 关 键 词： | 城市群 经济绩效 全要素生产率(TFP) 数据包络分析(DEA) Malmquist生产率指数法 中国 |
The dynamics and empirical analysis of input and output efficiency of urban agglomerations in China, 2000-2013: Based on the DEA model and Malmquist index method |
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| Jinchuan HUANG,Haoxi LIN,Ming CHEN.The dynamics and empirical analysis of input and output efficiency of urban agglomerations in China, 2000-2013: Based on the DEA model and Malmquist index method[J].Progress in Geography,2017,36(6):685-696. | |
| Authors: | Jinchuan HUANG Haoxi LIN Ming CHEN |
| Institution: | 1. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China 2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China 3. University of Chinese Academy of Sciences, Beijing 100049, China 4. China Academy of Urban Planning and Design, Beijing 100044, China |
| Abstract: | With the enormous radiation range, great potential and vigor, urban agglomerations are deemed as the core growth pole both at present and in the future under the background of globalization and new-type urbanization in China. Behind the seemingly ever-growing urban agglomerations, however, massive input elements such as resource, labor, capital, and other factors are needed. It is of profound significance to evaluate the economic performance of urban agglomerations, which may vigorously promote the sustainable and healthy development of urbanization. How to objectively evaluate the efficiencies such as industry scale concentration, resource allocation, and technological change of urban agglomerations has become an important question. Therefore, in this study we applied the data envelopment analysis (DEA) model to quantitatively measure the input and output efficiency of typical urban agglomerations in China from 2000 to 2013, based on time series data including capital input, urban construction land increment, labor supply, scientific-technological investment, and economic output value. Moreover, we analyzed urban agglomerations' total factor productivity index (TFP) dynamically by means of Malmquist productivity index method and lucubrated their spatial and temporal differentiation patterns, mechanisms of change, influencing factors, and other related contents. The results show that the urban agglomerations' stationery cross-section input and output efficiency made significant strides but an unbalanced spatial distribution pattern remained. In 2000, the average comprehensive technological efficiency was only 73.1% of the optimal level with barely three large urban agglomerations achieved the optimal DEA efficiency. After years of steady development, the corresponding index reached 96.8% of the optimal level with eight urban agglomerations achieved the optimal DEA efficiency. However, from the perspective of dynamic time series analysis, urban agglomerations' TFP decreased by 6% from 2000 to 2013 mainly due to the poor performance of technology change index. Unlike the disappointing performance of technology change index, other indices such as comprehensive technological efficiency change index, pure technological efficiency change index, and scale efficiency change index all shaped up, indicating that the level of resource allocation and utilization efficiency rose steadily during this period. The lag of the contribution of technological progress to a certain extent offset the positive effects brought by the expansion of urban agglomeration and the optimization of resource allocation. Specifically, only the Yangtze River Delta, the Pearl River Delta, and Beijing-Tianjin-Hebei urban agglomerations gained ascending contribution of technological progress, which illustrates that the current development stages and dynamic mechanisms of urban agglomerations in China maintained diversified characteristics. To conclude, this article puts forward a series of specific suggestions to optimize the development of urban agglomerations in China, namely, moderately expanding the scale of urban agglomerations, placing emphasis on input and output efficiency leaning against technological changes and transforming the present unbalanced regional development situation. |
| Keywords: | urban agglomeration economic performance total factor productivity (TFP) data envelopment analysis (DEA) Malmquist productivity index method China |
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