Is Younger Population Generating Higher CO2 Emissions? A Dynamic Panel Analysis on European Countries
Abstract
:1. Introduction
2. Literature Review
2.1. The Relationship between Population Age, CO2 Emissions, and Sustainable Economic Development
2.2. The Causes of CO2 Emissions and Their Connection with Energy Consumption, GDP/Capita, and Income Inequality
3. Materials and Methods
3.1. Analysis of the Data and Variables Used in Dynamic Panel Analysis
3.2. Methodology
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Authors | Relationships Identified and Analyzed | Method Used | Conclusions |
---|---|---|---|
Wang, Q and Li, L. [46] | Impact of population factors on carbon dioxide emissions. | STIRPAT (stochastic impacts by regression on population, affluence, and technology) model | The promoting effect of population urbanization has a significant effect on CO2 emissions. Inhibitory effect of population aging, density, and life expectancy on CO2 emissions. |
Wang, Q., Yang, T., Li, R., and Wang, L. [47] | CO2 emissions—explained variable; GDP/capita, energy consumption—explanatory variables; population aging—threshold variable; population size, technological innovation level, the degree of urbanization, industrial structure, and energy intensity—control variables. | Panel threshold regression method | Population aging is an key factor that affects the relationship between economic growth and CO2 emissions, as well as the significant role of energy consumption and CO2 emissions. |
Huang et al. [48] | Economic variables (GDP per capita, foreign direct investment, etc.), CO2 emissions | Fixed-effect regression method and GMM estimator | Negative effect of renewable energy on CO2 emissions. |
The predictive power of social variables and CO2 modeling | |||
Causes of CO2 emissions and power consumption | |||
Liu and Yuan [49] | CO2 emissions Economic growth (GDP) Non-renewable energy (EU) Renewable energy consumption | Quantile regression approach (REC) | The carbon impact of GDP and the EU is highest in underdeveloped countries and lowest in industrialized countries. |
Arminen and Menegaki [50] | Economic growth Power consumption CO2 emissions High- and upper–middle-income countries | Simultaneous equation framework Environmental Kuznets Curve (EKC) hypothesis | The countries under review base their economic growth on a two-way relationship with energy consumption. |
Yuan et al. [7] | Energy security (ENS) Economic policy uncertainty (EPU) Government ecological expenditure (GEE) CO2 emissions | Quantile autoregressive distributed lag approach (QARDL) | Energy consumption transition through investing in renewable energy, updating energy technologies, and supporting reform of the energy price system. |
Yu et al. [16] | Status of demographic structure Status of industrial structure Population aging Per capita wealth CO2 emissions impact factors | STIRPAT Model | Effects on CO2 emissions: Positive: population aging, industrial structure, and per capita wealth; Negative: energy intensity. |
Fan et al. [51] | Meteorology, biomass, land use, population density and GDP/capita, CO2 emissions impact factors | Generalized linear model Genetic algorithm Community Land Model version 4.5 | Global carbon emissions have fallen due to rising GDP per capita. The meteorological factor strongly influences the cases of higher warming. |
Hashmi, R. and Alam K. [52] | The effects of environmental regulation and innovation on carbon emissions | “Stochastic impacts by regression on population, affluence, regulation, and technology” (STIRPART) and generalized method of moments (GMM) models | The increase in the ecological patent determines the reduction in carbon emissions, and the increase in fiscal revenues from environmental taxes reduces carbon emissions for OECD countries. |
Abid, M. [53] | Economic growth; economic, financial, and institutional development; pollution | ordinary least squares (OLS); general method of moments | The empirical results show that the Environmental Kuznets Curve hypothesis does not hold for Sub-Saharan Africa economies, indicating that there is a linear relationship between CO2 emissions and GDP/capita. At the same time, the results of the study highlight that political stability and control of corruption negatively influence CO2 emissions. On the contrary, the regulatory quality and law have a positive effect on CO2 emissions. |
CO2/Capita | Adjusted Gross Disposable Income/Capita | GDP/Capita | Energy Use/Capita | Median Age | GINI | |
---|---|---|---|---|---|---|
Minimum | 2.32 | 10,959 | 2990 | 15,779.98 | 30.4 | 22 |
Maximum | 25.61 | 86,296.51 | 88,120 | 113,106.18 | 46.8 | 41.3 |
Mean | 7.36 | 30,309.5 | 25,359.68 | 40,559.78 | 39.22 | 31.05 |
St. Deviation | 3.57 | 13,035.84 | 17,850.48 | 16,948.29 | 2.8 | 3.88 |
Skewness | 1.91 | 0.627 | 1.551 | 1.58 | −0.389 | −0.106 |
Kurtosis | 5.77 | 0.526 | 2.786 | 3.217 | 0.238 | −0.31 |
Variables | Inverse Chi2 | p-Value | Result |
---|---|---|---|
CO2 emissions/capita | 46.3062 | 0.7623 | Non-stationary series |
lagged CO2 emissions/capita | 42.7204 | 0.8657 | Non-stationary series |
adjusted gross disposable income (GDI)/capita | 301.479 | 0.00 | Stationary series |
gross domestic product (GDP)/capita | 49.8185 | 0.6363 | Non-stationary series |
energy consumption/capita | 121.0411 | 0.00 | Stationary series |
median age | 11.1403 | 1.00 | Non-stationary series |
economic inequality in the population (GINI) | 158.2204 | 0.00 | Stationary series |
Number of obs. = 567 F (1,565) = 7632.53 Prob > F = 0.0000 R-squared = 0.9311 Adj. R-squared = 0.9310 | ||||||
Source | SS | Df | MS | |||
Model | 4405.607 | 1 | 4405.607 | |||
Residual | 326.126 | 565 | 0.577 | |||
Total | 4731.733 | 566 | 8.360 | |||
Residual terms | Coef. | Std. Err. | t | p > | [95% Conf. Interval]. | |
Residual terms L1 | 0.982 | 0.011 | 87.36 | 0.000 | 0.96 | 1.004 |
_cons | −0.126 | 0.032 | −3.95 | 0.000 | −0.189 | −0.063 |
Independent Variables | Regression Analysis | Robustness Test |
---|---|---|
CO2E/capita L1. | 0.92101 *** (0.000) | 0.92101 *** (0.000) |
GDI/capita | 0.000003 (0.224) | 0.000003 (0.437) |
GDP/capita | −0.00006 *** (0.000) | −0.00006 *** (0.001) |
Energy use/capita | 0.0000005 (0.434) | 0.0000005 (0.306) |
Median age | −0.0677 *** (0.007) | −0.0677 ** (0.055) |
Gini | −0.0088 (0.634) | −0.0088 (0.699) |
constant | 4.70449 *** (0.000) | 4.70449 *** (0.001) |
Wald chi2(6) | 2400.16 | 864.54 |
Prob > chi2 | 0.0000 | 0.0000 |
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Sabău-Popa, C.D.; Perțicaș, D.C.; Florea, A.; Rus, L.; Juma, H.W. Is Younger Population Generating Higher CO2 Emissions? A Dynamic Panel Analysis on European Countries. Sustainability 2024, 16, 7791. https://doi.org/10.3390/su16177791
Sabău-Popa CD, Perțicaș DC, Florea A, Rus L, Juma HW. Is Younger Population Generating Higher CO2 Emissions? A Dynamic Panel Analysis on European Countries. Sustainability. 2024; 16(17):7791. https://doi.org/10.3390/su16177791
Chicago/Turabian StyleSabău-Popa, Claudia Diana, Diana Claudia Perțicaș, Adrian Florea, Luminița Rus, and Hillary Wafula Juma. 2024. "Is Younger Population Generating Higher CO2 Emissions? A Dynamic Panel Analysis on European Countries" Sustainability 16, no. 17: 7791. https://doi.org/10.3390/su16177791