One of the new features implemented in ERIS by IIASA has been the capability to define R&D expenditures in absolute monetary units. Using this feature allows the model user to not only optimise a given R&D budget, but also to "freely" (without constraints) optimise the R&D support of a technology for which a 2FLC has been specified. This approach naturally does not realistically describe the decision process of EC policy makers, but experimental runs with ERIS in such a hypothetical world enhanced the basic understanding of the model's dynamics and plausible results in this respect boost the confidence in the model's results.
IIASA has performed a sensitivity analysis that tests the consequences on optimised R&D expenditures, of assuming the "wrong" learning rates (learning-by-doing rate and learning-by-research rate). In order to focus on testing the dynamics of ERIS in response to changing the parameters of two-factor learning curves, the model was used in an aggregate ("stylised") fashion.
Solar photovoltaic and wind electricity generation technologies were used to paraphrase a situation in which one technology has a long way to go before it reaches competitiveness but has a high potential for technological progress, and the other technology being closer to competitiveness, but learns at a slower pace.
The sensitivity of the results was then tested with respect to the learning parameters, with focus on the ranges around some reference values. Subsequently, the model was assessed with 2FLC applied to both technologies at the same time, to study how the optimised R&D support of one technology is influenced by the presence of a competitor. The procedure has generated useful results fully analysed in the final report.
IIASA has also extended the solution horizon of ERIS to 2100 allowing for the performance of R&D budgeting exercises with a very long-term perspective.