2022
DOI: 10.3390/ijerph19031226
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Spatial Heterogeneity of Carbon Emissions and Its Influencing Factors in China: Evidence from 286 Prefecture-Level Cities

Abstract: In the face of the severe challenge of global warming, promoting low-carbon emission reductions is an important measure to cope with global climate change and achieve a green cycle of sustainable development. The purpose of this study was to reveal the spatial heterogeneity of carbon emissions and the influencing factors in 286 prefecture-level-and-above cities in China, and to provide an empirical basis for the formulation of low-carbon emission reduction policies in China. This study used a combination of co… Show more

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Cited by 25 publications
(16 citation statements)
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“…The present study considers these factors and will help to reveal the influence mechanism of carbon emissions. Li combined all influencing factors and put all factors into the model for analysis, which can make the accuracy and stability of the results suffer [ 27 ]. We use the GRA algorithm to eliminate minor variables and optimize the model structure.…”
Section: Discussionmentioning
confidence: 99%
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“…The present study considers these factors and will help to reveal the influence mechanism of carbon emissions. Li combined all influencing factors and put all factors into the model for analysis, which can make the accuracy and stability of the results suffer [ 27 ]. We use the GRA algorithm to eliminate minor variables and optimize the model structure.…”
Section: Discussionmentioning
confidence: 99%
“…By analyzing the indirect carbon emissions of China’s residential consumption, Yuan [ 26 ] found that the driving role of consumption level and consumption structure on carbon emissions cannot be ignored. For the first time, Li [ 27 ] included the number of industrial enterprises above scale in the analysis of carbon emission factors and the results showed a significant effect on carbon emissions in some regions.…”
Section: Related Workmentioning
confidence: 99%
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“…The scatter plot is the most visual method used to express correlation analysis. The correlation coefficient is a collective term for a class of indicators that measure the correlation between variables ( 38 , 39 ).…”
Section: Methodsmentioning
confidence: 99%
“…The common correlations are linear correlation, curvilinear correlation, positive correlation, and negative correlation. The Pearson correlation coefficient, also known as the product–difference correlation coefficient, is a common metric for quantitatively describing the degree of linear correlation [ 69 , 70 , 71 , 72 ]. The formula for measuring the Pearson correlation coefficient is the following: …”
Section: Methods and Datamentioning
confidence: 99%