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Impact mechanism and spatial effects of urbanization on carbon emissions in Jiangsu, China
Based on panel data concerning urbanization and carbon dioxide emissions in the period 2000–2014, this study analyzes the effects of the spatial interaction between population urbanization and carbon emissions in Jiangsu province using Moran's global and local space autocorrelation index, and his scatter diagram. The results show a significant spatial interregional dependence. To test the spatial interaction of carbon emissions, this study constructs four spatial econometric models—a static panel model, a spatial lag model, a spatial autocorrelation model, and a dynamic spatial panel model—from both static and dynamic perspectives. The main factors affecting carbon dioxide emissions, such as the population, industry, FDI (Foreign Direct Investment), environment, and living standards, are analyzed according to the impact mechanism. Empirical results show a significant spatial spread effect and interaction effect among regions. Population urbanization, GDP (Gross Domestic Product) per capita, and the environment were found to be the most important factors affecting carbon emissions. In response, regions with high carbon emissions should make full use of this spread effect to correct imbalances in regional development so that the goals of urbanization and carbon emission reduction can be pursued and coordinated to promote a low-carbon economy.
Impact mechanism and spatial effects of urbanization on carbon emissions in Jiangsu, China
Based on panel data concerning urbanization and carbon dioxide emissions in the period 2000–2014, this study analyzes the effects of the spatial interaction between population urbanization and carbon emissions in Jiangsu province using Moran's global and local space autocorrelation index, and his scatter diagram. The results show a significant spatial interregional dependence. To test the spatial interaction of carbon emissions, this study constructs four spatial econometric models—a static panel model, a spatial lag model, a spatial autocorrelation model, and a dynamic spatial panel model—from both static and dynamic perspectives. The main factors affecting carbon dioxide emissions, such as the population, industry, FDI (Foreign Direct Investment), environment, and living standards, are analyzed according to the impact mechanism. Empirical results show a significant spatial spread effect and interaction effect among regions. Population urbanization, GDP (Gross Domestic Product) per capita, and the environment were found to be the most important factors affecting carbon emissions. In response, regions with high carbon emissions should make full use of this spread effect to correct imbalances in regional development so that the goals of urbanization and carbon emission reduction can be pursued and coordinated to promote a low-carbon economy.
Impact mechanism and spatial effects of urbanization on carbon emissions in Jiangsu, China
Wang, Shijin (author) / Li, Cunfang (author) / Ma, Yanyan (author)
2018-09-01
15 pages
Article (Journal)
Electronic Resource
English
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