Green change – endogenizing technical progress in the renewable power generation sector

Renewable energy deployment is growing rapidly on a global scale. China, Germany, Japan and the US are among the countries with highest capacity of renewables installed. In Germany, for example, the large growth in renewable power generation (RPG) capacities in the past has been mainly due to demand supporting policy measures (demand pull). Globally, increasing deployment also is accelerated by strongly decreasing costs of these technologies. Deployment, on the other hand leads to cost decreases via scale effects and this interdependence can be captured in learning curves, which is a concept used to model technological change. Using this concept it is possible to – at least partly – endogenize technological change in economic models. Introducing endogenous technological change is necessary to adequately analyze not only the direct effects of technological change, but also the indirect effects on important macro-economic indicators such as growth, employment, welfare and trade as well as their feedback to the electricity sector. In this paper a renewable power generation module for the INFORUM type econometric input-output models (see Eurostat, 2008, for more details) such as GINFORS (Lutz & Wiebe, 2012) and PANTA RHEI (Lehr et al., 2012) is developed. The energy sector modeling is based on the energy balances of the IEA. Renewables have only been a small aggregated part in the model previously, but are now included in more detail. In addition, this will be a first step to endogenize technological change in the model. Wind (on and offshore), PV, and concentrating solar power (CSP) generation technologies have been selected for further analysis. Their representation in the model is based on learning curves, which may, among other factors, depend on capacity installed, investment, R&D. We test both one factor and two factor learning curves (Wiesenthal et al., 2012) with learning rates estimated from the data and compared to existing studies. The electricity production is then calculated from capacity installed using the respective load hours of the year 2010, partly adapted if more information is available. This RPG extension of the econometric input-output model will contribute to a better understanding of the interaction between the deployment of renewable energy technologies and macro-economic indicators such as employment, GDP and sectoral production. The learning curves reflect both learning-by-doing and learning-by-searching. All of these factors develop endogenously in the model, but may also be influenced by policy measures. This approach will be a first step to endogenously determine the national investments in RPG technologies, electricity generation costs and global feedback loops of national policy measures (incl. export of policy measures) on RE investment and electricity production costs. Important results are exports and imports of RPG goods and services using assumptions regarding world market shares and trade shares, as well as the share of renewables in total electricity production and carbon emissions associated with electricity production. References Eurostat (2008): Eurostat Manual of Supply, Use and Input-Output Tables, Luxembourg. Lehr, U., Lutz, C. & Edler, D. (2012): Green jobs? Economic impacts of renewable energy in Germany. Energy Policy 40, DOI 10.1016/j.enpol.2012.04.076 Lutz, C. & Wiebe, K.S. (2012): Economic impacts of different Post-Kyoto regimes. International Journal of Energy Science 2(4), 163-168. Wiesenthal et al. (2012b). Technology Learning Curves for Energy Policy Support. EUR - Scientific and Technical Research Reports. JRC73231, doi:10.2790/59345.