In previous projects, different approaches for the endogenous treatment of technological progress have been studied in basic models and in the single-regional TUG-IPP-Model. The possibility of the implementation of these learning feature in the 15-regional EFDA-TIMES-Model shall be assessed, and, to the extent possible, the approaches implemented.
Additionally, new approaches for the endogenous treatment of learning processes shall be developed. First, a new treatment for learning-by-doing shall be developed, that allows endogenous learning treatments in big technology-rich and multi-regional models. This approach shall then be implemented in global models, and its impact on the model results shall be analysed. Secondly, an approach for the endogenous treatment of technical parameters shall be developed, implemented in TIMES, and its impact on the results of global models analysed.
As tool, the TIMES model generator and two global models, the TUG-IPP-Model and the 15-regional EFDA-TIMES-Model will be used.