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This research attempts to evaluate the impact of renewable electricity generation mix on economic growth and CO2 emissions in Iran from 1980 to 2016. In this regard, by using EViews 10, the Structural Vector Autoregressive model (SVAR) is estimated by imposing the Blanchard and Quah long-run restrictions. The yearly data on real Gross Domestic Production (GDP), the share of electricity generation from renewable sources, and carbon dioxide emissions (CO2) caused by liquid, solid, and gaseous fuels were used. The positive impact of one standard deviation shock of increasing the share of renewable electricity on economic growth was confirmed by using Impulse Response Function (IRF). Contrary to the expectation, the share of renewable electricity in the energy mix is not at a desirable level to lower CO2 emissions, which partly could be explained by the dominant role of fossil fuel in Iran (as an energy-driven country). Moreover, the findings of Variance Decomposition (VD) verified the low share of electricity generated by renewable energy in explaining forecast error variations in economic growth and CO2 emissions. It indicates that in this stage of development, increasing the share of renewable electricity could not be considered as an appropriate strategy to control environmental issues. Therefore, initiating and implementing environmental policies could be considered as the most proper policies to lower CO2 emissions and to achieve the goal of sustainable development.

Structural Vector Autoregressive Approach to Evaluate the Impact of Electricity Generation Mix on Economic Growth and CO2 Emissions in Iran