Document Type : Original Article


Department of Agricultural Economics, Faculty of Agricultural Management, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran


Introduction: In recent decades, global economic growth and industrialization have increased the demand for the consumption of energy . The increasing energy demand is met by burning fossil fuels, which emit air pollution and greenhouse gas emissions. After the industrial revolution, energy generation abnormally increased the amount of greenhouse gases emission, critically damaging the environment. In this regard, the purpose of this study is to investigate the determinants of carbon dioxide emissions (CO2) as an indicator of environmental quality and air pollution.
Material and methods: This study has proposed a long-run relationship between CO2, economic growth, energy consumption, trade openness, financial development, and urbanization for a global panel of 11 countries spanning the period 2000–2015 using Fully Modified Ordinary Least Square (FMOLS) and Dynamic Ordinary Least Square (DOLS). In the first step, the LLC and IPS unit root tests were performed to examine the non-stationarity properties of the dataset. Then, Pedroni and Kao co-integration tests were applied to identify if there is a correlation between variables in the long term. In addition, the F (Chow) test was used to detect the best model. The software package used for estimation and analysis of the models was Eviews 10.
Results and discussion: This paper first performed a panel unit root test proposed by Levin, Lin, and, Chu (LLC) and Im, Pesaran and, Shin (IPS) to examine the null hypothesis that all the series have a unit root. The results of IPS test indicated that the null hypothesis was rejected only for urbanization, implying that this variable was stationary. However, all tests confirmed that variables were stationary after the first-difference. It is hereby informed that variables were first-difference stationary. Our results suggest that there is a need to examine co-integration among variables. In addition, we conduct Pedroni and Kao co-integration tests, the results of which rejected the null hypothesis of no co-integration. The results of the F-test indicated that the panel model was the right choice. To help us choose between the fixed effects or random effects estimators, we conduct the Hausman test, where the null hypothesis was that the preferred model has random effects. Our results from the Hausman test did not reject the null hypothesis, suggesting that the random effects estimator was more appropriate for our data than the fixed effects estimator. The results from the FMOLS and DOLS estimations indicated that energy consumption from renewable sources, trade openness, financial development, and urbanization had a negative impact on CO2 emissions, while the energy consumption from non-renewable sources had a positive impact.
Conclusion: The results of the research imply that policymakers should focus more on public awareness of renewable energy, mainly in solar and wind power to alleviate environmental pressure and CO2 emission. The findings also suggest that the governments should set a price per ton on carbon i.e. a carbon tax. Furthermore, developed countries should transfer sophisticated technology to emerging and undeveloped countries to generate electricity and avoid unsafe climate change .


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