Impact of Population Aging on Carbon Emission in China: A Panel Data Analysis

Li, Weidong; Xin, Qi; Xiao-min, Zhao

doi:10.3390/su10072458

Cited by 20 publications

(8 citation statements)

References 25 publications

(22 reference statements)

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“…Zhang et al (2018) employed the Generalized method of moments (GMM) method and reported that for the active working group (age 15-64), their indirect effect on emission is contingent on the GDP per capita specific level in China, while the direct effect share on emission is positive. In a more recent study for China, Li et al (2018) reported similar outcomes using the STIRPAT model and data from 1999 to 2014. Kim et al (2020) studied the impact of population ageing in Korea using the Fully Modified Ordinary Least Squares (FMOLS) technique based on 1998 to 2016 data.…”

Section: Literature Reviewmentioning

confidence: 71%

Population dynamics and environmental quality in Africa

Dimnwobi

Ekesiobi

Madichie

et al. 2021

Science of The Total Environment

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Section: Literature Reviewmentioning

confidence: 71%

Population dynamics and environmental quality in Africa

Dimnwobi

Ekesiobi

Madichie

et al. 2021

Science of The Total Environment

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“…However, some scholars have shown that there were some regional differences in the impact of population aging on carbon emissions. For example, there was a significant inverted U-shaped curve relationship between population aging and carbon emissions, and the eastern region had a positive impact on carbon emissions, while the central and western regions had a negative impact [31]. Dalton used PET (Population, Environment, Technology) model and found that population aging had an inhibitory effect on carbon emissions in United States [32].…”

Section: The Impact Of Age Structure On Carbon Emissionsmentioning

confidence: 99%

How Do the Population Structure Changes of China Affect Carbon Emissions? An Empirical Study Based on Ridge Regression Analysis

et al. 2021

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This study focuses on the impact of population structure changes on carbon emissions in China from 1995 to 2018. This paper constructs the multiple regression model and uses the ridge regression to analyze the relationship between population structure changes and carbon emissions from four aspects: population size, population age structure, population consumption structure, and population employment structure. The results showed that these four variables all had a significant impact on carbon emissions in China. The ridge regression analysis confirmed that the population size, population age structure, and population employment structure promoted the increase in carbon emissions, and their contribution ratios were 3.316%, 2.468%, 1.280%, respectively. However, the influence of population consumption structure (−0.667%) on carbon emissions was negative. The results showed that the population size had the greatest impact on carbon emissions, which was the main driving factor of carbon emissions in China. Chinese population will bring huge pressure on the environment and resources in the future. Therefore, based on the comprehensive analysis, implementing the one-child policy will help slow down China’s population growth, control the number of populations, optimize the population structure, so as to reduce carbon emissions. In terms of employment structure and consumption structure, we should strengthen policy guidance and market incentives, raising people’s low-carbon awareness, optimizing energy-consumption structure, improving energy efficiency, so as to effectively control China’s carbon emissions.

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“…In some research, carbon emissions are related to population aging, and the working-age population is also considered an important indicator of future carbon emission mitigation [ 39 , 40 ]. Li et al found that the relationship between the aging structure and carbon emissions in China can be described by an inverted U-shaped curve [ 41 ]. In some developing countries, population quality also has a significant impact on carbon emissions [ 42 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Dependence and Influencing Factors of Carbon Emissions from the Perspective of Population Development

Zhao

Cui

Zhou

et al. 2021

IJERPH

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Working towards sustainable population development is an important part of carbon mitigation efforts, and decoupling carbon emissions from population development has great significance for carbon mitigation. Based on the construction of a comprehensive population development index (PDI), this study adopts a decoupling model to explore the dependence between carbon emissions and PDI across 30 Chinese provinces from 2001 to 2017. Then, the stochastic impacts by regression on population, affluence and technology (STIRPAT) model is used to investigate the impact of population factors on carbon emissions. The results show that the decoupling relationship between carbon emissions and PDI has experienced a transformation from expansive negative coupling to expansive coupling and then to weak decoupling at the national level, while some provinces have experienced the same evolutionary process, but the decoupling state in most provinces is not ideal. Sending talent to western provinces and developing low-carbon supporting industries will accelerate carbon decoupling. At the national level, incorporating environmental protection into the existing education system as part of classroom teaching could contribute to carbon decoupling.

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Impact of Population Aging on Carbon Emission in China: A Panel Data Analysis

Cited by 20 publications

References 25 publications

Population dynamics and environmental quality in Africa

Population dynamics and environmental quality in Africa

How Do the Population Structure Changes of China Affect Carbon Emissions? An Empirical Study Based on Ridge Regression Analysis

Exploring the Dependence and Influencing Factors of Carbon Emissions from the Perspective of Population Development

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