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Induced Technological Change in a Limited Foresight Optimization Model

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  • Fredrik Hedenus, Christian Azar and Kristian Lindgren

Abstract

The threat of global warming calls for a major transformation of the energy system in the coming century. The treatment of technological change in energy system models is a critical challenge. Technological change may be treated as induced by climate policy or as exogenous. We investigate the importance of induced technological change (ITC) in GET-LFL, an iterative optimization model with Limited Foresight that incorporates Learning-by-doing. Scenarios for stabilization of atmospheric CO2 concentrations at 400, 450, 500 and 550 ppm are studied. We find that the introduction of ITC reduces the total net present value of the abatement cost over this century by 3-9% compared to a case where technological learning is exogenous. Technology specific policies which force the introduction of fuel cell cars and solar PV in combination with ITC reduce the costs further by 4-7% and lead to significantly different technological solutions, primarily in the transport sector.

Suggested Citation

  • Fredrik Hedenus, Christian Azar and Kristian Lindgren, 2006. "Induced Technological Change in a Limited Foresight Optimization Model," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 109-122.
    • Handle: RePEc:aen:journl:2006se-a04
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    Cited by:

    1. Wei, Yi-Ming & Mi, Zhi-Fu & Huang, Zhimin, 2015. "Climate policy modeling: An online SCI-E and SSCI based literature review," Omega, Elsevier, vol. 57(PA), pages 70-84.
    2. Chen, Huayi & Ma, Tieju, 2021. "Technology adoption and carbon emissions with dynamic trading among heterogeneous agents," Energy Economics, Elsevier, vol. 99(C).
    3. Alexander Golub & Oleg Lugovoy & Anil Markandya & Ramon Arigoni Ortiz & James Wang, 2013. "Regional IAM: analysis of risk-adjusted costs and benefits of climate policies," Working Papers 2013-06, BC3.
    4. Chen, Huayi & Ma, Tieju, 2017. "Optimizing systematic technology adoption with heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 257(1), pages 287-296.
    5. Price, James & Keppo, Ilkka, 2017. "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models," Applied Energy, Elsevier, vol. 195(C), pages 356-369.
    6. Jeroen C.J.M. van den Bergh & Arild Angelsen & Andrea Baranzini & W.J. Wouter Botzen & Stefano Carattini & Stefan Drews & Tessa Dunlop & Eric Galbraith & Elisabeth Gsottbauer & Richard B. Howarth & Em, 2018. "Parallel tracks towards a global treaty on carbon pricing," Working Papers 2018/12, Institut d'Economia de Barcelona (IEB).
    7. Chen, Huayi & Ma, Tieju, 2014. "Technology adoption with limited foresight and uncertain technological learning," European Journal of Operational Research, Elsevier, vol. 239(1), pages 266-275.
    8. DeCarolis, Joseph & Daly, Hannah & Dodds, Paul & Keppo, Ilkka & Li, Francis & McDowall, Will & Pye, Steve & Strachan, Neil & Trutnevyte, Evelina & Usher, Will & Winning, Matthew & Yeh, Sonia & Zeyring, 2017. "Formalizing best practice for energy system optimization modelling," Applied Energy, Elsevier, vol. 194(C), pages 184-198.
    9. Kuik, Onno & Brander, Luke & Tol, Richard S.J., 2009. "Marginal abatement costs of greenhouse gas emissions: A meta-analysis," Energy Policy, Elsevier, vol. 37(4), pages 1395-1403, April.
    10. Limpens, Gauthier & Rixhon, Xavier & Contino, Francesco & Jeanmart, Hervé, 2024. "EnergyScope Pathway: An open-source model to optimise the energy transition pathways of a regional whole-energy system," Applied Energy, Elsevier, vol. 358(C).
    11. Chen, Huayi & Zhou, P., 2019. "Modeling systematic technology adoption: Can one calibrated representative agent represent heterogeneous agents?," Omega, Elsevier, vol. 89(C), pages 257-270.
    12. Svensson, Elin & Strömberg, Ann-Brith & Patriksson, Michael, 2011. "A model for optimization of process integration investments under uncertainty," Energy, Elsevier, vol. 36(5), pages 2733-2746.
    13. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    14. Tokimatsu, Koji & Konishi, Satoshi & Ishihara, Keiichi & Tezuka, Tetsuo & Yasuoka, Rieko & Nishio, Masahiro, 2016. "Role of innovative technologies under the global zero emissions scenarios," Applied Energy, Elsevier, vol. 162(C), pages 1483-1493.
    15. Joanna Sitarz & Michael Pahle & Sebastian Osorio & Gunnar Luderer & Robert Pietzcker, 2024. "EU carbon prices signal high policy credibility and farsighted actors," Nature Energy, Nature, vol. 9(6), pages 691-702, June.
    16. Karali, Nihan & Park, Won Young & McNeil, Michael, 2017. "Modeling technological change and its impact on energy savings in the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 202(C), pages 447-458.
    17. Zhang, Shuwei & Bauer, Nico & Yin, Guangzhi & Xie, Xi, 2020. "Technology learning and diffusion at the global and local scales: A modeling exercise in the REMIND model," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    18. Valeriya Azarova & Mathias Mier, 2021. "Unraveling the Black Box of Power Market Models," ifo Working Paper Series 357, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    19. Sitarz, Joanna & Pahle, Michael & Osorio, Sebastian & Luderer, Gunnar & Pietzcker, Robert, 2023. "EU carbon prices signal high policy credibility and farsighted actors," EconStor Preprints 280455, ZBW - Leibniz Information Centre for Economics.
    20. Keppo, Ilkka & Strubegger, Manfred, 2010. "Short term decisions for long term problems – The effect of foresight on model based energy systems analysis," Energy, Elsevier, vol. 35(5), pages 2033-2042.

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    • F0 - International Economics - - General

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