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Calculation of Urban Green Competitiveness and Analysis of Spatial and Temporal Evolution Characteristics in China
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It is particularly important to analyze the influencing factors of urban green competitiveness and the spatial distribution characteristics under the constraint of carbon emissions. The research ideas of this paper: firstly, this paper selects the carbon emission intensity and urban green competitiveness data in 2010, 2013, 2015, 2018 and 2020 for panel data regression; secondly, this paper applies a variety of methods to carry out the robustness test, and the results show that the regression model is better, and analyzes the development of urban green competitiveness for the heterogeneity of large cities and small cities; subsequently, the use of inverse geographic matrix to analyze the spatial correlation between the global Moran index and local Moran index for urban green competitiveness, and to analyze the spatial and temporal pattern evolution of urban green competitiveness. The conclusions of the study show that, from the viewpoint of influencing factors, carbon emission intensity presents a significant negative effect on the development of urban green competitiveness, and has a greater impact on the green competitiveness of large cities than that of small cities. From the perspective of spatial correlation, urban green competitiveness presents positive spatial correlation and shows a growing trend over time. Finally, this paper puts forward relevant policy recommendations based on the findings of the study.
Science Publishing Group
Title: Calculation of Urban Green Competitiveness and Analysis of Spatial and Temporal Evolution Characteristics in China
Description:
It is particularly important to analyze the influencing factors of urban green competitiveness and the spatial distribution characteristics under the constraint of carbon emissions.
The research ideas of this paper: firstly, this paper selects the carbon emission intensity and urban green competitiveness data in 2010, 2013, 2015, 2018 and 2020 for panel data regression; secondly, this paper applies a variety of methods to carry out the robustness test, and the results show that the regression model is better, and analyzes the development of urban green competitiveness for the heterogeneity of large cities and small cities; subsequently, the use of inverse geographic matrix to analyze the spatial correlation between the global Moran index and local Moran index for urban green competitiveness, and to analyze the spatial and temporal pattern evolution of urban green competitiveness.
The conclusions of the study show that, from the viewpoint of influencing factors, carbon emission intensity presents a significant negative effect on the development of urban green competitiveness, and has a greater impact on the green competitiveness of large cities than that of small cities.
From the perspective of spatial correlation, urban green competitiveness presents positive spatial correlation and shows a growing trend over time.
Finally, this paper puts forward relevant policy recommendations based on the findings of the study.
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