Per-capita carbon emissions in 147 countries: The effect of economic, energy, social, and trade structural changes

Li, Rongrong; Wang, Qiang; Liu, Yi; Jiang, Rui

doi:10.1016/j.spc.2021.02.031

Cited by 472 publications

(157 citation statements)

References 76 publications

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“…As a matter of fact, with the influx of migrants into cities, the construction of infrastructure, such as buildings and transportation, is accelerating, which will bring about the rapid development of the urban economy and inevitably increase CO 2 emissions. (6) H6: Economic growth has a positive impact on CO 2 emissions, which is consistent with the findings of Cui et al [16] and Li et al [71]. China is currently at the central stage of economic development and undergoing large-scale economic growth and rapid development.…”

Section: Theoretical Implicationssupporting

confidence: 85%

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: Theoretical Implicationssupporting

confidence: 85%

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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show abstract

“…In the four municipalities directly under the Central Government, Shanghai has always ranked first in carbon The proportion of fuel j consumption in urban and rural areas in municipality n CI n ij Sector emission coefficient for fuel j in sector i in province n emissions, followed by Chongqing, which is mainly due to Shanghai's unique economic status and industrial structure. Li et al (2021) show that there is a long-term equilibrium relationship between the intensity of carbon emissions and the three factors of energy consumption structure, energy intensity, and industrial structure. The three driving factors can be arranged according to their importance as follows: industrial structure, energy consumption structure, and energy intensity.…”

Section: Analysis Of Carbon Emissions Characteristics Of Each Municipalitymentioning

confidence: 98%

Driving factors of carbon emissions in China’s municipalities: a LMDI approach

Liu

Jiang

Liu

et al. 2021

Environ Sci Pollut Res

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“…The study found that all variables have affected carbon emissions. Li et al (2021) discussed the effect of economic growth, economic structure, and other factors on per capita carbon emissions in 147 countries from 1990 to 2015. The results showed that at the global level, economic growth and economic structure were respectively the most significant positive and negative factors affecting carbon emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

The Major Driving Factors of Carbon Emissions in China and Their Relative Importance: An Application of the LASSO Model

Shum

Ma²,

Lin³

et al. 2021

Front. Energy Res.

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China is one of the biggest energy consumers and carbon emitters in the world. Understanding the factors affecting carbon emissions is critical for policymakers to control the rising trend of carbon emissions. This paper investigates the relative importance of carbon emissions drivers in China. Literature review has been carried out to determine a set of predominant independent variables; the LASSO model is then introduced to rank the relative importance among the set of independent variables. The results find that 1) carbon emissions were mainly driven by economic growth and energy consumption followed by population size and industrialization; and 2) income growth slowed down carbon emissions during the studied period, but it is the least significant factor among the other factors. The ranking allows policy makers to focus on the most critical contributors to carbon emissions and gives policymakers more flexibility in determining policy interventions.

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Per-capita carbon emissions in 147 countries: The effect of economic, energy, social, and trade structural changes

Cited by 472 publications

References 76 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

Driving factors of carbon emissions in China’s municipalities: a LMDI approach

The Major Driving Factors of Carbon Emissions in China and Their Relative Importance: An Application of the LASSO Model

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