Determinants of air pollution: Empirical evidence from the largest CO2 emitters

In recent decades, global economic growth and industrialization have increased the demand for consumption of energy. The increasing demand for energy is met by burning fossil fuels which emit air pollution and greenhouse gas emissions. After the industrial revolution, energy generation has increased the amount of greenhouse gases abnormally, critically damaging the environment of countries. In this regard, the purpose of this study is investigating the determinants of carbon dioxide emissions (CO2) as an indicator of environmental quality and air pollution.

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 a first step, the LLC and IPS unit root tests were performed to examine the non-stationarity properties of our 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 is Eviews 10.

Our manuscript first performs 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 unit root. The results of IPS test indicate that the null hypothesis is rejected only for urbanization, implying that this variable is stationary, while for the other six variables the panel unit root test null hypothesis is not rejected. However, all tests confirmed that variables are stationary after first difference. It is hereby inferred that variables are first differenced 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 whose results reject the null hypothesis of no co-integration. The results of the F-test indicate that the panel model is the right choice. To help us choose between the fixed effects or random effects estimators, we conduct the Hausman test where the null hypothesis is that the preferred model is random effects. Our results from the Hausman test do not reject the null hypothesis, suggesting that the random effects estimator is more appropriate for our data than the fixed effects estimator. The results from the FMOLS and DOLS estimations indicate 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. The positive impact of economic development on CO2 emissions was also investigated.

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.