Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Controlling carbon dioxide (CO2) emissions is the foundation of China’s goals to reach its carbon peak by 2030 and carbon neutrality by 2060. This study aimed to explore the spatial and temporal patterns and driving factors of CO2 emissions in China. Fir...

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Autores principales:	Bin Wang, Qiuxia Zheng, Ao Sun, Jie Bao, Dianting Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	CO<sub>2</sub> emissions ESDA PLS-SEM China Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/69980249167b462cb83dbbaef5dd3747
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spelling	oai:doaj.org-article:69980249167b462cb83dbbaef5dd37472021-11-11T18:16:09ZSpatio-Temporal Patterns of CO<sub>2</sub> Emissions and Influencing Factors in China Using ESDA and PLS-SEM10.3390/math92127112227-7390https://doaj.org/article/69980249167b462cb83dbbaef5dd37472021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2711https://doaj.org/toc/2227-7390Controlling carbon dioxide (CO<sub>2</sub>) emissions is the foundation of China’s goals to reach its carbon peak by 2030 and carbon neutrality by 2060. This study aimed to explore the spatial and temporal patterns and driving factors of CO<sub>2</sub> emissions in China. First, we constructed a conceptual model of the factors influencing CO<sub>2</sub> emissions, including economic growth, industrial structure, energy consumption, urban development, foreign trade, and government management. Second, we selected 30 provinces in China from 2006 to 2019 as research objects and adopted exploratory spatial data analysis (ESDA) methods to analyse the spatio-temporal patterns and agglomeration characteristics of CO<sub>2</sub> emissions. Third, on the basis of 420 data samples from China, we used partial least squares structural equation modelling (PLS-SEM) to verify the validity of the conceptual model, analyse the reliability and validity of the measurement model, calculate the path coefficient, test the hypothesis, and estimate the predictive power of the structural model. Fourth, multigroup analysis (MGA) was used to compare differences in the influencing factors for CO<sub>2</sub> emissions during different periods and in various regions of China. The results and conclusions are as follows: (1) CO<sub>2</sub> emissions in China increased year by year from 2006 to 2019 but gradually decreased in the eastern, central, and western regions. The eastern coastal provinces show spatial agglomeration and CO<sub>2</sub> emission hotspots. (2) Confirmatory analysis showed that the measurement model had high reliability and validity; four latent variables (industrial structure, energy consumption, economic growth, and government management) passed the hypothesis test in the structural model and are the determinants of CO<sub>2</sub> emissions in China. Meanwhile, economic growth is a mediating variable of industrial structure, energy consumption, foreign trade, and government administration on CO<sub>2</sub> emissions. (3) The calculated results of the R<sup>2</sup> and Q<sup>2</sup> values were 76.3% and 75.4%, respectively, indicating that the structural equation model had substantial explanatory and high predictive power. (4) Taking two development stages and three main regions as control groups, we found significant differences between the paths affecting CO<sub>2</sub> emissions, which is consistent with China’s actual development and regional economic pattern. This study provides policy suggestions for CO<sub>2</sub> emission reduction and sustainable development in China.Bin WangQiuxia ZhengAo SunJie BaoDianting WuMDPI AGarticleCO<sub>2</sub> emissionsESDAPLS-SEMChinaMathematicsQA1-939ENMathematics, Vol 9, Iss 2711, p 2711 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CO<sub>2</sub> emissions ESDA PLS-SEM China Mathematics QA1-939
spellingShingle	CO<sub>2</sub> emissions ESDA PLS-SEM China Mathematics QA1-939 Bin Wang Qiuxia Zheng Ao Sun Jie Bao Dianting Wu Spatio-Temporal Patterns of CO<sub>2</sub> Emissions and Influencing Factors in China Using ESDA and PLS-SEM
description	Controlling carbon dioxide (CO<sub>2</sub>) emissions is the foundation of China’s goals to reach its carbon peak by 2030 and carbon neutrality by 2060. This study aimed to explore the spatial and temporal patterns and driving factors of CO<sub>2</sub> emissions in China. First, we constructed a conceptual model of the factors influencing CO<sub>2</sub> emissions, including economic growth, industrial structure, energy consumption, urban development, foreign trade, and government management. Second, we selected 30 provinces in China from 2006 to 2019 as research objects and adopted exploratory spatial data analysis (ESDA) methods to analyse the spatio-temporal patterns and agglomeration characteristics of CO<sub>2</sub> emissions. Third, on the basis of 420 data samples from China, we used partial least squares structural equation modelling (PLS-SEM) to verify the validity of the conceptual model, analyse the reliability and validity of the measurement model, calculate the path coefficient, test the hypothesis, and estimate the predictive power of the structural model. Fourth, multigroup analysis (MGA) was used to compare differences in the influencing factors for CO<sub>2</sub> emissions during different periods and in various regions of China. The results and conclusions are as follows: (1) CO<sub>2</sub> emissions in China increased year by year from 2006 to 2019 but gradually decreased in the eastern, central, and western regions. The eastern coastal provinces show spatial agglomeration and CO<sub>2</sub> emission hotspots. (2) Confirmatory analysis showed that the measurement model had high reliability and validity; four latent variables (industrial structure, energy consumption, economic growth, and government management) passed the hypothesis test in the structural model and are the determinants of CO<sub>2</sub> emissions in China. Meanwhile, economic growth is a mediating variable of industrial structure, energy consumption, foreign trade, and government administration on CO<sub>2</sub> emissions. (3) The calculated results of the R<sup>2</sup> and Q<sup>2</sup> values were 76.3% and 75.4%, respectively, indicating that the structural equation model had substantial explanatory and high predictive power. (4) Taking two development stages and three main regions as control groups, we found significant differences between the paths affecting CO<sub>2</sub> emissions, which is consistent with China’s actual development and regional economic pattern. This study provides policy suggestions for CO<sub>2</sub> emission reduction and sustainable development in China.
format	article
author	Bin Wang Qiuxia Zheng Ao Sun Jie Bao Dianting Wu
author_facet	Bin Wang Qiuxia Zheng Ao Sun Jie Bao Dianting Wu
author_sort	Bin Wang
title	Spatio-Temporal Patterns of CO<sub>2</sub> Emissions and Influencing Factors in China Using ESDA and PLS-SEM
title_short	Spatio-Temporal Patterns of CO<sub>2</sub> Emissions and Influencing Factors in China Using ESDA and PLS-SEM
title_full	Spatio-Temporal Patterns of CO<sub>2</sub> Emissions and Influencing Factors in China Using ESDA and PLS-SEM
title_fullStr	Spatio-Temporal Patterns of CO<sub>2</sub> Emissions and Influencing Factors in China Using ESDA and PLS-SEM
title_full_unstemmed	Spatio-Temporal Patterns of CO<sub>2</sub> Emissions and Influencing Factors in China Using ESDA and PLS-SEM
title_sort	spatio-temporal patterns of co<sub>2</sub> emissions and influencing factors in china using esda and pls-sem
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/69980249167b462cb83dbbaef5dd3747
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Spatio-Temporal Patterns of CO<sub>2</sub> Emissions and Influencing Factors in China Using ESDA and PLS-SEM

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