The Renewable Power Generation Module (RPGM) – An extension to the GWS model family to endogenize technological change in the renewable power generation sector
Kirsten Wiebe and
Christian Lutz ()
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Christian Lutz: GWS - Institute of Economic Structures Research
No 13-7, GWS Discussion Paper Series from GWS - Institute of Economic Structures Research
In Germany, 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 analyse 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. This is a first step to endogenize technological change in the model. Wind (onshore) and PV 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. 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 contributes to endogenously determining 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.
Keywords: Deployment; endogenous technological change; renewable power generation; econometric input-output model (search for similar items in EconPapers)
JEL-codes: C51 O30 Q41 (search for similar items in EconPapers)
Pages: 31 pages
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