On the relationship between energy consumption, CO2 emissions and economic growth in Europe

WOS: 000286343000107 This study examines the causal relationship between carbon dioxide emissions, energy consumption, and economic growth by using autoregressive distributed lag (ARDL) bounds testing approach of cointegration for nineteen European countries. The bounds F-test for cointegration test...

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Bibliographic Details
Published in:Energy
Main Authors: Acaravci, Ali, Öztürk, İlhan
Other Authors: Meslek Yüksekokulu, Ozturk, Ilhan
Format: Article in Journal/Newspaper
Language:English
Published: PERGAMON-ELSEVIER SCIENCE LTD 2010
Subjects:
Online Access:https://hdl.handle.net/20.500.12507/640
https://doi.org/10.1016/j.energy.2010.07.009
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Summary:WOS: 000286343000107 This study examines the causal relationship between carbon dioxide emissions, energy consumption, and economic growth by using autoregressive distributed lag (ARDL) bounds testing approach of cointegration for nineteen European countries. The bounds F-test for cointegration test yields evidence of a long-run relationship between carbon emissions per capita, energy consumption per capita, real gross domestic product (GDP) per capita and the square of per capita real GDP only for Denmark, Germany, Greece, Iceland, Italy, Portugal and Switzerland. The cumulative sum and cumulative sum of squares tests also show that the estimated parameters are stable for the sample period. We found a positive long-run elasticity estimate of emissions with respect to energy consumption at 1% significant level in Denmark, Germany, Greece, Italy and Portugal. Positive long-run elasticity estimates of carbon emissions with respect to real GDP and the negative long-run elasticity estimates of carbon emissions with respect to the square of per capita real GDP at 1% significance level in Denmark and 5% significant level in Italy are also found. These results support that the validity of environmental Kuznets curve (EKC) hypothesis in Denmark and Italy. This study also explores causal relationship between the variables by using error-correction based Granger causality models.