Research and Analysis on the Influencing Factors of China’s Carbon Emissions Based on a Panel Quantile Model

Liu, Yunlong; Chang, Xianlin; Huang, Chengfeng

doi:10.3390/su14137791

Cited by 11 publications

(6 citation statements)

References 32 publications

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“…Liu et al studied the influencing factors of carbon emissions in China on the basis of the fixed effect panel quantile regression model. The results show that per capita GDP, population and 'Atmosphere ten' have a higher impact on carbon emissions in low carbon emission areas than in high carbon emission areas, and fixed asset investment and energy intensity have a stronger impact on carbon emissions in high carbon emission areas [25].…”

Section: Introductionmentioning

confidence: 91%

Carbon Emission Efficiency and Low-Carbon Optimization in Shanxi Province under “Dual Carbon” Background

Guo

Wang

et al. 2022

Energies

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As an important energy base in China, Shanxi Province, in its economic development, has depended heavily on coal resources. However, enormous coal consumption produces a large amount of carbon dioxide and an aggravated ecological problem. In this paper, the super efficiency slack-based measure and data envelopment analysis (SBM-DEA) model and Malmquist index were used to calculate the energy consumption and carbon emission efficiency of 11 prefecture-level cities in Shanxi in the period from 2000 to 2020. The results were as follows: (1) The primary form of energy consumption in Shanxi Province is the burning of coal, and the carbon emission efficiencies of the 11 prefecture-level cities are quite different. The technical efficiency, technological progress, and total factor productivity of Taiyuan and Shuozhou were found to be greater than 1 compared to other cities in Shanxi. (2) On the whole, although the carbon emission rate of Shanxi Province has slowed down, it still faces the problems of a large total emission base, high carbon emission intensity, and low efficiency of the unit carbon emission output. Industrial structure is the key factor restricting improvements in the efficiency of carbon emissions in Shanxi Province. (3) From the spatial and temporal evolution of the carbon emission efficiency in Shanxi Province, we can see that the carbon emission efficiency of the whole province gradually dispersed and improved from the central and northern regions to the southern regions during the period of 2000–2020. In order to achieve a low-carbon economy, Shanxi Province must optimize its industrial structure, accelerate research and development in low-carbon technologies, adjust the energy structure, and promote the construction of an energy supply system with clean low-carbon energy as the main source.

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Section: Introductionmentioning

confidence: 91%

Carbon Emission Efficiency and Low-Carbon Optimization in Shanxi Province under “Dual Carbon” Background

Guo

Wang

et al. 2022

Energies

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“…Furthermore, quantile regression allows for the observation of the tails of the dependent variable, reflecting more accurately the effect of the independent variable on the shape of the conditional distribution of the dependent variable. Moreover, it does not make any assumptions about the distribution of the random error term; the results are not easily affected by the extreme value, and the regression is more robust to reflect the data information more comprehensively [68]. To fix the inaccuracy of mean regression estimation and the shortcoming of only being able to examine the effect of the covariates on the dependent variable around the mean, scholars usually use quantile regression instead of mean regression.…”

Section: Quantile Regression Modelmentioning

confidence: 99%

Research on Financial Poverty Alleviation Aid for Increasing the Incomes of Low-Income Chinese Farmers

Pan,

Yao,

Zhang

et al. 2024

Sustainability

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Unlike the definition of absolute poverty in international society, rural poverty in China is characterized by farmers’ low ability to increase their income, and the unsustainability of income increases. This study examines farming households’ issues with increasing their incomes via financial aid from the International Fund for Agricultural Development (IFAD). Through quantile regression and stepwise regression models, this paper studies two ways in which IFAD loan projects can affect farmers’ income: by directly promoting increases in farmers’ income and by indirectly promoting increases in farmers’ income through newly operational agricultural entities. This paper uses the entropy weight and comprehensive evaluation methods to construct an IFAD evaluation index system to evaluate the endogenous development ability of farmers participating in IFAD loan projects. Our empirical results show that IFAD projects significantly and positively affect farmers’ income. Our heterogeneity analysis shows that IFAD projects have varied effects on farmers’ income growth at different income levels; the lower the income level, the more pronounced the promotion. IFAD loan projects promote farmers’ incomes through newly operational agricultural entities, the mechanism of which is their ESG performances. On average, the anti-poverty masses and areas participating in IFAD loan projects show a robust endogenous development impetus.

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“…Yang and Tan [6] used principal component analysis and concluded that the driving force of low-carbon economic development in Hunan Province mainly comes from economic efficiency factors. Liu [7] studied various factors affecting carbon emissions in China by LMDI factor decomposition method, and used scenario analysis to analyze carbon emissions in different contexts. Cao et al [8] used an extended multiplier structure decomposition method to identify the overall and regional carbon intensity changes in China from multiple levels, and decomposed the carbon intensity changes into input structure effect, intensity effect, and final demand effect.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Driving Factors of Low-Carbon Development in Fujian Province

2023

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Based on the data related to carbon emissions in Fujian Province from 2000 to 2021, factor analysis and regression analysis were used to explore the key factors affecting low carbon development in Fujian Province. The results show that carbon emissions in Fujian Province are closely related to a variety of factors, including the consumption level of residents, international trade, urbanization, population size, fixed asset investment, energy intensity and economic development level, which have a relatively large impact on carbon emissions in Fujian Province, while the energy structure and industrial structure have a relatively small impact. Through the empirical analysis, we propose corresponding solutions to the current problems in order to promote the low-carbon development of Fujian Province.

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Research and Analysis on the Influencing Factors of China’s Carbon Emissions Based on a Panel Quantile Model

Cited by 11 publications

References 32 publications

Carbon Emission Efficiency and Low-Carbon Optimization in Shanxi Province under “Dual Carbon” Background

Carbon Emission Efficiency and Low-Carbon Optimization in Shanxi Province under “Dual Carbon” Background

Research on Financial Poverty Alleviation Aid for Increasing the Incomes of Low-Income Chinese Farmers

Research on Driving Factors of Low-Carbon Development in Fujian Province

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