The Impact of Renewable Energy Sources on Economic Growth and CO2 Emissions-A SVAR Approach

Article excerpt

1. Framework

The Kyoto Protocol set targets for Greenhouse Gas (GHG) emission, particularly Carbon Dioxide (CO2), for industrialized countries. A large share of anthropogenic emissions is due to the energy sector, in concrete, due to the combustion of fossil fuels (Halicioglu, 2009; Soytas and Sari, 2009; Jaccard et al., 2003; Kohler et al., 2006) (4). Since the Protocol, the replacement of the traditional sources for Renewable Energy Sources (RES) has appeared as a viable solution to reduce emissions, particularly in the electricity sector (Bohringer and Loschel, 2006, Neuhoff, 2005; Stocker et al., 2008). But what are the consequences for economic growth of an increasingly dependence on these sources? Are these sources really effective in reducing emissions?

To evaluate the existence and extent of economic and environmental effects of a growing dependence on RES, we take a sample of four countries with distinct economic and social structures as well as different levels of economic development: Denmark, Portugal, Spain and USA. The single country analysis allows assessing if countries with diverse geographic, economic and social conditions react differently to an increase in the RES share. We use a three variable Structural Vector Autoregressive (SVAR) model which includes the share of RES on Electricity generation (RES-E), CO2 emissions per capita, and GDP per capita.

The relationship between energy, economic growth and carbon emissions has been treated in the literature using different methodological approaches (see, for example, Payne, 2010; Ozturk, 2010; Halicioglu, 2009; Jalil and Mahmud, 2009; Bowden and Payne, 2009; Narayan et al., 2008; Erbaykal, 2008; Narayan and Prasad, 2007; Stern and Cleveland, 2004; Soytas and Sari, 2003; Ortega-Cerda and Ramos-Martin, 2003; Aqeel and Butt, 2001; Cheng and Andrews, 1998; Stern, 1993). The results have differed significantly depending on the country, period, variables and method used for the analysis (Ozturk, 2010; Bowden and Payne, 2009; Chontanawat et al., 2008). However, most studies ignored the disaggregation of energy sources, in particular, between renewable and non renewable sources. Some exceptions are Chien and Hu (2008), Sari et al. (2008), Chang et al. (2009) and Sadorsky (2009a).

To our knowledge, the SVAR methodology has never been used with the variables included in our model and for the countries under analysis.

Our results show that, except for the USA, the increasing share of RES-E had an economic cost. Notwithstanding it has been an effective measure to decrease CO2 emissions. Additionally, we tested the variables for the existence of unit roots and performed forecast error variance decomposition.

The article is organized as follows. Section II describes the model; section III depicts the sample used. The empirical results are presented in section IV. Conclusions and policy implications are presented in section V.

2. The Model

In this article we analyze the relationship between the fuel mix for electricity generation, economic growth and CO2 emissions using a SVAR methodology.

The SVAR methodology considers the interactions between all variables and its restrictions are based on economic theory or reveal information about the dynamic properties of the economy investigated. Thus, the SVAR can be used to predict the effects of specific policy actions or of important changes in the economy which is the case of a change in the energy supply mix (Narayan et al, 2008; Buckle et al., 2002).

Our model used Gross Domestic Product (gdp), CO2 emissions (co2) and the weight of renewable sources on total electricity generation

rentotat: (rentotal) = ren/[ren + ther]

Where ren is the electricity generated from RES (hydro power, wind power, geothermal power, photovoltaic, biomass, tidal and wave power) and ther is the electricity generation from non-renewable sources (5). …