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...

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Autor principal: Baioni, Tomás
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2021
Materias:
Ciencias Económicas
emissions
panel data
dynamic model
short run and long run effects
ARDL
emisiones
modelos en panel
modelo dinámico
efectos de corto y largo plazo
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/173770
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-173770
record_format dspace
spelling I19-R120-10915-1737702024-11-27T20:12:53Z http://sedici.unlp.edu.ar/handle/10915/173770 A dynamic fixed effects and nonlinear causality approach to analyze CO2 emissions Baioni, Tomás 2021-11 2021 2024-11-27T18:04:55Z en Ciencias Económicas emissions panel data dynamic model short run and long run effects ARDL emisiones modelos en panel modelo dinámico efectos de corto y largo plazo 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. Con el objetivo de estimar tanto efectos de corto plazo como de largo plazo en las Emisiones de CO2 de diferentes varables incluyendo Flujos de IED, PBI Per Cápita, Formación Bruta de Capital, Apertura Comercial, Consumo de Combustibles, Energía Renovable, Densidad Pobla- cional y Precio del Petróleo, hacemos uso de un estimador Dinámico de Efectos Fijos (ARDL) para una base de datos de 43 países durante el período 1980-2019. Nuestros resultados principales muestran que el Consumo de Combustibles Fósiles y el Crecimiento Económico favorecen significativamente a las Emisiones de Dióxido de Carbono, aunque esta conclusión se invierte una vez que se analiza por submuestras. Asimismo, evidencia de mitigación por parte de Fuentes de Energías Renovables es confirmada para la muestra en su conjunto. A su vez, desarrollamos un modelo de causalidad no linear, el cual tiende a superar el enfoque clásico de Causalidad ”á la” Granger al analizar sistemas complejos, para constatar correctamente causalidad entre las variables. A pesar de ello, a partir de nuestras estimaciones, evidencia de no linearidad es rechazada para un conjunto de variables. Por tal motivo, estimamos causalidad a través del clásico Enfoque de Granger. Con esta técnica, evidencia de una relación dual entre Fuentes de Energías Renovables y Emisiones de Carbono es confirmada. Facultad de Ciencias Económicas Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Económicas
emissions
panel data
dynamic model
short run and long run effects
ARDL
emisiones
modelos en panel
modelo dinámico
efectos de corto y largo plazo
spellingShingle Ciencias Económicas
emissions
panel data
dynamic model
short run and long run effects
ARDL
emisiones
modelos en panel
modelo dinámico
efectos de corto y largo plazo
Baioni, Tomás
A dynamic fixed effects and nonlinear causality approach to analyze CO2 emissions
topic_facet Ciencias Económicas
emissions
panel data
dynamic model
short run and long run effects
ARDL
emisiones
modelos en panel
modelo dinámico
efectos de corto y largo plazo
description 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.
format Objeto de conferencia
Objeto de conferencia
author Baioni, Tomás
author_facet Baioni, Tomás
author_sort Baioni, Tomás
title A dynamic fixed effects and nonlinear causality approach to analyze CO2 emissions
title_short A dynamic fixed effects and nonlinear causality approach to analyze CO2 emissions
title_full A dynamic fixed effects and nonlinear causality approach to analyze CO2 emissions
title_fullStr A dynamic fixed effects and nonlinear causality approach to analyze CO2 emissions
title_full_unstemmed A dynamic fixed effects and nonlinear causality approach to analyze CO2 emissions
title_sort dynamic fixed effects and nonlinear causality approach to analyze co2 emissions
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/173770
work_keys_str_mv AT baionitomas adynamicfixedeffectsandnonlinearcausalityapproachtoanalyzeco2emissions
AT baionitomas dynamicfixedeffectsandnonlinearcausalityapproachtoanalyzeco2emissions
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