How to cite this WIREs title:
WIREs Energy Environ.

Impact Factor: 2.922

Future energy system development depends on past learning opportunities

Advanced Review

Clas‐Otto Wene

Published Online: Mar 21 2015

DOI: 10.1002/wene.172

Full article on Wiley Online Library: HTML PDF

Can't access this content? Tell your librarian.

Learning curves pervade all levels of industry and suggest deployment programs to buy down the cost of presently too expensive environment‐friendly technologies. However, the legitimate role of learning curves in a low‐carbon strategy depends on the validity of extrapolations and forecasts of the curves. Moving toward a technology‐led strategy for a low‐carbon energy system requires understanding of the learning mechanisms and their stability. The first part of this review provides examples of pervasive learning and a brief literature survey of recent learning curve measurements for energy technologies and the use of learning curves for scenario and policy analysis. The second part reviews how authors from two different perspectives understand the learning curves and come to different conclusions about their stability and legitimate role in scenario analysis and policy making. WIREs Energy Environ 2016, 5:16–32. doi: 10.1002/wene.172 This article is categorized under: Energy Systems Economics > Economics and Policy Energy and Climate > Economics and Policy Energy Policy and Planning > Economics and Policy Energy Research & Innovation > Economics and Policy

(a) Learning curve for installation of offshore wind turbines. (Adapted from Ref ) (b) Experience curve for production of coated glass. (Data from Ref ) (c) Decarbonization of world GDP. (d) Learning curve for building man‐of‐war, frigate plus ship‐of the‐line, at Karlskrona shipyard 1782–1785. (Data from Ref ) I am grateful to Lorens Borg for calling my attention to this work and to the performance data available. (e) Learning curve for wildcats in the USA. The y‐axis is 1/performance following the definitions in Eqs and . (f) Experience curve analysis of oil exploration. (Data from Ref )

[ Normal View | Magnified View ]

Experience curve and growth in global sales for photovoltaic modules. (Data for experience curve from Ref )

[ Normal View | Magnified View ]

Theoretical experience curve and observed prices for PV modules. The theoretical curve is fitted to price in 1976 (red square). The numbers in parentheses show the cumulative world shipments in MW_p at eigentime 5, 10, 15, 20. (From Ref ; price data from Ref )

[ Normal View | Magnified View ]

The learning system and government policies. (Adapted from IEA and Wene)

[ Normal View | Magnified View ]

Core elements of strategies to manage climate stability.

[ Normal View | Magnified View ]

References

Schaeffer, GJ, Seebregts, AJ, Beurskens, LWM, de Moor, HHC, Alsema, EA, van Sark, WGJHM, Durstewitz, M, Perrin, M, Boulanger, P, Laukamp, H, et al. Learning from the Sun; Analysis of the use of experience curves for energy policy purposes: the case of photovoltaic power. Final report of the Photex project, ECN‐C‐‐04‐035, 2004, ECN, Petten.
Junginger, M, Faaij, A, Turkenburg, WC. Global experience curves for wind farms. Energ Policy 2005, 33:133–150.
Wright, TP. Factors affecting the cost of airplanes. J Aeronaut Sci 1936, 3:122.
BCG. Perspectives on Experience. Boston, MA: Boston Consulting Group; 1968.
IEA. Experience Curves for Energy Technology Policy. Paris: International Energy Agency/Organisation for Economic Co‐operation and Development; 2000.
Morgan, G. Images of Organization. Newbury Park/London: Sage Publications; 1986.
Kim, DH. The link between individual and organizational learning. Sloan Manage Rev 1993, 35:37–50.
Espejo, R, Schuhmann, W, Schwaninger, M, Bilello, U. Organisational Transformation and Learning – A Cybernetic Approach to Management. Chichester: John Wiley %26 Sons; 1996.
Cranwell, RM, Guzowski, RV, Campbell, JE, Ortiz, NR. Risk methodology for geologic disposal of Radioactive waste: scenario selection procedure, Report of US Nuclear Regulatory Commission NUREG/CR‐1667 (SAND‐1429), Washington: NRC; 1990.
Rosenberg, N. Inside the Black Box: Technology and Economics. New York: Cambridge University Press; 1982.
Arthur, WB. Competing technologies: an overview. In: Dosi, G, Freeman, C, Nelson, R, Silverberg, G, Soete, L, eds. Technical Change and Economic Theory. London: Pinter Publishers; 1990, 590–607.
Lundvall, BÅ. National Systems of Innovation. Towards a Theory of Innovation and Interactive Learning. London: Pinter Publishers; 1992.
Hall, G, Howell, S. The experience curve from the economist`s perspective. Strateg Manag J 1985, 6:197–212.
Nemet, GF. Beyond the learning curve: factors influencing cost reductions in photovoltaics. Energ Policy 2006, 34:3218–3232.
Lapré, MA. Inside the learning curve: opening the black box of the learning curve. In: Jaber, MY, ed. Learning Curves: Theory, Models, and Applications. Boca Raton/London/New York: CRC Press; 2011.
Ferioli, F, van der Zwaan, BCC. Learning in times of change – a dynamic explanation for technological progress. Environ Sci Technol 2009, 43:4002–4008.
Wene, C‐O. Technology learning systems as non‐trivial machines. Kybernetes 2007, 36:348–363.
Wene, C‐O. Learning curves tracing the optimal path for technology learning systems. In: Proceedings of the 13th IAEE European Conference, Düsseldorf, Germany, 18–21 August, 2013, Available at: http://www.iaee.org/en/publications/proceedingssearch.aspx. (Accessed March 13, 2015).
Sagar, AD, van der Zwaan, BCC. Technology innovation in the energy sector: R%26D, deployment, and learning‐by‐doing. Energ Policy 2006, 34:2601–2608.
Breyer, Ch., Gerlach, A.. Global overview on grid‐parity event dynamics. In: 25th EUPVSEC/WCPEC‐5, Valencia, 6–10 September, 2010.
Maycock, P, Bradford, T. PV market update: demand grows quickly and supply catches up. Renew Energ World 2007, 10:60–74.
Junginger, M, van Sark, W, Faaij, A. Technological Learning in the Energy Sector: Lessons for Policy, Industry and Science. Cheltenham: Edward Elgar; 2010.
Dutton, JM, Thomas, A. Treating progress functions as a managerial opportunity. Acad Manag Rev 1984, 9:235–247.
Weiss, M, Junginger, M, Patel, MK, Blok, K. A review of experience curve analyses for energy demand technologies. Technol Forecast Soc Chang 2010, 77:411–428.
Junginger, M, Faaij, A, Turkenburg, WC. Cost reduction prospects for offshore wind farms. Wind Eng 2004, 28:97–118.
Wene, C‐O. A cybernetic perspective on technology learning. In: Foxon, TJ, Köhler, J, Oughton, C, eds. Innovations for a Low Carbon Economy: Economic, Institutional and Management Approaches. Cheltenham: Edward Elgar; 2008.
Blessing, R. Lerneffekte bei Wärmeschutzverglasung aus Herstellersicht. In: Ostertag, K, Gruber, E, Schleich, J, eds. Bericht zum Forum Hemmnisabbau Zukünftige Optionen für die rationelle Energienutzung. Karlsruhe: Fraunhofer Institut für Systemtechnik und Innovationsforschung; 2002, 36–39.
Wene, C‐O. Energy technology learning – key to transform into a low – carbon society. In: Blanco J, Kheradmand H, eds. Climate Change – Research and Technology for Adaptation and Mitigation. InTech, 2011. Available at: http://www.intechopen.com/articles/show/title/energy‐technology‐learning‐key‐to‐transform‐into‐a‐low‐carbon‐society. (Accessed March 13, 2015).
Harris, DG. Fredrik Henrik af Chapman: The First Naval Architect and His Work. Revised ed. Stockholm: Literatim; 2001.
Wene, C‐O. Technology Learning and investments needs for upstream oil and the LNG chain. In: Ellersdorfer, I, Fahl, U, eds. Proceedings Ansätze zur Modellierung von Innovation in der Energiewirtschaft. Berlin: Mensch und Buch Verlag; 2005, 99–118.
Smith, M. Oil %26 Gas Group Seminar, Paris, 1 June 2001.
Lundberg, E. Produktivitet och Räntabilitet. Stockholm: P.A. Norstedts och Söner; 1961.
Nilsson, H, Wene, C‐O. It is not the barriers, but the disorganized learning that holds us back! In: Proceedings ECEEE Summer Study, Belambra Presqu`ile de Giens, France, 5–10 June, 2011, 2069–2078.
Ferioli, F, Schoots, K, van der Zwaan, BCC. Use and limitations of learning curves for energy technology policy: a component‐learning hypothesis. Energ Policy 2009, 37:2525–2535.
Nakicenovic, N. Technological change and learning. In: Nakicenovic, N, Nordhaus, WD, Richels, R, Toth, FL, eds. Climate Change: Integrating Science, Economics and Policy. Laxenburg: IIASA; 1996, CP‐96‐1, 271–294.
IEA. World Energy Outlook. Paris: International Energy Agency/Organisation for Economic Co‐operation and Development; 2010.
Beeching, J. The Galleys at Lepanto. New York: Charles Scriber`s Sons; 1983.
Schwaninger, M, Groesser, SN. Operational closure and self‐reference: on the logic of organizational change. Syst Res Behav Sci 2012, 24:342–367.
Beer, S. Heart of Enterprise. Chichester: John Wiley %26 Sons; 1979.
IEA. Creating Markets for Energy Technologies. Paris: International Energy Agency/Organisation for Economic Co‐operation and Development; 2003.
Bergek, A, Hekkert, M, Jacobsson, S. Functions in innovation systems: a framework for analysing energy systems dynamics and identifying goals for system‐building activities by entrepreneus and policymakers. In: Foxon, TJ, Köhler, J, Oughton, C, eds. Innovations for a Low Carbon Economy: Economic, Institutional and Management Approaches. Cheltenham: Edward Elgar; 2008.
Espejo, R. Self‐organisation and policy processes: recursive structures and self‐reference. Submitted to Kybernetes 2015, 44, Special issue on The Cybernetics of Self‐Organisation.
Cowan, R, Gunby, P. Sprayed to death: pest control strategies and technological lock‐in. Econ J 1996, 106:521–542.
IEA. Energy Technology Perspectives 2006 – In Support of the G8 Plan of Action, Scenarios and Strategies to 2050. Paris: International Energy Agency/Organisation for Economic Co‐operation and Development; 2006.
IEA. Energy Technology Perspectives 2008 – In Support of the G8 Plan of Action, Scenarios and Strategies to 2050. Paris: International Energy Agency/Organisation for Economic Co‐operation and Development; 2008.
Stern, N. The Economics of Climate Change: The Stern Review. Cambridge: Cambridge University Press; 2006.
EU Road Map. 2050. Available at: http://www.roadmap2050.eu/. (Accessed March 13, 2015).
Maycock, PD, Wakefield, GF. Business analysis of solar photovoltaic energy conversion. In: 11th Photovoltaic Specialists Conference, Scottsdale, 6–8 May 1975, New York: Institute of Electrical and Electronics Engineers, 1975, 252–255, Conference Record. (A76‐14727 04–44).
Williams, RH, Terzian, G. A Benefit/Cost Analysis of Accelerated Development of Photovoltaic Technology, PU/CEES Report No. 281, New Jersey: Princeton University Press; 1993.
Tsuchiya, H. Photovoltaic cost based on the learning curve. In: Proceedings of the International Solar Energy Society Clean %26 Safe Energy Forever Symposium, Kobe City, 4–8 September, 1989, 402.
Lund, PD. An improved market penetration model for wind energy technology forecasting. In: European Wind Energy Association Special Conference “The Economics of Wind Energy”, Espoo, Finland, 5–7 September, 1995. D5‐1–D5‐6.
Neij, L. Use of experience curves to analyze the prospects for diffusion and adoption of renewable energy technology. Energ Policy 1997, 23:1099–1107.
Neij, L. Cost dynamics of wind power. Energy 1999, 24:375–389.
Durstewitz, M, Hoppe‐Kilpper, M. Using information of Germany`s ‘250 MW Wind’‐Programme for the construction of wind power experience curves. In: Wene C‐O, Voss A, Fried T, eds. Proceedings IEA Workshop on Experience Curves for Policy Making – The Case of Energy Technologies, Stuttgart, 10–11 May 1999, 129–134, Forschungsbericht 67, Institut für Energiewirtschaft und Rationelle Energieanwendung, Universität Stuttgart.
Kydes, AS. Modelling technology learning in the national energy modelling system. In: Wene C‐O, Voss A, Fried T, eds. Proceedings IEA Workshop on Experience Curves for Policy Making – The Case of Energy Technologies, Stuttgart, 1999, 181–202, Forschungsbericht 67, Institut für Energiewirtschaft und Rationelle Energieanwendung, Universität Stuttgart.
Messner, S. Endogenized technological learning in an energy systems model. J Evol Econ 1997, 7:291–313.
Mattsson, N, Wene, C‐O. Assessing new energy technologies using an energy system model with endogenized experience curves. Int J Energ Res 1997, 21:385–393.
van der Zwaan, BCC, Gerlagh, R, Klaassen, G, Schrattenholzer, L. Endogenous technological change in climate change modelling. Energ Econ 2002, 24:1–19.
Kahouli‐Brahmi, S. Technology learning in energy‐environment‐economy modeling: a survey. Energ Policy 2008, 36:138–162.
Edenhofer, O, Lessmann, K, Kemfert, C, Grubb, M, Köhler, J. Induced technological change: exploring its implications for the economics of atmospheric stabilization. Energy J, Endogenous Technological Change and the Economics of Atmospheric Stabilisation Special Issue 2006:57–122.
Edenhofer, O, Knopf, B, Barker, T, Baumstark, L, Bellevrat, E, Chateau, B, Criqui, P, Isaac, M, Kitous, A, Kypreos, S, et al. The economics of low stabilisation: model comparison of mitigation strategies and costs. Energy J 2010, 31:11–48.
Martinsen, T. Technology learning in a small open economy – the systems, modeling and exploiting the learning effect. Energ Policy 2011, 39:2361–2372.
Neij, L, Andersen, PD, Durstewitz, M, Helby, P, Hoppe‐Kilpper, M, Morthorst, PE. Experience curves: a tool for energy policy assessment, IMES/EESS Report No. 40, Department of Technology and Society, Lund University, Sweden, 2004.
McDonald, A, Schattenholzer, L. Learning rates for energy technologies. Energ Policy 2001, 29:255–261.
Nemet, G. (2009), Interim monitoring of cost dynamics for publicly‐supported energy technologies, Energ Policy, 37(3): 825–835.
Ayres, RU, Martinas, K. Experience and life cycle: some analytical implications. Technovation 1992, 12:465–486.
Yeh, S, Rubin, E. A review of uncertainties in technology experience curves. Energ Econ 2012, 34:762–771.
Nordhaus, WD. The perils of the learning model for modeling endogenous technological change. Energy J 2014, 35:1–13.
Nemet, GF, Baker, E. Demand subsidies versus R%26D: comparing the uncertain impacts of policy on a pre‐commercial low‐carbon energy technology. Energy J 2009, 30:49–80.
Wene, C‐O. Energy technology learning through deployment in competitive markets. Eng Economist 2008, 53:340–364.
Watanabe, C, Wakabayashi, K, Miyazawa, T. Industrial dynamism and the creation of a ‘virtuous cycle’ between R%26D, market growth and price reduction – the case of photovoltaic power generation (PV) development in Japan. Technovation 2000, 20:299–312.
von Förster, H. Principles of self‐organization: in a social‐managerial context. In: Ulrich, H, Probst, JB, eds. Self‐Organization and Management of Social Systems. Berlin: Springer; 1984, 2–24.
von Förster, H. Understanding Understanding. New York/Berlin/Heidelberg: Springer; 2003.
Varela, F. Principles of Biological Autonomy. New York: Elsevier‐North Holland; 1979.
Varela, F. Two principles for self‐organization. In: Ulrich, H, Probst, JB, eds. Self‐Organization and Management of Social Systems. Berlin: Springer; 1984, 25–32.
Luhmann, N. Theories of Distinction. Stanford: Stanfod University Press; 2002, edited with an introduction of W. Rasch.
Baecker, D. A note on composition. Syst Res 1996, 13:195–204.
Wene, C‐O. Adaptation in technology learning systems. In: Proceedings of the 11th IAEE European Conference, Vilnius, Lithuania, 25–28 August, 2010, Available at: www.wenergy.se/pdf/Wene‐IAEE2010.pdf. (Accessed March 14, 2015).
Fermi, E. Thermodynamics. New York: Dover Publications; 1936.
Beer, S. Platform for Change. London: John Wiley %26 Sons; 1975.
Yelle, LE. The learning curve: historical review and comprehensive survey. Decis Sci 1979, 10:302–328.
Jaber, MY, ed. Learning Curves – Theory, Models, and Applications. Boca Raton: CRC Press; 2011.
Muth, JF. Search theory and the manufacturing progress function. Manag Sci 1986, 32:948–962.
Auerswald, P, Kauffman, S, Lobo, J, Shell, K. The production recipes approach to modeling technological innovation: an application to learning by doing. J Econ Dyn Control 2000, 24:389–450.
McNerney, J, Farmer, JD, Redner, S, Trancik, JE. Role of design complexity in technology improvement. PNAS 2011, 108:9009–9013.
Bendler, JT, Schlesinger, MF. Fractal clusters in the learning curve. Physica A 1991, 177:585–588.
Huberman, BA. The dynamics of organizational learning. Comput Math Organ Theory 2001, 7:145–153.
Ferioli, F, van der Zwaan, BCC. A consumer‐producer model for induced technological progress – market investments, cost reductions and the learning curve. In: Proceedings of the 31st International Energy Workshop, Cape Town, 19–21 June 2012.
Fock, VA. Die eigenzeit in der klassischen und der quantenmechanik. Zeitschrift Physik Sovietunion 1937, 12:404–425.
Prigogine, I. Etude Thermodynamique des Phénomènes Irréversibles. Paris: Dunod; 1947.
Andresen, B, Shiner, JS, Uehlinger, DE. Allometric scaling and maximum efficiency in physiological eigentime. Proc Natl Acad Sci U S A 2002, 99:5822–5824.
Onsager, L. Reciprocal relations in irreversible processes. I. Phys Rev 1931, 37:405–426.
Prigogine, I. From Being to Becoming. Time and Complexity in the Physical Sciences. New York: W.H. Freeman %26 Company; 1980.
Andresen, B, Gordon, JM. Constant thermodynamic speed for minimizing entropy production in thermodynamic processes and simulated annealing. Phys Rev E 1994, 50:4346–4351.

Author Notes

This WIREs Energy and Environment article includes this figure from the table of contents:

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts