资源科学 ›› 2020, Vol. 42 ›› Issue (6): 1087-1098.doi: 10.18402/resci.2020.06.07

• 资源经济 • 上一篇    下一篇

长江经济带城市生态效率的空间格局及演进趋势

陈明华(), 刘文斐, 王山, 刘玉鑫   

  1. 山东财经大学 经济学院,济南 250014
  • 收稿日期:2019-07-01 修回日期:2020-04-13 出版日期:2020-06-25 发布日期:2020-08-25
  • 作者简介:陈明华,男,山东临沂人,博士,教授,博士生导师,主要研究方向为经济增长与绿色发展。E-mail: chenminghua1978@163.com
  • 基金资助:
    国家社会科学基金项目(19BJY087);山东省社会科学规划研究优势学科项目(19BYSJ37);山东省社会科学规划研究项目(20CJJJ29)

Spatial pattern and temporal trend of urban ecological efficiency in the Yangtze River Economic Belt

CHEN Minghua(), LIU Wenfei, WANG Shan, LIU Yuxin   

  1. School of Economics, Shandong University of Finance and Economics, Jinan 250014, China
  • Received:2019-07-01 Revised:2020-04-13 Online:2020-06-25 Published:2020-08-25

摘要:

提升城市生态效率是长江经济带高质量发展的重要内容。本文运用MinDS模型测算了长江经济带城市生态效率,采用Dagum基尼系数、Kernel密度估计、Markov链分析等方法考察其空间格局及演进趋势,并借助分位数回归检验了关键影响因素。研究发现:①下游城市生态效率水平明显高于上、中游地区;②城市生态效率的总体差异较大但呈缩小趋势,下游城市生态效率的区域内差异始终最大,上游与下游的区域间差异最大。随着生态效率水平的提高,技术创新、公众环保意识等因素的影响系数逐渐下降,是总体及区域内差异缩小的重要原因;各地区影响因素及其作用大小和方向不尽相同,为城市生态效率的区域间差异提供了一定解释;③技术创新、经济发展水平等因素对低水平、高水平城市生态效率的影响强度相对较大,助推下游、上游地区分别呈现两极分化、多极分化趋势。经济发展水平、资源禀赋、产业结构等因素对低、中、高水平城市生态效率的正向影响系数均较小,城市生态效率发生转移的趋势并不明显,从而导致总体及各地区均存在“俱乐部趋同”和“马太效应”。本文采用至强有效前沿面最小距离模型对测度城市生态效率水平,有效避免传统DEA模型的缺陷,在对其总体空间差异识别的基础上进一步分解差异来源,并预测其长期转移趋势,由此深化了对长江经济带生态效率时空演进规律的认识和理解,对于协同推进长江经济带城市绿色发展具有一定借鉴意义。

关键词: 城市生态效率, MinDS模型, 空间格局, 演进趋势, 长江经济带

Abstract:

Improving urban ecological efficiency is an important aspect of high-quality development of the Yangtze River Economic Belt. In this study, the minimum distance to strong efficient frontier data envelopment analysis (MinDS) model was used to measure the ecological efficiency of the cities in the Yangtze River Economic Belt. The Dagum Gini coefficient, Kernel density estimation, and Markov chain analysis were used to examine its spatial pattern and temporal trend, and the key influencing factors were tested using quantile regression. The study found that: (1) The level of ecological efficiency in downstream cities was significantly higher than that in the upper and middle reaches. (2) The overall difference in urban ecological efficiency was large but gradually decreasing. The intraregional difference in urban ecological efficiency was always the largest in the downstream areas, and the largest regional difference was between the upstream and the downstream regions. With the improvement of the level of ecological efficiency, the impact coefficients of factors such as technological innovation and public awareness of environmental protection had gradually decreased, which is an important reason for the reduction of the overall and intraregional differences. The influencing factors in different regions and their roles and directions were different, which provides a certain explanation for the regional differences in urban ecological efficiency. (3) Factors such as technological innovation and economic development level had relatively greater impact on cities of low-level and high-level urban ecological efficiency, which resulted in the trend of polarization and multi-polarization in the downstream and upstream regions, respectively. Factors such as economic development level, resource endowment, and industrial structure had low positive impact coefficients on cities of low, medium, and high levels of urban ecological efficiency, and the trend of urban ecological efficiency shift was not obvious, resulting in the overall and regional “club convergence” and “Matthew effect.” This study used the MinDS model to measure the urban ecological efficiency level, effectively avoided the defects of the traditional DEA model, further decomposed the source of the difference on the basis of its overall spatial difference identification, and predicted its long-term transfer trend. This deepened the understanding of the pattern of spatial and temporal change of ecological efficiency in the Yangtze River Economic Belt, which provides some reference for the coordinated promotion of green development of the Yangtze River Economic Belt.

Key words: urban ecological efficiency, MinDS model, spatial pattern, evolution trend, Yangtze River Economic Belt