The impact of renewable energy sources on economic growth and CO2 emissions - a SVAR approach

Over the last years renewable energy sources (RES) have increased their share on electricity generation of most developed economies due to environmental and security of supply concerns. The aim of this paper was to analyze how an increasing share of RES on electricity generation (RES-E) affects Gross Domestic Product (GDP) and carbon dioxide (CO2) emissions. Several methodologies could be used for this purpose. The Structural Vector Autoregressive (SVAR) methodology considers the interactions among all variables in the model and is well suited to predict the effects of specific policy actions or important changes in the economy. Therefore, we chose to implement this methodology. We used a 3 variable SVAR model for a sample of four countries along the period 1960-2004. The existence of unit roots was tested to infer the stationarity of the variables. The countries chosen have rather different levels of economic development and social and economic structures but a common effort of investment in RES in the last decades. Through the impulse response functions (IRF), the SVAR estimation showed that, for all countries in the sample, except for the USA, the increasing RES-E share had economic costs in terms of GDP per capita. As expected, there was also an evident decrease of CO2 emissions per capita. The variance decomposition showed that a significant part of the forecast error variance of GDP per capita and a relatively smaller part of the forecast error variance of CO2 per capita were explained by the share of RES-E.