Endogenous Technological Change in Energy Systems Models: Synthesis of Experience with ERIS, MARKAL, and MESSAGE

Seebregts, A.J., Kram, T., Schaeffer, G.J., Stoffer, A., Kypreos, S., Barreto, L., Messner, S., & Schrattenholzer, L. (1998). Endogenous Technological Change in Energy Systems Models: Synthesis of Experience with ERIS, MARKAL, and MESSAGE. Paul Scherrer Institute, IIASA , Villigen, Switzerland and IIASA, Laxenburg, Austria. ECN-C-99-025.

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Abstract

Technological change is widely recognised as a key factor in economic progress, as it enhances the productivity of factor inputs. In recent years also the notion has developed that targeted technological development is a main means to reconcile economic ambitions with ecological considerations. This raises the issue that assessments of future trajectories of for example en-ergy systems should take into account context-specific technological progress. Rather than tak-ing characteristics of existing and emerging technologies as a given, their development should be a function of dedicated Research, Development and Demonstration (RD&D) and market de-ployment under varying external conditions.
Endogenous technological learning has recently shown to be a very promising new feature in energy system models. A learning, or experience curve, describes the specific (investment) cost as a function of the cumulative capacity for a given technology. It reflects the fact that tech-nologies may experience declining costs as a result of its increasing adoption into the society due to the accumulation of knowledge through, among others, processes of learning-by-doing and learning-by-using.
This report synthesises the results and findings from experiments with endogenous technologi-cal learning, as reported separately within the EU TEEM project. These experiments have been carried out by three TEEM partners using three models: ERIS (PSI), MARKAL (ECN and PSI), and MESSAGE (IIASA). The main objectives of this synthesis are: to derive common methodo-logical insights; to indicate and assess benefits of the new feature, but also its limitations and issues to solve; and to recommend further research to solve the main issues.
This synthesis shows that all model applications are examples of successful first experiments to incorporate the learning-by-doing concept in energy system models. Incorporating the learning-by-doing concept makes an important difference. The experiments demonstrate and quantify the benefits of investing early in emerging technologies that are not competitive at the moment of their deployment. They also show that the long-term impact of policy instruments, such as CO2 taxes or emission limits and RD&D instruments, on technological development can be assessed adequately with models including technology learning.
Adopting the concept of endogenous learning, several types of RD&D interventions can be addressed that aim at accelerating the market penetration of new technologies. The directions into which such interventions might lead have been illustrated in some of the experiments. However, quantitative relationships between R&D policy and learning data parameters are still unknown

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