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Titel:
Technology R&D and CO2 policy scenarios. The MARKAL model work for SAPIENTIA
 
Auteur(s):
 
Gepubliceerd door: Publicatie datum:
ECN Beleidsstudies 1-7-2005
 
ECN publicatienummer: Publicatie type:
ECN-C--05-059 ECN rapport
 
Aantal pagina's: Volledige tekst:
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Samenvatting:

The SAPIENTIA project is a follow-up of the SAPIENT project with an aim to explore the impact of a two learning curve (learning by doing and learning by searching) for specific technologies and technology components. These components form technology clusters in which technology spill over, both intra-sectoral as cross sectoral, can occur. By means of a two factor learning curve a distinction is made between the deployment and R&D efforts as driving forces behind cost reductions of technologies.

The necessary basic information to start the MARKAL model extension work for SAPIENTIA was obtained from a preceding study by IER and ECN (WP1 of the SAPIENTIA project, see ECN report ECN-C-05-056). This model work included the extension of the coverage of the technology clusters for the ten existing, i.e. pre-SAPIENTIA, learning key components and the addition of a substantial number of new key components (20) and of the corresponding technology clusters. As a result, 30 key components covering 354 technologies spread over 6 sectors (supply and demand) are integrated in a large Western European energy model.

The scenarios analysed, a reference case and two different CO2 tax cases, show that technology-wise, a much richer insight in the results can be given. The CO2 tax cases lead to substantial CO2 emission reductions (50 to 80% by 2050). The primary energy mix, although reduced with 10 to 15%, shows an improvement in diversification, resulting in better security of supply indicators. Nevertheless Western Europe will remain dependent on external imports (55 to 40 % in 2050).

R&D shocks as an approximation of two factor learning curves (TFLC) in MARKAL show to have only a limited impact on cost reductions. The R&D shock improves the overall learning ratio marginally and other scenario assumptions prevail over the sensitivity to these changes. Only a few key components, mainly in the power sector, show benefits, i.e. an improved de-ployment, from the inclusion of this approach. It is not clear yet whether these conclusions are generally applicable or only hold for the type of technologies studied in this project. What the role and impact of the technology characteristics are on the share of R&D and of deployment in the two factor learning approach, and hence in the R&D shock approach, is a subject for further research, analysis and assessment.


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