Abstract
This paper uses a panel data sample of 30 resource-based cities (hereinafter referred to as R-B cities) in China from 2009 to 2019, constructs the green economic efficiency level (hereinafter referred to as GEE) using the super-SBM model, incorporates the GEE value into the endogenous economic growth model, combines the difference equation and the level equation, and estimates the relationship between the green efficiency level and economic growth using the systematic GMM method. The study came to the following major conclusions: First, green development in Chinese resource-based cities is moderately high, and green economic efficiency varies by region, with a relatively low level of GEE in the central region and a relatively high level of GEE in the eastern and western regions. Second, on both static and dynamic dimensions, Chinese resource-based cities can be classified into seven types based on their level of green development. Third, the GEE of Chinese resource-based cities has a significant positive relationship with economic growth, with the effect of green economic efficiency on economic growth being stronger in the central and northeastern regions.
Highlights
Green economy is based on the traditional economy, making full use of traditional factors such as social labor, capital, and natural resources, while using clean production technology to reproduce various resources and promote economic growth while avoiding resource waste and environmental pollution [1]
Khan et al concentrated on the connection between financial development and natural maintainability based on panel data of 43 different countries around the world [3]; Wang et al studied the relationship between foreign opening and green technological progress based on the comparison of two countries [4]; Huang and Hua studied the bioenergy intensity of European countries [5]; from the study of China, at the macronational level, Yuan and Xiang explored the impact of FDI on carbon emissions in China [6]; at the regional and provincial levels, Sun et al studied the impact of innovation on ecoefficiency from a provincial perspective [7]; Zhao et al explored the green transition in China [8]; and Cui et al studied the utility of carbon emissions on the provincial economy of China [9]
Further study shows that the presence of unobserved individual fixed effects will lead to upward biased estimates of the mixed OLS estimates of the lagged coefficients of the dependent variable, while the fixed-effects estimates in the “small time dimension and large crosssectional dimension” panels will produce downward biased estimates. erefore, the consistent estimates of the coefficients of the lagged terms of the dependent variable will be in between the POOLED estimates and the FE estimates. is paper performs mixed OLS and fixed-effects estimation on the dynamic panel model to test the stability of the systematic GMM estimates
Summary
Green economy is based on the traditional economy, making full use of traditional factors such as social labor, capital, and natural resources, while using clean production technology to reproduce various resources and promote economic growth while avoiding resource waste and environmental pollution [1]. Is paper breaks this tradition and proposes that the green economy should be based on clean production technology from the perspective of inputs and outputs of production functions, making full use of various inputs, preventing environmental pollution and resource waste, and achieving green and sustainable economic development. Is paper mainly studies technical efficiency to reflect the development level of green economy in resource-based cities. Green economic efficiency is based on resource input and environmental cost considerations and evaluates the economic efficiency indicators of a country or region It includes two aspects: (1) the effectiveness of the input elements in the production process; (2) the “green” economic benefits after the integration of environmental element inputs and environmental pollution factors. + β ln Li,t + c ln Hi,t + φ1 ln RDi,t + φ2 ln ISi,t + εi,t
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