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
| [1] | Ni Peng Fei. An Emperical Test about Contribution of Infrastructure to Urban Competitiveness [J]. China Industrial Economy, 2002(05): 62-69. https://doi.org/10.19744/j.cnki.11-1235/f.2015.11.004 |
| [2] | Porter M E, Linde C. Toward a new conception of the environment-competitiveness relationship [J]. Journal of economic perspectives, 1995, 9(4): 97-118. https://doi.org/10.1257/jep.9.4.97 |
| [3] | Li Yan, Zhu Jianmin. Construction and empirical research of green development competitiveness evaluation index system under eco-city planning [J]. Journal of Central University of Finance and Economics, 2017, No. 364(12): 130-138. |
| [4] | Wang Y, Bai Y, Quan T, et al. Influence and effect of industrial agglomeration on urban green total factor productivity—On the regulatory role of innovation agglomeration and institutional distance [J]. Economic Analysis and Policy, 2023, 78: 1158-1173. https://doi.org/https://doi.org/10.1016/j.eap.2023.04.024 |
| [5] | LI Jian, LI Yanxia, ZHANG Jie. Measurement and evolution analysis of green competitiveness index of three major city clusters [J]. Statistics and Decision Making, 2022, 38(20): 93-97. https://doi.org/10.13546/j.cnki.tjyjc.2022.20.018 |
| [6] | ZHANG Zeyi, LUO Xuehua. Measuring the efficiency of green development in Chinese cities [J]. Urban Issues, 2019(02): 12-20. https://doi.org/10.13239/j.bjsshkxy.cswt.190202 |
| [7] | ZHOU Liang, CHE Lei, ZHOU Chenghu. Characteristics of spatial and temporal evolution of green development efficiency and influencing factors in Chinese cities [J]. Journal of Geography, 2019, 74(10): 2027-2044. https://doi.org/10.13239/j.bjsshkxy.cswt.190202 |
| [8] | Cheng X, Long R, Chen H. Obstacle diagnosis of green competition promotion: a case study of provinces in China based on catastrophe progression and fuzzy rough set methods [J]. Environmental Science & Pollution Research, 2017. |
| [9] | WANG Shaojian, HUANG Yongyuan. Spatial spillover effects and drivers of carbon emission intensity in Chinese cities [J]. Journal of Geography, 2019, 74(06): 1131-1148. |
| [10] | JIANG Sanliang, JIA Fangfang. How digital economy promotes carbon emission reduction-an examination based on urban carbon emission intensity and carbon emission efficiency [J]. Research World, 2023(01): 14-21. https://doi.org/10.13778/j.cnki.11-3705/c.2023.01.002 |
| [11] | YU Xiang, CHEN Nan, LI Manqi. Carbon Emission Characteristics and Carbon Emission Reduction Paths of Low-Carbon Pilot Cities in China [J]. China Population-Resources and Environment, 2020, 30(07): 1-9. |
| [12] | Qiu S, Wang Z, Liu S. The policy outcomes of low-carbon city construction on urban green development: Evidence from a quasi-natural experiment conducted in China [J]. Sustainable Cities and Society, 2021, 66: 102699. https://doi.org/10.1016/j.scs.2020.102699 |
| [13] | Lan T, Shao G F, Xu Z B, 2023. Considerable role of urban functional form in low-carbon city development [J]. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2023.136256 |
| [14] | Yuan K, Hu B, Niu T, et al. Competitiveness evaluation and obstacle factor analysis of urban green and low-carbon development in Beijing-Tianjin-Hebei Cities [J]. Mathematical Problems in Engineering, 2022, 2022: 1-15. https://doi.org/10.1155/2022/5230314 |
| [15] | Shi C, Guo N, Gao X, et al. How carbon emission reduction is going to affect urban resilience [J]. Journal of Cleaner Production, 2022, 372: 133737. https://doi.org/https://doi.org/10.1016/j.jclepro.2022.133737 |
| [16] | Li J, Shi J, Duan K, et al. Efficiency of China's urban development under carbon emission constraints: A city-level analysis [J]. Physics and Chemistry of the Earth, Parts A/B/C, 2022, 127: 103182. https://doi.org/10.1016/j.pce.2022.103182 |
| [17] | Wang Z, Liang F, Li C, et al. Does China's low-carbon city pilot policy promote green development? Evidence from the digital industry [J]. Journal of Innovation & Knowledge, 2023, 8(2): 100339. https://doi.org/10.1016/j.jik.2023.100339 |
| [18] | Jia Z, Wen S, Liu Y. China's urban-rural inequality caused by carbon neutrality: A perspective from carbon footprint and decomposed social welfare [J]. Energy Economics, 2022, 113: 106193. https://doi.org/10.1016/j.eneco.2022.106193 |
| [19] | Chen H, Qi S, Zhang J. Towards carbon neutrality with Chinese characteristics: From an integrated perspective of economic growth-equity-environment [J]. Applied Energy, 2022, 324: 119719. https://doi.org/10.1016/j.apenergy.2022.119719 |
| [20] | Zhang Z, Zuo Q, Li D, et al. The relationship between resource utilization and high-quality development in the context of carbon neutrality: Measurement, assessment and identification [J]. Sustainable Cities and Society, 2023, 94: 104551. https://doi.org/10.1016/j.scs.2023.104551 |
| [21] | CHEN Yunping, HUANG Xiaoyong. Exploratory analysis of factors influencing regional green competitiveness [J]. Macroeconomic Research, 2012(12): 60-67. https://doi.org/10.16304/j.cnki.11-3952/f.2012.12.007 |
| [22] | HUANG Xiaoyong, CHA Yuxin. Research on the impact of environmental policy innovation on urban green competitiveness in China [J]. China Soft Science, 2022(08): 140-150. |
| [23] | Fang G, Gao Z, Wang L, et al. How does green innovation drive urban carbon emission efficiency?—Evidence from the Yangtze River Economic Belt [J]. Journal of Cleaner Production, 2022, 375: 134196. https://doi.org/10.1016/j.jclepro.2022.134196 |
| [24] | Hu R, Xu W, Liu L, et al. The impact of urban agglomerations on carbon emissions in China: Spatial scope and mechanism [J]. Journal of Cleaner Production, 2023, 419: 138250. https://doi.org/10.1016/j.jclepro.2023.138250 |
| [25] | Chen, J., Gao, M., Cheng, S. Hou, W., Song, M., Liu, X., Liu, Y., Shan, Y. (2020). “County-level CO2 emissions and sequestration in China during 1997–2017.”Scientific Data, 7, 391. https://doi.org/10.1038/s41597-020-00736-3 |
| [26] | LU Jin, WANG Xiaofei, LIU Lu. The industrial structure upgrading effect of low-carbon city policy--a quasi-natural experiment based on low-carbon city pilot [J]. Journal of Xi'an Jiaotong University (Social Science Edition), 2020, 40(02): 104-115. https://doi.org/10.15896/j.xjtuskxb.202002012 |
| [27] | XU Min, JIANG Yong. Can China's industrial structure upgrade narrow the urban-rural consumption gap? [J]. Research on Quantitative and Technical Economics, 2015, 32(03): 3-21. https://doi.org/10.13653/j.cnki.jqte.2015.03.001 |
| [28] | CHEN Shuo, GAO Lin. Central-Local Relations: Reassessment of the Measurement and Mechanism of Fiscal Decentralization [J]. Management World, 2012(06): 43-59. https://doi.org/10.19744/j.cnki.11-1235/f.2012.06.005 |
| [29] | YUE Shujing, ZOU Yulin, HU Yaoyu. The impact of industrial agglomeration on the efficiency of green development in Chinese cities [J]. Urban Issues, 2015(10): 49-54. https://doi.org/10.13239/j.bjsshkxy.cswt.151008 |
| [30] | Wu H. Carbon Emission Reduction in Chinese Provinces: Spatial and Temporal Patterns, Evolution Mechanisms and Policy Suggestions-Based on the Theory and Methods of Spatial Econometrics [J]. Management World, 2015(11): 3-10. https://doi.org/10.19744/j.cnki.11-1235/f.2015.11.004 |
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
