Assessing the impact of macroeconomic variables on environmental quality in the MENA using logarithmic mean divisia and co-integration panel

Introduction: Based on Kaya equation, this paper evaluates the effects of macroeconomic variables on the environment quality in MENA region. The process of economic growth and development in developing countries, including the Islamic Republic of Iran and other the Middle East and North Africa countries, have created increasing pressure on the environment. The costs of environmental degradation in the MENA region is about %5 of gross domestic production and in the Islamic Republic of Iran is over of %7 (Croitoru, Lelia et al, 2010). In the other hand, Environment damages and emissions have faced sustainable growth and development with doubt. Given the importance of the environment and energy resources in sustainable development, we try to identify impacts of the factors such as population, gross domestic production, energy intensity and carbon intensity on carbon dioxide emission as an important indicator to measure performance consistent with environment quality and sustainable development, because of all the greenhouse gases, the share of carbon dioxide emission in global warming is %94.7 (Nordhaus, 1990).Materials and methods: According to the above, on basis of Kaya relationship and by using the data from (1990-2011), we assessed the contribution of macro factors, the kaya identity has been widely discussed in analyses of energy-related carbon dioxide (CO2) emissions (O'Neill et al., 2000). In the first step, the share of each macroeconomic variables was investigated by using the Logarithmic Mean Divisia Index, Which is considered one of the most widely used decomposition techniques in the short term. To long-term analysis, After determining the input values related to MENA region, The panel data method was used that indicate the presence of co-integration in the model, Co-integration concept is reminiscent of a long-run equilibrium relationship between economic systems move over time towards it (Noferesti, 1999). The models such as fully modified ordinary least square (FMOLS) or dynamic ordinary least square (DOLS) is more effective methodin the case of co-integration panel data estimation (Chen et al., 1999). So FMOLS co-integrated model was applied on the variables and the results of parameter estimation was achieved in the long run.Results and discussion: Background checks in developed countries and areas shows that gross domestic production and demographic variances have increased the carbon dioxide emissions in these countries, whereas this change is largely offset by the decrease in energy intensity and substitution of renewable energies, in the long- run term. Parameter estimation results in this research suggest a significant long-term impact on GDP, population and the carbon intensity on carbon dioxide emission. In the region, Results of the Logarithmic Mean Divisia method in the short term show that the demographic factor has the greatest impact on emissions and gross domestic production, energy intensity and carbon intensity are. At five-year intervals and on average, demographic, gross domestic production and energy intensity have been increasing emissions and carbon intensity has been the reverse impact. Coefficients of panel models show that in the long term population growth in the MENA region has the greatest impact on carbon emissions, Energy intensity is the next .Conclusion: According to the results of short-term and long-term, compared with developed countries, energy intensity index can play a key role in enhancing the quality of the environment in MENA countries. hence, the region needs attention from policy-makers to improve energy intensity index.