Advanced Search
MyIDEAS: Login to save this article or follow this journal

Use and limitations of learning curves for energy technology policy: A component-learning hypothesis

Contents:

Author Info

  • Ferioli, F.
  • Schoots, K.
  • van der Zwaan, B.C.C.

Abstract

In this paper, we investigate the use of learning curves for the description of observed cost reductions for a variety of energy technologies. Starting point of our analysis is the representation of energy processes and technologies as the sum of different components. While we recognize that in many cases "learning-by-doing" may improve the overall costs or efficiency of a technology, we argue that so far insufficient attention has been devoted to study the effects of single component improvements that together may explain an aggregated form of learning. Indeed, for an entire technology the phenomenon of learning-by-doing may well result from learning of one or a few individual components only. We analyze under what conditions it is possible to combine learning curves for single components to derive one comprehensive learning curve for the total product. The possibility that for certain technologies some components (e.g., the primary natural resources that serve as essential input) do not exhibit cost improvements might account for the apparent time dependence of learning rates reported in several studies (the learning rate might also change considerably over time depending on the data set considered, a crucial issue to be aware of when one uses the learning curve methodology). Such an explanation may have important consequences for the extent to which learning curves can be extrapolated into the future. This argumentation suggests that cost reductions may not continue indefinitely and that well-behaved learning curves do not necessarily exist for every product or technology. In addition, even for diffusing and maturing technologies that display clear learning effects, market and resource constraints can eventually significantly reduce the scope for further improvements in their fabrication or use. It appears likely that some technologies, such as wind turbines and photovoltaic cells, are significantly more amenable than others to industry-wide learning. For such technologies we assess the reliability of using learning curves at large to forecast energy technology cost reductions.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www.sciencedirect.com/science/article/B6V2W-4W0R3J9-1/2/a73ec8bf955f7674f76dd2773c2bf2fe
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Bibliographic Info

Article provided by Elsevier in its journal Energy Policy.

Volume (Year): 37 (2009)
Issue (Month): 7 (July)
Pages: 2525-2535

as in new window
Handle: RePEc:eee:enepol:v:37:y:2009:i:7:p:2525-2535

Contact details of provider:
Web page: http://www.elsevier.com/locate/enpol

Related research

Keywords: Energy technology Learning by doing Experience curve;

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Neij, Lena, 1997. "Use of experience curves to analyse the prospects for diffusion and adoption of renewable energy technology," Energy Policy, Elsevier, vol. 25(13), pages 1099-1107, November.
  2. Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
  3. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
  4. van der Zwaan, B. C. C. & Gerlagh, R. & G. & Klaassen & Schrattenholzer, L., 2002. "Endogenous technological change in climate change modelling," Energy Economics, Elsevier, vol. 24(1), pages 1-19, January.
  5. C. Harmon, 2000. "Experience Curves of Photovoltaic Technology," Working Papers ir00014, International Institute for Applied Systems Analysis.
  6. Neij, Lena, 2008. "Cost development of future technologies for power generation--A study based on experience curves and complementary bottom-up assessments," Energy Policy, Elsevier, vol. 36(6), pages 2200-2211, June.
  7. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
  8. van der Zwaan, Bob & Rabl, Ari, 2004. "The learning potential of photovoltaics: implications for energy policy," Energy Policy, Elsevier, vol. 32(13), pages 1545-1554, September.
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 in new window

Cited by:
  1. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
  2. Laude, Audrey & Jonen, Christian, 2013. "Biomass and CCS: The influence of technical change," Energy Policy, Elsevier, vol. 60(C), pages 916-924.
  3. Ayompe, L.M. & Duffy, A. & McCormack, S.J. & Conlon, M., 2010. "Projected costs of a grid-connected domestic PV system under different scenarios in Ireland, using measured data from a trial installation," Energy Policy, Elsevier, vol. 38(7), pages 3731-3743, July.
  4. Audrey Laude & Christian Jonen, 2011. "Biomass and CCS: The influence of the learning effect," Working Papers halshs-00829779, HAL.
  5. Giraudet, Louis-Gaëtan & Guivarch, Céline & Quirion, Philippe, 2012. "Exploring the potential for energy conservation in French households through hybrid modeling," Energy Economics, Elsevier, vol. 34(2), pages 426-445.
  6. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
  7. Siderius, Hans-Paul, 2013. "The role of experience curves for setting MEPS for appliances," Energy Policy, Elsevier, vol. 59(C), pages 762-772.
  8. Samantha DeMartino, David Le Blanc, 2010. "Estimating the Amount of a Global Feed-in Tariff for Renewable Electricity," Working Papers 95, United Nations, Department of Economics and Social Affairs.
  9. Felix Groba & Barbara Breitschopf, 2013. "Impact of Renewable Energy Policy and Use on Innovation: A Literature Review," Discussion Papers of DIW Berlin 1318, DIW Berlin, German Institute for Economic Research.
  10. Zheng, Cheng & Kammen, Daniel M., 2014. "An innovation-focused roadmap for a sustainable global photovoltaic industry," Energy Policy, Elsevier, vol. 67(C), pages 159-169.
  11. 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.
  12. Hayward, Jennifer A. & Graham, Paul W., 2013. "A global and local endogenous experience curve model for projecting future uptake and cost of electricity generation technologies," Energy Economics, Elsevier, vol. 40(C), pages 537-548.
  13. Schoots, K. & Kramer, G.J. & van der Zwaan, B.C.C., 2010. "Technology learning for fuel cells: An assessment of past and potential cost reductions," Energy Policy, Elsevier, vol. 38(6), pages 2887-2897, June.
  14. Massimo Tavoni & Bob van der Zwaan, 2009. "Nuclear versus Coal plus CCS: A Comparison of Two Competitive Base-load Climate Control Options," Working Papers 2009.100, Fondazione Eni Enrico Mattei.
  15. Arnaud De La Tour & Matthieu Glachant & Yann Ménière, 2013. "What cost for photovoltaic modules in 2020? Lessons from experience curve models," Working Papers hal-00805668, HAL.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:37:y:2009:i:7:p:2525-2535. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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