The aim of this paper is to investigate the existence of environmental Kuznets curve (EKC) in an open economy like Tunisia using annual time series data for the period of 1971-2010. The ARDL bounds testing approach to cointegration is applied to test long run relationship in the presence of structural breaks and vector error correction model (VECM) to detect the causality among the variables. The robustness of causality analysis has been tested by applying the innovative accounting approach (IAA). The findings of this paper confirmed the long run relationship between economic growth, energy consumption, trade openness and CO 2 emissions in Tunisian Economy. The results also indicated the existence of EKC confirmed by the VECM and IAA approaches. The study has significant contribution for policy implications to curtail energy pollutants by implementing environment friendly regulations to sustain the economic development in Tunisia.
We evaluate the N-shaped environmental Kuznets curve (EKC) using panel quantile regression analysis. We investigate the relationship between CO2 emissions and GDP per capita for 74 countries over the period of 1994–2012. We include additional explanatory variables, such as renewable energy consumption, technological development, trade, and institutional quality. We find evidence for the N-shaped EKC in all income groups, except for the upper-middle-income countries. Heterogeneous characteristics are, however, observed over the N-shaped EKC. Finally, we find a negative relationship between renewable energy consumption and CO2 emissions, which highlights the importance of promoting greener energy in order to combat global warming.
This paper analyzes the causal relationship between renewable energy consumption, oil prices, and economic activity in the United States from July 1989 to July 2016, considering all quantiles of the distribution. Although the concept of Granger-causality is defined for the conditional distribution, the majority of papers have tested Granger-causality using conditional mean regression models in which the causal relations are linear. We apply a Granger-causality in quantiles analysis that evaluates causal relations in each quantile of the distribution. Under this approach, we can discriminate between causality affecting the median and the tails of the conditional distribution. We find evidence of bi-directional causality between changes in renewable energy consumption and economic growth at the lowest tail of the distribution; besides, changes in renewable energy consumption lead economic growth at the highest tail of the distribution. Our results also support the unidirectional causality from fluctuations in oil prices to economic growth at the extreme quantiles of the distribution. Finally, we find evidence of lower-tail dependence from changes in oil prices to changes in renewable energy consumption. Our findings call for government policies aimed at developing renewable energy markets, to increase energy efficiency in the U.S.
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