A model-based assessment of the impact of revitalised R&D investments on the European power sector

Abstract This paper presents an analysis of the effect of enhanced research and development (R&D) efforts for a set of low-carbon power technologies on the development of the European energy sector. It applies a methodology using the concept of Two-Factor-Learning, which quantitatively links trends in technology cost to both accumulated R&D investments and production volumes. The impacts of the latter on the energy sector are then simulated in a consistent manner with the POLES global energy model. On this basis, it compares the total system costs of an assumed increase in worldwide R&D investments that for the EU are in line with proposals made in its European Strategic Energy Technology Plan to a baseline development. It finds that an increase in research efforts at a global level will contribute to reducing the costs of currently less mature low-carbon technologies, thus accelerating their market entry. When comparing two scenarios that both fulfil the EU's 2020 energy and climate objectives and differing only in their R&D investment levels, the reduced technology costs allow EU support policies for renewables and carbon values to be reduced, and the cumulative (discounted) benefit of the accelerated research efforts is positive in the long term.

[1]  Leo Schrattenholzer,et al.  Energy technology dynamics , 2000 .

[2]  Richard L. Ottinger,et al.  Compendium of Sustainable Energy Laws: Directive 2003/87/EC of the European Parliament and of the Council of 13 October 2003 Establishing a Scheme for Greenhouse Gas Emission Allowance Trading Within the Community and Amending Council Directive 96/61/EC , 2005 .

[3]  Jaeger-Waldau Arnulf,et al.  PV Status - Research, Solar Cell Production and Market Implementation of Photovoltaics , 2005 .

[4]  Ottmar Edenhofer,et al.  Towards a Global Green Recovery. Recommendations for Immediate G20 Action. Report submitted to the G20 London Summit , 2009 .

[5]  K. Arrow.  The Economic Implications of Learning by Doing , 1962 .

[6]  Aie,et al.  Energy Technology Perspectives 2012 , 2006 .

[7]  B. Schade.  Volkswirtschaftliche Bewertung von Szenarien mit System Dynamics. Bewertung von nachhaltigen Verkehrsszenarien mit ESCOT (Economic assessment of Sustainability poliCies Of Transport) , 2005 .

[8]  S. Kahouli-Brahmi.  Technological learning in energy–environment–economy modelling: A survey , 2008 .

[9]  E. Cerdá,et al.  Climate change policies : global challenges and future prospects , 2010 .

[10]  Leo Schrattenholzer,et al.  Experiments with a methodology to model the role of RD first results , 2004 .

[11]  Patrik Söderholm,et al.  Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies , 2007 .

[12]  C. Wene.  Experience Curves for Energy Technology Policy , 2000 .

[13]  Ulrich Fahl,et al.  Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model , 2009 .

[14]  Burkhard Schade,et al.  Quantitative Assessment of the Impact of the Strategic Energy Technology Plan on the European Power Sector , 2010 .

[15]  T. P. Wright,et al.  Factors affecting the cost of airplanes , 1936 .

[16]  Bert Saveyn,et al.  Economic Assessment of Post-2012 Global Climate Policies - Analysis of Gas Greenhouse Gas Emission Reduction Scenarios with the POLES and GEM-E3 models , 2009 .

[17]  Nebojsa Nakicenovic,et al.  Dynamics of energy technologies and global change , 1999 .

[18]  Tobias Wiesenthal,et al.  RandD Investment in the Priority Technologies of the European Strategic Energy Technology Plan , 2009 .

[19]  Chihiro Watanabe,et al.  Industrial dynamism and the creation of a “virtuous cycle” between R&D, market growth and price reduction: The case of photovoltaic power generation (PV) development in Japan , 2000 .

[20]  Bert Saveyn,et al.  Present and Future of Applied Climate Mitigation Policies: The European Union , 2010 .

[21]  Melissa A. Schilling,et al.  Technology S-Curves in Renewable Energy Alternatives: Analysis and Implications for Industry and Government. , 2009 .

[22]  Asami Miketa,et al.  The impact of R&D on innovation for wind energy in Denmark, Germany and the United Kingdom , 2005 .

[23]  Daniel A. Levinthal,et al.  Innovation and Learning: The Two Faces of R&D , 1989 .

[24]  Antonio Soria,et al.  Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching , 2000 .