IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v65y2016icp507-524.html
   My bibliography  Save this article

Photovoltaic learning rate estimation: Issues and implications

Author

Listed:
  • Mauleón, Ignacio

Abstract

This paper surveys the results of estimating learning rate (LR) equations for the photovoltaic (PV) industry at the world level, and reports new results, placing emphasis on estimation issues, and other shortcomings surveyed recently. The results are reported in detail, one relevant finding being that the learning rate parameter might reach values substantially higher than those usually reported (18–20%). This result, however, does not necessarily translate to other energies. The relevance of selecting the estimation sample, dynamic specification, and omitted variables in simple standard specifications for the estimated learning rate is highlighted. A solution for the LR in dynamic non stationary models is presented. The modeling of silicon prices is also discussed, and the concept of the total learning rate (TLR) is introduced. Probability confidence intervals for the main estimated learning rate parameters are analyzed, and the time decomposition of PV module prices is discussed, highlighting the role of fossil energy prices. It is found that the total LR might reach values above 27% with a 95% probability.

Suggested Citation

  • Mauleón, Ignacio, 2016. "Photovoltaic learning rate estimation: Issues and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 507-524.
    • Handle: RePEc:eee:rensus:v:65:y:2016:i:c:p:507-524
    DOI: 10.1016/j.rser.2016.06.070
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032116303008
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2016.06.070?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    3. Witajewski-Baltvilks, Jan & Verdolini, Elena & Tavoni, Massimo, 2015. "Bending the learning curve," Energy Economics, Elsevier, vol. 52(S1), pages 86-99.
    4. Movilla, Santiago & Miguel, Luis J. & Blázquez, L. Felipe, 2013. "A system dynamics approach for the photovoltaic energy market in Spain¤," Energy Policy, Elsevier, vol. 60(C), pages 142-154.
    5. 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.
    6. Gan, Peck Yean & Li, ZhiDong, 2015. "Quantitative study on long term global solar photovoltaic market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 88-99.
    7. Albrecht, Johan, 2007. "The future role of photovoltaics: A learning curve versus portfolio perspective," Energy Policy, Elsevier, vol. 35(4), pages 2296-2304, April.
    8. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    9. Reboredo, Juan C., 2015. "Is there dependence and systemic risk between oil and renewable energy stock prices?," Energy Economics, Elsevier, vol. 48(C), pages 32-45.
    10. Hettinga, W.G. & Junginger, H.M. & Dekker, S.C. & Hoogwijk, M. & McAloon, A.J. & Hicks, K.B., 2009. "Understanding the reductions in US corn ethanol production costs: An experience curve approach," Energy Policy, Elsevier, vol. 37(1), pages 190-203, January.
    11. Wolfgang Keller, 2004. "International Technology Diffusion," Journal of Economic Literature, American Economic Association, vol. 42(3), pages 752-782, September.
    12. Lena Neij & Per Dannemand Andersen & Michael Durstewitz, 2004. "Experience curves for wind power," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 2(1/2), pages 15-32.
    13. de la Tour, Arnaud & Glachant, Matthieu & Ménière, Yann, 2011. "Innovation and international technology transfer: The case of the Chinese photovoltaic industry," Energy Policy, Elsevier, vol. 39(2), pages 761-770, February.
    14. Coulomb, L. & Neuhoff, K., 2006. "Learning curves and changing product attributes: the case of wind turbines," Cambridge Working Papers in Economics 0618, Faculty of Economics, University of Cambridge.
    15. 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.
    16. 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.
    17. 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.
    18. van Sark, Wilfried G.J.H.M. & Muizebelt, Peter & Cace, Jadranka & de Vries, Arthur & de Rijk, Peer, 2014. "Price development of photovoltaic modules, inverters, and systems in the Netherlands in 2012," Renewable Energy, Elsevier, vol. 71(C), pages 18-22.
    19. 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.
    20. de La Tour, Arnaud & Glachant, Matthieu & Ménière, Yann, 2013. "Predicting the costs of photovoltaic solar modules in 2020 using experience curve models," Energy, Elsevier, vol. 62(C), pages 341-348.
    21. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    22. Pillai, Unni, 2015. "Drivers of cost reduction in solar photovoltaics," Energy Economics, Elsevier, vol. 50(C), pages 286-293.
    23. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    24. Tooraj Jamasb, 2007. "Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 51-72.
    25. Nemet, Gregory F., 2009. "Interim monitoring of cost dynamics for publicly supported energy technologies," Energy Policy, Elsevier, vol. 37(3), pages 825-835, March.
    26. 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.
    27. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    28. William D. Nordhaus, 2014. "The Perils of the Learning Model for Modeling Endogenous Technological Change," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    29. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    30. Davidsson, Simon & Grandell, Leena & Wachtmeister, Henrik & Höök, Mikael, 2014. "Growth curves and sustained commissioning modelling of renewable energy: Investigating resource constraints for wind energy," Energy Policy, Elsevier, vol. 73(C), pages 767-776.
    31. Ferioli, F. & Schoots, K. & van der Zwaan, B.C.C., 2009. "Use and limitations of learning curves for energy technology policy: A component-learning hypothesis," Energy Policy, Elsevier, vol. 37(7), pages 2525-2535, July.
    32. 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.
    33. Ignacio Mauleón, 2010. "Assessing the value of Hermite densities for predictive distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(8), pages 689-714, December.
    34. Partridge, Ian, 2013. "Renewable electricity generation in India—A learning rate analysis," Energy Policy, Elsevier, vol. 60(C), pages 906-915.
    35. Georg Zachmann & Amma Serwaah & Michele Perruzi, "undated". "When & how to support renewables? Letting the data speak," SIMPATIC Working Papers 951, Bruegel.
    36. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    37. MacKinnon, James G, 1996. "Numerical Distribution Functions for Unit Root and Cointegration Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 601-618, Nov.-Dec..
    38. Candelise, Chiara & Winskel, Mark & Gross, Robert J.K., 2013. "The dynamics of solar PV costs and prices as a challenge for technology forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 96-107.
    39. Dutton, John M. & Thomas, Annie & Butler, John E., 1984. "The History of Progress Functions as a Managerial Technology," Business History Review, Cambridge University Press, vol. 58(2), pages 204-233, July.
    40. Bianchini, Augusto & Gambuti, Michele & Pellegrini, Marco & Saccani, Cesare, 2016. "Performance analysis and economic assessment of different photovoltaic technologies based on experimental measurements," Renewable Energy, Elsevier, vol. 85(C), pages 1-11.
    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. Mauleón, Ignacio, 2019. "Optimizing individual renewable energies roadmaps: Criteria, methods, and end targets," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Sommerfeld, Jeff & Buys, Laurie & Vine, Desley, 2017. "Residential consumers’ experiences in the adoption and use of solar PV," Energy Policy, Elsevier, vol. 105(C), pages 10-16.
    3. Ignacio Mauleón, 2020. "Economic Issues in Deep Low-Carbon Energy Systems," Energies, MDPI, vol. 13(16), pages 1-32, August.
    4. Peffley, Trevor B. & Pearce, Joshua M., 2020. "The potential for grid defection of small and medium sized enterprises using solar photovoltaic, battery and generator hybrid systems," Renewable Energy, Elsevier, vol. 148(C), pages 193-204.
    5. Adesanya, Adewale A. & Pearce, Joshua M., 2019. "Economic viability of captive off-grid solar photovoltaic and diesel hybrid energy systems for the Nigerian private sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    6. Nemet, Gregory F. & Lu, Jiaqi & Rai, Varun & Rao, Rohan, 2020. "Knowledge spillovers between PV installers can reduce the cost of installing solar PV," Energy Policy, Elsevier, vol. 144(C).
    7. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    8. Mauleón, Ignacio, 2019. "Assessing PV and wind roadmaps: Learning rates, risk, and social discounting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 71-89.
    9. Choi, Donghyun & Kim, Yeong Jae, 2023. "Local and global experience curves for lumpy and granular energy technologies," Energy Policy, Elsevier, vol. 174(C).
    10. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    11. Chiara Modanese & Hannu S. Laine & Toni P. Pasanen & Hele Savin & Joshua M. Pearce, 2018. "Economic Advantages of Dry-Etched Black Silicon in Passivated Emitter Rear Cell (PERC) Photovoltaic Manufacturing," Energies, MDPI, vol. 11(9), pages 1-18, September.
    12. Berry, Stephen & Moore, Trivess & Ambrose, Michael, 2019. "Flexibility versus certainty: The experience of mandating a building sustainability index to deliver thermally comfortable homes," Energy Policy, Elsevier, vol. 133(C).
    13. Joshua M. Pearce & Emily Prehoda, 2019. "Could 79 People Solarize the U.S. Electric Grid?," Societies, MDPI, vol. 9(1), pages 1-27, March.
    14. Mauleón, Ignacio & Hamoudi, Hamid, 2017. "Photovoltaic and wind cost decrease estimation: Implications for investment analysis," Energy, Elsevier, vol. 137(C), pages 1054-1065.
    15. Leonidas Paroussos & Kostas Fragkiadakis & Panagiotis Fragkos, 2020. "Macro-economic analysis of green growth policies: the role of finance and technical progress in Italian green growth," Climatic Change, Springer, vol. 160(4), pages 591-608, June.
    16. Blessing Ugwoke & Adedoyin Adeleke & Stefano P. Corgnati & Joshua M. Pearce & Pierluigi Leone, 2020. "Decentralized Renewable Hybrid Mini-Grids for Rural Communities: Culmination of the IREP Framework and Scale up to Urban Communities," Sustainability, MDPI, vol. 12(18), pages 1-26, September.

    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. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    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. 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).
    4. Sascha Samadi, 2016. "A Review of Factors Influencing the Cost Development of Electricity Generation Technologies," Energies, MDPI, vol. 9(11), pages 1-25, November.
    5. Mauleón, Ignacio & Hamoudi, Hamid, 2017. "Photovoltaic and wind cost decrease estimation: Implications for investment analysis," Energy, Elsevier, vol. 137(C), pages 1054-1065.
    6. 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).
    7. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    8. Strupeit, Lars & Neij, Lena, 2017. "Cost dynamics in the deployment of photovoltaics: Insights from the German market for building-sited systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 948-960.
    9. Elia, A. & Taylor, M. & Ó Gallachóir, B. & Rogan, F., 2020. "Wind turbine cost reduction: A detailed bottom-up analysis of innovation drivers," Energy Policy, Elsevier, vol. 147(C).
    10. Schauf, Magnus & Schwenen, Sebastian, 2021. "Mills of progress grind slowly? Estimating learning rates for onshore wind energy," Energy Economics, Elsevier, vol. 104(C).
    11. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    12. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    13. Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
    14. 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).
    15. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    16. Strupeit, Lars, 2017. "An innovation system perspective on the drivers of soft cost reduction for photovoltaic deployment: The case of Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 273-286.
    17. De Cian, Enrica & Buhl, Johannes & Carrara, Samuel & Michela Bevione, Michela & Monetti, Silvia & Berg, Holger, 2016. "Knowledge Creation between Integrated Assessment Models and Initiative-Based Learning - An Interdisciplinary Approach," MITP: Mitigation, Innovation and Transformation Pathways 249784, Fondazione Eni Enrico Mattei (FEEM).
    18. 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.
    19. Gan, Peck Yean & Li, ZhiDong, 2015. "Quantitative study on long term global solar photovoltaic market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 88-99.
    20. Odam, Neil & de Vries, Frans P., 2020. "Innovation modelling and multi-factor learning in wind energy technology," Energy Economics, Elsevier, vol. 85(C).

    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:eee:rensus:v:65:y:2016:i:c:p:507-524. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.