IDEAS home Printed from https://ideas.repec.org/p/ipt/iptwpa/jrc61065.html
   My bibliography  Save this paper

Quantitative Assessment of the Impact of the Strategic Energy Technology Plan on the European Power Sector

Author

Listed:

Abstract

The goal of this analysis is to capture the effect of increasing research, development and demonstration (RD&D) efforts for a set of low-carbon power technologies on the development of the European energy sector. The report finds that an increase in research efforts on a global level, that for the EU are in line with the RD&D investments proposed in the context of the European Strategic Energy Technology Plan, will contribute to reducing the costs of currently less mature low-carbon technologies, and therefore accelerate their market entry. Following from the lower technology investment costs, the economic rate of return of the additional SET-Plan investments in the EU would be positive, reaching around 15% for a time horizon between 2010 and 2030. The cumulative (discounted) benefit of the RD&D investments would be negative in early years before turning positive around the year 2020 and remaining so thereafter.

Suggested Citation

  • Tobias Wiesnethal & Arnaud Mercier & Burkhard Schade & H. Petric & Lazlo Szabo, 2010. "Quantitative Assessment of the Impact of the Strategic Energy Technology Plan on the European Power Sector," JRC Research Reports JRC61065, Joint Research Centre.
  • Handle: RePEc:ipt:iptwpa:jrc61065
    as

    Download full text from publisher

    File URL: https://publications.jrc.ec.europa.eu/repository/handle/JRC61065
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Markard, Jochen & Truffer, Bernhard, 2006. "Innovation processes in large technical systems: Market liberalization as a driver for radical change?," Research Policy, Elsevier, vol. 35(5), pages 609-625, June.
    2. Szabo, Laszlo & Hidalgo, Ignacio & Ciscar, Juan Carlos & Soria, Antonio, 2006. "CO2 emission trading within the European Union and Annex B countries: the cement industry case," Energy Policy, Elsevier, vol. 34(1), pages 72-87, January.
    3. Cohen, Wesley M & Levinthal, Daniel A, 1989. "Innovation and Learning: The Two Faces of R&D," Economic Journal, Royal Economic Society, vol. 99(397), pages 569-596, September.
    4. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    5. Tooraj Jamasb and Michael Pollitt, 2005. "Electricity Market Reform in the European Union: Review of Progress toward Liberalization & Integration," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 11-42.
    6. Claudia Kettner & Angela Köppl & Stefan Schleicher, 2008. "Technological Change and Learning Curves in the Context of the TranSust.Scan Modelling Network. TranSust.Scan Working Paper," WIFO Studies, WIFO, number 38921, April.
    7. Tobias Wiesenthal & Guillaume Leduc & Hans-Gunther Schwarz & Karel Haegeman, 2009. "RandD Investment in the Priority Technologies of the European Strategic Energy Technology Plan," JRC Research Reports JRC52225, Joint Research Centre.
    8. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    9. Jamasb, T. & Köhler, J., 2007. "Learning Curves For Energy Technology and Policy Analysis: A Critical Assessment," Cambridge Working Papers in Economics 0752, Faculty of Economics, University of Cambridge.
    10. Söderholm, Patrik & Sundqvist, Thomas, 2007. "Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies," Renewable Energy, Elsevier, vol. 32(15), pages 2559-2578.
    11. Sahal, Devendra, 1985. "Technological guideposts and innovation avenues," Research Policy, Elsevier, vol. 14(2), pages 61-82, April.
    12. Kypreos, Socrates, 2007. "A MERGE model with endogenous technological change and the cost of carbon stabilization," Energy Policy, Elsevier, vol. 35(11), pages 5327-5336, November.
    13. Isoard, Stephane & Soria, Antonio, 2001. "Technical change dynamics: evidence from the emerging renewable energy technologies," Energy Economics, Elsevier, vol. 23(6), pages 619-636, November.
    14. Nikolaos Kouvaritakis & Antonio Soria & Stephane Isoard, 2000. "Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 104-115.
    15. Klaassen, Ger & Miketa, Asami & Larsen, Katarina & Sundqvist, Thomas, 2005. "The impact of R&D on innovation for wind energy in Denmark, Germany and the United Kingdom," Ecological Economics, Elsevier, vol. 54(2-3), pages 227-240, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Choe, Hochull & Lee, Duk Hee & Seo, Il Won & Kim, Hee Dae, 2013. "Patent citation network analysis for the domain of organic photovoltaic cells: Country, institution, and technology field," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 492-505.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lohwasser, Richard & Madlener, Reinhard, 2013. "Relating R&D and investment policies to CCS market diffusion through two-factor learning," Energy Policy, Elsevier, vol. 52(C), pages 439-452.
    2. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    3. Hong, Sungjun & Chung, Yanghon & Woo, Chungwon, 2015. "Scenario analysis for estimating the learning rate of photovoltaic power generation based on learning curve theory in South Korea," Energy, Elsevier, vol. 79(C), pages 80-89.
    4. Kahouli, Sondès, 2011. "Effects of technological learning and uranium price on nuclear cost: Preliminary insights from a multiple factors learning curve and uranium market modeling," Energy Economics, Elsevier, vol. 33(5), pages 840-852, September.
    5. Yu, C.F. & van Sark, W.G.J.H.M. & Alsema, E.A., 2011. "Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 324-337, January.
    6. Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    7. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    8. Wei, Yi-Ming & Qiao, Lu & Lv, Xin, 2020. "The impact of mergers and acquisitions on technology learning in the petroleum industry," Energy Economics, Elsevier, vol. 88(C).
    9. Kahouli-Brahmi, Sondes, 2009. "Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach," Ecological Economics, Elsevier, vol. 68(4), pages 1195-1212, February.
    10. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng, 2022. "Effects of learning curve models on onshore wind and solar PV cost developments in the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    11. Williams, Eric & Hittinger, Eric & Carvalho, Rexon & Williams, Ryan, 2017. "Wind power costs expected to decrease due to technological progress," Energy Policy, Elsevier, vol. 106(C), pages 427-435.
    12. Shayegh, Soheil & Sanchez, Daniel L. & Caldeira, Ken, 2017. "Evaluating relative benefits of different types of R&D for clean energy technologies," Energy Policy, Elsevier, vol. 107(C), pages 532-538.
    13. Odam, Neil & de Vries, Frans P., 2020. "Innovation modelling and multi-factor learning in wind energy technology," Energy Economics, Elsevier, vol. 85(C).
    14. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    15. Elofsson, Katarina, 2014. "International knowledge diffusion and its impact on the cost-effective clean-up of the Baltic Sea," Working Paper Series 2014:06, Swedish University of Agricultural Sciences, Department Economics.
    16. Elia, A. & Kamidelivand, M. & Rogan, F. & Ó Gallachóir, B., 2021. "Impacts of innovation on renewable energy technology cost reductions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    17. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    18. Heuberger, Clara F. & Rubin, Edward S. & Staffell, Iain & Shah, Nilay & Mac Dowell, Niall, 2017. "Power capacity expansion planning considering endogenous technology cost learning," Applied Energy, Elsevier, vol. 204(C), pages 831-845.
    19. Chen, Xiaoguang & Khanna, Madhu, 2012. "Explaining the reductions in US corn ethanol processing costs: Testing competing hypotheses," Energy Policy, Elsevier, vol. 44(C), pages 153-159.
    20. Upstill, Garrett & Hall, Peter, 2018. "Estimating the learning rate of a technology with multiple variants: The case of carbon storage," Energy Policy, Elsevier, vol. 121(C), pages 498-505.

    More about this item

    Keywords

    Quantitative assessment; SET Plan; RD&D; low-carbon power; Climate Policy; climate change; greenhouse gas emission (GHG); POLES; GEM-E3; scenario;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ipt:iptwpa:jrc61065. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Publication Officer (email available below). General contact details of provider: https://edirc.repec.org/data/ipjrces.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.