[1]陈明华,张晓萌,刘玉鑫,等.绿色TFP增长的动态演进及趋势预测——基于中国五大城市群的实证研究[J].南开经济研究官网,2020,(01):20-44.
 Chen Minghua,Zhang Xiaomeng,Liu Yuxin and Zhong Chongyang.Dynamic Evolution and Trend Prediction of Green TFP Growth —Empirical Research Based on Five Urban Agglomerations in China[J].Nankai Economic Studies,2020,(01):20-44.
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绿色TFP增长的动态演进及趋势预测——基于中国五大城市群的实证研究(  )
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《南开经济研究》官网[ISSN:1001-4691/CN:12-1028/F]

卷:
期数:
2020年01
页码:
20-44
栏目:
出版日期:
2020-02-22

文章信息/Info

Title:
Dynamic Evolution and Trend Prediction of Green TFP Growth —Empirical Research Based on Five Urban Agglomerations in China
作者:
陈明华张晓萌刘玉鑫仲崇阳
陈明华,山东财经大学经济学院(邮编: 250014), E-mail: chenminghua1978@163.com;张晓萌,中国人民大学应用经济学院(邮编: 100872), E-mail:zhangxiao_mm06@163.com;刘玉鑫,山东财经大学经济学院(邮编: 250014), E-mail: 18769769216@163.com;仲崇阳,上海财经大学财经研究所(邮编: 200433), Email: chongy_zhong@163.com
Author(s):
Chen Minghua1Zhang Xiaomeng2Liu Yuxin1 and Zhong Chongyang3
1.School of Economics,Shandong University of Finance and Economics,Jinan 250014,China;2.School of Applied Economics,Renmin University of China,Beijing 100872,China;3. Institute of Finance and Economics,Shanghai University of Finance and Economics,Shanghai 200433,China
关键词:
城市群Kernel密度估计传统马尔科夫链空间马尔科夫链
Keywords:
Urban Agglomeration Kernel Density Estimation Traditional Markov Chain Space Markov Chain
文献标志码:
A
摘要:
随着经济增长与资源环境约束矛盾的日益增大,提升绿色TFP已经成为城市群经济发展的必然选择。文章基于2003-2016年城市数据采用DEA的非期望产出-超效率SBM模型测算了五大城市群的绿色TFP指数,并使用Kernel密度估计、传统马尔科夫链和空间马尔科夫链方法从时间和空间两个维度对城市群绿色TFP增长的分布动态演进和趋势预测进行了分析。研究结论显示:①五大城市群整体及各城市群绿色TFP指数均呈现上升趋势,大部分城市群的技术进步变化指数和技术效率变化指数也呈现上升趋势。城市群绿色TFP差异均呈整体扩大趋势,技术效率差异的扩大是其主要原因。五大城市群整体及各城市群分布都呈现出右拖尾和极化趋势。②除成渝城市群外,其他城市群绿色TFP增长均存在俱乐部趋同效应。金融发展、要素禀赋、技术进步均没有显著拉动低水平城市绿色TFP提升,导致低水平趋同现象显著。但随着绿色TFP指数的提高,各影响因素的拉动作用逐渐增大。③城市群绿色TFP指数转移具有显著的空间依赖性,低水平的邻域会阻碍本地区绿色TFP增长,而高水平的邻域对本地区绿色TFP具有明显的正向拉动作用。
Abstract:
With the increasing contradiction between economic growth and resource and environment constraints, upgrading green TFP has become an inevitable choice for the economic development of urban agglomerations. Based on the 2003-2016 urban data, the DEA undesired output-super-efficient SBM model measures the green TFP index of the five major urban agglomerations, and uses Kernel density estimation, traditional Markov chain and space Markov chain method from time. The two dimensions of space are used to analyze the dynamic evolution and trend prediction of the growth of urban green TFP. The conclusions of the study show that: first, the green TFP of the five major urban agglomerations as well as the urban agglomerations of all urban agglomerations are on the rise, and the technological progress index and technical efficiency index of most urban agglomerations are also on the rise. The difference of green TFP in urban agglomerations is generally expanding, and the difference in technical efficiency is the main reason. The distribution of the five major urban agglomerations and the urban agglomerations showed a right tailing and polarization trend. Second,in addition to the Chengdu urban agglomeration, the green TFP growth of other urban agglomerations has a club convergence effect. Financial development, factor endowment and technological progress did not significantly promote the green TFP of low-level cities, resulting in a significant level of convergence. However, as the green TFP index increases, the pulling effect of various influencing factors gradually increases. Third, the green TFP index transfer of urban agglomerations has significant spatial dependence. Low-level neighborhoods will hinder the growth of green TFP in the region, while high-level neighborhoods have a significant positive pull effect on green TFP in the region.

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更新日期/Last Update: 2020-01-13