A dynamic fixed effects and nonlinear causality approach to analyze CO2 emissions
In order to estimate both short run and long run effects on CO2 Emissions of several variables including EDI Inflows, Per Capita GDP, Gross Capital Formation, Trade Openness, Fossil Fuels Consumption, Renewable Energy, Population Density and Oil Price, we make use of a Dynamic Fixed Effects estimato...
Guardado en:
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/173770 |
| Aporte de: |
| Sumario: | In order to estimate both short run and long run effects on CO2 Emissions of several variables including EDI Inflows, Per Capita GDP, Gross Capital Formation, Trade Openness, Fossil Fuels Consumption, Renewable Energy, Population Density and Oil Price, we make use of a Dynamic Fixed Effects estimator (ARDL) for a dataset of 43 countries during the period 19802019. Our main results show that Fossil Fuels Consumption and Economic Growth significantly favors Carbon Dioxide Emissions, although this conclusion is inverted once we account for subsamples. Moreover, mitigation evidence from Renewable Energy sources is confirmed for the full sample. We develop as well a non-linear causality model, which tends to overcome the classical Granger approach while working with complex systems, to correctly assess causality between our variables. However, from our estimations, evidence of nonlinearity is ruled out for a set of variables. Hence, we address causality with the classical Granger Approach. With this technique, evidence of a two-way relation between Renewable Energy Sources and Carbon Emissions is confirmed. |
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