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.
| Published in | International Journal of Environmental Protection and Policy (Volume 12, Issue 1) |
| DOI | 10.11648/j.ijepp.20241201.12 |
| Page(s) | 7-20 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Carbon Intensity, Urban Green Competitiveness, Panel Regression Model, Moran Index
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APA Style
Tao, S., Yu, W., Pengyan, W., Yuxiao, L., Nuo, W. (2024). Calculation of Urban Green Competitiveness and Analysis of Spatial and Temporal Evolution Characteristics in China. International Journal of Environmental Protection and Policy, 12(1), 7-20. https://doi.org/10.11648/j.ijepp.20241201.12
ACS Style
Tao, S.; Yu, W.; Pengyan, W.; Yuxiao, L.; Nuo, W. Calculation of Urban Green Competitiveness and Analysis of Spatial and Temporal Evolution Characteristics in China. Int. J. Environ. Prot. Policy 2024, 12(1), 7-20. doi: 10.11648/j.ijepp.20241201.12
AMA Style
Tao S, Yu W, Pengyan W, Yuxiao L, Nuo W. Calculation of Urban Green Competitiveness and Analysis of Spatial and Temporal Evolution Characteristics in China. Int J Environ Prot Policy. 2024;12(1):7-20. doi: 10.11648/j.ijepp.20241201.12
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Download@article{10.11648/j.ijepp.20241201.12,
author = {Song Tao and Wang Yu and Wang Pengyan and Lei Yuxiao and Wang Nuo},
title = {Calculation of Urban Green Competitiveness and Analysis of Spatial and Temporal Evolution Characteristics in China},
journal = {International Journal of Environmental Protection and Policy},
volume = {12},
number = {1},
pages = {7-20},
doi = {10.11648/j.ijepp.20241201.12},
url = {https://doi.org/10.11648/j.ijepp.20241201.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepp.20241201.12},
abstract = {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.
},
year = {2024}
}
TY - JOUR T1 - Calculation of Urban Green Competitiveness and Analysis of Spatial and Temporal Evolution Characteristics in China AU - Song Tao AU - Wang Yu AU - Wang Pengyan AU - Lei Yuxiao AU - Wang Nuo Y1 - 2024/03/13 PY - 2024 N1 - https://doi.org/10.11648/j.ijepp.20241201.12 DO - 10.11648/j.ijepp.20241201.12 T2 - International Journal of Environmental Protection and Policy JF - International Journal of Environmental Protection and Policy JO - International Journal of Environmental Protection and Policy SP - 7 EP - 20 PB - Science Publishing Group SN - 2330-7536 UR - https://doi.org/10.11648/j.ijepp.20241201.12 AB - 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. VL - 12 IS - 1 ER -
School of Economics and Resource Management, Beijing Normal University, Beijing, China
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