Using Quantile Regression to Analyze the Relationship between Socioeconomic Indicators and Carbon Dioxide Emissions in G20 Countries
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data and Variable Selection
2.2. Empirical Model
2.3. Quantile Regression
3. Results and Discussion
3.1. Data Statistics
3.2. Control Variables Selection
3.3. Empirical Analysis
3.4. Discussion
4. Main Conclusions and Policy Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
References
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Variable | Description | Unit | Years | Data Source |
---|---|---|---|---|
CO2 | CO2 emissions per capita including sources from fossil fuel use and industrial processes | ton CO2/capita | 2000–2019 | [33] |
LPI | Legatum Prosperity Index | Percentage | 2007–2019 | [7] |
HDI | Human Development Index | Percentage | 2000–2019 | [8] |
GDP | GDP per capita expressed in current USD converted by PPP conversion factor | USD/capita | 2000–2019 | [34] |
FC | Fossil fuel consumption per capita | kWh/capita | 2000–2019 | [35] |
TR | The sum of exports and imports of goods and services measured as a share of GDP | Percentage of GDP | 2000–2019 | [34] |
URB | People living in urban areas (% of total population) | Percentage | 2000–2019 | [34] |
PD | The number of persons per square km | Persons/km2 | 2000–2019 | [34] |
Variables | CO2 | LPI | HDI | GDP | URB | FC | TR | PD |
---|---|---|---|---|---|---|---|---|
Mean | 8.77 | 66.13 | 80.4 | 22209 | 73.1 | 35421 | 52.1 | 139.6 |
Std. dev. | 5.49 | 10.52 | 10.9 | 17673 | 15.3 | 23887 | 17.6 | 147.5 |
Median | 8.19 | 60.2 | 83.7 | 15698 | 78.9 | 28709 | 52.1 | 93.3 |
Skewness | 0.51 | 0.11 | −0.68 | 0.49 | −1.43 | 0.64 | 0.31 | 1.12 |
Kurtosis | −0.83 | −1.67 | −0.53 | −1.05 | 1.35 | −0.64 | −0.17 | 0.24 |
Jarque–Bera | 248 *** | 225 *** | 227 *** | 274 *** | 173 *** | 236 *** | 165 *** | 200 *** |
Observations | 380 | 247 | 380 | 380 | 380 | 380 | 380 | 380 |
Units | ton CO2/capita | Percentage | Percentage | USD/capita | Percentage | kWh/capita | Percentage of GDP | Persons/km2 |
Variable | q0.05 | q0.25 | q0.50 | q0.75 | q0.95 |
---|---|---|---|---|---|
(Intercept) | −3.066 *** | −3.320 *** | −3.367 *** | −4.481 *** | −4.866 *** |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
GDP | 0.102 | 0.199 * | 0.177 *** | 1.033 *** | 0.640 *** |
(0.153) | (0.0.064) | (0.002) | (0.000) | (0.000) | |
GDP2 | −0.010 | −0.026 ** | −0.025 *** | −0.129 *** | −0.094 *** |
(0.220) | (0.0049) | (0.001) | (0.000) | (0.000) | |
URB | −0.203 *** | −0.244 *** | −0.228 *** | −0.593 *** | −0.261 *** |
(0.002) | (0.000) | (0.000) | (0.000) | (0.000) | |
LPI | −0.102 * | −0.285 *** | −0.583 *** | −0.520 *** | −0.955 *** |
(0.040) | (0.015) | (0.002) | (0.000) | (0.000) | |
FC | 0.957 *** | 0.995 *** | 1.013 *** | 1.131 *** | 1.211 *** |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
TR | −0.158 *** | −0.116 *** | −0.101 *** | −0.139 *** | −0.066 *** |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
Observations | 247 | 247 | 247 | 247 | 247 |
Variable | q0.05 | q0.25 | q0.50 | q0.75 | q0.95 |
---|---|---|---|---|---|
(Intercept) | −3.466 *** | −3.390 *** | −2.987 *** | −3.566 *** | −3.462 *** |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
GDP | −0.056 | 0.209 *** | 0.197 *** | 0.485 *** | 0.662 *** |
(0.247) | (0.000) | (0.003) | (0.000) | (0.000) | |
GDP2 | 0.006 | −0.028 *** | −0.026 *** | −0.065 *** | −0.082 *** |
(0.314) | (0.000) | (0.001) | (0.000) | (0.000) | |
URB | −0.191 *** | −0.180 *** | −0.157 *** | −0.295 *** | −0.274 *** |
(0.003) | (0.000) | (0.000) | (0.000) | (0.000) | |
HDI | 0.420 ** | −0.054 *** | −0.353 *** | −0.351 *** | −0.706 *** |
(0.030) | (0.003) | (0.001) | (0.001) | (0.000) | |
FC | 0.942 *** | 0.999 *** | 1.030 *** | 1.110 *** | 1.130 *** |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
TR | −0.139 *** | −0.097 *** | −0.090 *** | −0.089 *** | −0.064 *** |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
Observations | 380 | 380 | 380 | 380 | 380 |
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Alotaibi, A.A.; Alajlan, N. Using Quantile Regression to Analyze the Relationship between Socioeconomic Indicators and Carbon Dioxide Emissions in G20 Countries. Sustainability 2021, 13, 7011. https://doi.org/10.3390/su13137011
Alotaibi AA, Alajlan N. Using Quantile Regression to Analyze the Relationship between Socioeconomic Indicators and Carbon Dioxide Emissions in G20 Countries. Sustainability. 2021; 13(13):7011. https://doi.org/10.3390/su13137011
Chicago/Turabian StyleAlotaibi, Abdulaziz A., and Naif Alajlan. 2021. "Using Quantile Regression to Analyze the Relationship between Socioeconomic Indicators and Carbon Dioxide Emissions in G20 Countries" Sustainability 13, no. 13: 7011. https://doi.org/10.3390/su13137011