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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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  • Karolina Safarzyńska & Jeroen Bergh, 2013. "An evolutionary model of energy transitions with interactive innovation-selection dynamics," Journal of Evolutionary Economics, Springer, vol. 23(2), pages 271-293, April.
    • Handle: RePEc:spr:joevec:v:23:y:2013:i:2:p:271-293
    DOI: 10.1007/s00191-012-0298-9
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    Cited by:

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    2. Tommaso Ciarli & Karolina Safarzynska, 2020. "Sustainability and Industrial Challenge: The Hindering Role of Complexity," SPRU Working Paper Series 2020-18, SPRU - Science Policy Research Unit, University of Sussex Business School.
    3. Max Rånge & Mikael Sandberg, 2016. "Windfall gains or eco-innovation? ‘Green’ evolution in the Swedish innovation system," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 18(2), pages 229-246, April.
    4. F. Knobloch & J. -F. Mercure, 2016. "The behavioural aspect of green technology investments: a general positive model in the context of heterogeneous agents," Papers 1603.06888, arXiv.org.
    5. Frank Beckenbach & Maria Daskalakis & David Hofmann, 2018. "Agent-Based Analysis of Industrial Dynamics and Paths of Environmental Policy: The Case of Non-renewable Energy Production in Germany," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 953-994, October.
    6. 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.
    7. Moallemi, Enayat A. & de Haan, Fjalar J. & Webb, John M. & George, Biju A. & Aye, Lu, 2017. "Transition dynamics in state-influenced niche empowerments: Experiences from India's electricity sector," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 129-141.
    8. Wood, Aaron D. & Mason, Charles F. & Finnoff, David, 2016. "OPEC, the Seven Sisters, and oil market dominance: An evolutionary game theory and agent-based modeling approach," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 66-78.
    9. Herrmann, Johannes & Savin, Ivan, 2015. "Evolution of the electricity market in Germany: Identifying policy implications by an agent-based model," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112959, Verein für Socialpolitik / German Economic Association.
    10. Jean-François Mercure, 2015. "An age structured demographic theory of technological change," Journal of Evolutionary Economics, Springer, vol. 25(4), pages 787-820, September.
    11. Mercure, J.-F. & Pollitt, H. & Chewpreecha, U. & Salas, P. & Foley, A.M. & Holden, P.B. & Edwards, N.R., 2014. "The dynamics of technology diffusion and the impacts of climate policy instruments in the decarbonisation of the global electricity sector," Energy Policy, Elsevier, vol. 73(C), pages 686-700.
    12. Bernardo, Giovanni & D'Alessandro, Simone, 2014. "Transition to sustainability? Feasible scenarios towards a low-carbon economy," MPRA Paper 53746, University Library of Munich, Germany.
    13. 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).

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    More about this item

    Keywords

    Bounded rationality; Energy transition; Investment theory; Learning curve; B52; L94; O32; O33; Q42; Q54;
    All these keywords.

    JEL classification:

    • B52 - Schools of Economic Thought and Methodology - - Current Heterodox Approaches - - - Historical; Institutional; Evolutionary; Modern Monetary Theory;
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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