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An evolutionary model of energy transitions with interactive innovation-selection dynamics

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  • Karolina Safarzyńska

    ()

  • Jeroen Bergh

    ()

Abstract

We develop a stylized application of a new evolutionary model to study an energy transition in electricity production. The framework describes a population of boundedly rational electricity producers who decide each period on the allocation of profits among different energy technologies. They tend to invest in below-average cost energy technologies, while also devoting a small fraction of profits to alternative technological options and research on recombinant innovation. Energy technologies are characterized by costs falling with cumulative investments. Without the latter, new technologies have no chance to become cost competitive. We study the conditions under which a new energy technology emerges and technologies coexist. In addition, we determine which investment heuristics are optimal in the sense of minimizing the total cost of electricity production. This is motivated by the idea that, while diversity contributes to system adaptability (innovation) and resilience to unforeseen contingencies (keeping options open), a high cost will discourage investments in it. Copyright Springer-Verlag Berlin Heidelberg 2013

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Bibliographic Info

Article provided by Springer in its journal Journal of Evolutionary Economics.

Volume (Year): 23 (2013)
Issue (Month): 2 (April)
Pages: 271-293

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Handle: RePEc:spr:joevec:v:23:y:2013:i:2:p:271-293

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Related research

Keywords: Bounded rationality; Energy transition; Investment theory; Learning curve; B52; L94; O32; O33; Q42; Q54;

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References

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  1. Weitzman, Martin L., 1998. "Recombinant Growth," Scholarly Articles 3708468, Harvard University Department of Economics.
  2. D. Canning, 2010. "Average Behavior in Learning Models," Levine's Working Paper Archive 490, David K. Levine.
  3. Yildizoglu, Murat, 2002. "Competing R&D Strategies in an Evolutionary Industry Model," Computational Economics, Society for Computational Economics, vol. 19(1), pages 51-65, February.
  4. Sabine Messner, 1997. "Endogenized technological learning in an energy systems model," Journal of Evolutionary Economics, Springer, vol. 7(3), pages 291-313.
  5. van den Bergh, Jeroen C.J.M., 2008. "Optimal diversity: Increasing returns versus recombinant innovation," Journal of Economic Behavior & Organization, Elsevier, vol. 68(3-4), pages 565-580, December.
  6. Olsson, Ola & Frey, Bruno S., 2001. "Entrepreneurship As Recombinant Growth," Working Papers in Economics 51, University of Gothenburg, Department of Economics.
  7. Jonathan Kohler, Michael Grubb, David Popp and Ottmar Edenhofer , 2006. "The Transition to Endogenous Technical Change in Climate-Economy Models: A Technical Overview to the Innovation Modeling Comparison Project," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 17-56.
  8. 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.
  9. Young, H Peyton, 1993. "The Evolution of Conventions," Econometrica, Econometric Society, vol. 61(1), pages 57-84, January.
  10. Shimon Awerbuch, 2006. "Portfolio-Based Electricity Generation Planning: Policy Implications For Renewables And Energy Security," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 11(3), pages 693-710, May.
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Cited by:
  1. Valeria Costantini & Francesco Crespi, 2013. "Public policies for a sustainable energy sector: regulation, diversity and fostering of innovation," Journal of Evolutionary Economics, Springer, vol. 23(2), pages 401-429, April.
  2. Bernardo, Giovanni & D'Alessandro, Simone, 2014. "Transition to sustainability? Feasible scenarios towards a low-carbon economy," MPRA Paper 53746, University Library of Munich, Germany.

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