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

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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.

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Notes

  1. One measure of energy density or concentration is the energy return on (energy) investment (EROEI). It is defined as the energy output obtained in a process divided by the energy input required to extract, produce, deliver and use the output. Historical oil exploration was characterized by easily reachable fields with good concentrations and thus scores the best with an EROEI of over 100, followed by coal, which has had a constant EROEI of about 80 since the 1950s. The global average EROEI of oil has declined from over 100 to less than 40, despite technological progress in exploration, drilling and transport technologies. But it is still considerably higher than the EROEI of alternatives: nuclear fission varies between 5 and 15, hydropower is above 100 (but has limited application), wind is up to 18, solar PV is about 7, solar (flate plate) thermal collectors 1.9, solar concentrated heat power 1.6, sugarcane (corn-based) ethanol varies between less than 1 and 10, and biodiesel 1.3 (Murphy and Hall 2010).

  2. The implicit assumption here is that an amount of \(\delta_{j,i}^e f_j x_j \) of capital i and \(\delta_{i,j}^e f_i x_i \) of capital j are combined to generate a new technology e. For this reason, the δ parameters do not appear in Eq. 2.

  3. The results were obtained using software Mathematica 7.0 and LSD.

  4. Note that the average cost is proportional to the total cost because of the assumption of an infinite population.

  5. We do not provide the precise expressions because of their length.

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Authors and Affiliations

  1. Institute for the Environment and Regional Development, WU Vienna University of Economics and Business, Nordbergstrasse 15 (UZA4, 4B), A-1090, Vienna, Austria

    Karolina Safarzyńska

  2. ICREA, Barcelona, Spain

    Jeroen C. J. M. van den Bergh

  3. Institute for Environmental Science and Technology, & Department of Economics and Economic History, Universitat Autònoma de Barcelona, Edifici Cn - Campus UAB, 08193, Bellaterra, Spain

    Jeroen C. J. M. van den Bergh

  4. Faculty of Economics and Business Administration, and Institute for Environmental Studies, Fellow of Tinbergen Institute and NAKE, VU University Amsterdam, Amsterdam, The Netherlands

    Jeroen C. J. M. van den Bergh

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  1. Karolina Safarzyńska
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Correspondence to Karolina Safarzyńska.

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Safarzyńska, K., van den Bergh, J.C.J.M. An evolutionary model of energy transitions with interactive innovation-selection dynamics. J Evol Econ 23, 271–293 (2013). https://doi.org/10.1007/s00191-012-0298-9

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