Analysis of China’s carbon emission driving factors based on the perspective of eight major economic regions

Chen, Jingquan; Lian, Xinyan; Su, Hanning; Zhang, Ziyan; Ma, Xiaojun; Chang, Baishu

doi:10.1007/s11356-020-11044-z

Cited by 43 publications

(28 citation statements)

References 39 publications

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“…Quantitative studies of CO 2 emissions have been a hot topic in recent years. Previous studies widely used include the Kaya identity [38][39][40]; the IPAT model [41][42][43]; the arithmetic mean divisia index (AMDI); the logarithmic mean divisia index (LMDI) [44][45][46][47]; and the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model [48][49][50][51]. This study integrated ESDA and PLS-SEM to study the spatio-temporal differences and influencing factors of CO 2 emissions in China.…”

Section: Methodsmentioning

confidence: 99%

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

Wang

Zheng²,

Sun

et al. 2021

Mathematics

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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. First, we constructed a conceptual model of the factors influencing CO2 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 CO2 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 CO2 emissions during different periods and in various regions of China. The results and conclusions are as follows: (1) CO2 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 CO2 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 CO2 emissions in China. Meanwhile, economic growth is a mediating variable of industrial structure, energy consumption, foreign trade, and government administration on CO2 emissions. (3) The calculated results of the R2 and Q2 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 CO2 emissions, which is consistent with China’s actual development and regional economic pattern. This study provides policy suggestions for CO2 emission reduction and sustainable development in China.

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Section: Methodsmentioning

confidence: 99%

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

Wang

Zheng²,

Sun

et al. 2021

Mathematics

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“…China pledges to reach the CO 2 emission peak no later than 2030, and strive to achieve carbon neutrality by 2060. Therefore, the sustainable development of economy and environment has become a top priority and how to reduce carbon emission while promoting economic growth is a key issue (Chen et al 2021;Fang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Industrialization, urbanization, and carbon emission efficiency of Yangtze River Economic Belt—empirical analysis based on stochastic frontier model

Zhang

Chen

2021

Environ Sci Pollut Res

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Carbon emission efficiency directly determines the level of green economic development. Based on the panel data of China's Yangtze River Economic Belt (YEB) from 2008 to 2017, this paper uses the stochastic frontier analysis (SFA) model to analyze the overall carbon emission efficiency level, influencing factors, and changing trends, with a view to discussing the relationship between economic development and carbon emission efficiency. The results suggest, first, the overall carbon emission efficiency of the YEB is on an upward trend, but there is still much room for improvement. Second, the impact of industrialization and urbanization on carbon emission efficiency follows a U-shaped. As industrialization and urbanization progress, the impact on carbon emission efficiency shows a downward and then upward trend. Third, due to the rebound effect, technological progress has a slight negative impact on carbon emission efficiency. Energy consumption structure, government intervention, and foreign trade are all negative incentive factors. Therefore, efforts to improve carbon emission efficiency in the YEB should focus on transforming the economic growth model, adjusting the industrial structure, improving the energy consumption structure, and innovating green technology. The research results can provide a reference for the government policymakers to develop a green economy.

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“…This detection method can dynamically analyze the influence of a single driving factor from the time gradient and observe the multiple spatial superposition interaction effects between the key interaction factors. To clarify the differences and advantages of this study, the results are analyzed and compared with Liu et al ( 2020 ), Chen et al ( 2021a , b ), Han et al ( 2020 ), Zhao and Wu ( 2018 ), Yang et al ( 2019 ), and Yang and Liu ( 2012 ). Since the research method of this paper is most similar to that of Zhao and Wu ( 2018 ), the conclusion of this paper is mainly compared with the research results of Zhao and Wu ( 2018 ) in detail.…”

Section: Discussionmentioning

confidence: 99%

“…And at this stage, China’s main primary energy consumption is still coal, accounting for more than 50%, so China’s carbon emission reduction pressure is enormous. China’s resource endowment, energy structure, industrial structure, and other aspects are significantly different from Western countries, so we can’t copy the experience of western countries mechanically and need to find a carbon–neutral path suitable for China’s national conditions (Chen et al 2021a , b ).…”

Section: Introductionmentioning

confidence: 99%

Research on carbon emission differences decomposition and spatial heterogeneity pattern of China’s eight economic regions

Zhang

2022

Environ Sci Pollut Res

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To explore the sources of regional carbon emission differences and the evolution characteristics of spatial heterogeneity pattern, this paper first calculates the corresponding carbon emissions according to the relevant statistical data of eight economic regions in China from 2005 to 2019. It analyzes the overall differences and temporal and spatial evolution characteristics of regional carbon emissions combined with the visualization method of GIS. Then, the total carbon emission difference is decomposed by the Theil index to find out the primary sources affecting the regional carbon emission difference. Finally, the driving factors affecting the spatial heterogeneity pattern of regional carbon emissions are studied with the help of the Geodetector method. The results show that (1) significant differences in carbon emissions among China’s eight economic regions. The contribution rate of inter-regional and intra-regional differences of carbon emissions in different regions to the overall carbon emission difference is diverse. (2) Regional carbon emissions are affected by single driving factors and the interaction of two driving factors. The interaction has an increasing impact on the determinant of regional carbon emission spatial differentiation. (3) The factor detection results and interaction detection results, respectively, show that the level of energy consumption, industrialization, and technological development has always been the main driving factors affecting the spatial heterogeneity pattern of regional carbon emissions, and the critical interaction factors have multiple spatial superposition interaction effects. Therefore, regional carbon emission reduction should consider the national strategic objectives and own regional characteristics and implement differentiated emission reduction schemes.

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Analysis of China’s carbon emission driving factors based on the perspective of eight major economic regions

Cited by 43 publications

References 39 publications

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

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

Industrialization, urbanization, and carbon emission efficiency of Yangtze River Economic Belt—empirical analysis based on stochastic frontier model

Research on carbon emission differences decomposition and spatial heterogeneity pattern of China’s eight economic regions

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