Analysis of Spatial-Temporal Evolution and Its Influencing Factors of Cities’ Green Economic Efficiency: A Case Study of Shandong Province, China
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School of Economics and Management, Shandong Agricultural University, Tai’an 271018, China
Irvine Valley College, CA,92618, USA
Submission date: 2023-11-04
Final revision date: 2023-12-08
Acceptance date: 2023-12-29
Online publication date: 2024-05-22
Publication date: 2024-06-27
Corresponding author
Ying Li   

School of Economics and Management, Shandong Agricultural University, Tai’an 271018, China
Pol. J. Environ. Stud. 2024;33(5):5473-5483
Based on panel data from 16 prefecture-level cities in Shandong Province from 2011 to 2020, the paper utilizes the super-efficiency SBM model with undesirable outputs to measure the green economic efficiency of each city. Spatial autocorrelation analysis and the natural breaks method are applied to analyze the spatial-temporal evolution of green economic efficiency. Lastly, a panel Tobit model is used to analyze the factors affecting green economic efficiency. The study’s outcomes are as outlined below: (1) The green economy efficiency of the 16 cities in Shandong Province showed an overall increasing trend from 2011 to 2020. However, there is a noticeable disparity in green economic efficiency among cities, with developed cities exhibiting relatively higher levels of efficiency. (2) The proportion of cities with high green economic efficiency steadily increases, and these highefficiency regions gradually cluster around the provincial capital and the eastern coastal areas. While there is heterogeneous clustering of green economic efficiency, the degree of this heterogeneity decreases over time. (3) Social security, economic development, and technological advancement significantly enhance green economic efficiency, whereas the industrial structure noticeably impedes efficiency. Environmental regulations and urbanization levels have a less pronounced impact on efficiency. Drawing from these findings, this chapter presents targeted policy recommendations.
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