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Technology learning in a small open economy--The systems, modelling and exploiting the learning effect

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  • Martinsen, Thomas

Abstract

This paper reviews the characteristics of technology learning and discusses its application in energy system modelling in a global-local perspective. Its influence on the national energy system, exemplified by Norway, is investigated using a global and national Markal model. The dynamic nature of the learning system boundary and coupling between the national energy system and the global development and manufacturing system is elaborated. Some criteria important for modelling of spillover1 are suggested. Particularly, to ensure balance in global energy demand and supply and accurately reflect alternative global pathways spillover for all technologies as well as energy carrier cost/prices should be estimated under the same global scenario. The technology composition, CO2 emissions and system cost in Norway up to 2050 exhibit sensitivity to spillover. Moreover, spillover may reduce both CO2 emissions and total system cost. National energy system analysis of low carbon society should therefore consider technology development paths in global policy scenarios. Without the spillover from international deployment a domestic technology relies only on endogenous national learning. However, with high but realistic learning rates offshore floating wind may become cost-efficient even if initially deployed only in Norwegian niche markets.

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  • Martinsen, Thomas, 2011. "Technology learning in a small open economy--The systems, modelling and exploiting the learning effect," Energy Policy, Elsevier, vol. 39(5), pages 2361-2372, May.
  • Handle: RePEc:eee:enepol:v:39:y:2011:i:5:p:2361-2372
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    1. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    2. Martinsen, Thomas, 2010. "Global technology learning and national policy--An incentive scheme for governments to assume the high cost of early deployment exemplified by Norway," Energy Policy, Elsevier, vol. 38(8), pages 4163-4172, August.
    3. 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.
    4. Cowan, Robin & Gunby, Philip, 1996. "Sprayed to Death: Path Dependence, Lock-In and Pest Control Strategies," Economic Journal, Royal Economic Society, vol. 106(436), pages 521-542, May.
    5. Cimoli, Mario & Dosi, Giovanni, 1995. "Technological Paradigms, Patterns of Learning and Development: An Introductory Roadmap," Journal of Evolutionary Economics, Springer, vol. 5(3), pages 243-268, September.
    6. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    7. 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.
    8. Sabine Messner, 1997. "Endogenized technological learning in an energy systems model," Journal of Evolutionary Economics, Springer, vol. 7(3), pages 291-313.
    9. Shilpa Rao, Ilkka Keppo and Keywan Riahi, 2006. "Importance of Technological Change and Spillovers in Long-Term Climate Policy," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 123-140.
    10. Wene, Clas-Otto & Ryden, Bo, 1988. "A comprehensive energy model in the municipal energy planning process," European Journal of Operational Research, Elsevier, vol. 33(2), pages 212-222, January.
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    5. Kostevšek, Anja & Petek, Janez & Čuček, Lidija & Pivec, Aleksandra, 2013. "Conceptual design of a municipal energy and environmental system as an efficient basis for advanced energy planning," Energy, Elsevier, vol. 60(C), pages 148-158.
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