The Role of Renewables in a Low-Carbon Society: Evidence from a Multivariate Panel Data Analysis at the EU Level
Abstract
:1. Introduction
2. Literature Review and Hypotheses Development
3. Research Method
3.1. Sample Description
3.2. Dependent and Independent Variables
3.3. Research Methodology
- Yi = the dependent variable;
- Xi = independent variables;
- αi, βi = parametric coefficients;
- ui = error term;
- .
- H0: There is no correlation between independent variables and unit effects;
- H1: There is a correlation between independent variables and unit effects.
4. Results
5. Discussion
6. Conclusions
Funding
Conflicts of Interest
References
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Hypotheses | |
---|---|
H1 | Renewable energy use at the EU level has a significant and strong impact on CO2 emissions. |
H2 | Biofuel production at the EU level has a significant and strong impact on CO2 emissions. |
H3 | Resources productivity has a significant impact on CO2 emissions. |
H4 | Bioenergy productivity is strongly correlated to the levels of CO2 emissions. |
H5 | Urbanization level in EU countries has a significant impact on CO2 emissions. |
H6 | Population level in EU countries has a significant impact on CO2 emissions. |
Variable | Name | Definition | Unit |
---|---|---|---|
(Y) | CO2 | The total CO2 emissions in each EU country, measured in millions of tons | Millions of tons |
(X1) | Renewable | The rate of renewable energy in total energy consumed. This indicator represents the percentage (%) of renewable energy in total energy consumption at the EU level | Percentages (%) |
(X2) | Biofuels | Total production of biofuels in EU countries | Thousand tons |
(X3) | Resources | The quotient between gross domestic product (GDP) and domestic material consumption (DMC) | Percentages (%) |
(X4) | Bioenergy | This indicator results from the division of the GDP by the gross inland consumption of bioenergy for a given calendar year | Euro/kg |
(X5) | Urbanization | The percentage of urban population in the total population | Millions |
(X6) | Population | The total population in each EU country | Millions |
Variable * | Mean | Median | Standard Deviation | N |
---|---|---|---|---|
CO2 (Y) | 122.343 | 120.233 | 12.134 | 27 |
Renewable (X1) | 17.322 | 15.345 | 3.302 | 27 |
Biofuels (X2) | 234.654 | 230.645 | 56.563 | 27 |
Resources (X3) | 352.765 | 344.891 | 12.547 | 27 |
Bioenergy (X4) | 34.541 | 32.653 | 7.329 | 27 |
Urbanization (X5) | 23.524 | 26.344 | 5.347 | 27 |
Population (X6) | 543.8761 | 540.239 | 0.0987 | 27 |
Variable | Y | X1 | X2 | X3 | X4 | X5 | X6 |
---|---|---|---|---|---|---|---|
Y | 1 | ||||||
X1 | 0.687 | 1 | |||||
X2 | 0.702 | 0.044 | 1 | ||||
X3 | 0.545 | 0.088 | 0.053 | 1 | |||
X4 | 0.612 | 0.132 | 0.176 | 0.054 | 1 | ||
X5 | 0.785 | 0.129 | 0.080 | 0.072 | 0.105 | 1 | |
X6 | 0.856 | 0.188 | 0.094 | 0.097 | 0.118 | 0.132 | 1 |
F Statistics | 4.16 |
Probability | 0.321 |
Chi-Square Statistic | 1.7982 |
Chi-Square Statistic Probability | 0.8975 |
Cross Section | Time | Both | |
---|---|---|---|
Coefficients | 28.18 | 46.432 | 87.95 |
Probability | 0.091 | 0.756 | 0.064 |
Dependent Variable: CO2 Method: Pooled least squares Sample: 2008 2017 Total panel observations: 270 CO2 = C(1) + C(2)*RENEWABLE + C(3)*BIOFUELS+ C(4)*RESOURCES + C(5)*BIOENERGY+C(6)*URBANIZATION+C(7)*POPULATION | ||||
Coefficient | Std. Error | t-Statistic | Prob. | |
C | 23.9335 | 91.346 | 3.12376 | 0.0092 |
RENEWABLE (X1) | −0.654523 | 1.543 | 4.243033 | 0.0098 |
BIOFUELS (X2) | −0.983504 | 1.762 | 4.762098 | 0.0153 |
RESOURCES (X3) | −0.567837 | 1.073 | 5.120943 | 0.0082 |
BIOENERGY (X4) | −0.805490 | 1.476 | 6.092085 | 0.0027 |
URBANIZATION (X5) | 1.665434 | 1.814 | 6.165498 | 0.0090 |
POPULATION (X6) | 1.734502 | 1.654 | 7.109345 | 0.0085 |
R-squared | 0.778297 | Mean dependent variables | 7.21987 | |
Adjusted R-squared | 0.693918 | S.D.2 dependent var | 0.80932 | |
S.E.1 of regression | 0.098723 | Akaike info criterion | 1.98345 | |
Sum squared residual | 1.503963 | Schwarz criterion | 1.87403 | |
Log likelihood | 104.4508 | Hannan–Quinn criterion | 1.98765 | |
Durbin–Watson stat | 2.1174520 | |||
Prob (F-statistic) | 0.0000000 |
Variance Inflation Factors Date: 9 May 2019 Time: 09:32 Sample: 2008 2017 Included observations: 270 | |||
Variable | Coefficient Variance | Uncentered VIF | Centered VIF |
C | 6.956 | NA | |
RENEWABLE | 1.543 | 2.508 | 1.348 |
BIOFUELS | 1.762 | 2.876 | 1.876 |
RESOURCES | 1.073 | 2.012 | 1.012 |
BIOENERGY | 1.476 | 2.817 | 1.817 |
URBANIZATION | 1.814 | 2.265 | 1.265 |
POPULATION | 1.654 | 1.983 | 1.103 |
Hypothesis | Validated (Yes/No) |
---|---|
H1 | Yes |
H2 | Yes |
H3 | Yes |
H4 | Yes |
H5 | Yes |
H6 | Yes |
© 2019 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Busu, M. The Role of Renewables in a Low-Carbon Society: Evidence from a Multivariate Panel Data Analysis at the EU Level. Sustainability 2019, 11, 5260. https://doi.org/10.3390/su11195260
Busu M. The Role of Renewables in a Low-Carbon Society: Evidence from a Multivariate Panel Data Analysis at the EU Level. Sustainability. 2019; 11(19):5260. https://doi.org/10.3390/su11195260
Chicago/Turabian StyleBusu, Mihail. 2019. "The Role of Renewables in a Low-Carbon Society: Evidence from a Multivariate Panel Data Analysis at the EU Level" Sustainability 11, no. 19: 5260. https://doi.org/10.3390/su11195260
APA StyleBusu, M. (2019). The Role of Renewables in a Low-Carbon Society: Evidence from a Multivariate Panel Data Analysis at the EU Level. Sustainability, 11(19), 5260. https://doi.org/10.3390/su11195260