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Title:
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Endogenous technological change in energy system models: synthesis of experience with ERIS, MARKAL, and MESSAGE
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Author(s):
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Published by:
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Publication date:
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ECN
Policy Studies
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1-4-1999
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ECN report number:
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Document type:
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ECN-C--99-025
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ECN publication
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Number of pages:
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Full text:
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29
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Download PDF
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Abstract:
Technological change is widely recognised as a key factor in economicprogress, as it enhances the productivity of factor inputs. In recent years
the notion has also 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
energy systems should take into account context-specific technological
progress. Rather than taking characteristics of existing and emerging
technologies as a given, their development should be a function of dedicated
Research, Development and Demonstration (R, D and D) and market deployment
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 technologies 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 technological 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 methodological 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 RDandD 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 RandD policy and
learning data parameters are still unknown. 26 refs.
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