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Endogenous learning in world post-Kyoto scenarios: application of the POLES model under adaptive expectations

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  • Nikolaos Kouvaritakis
  • Antonio Soria
  • Stephane Isoard
  • Claude Thonet

Abstract

This paper describes the endogenous technical change module that has been incorporated in POLES and the main quantitative results of the new version of the model and corresponding exercises. Section 2 presents the methodology that has been used in order to assess the returns to R&D for the main power generation technologies identified in the model. R&D budget allocation is then analysed for the base case in Section 3, which also illustrates the differences in the behaviour, respectively of the least and most risk-averse agents. Section 4, analyses in detail the changes in budget allocation that are induced by the introduction of CO2 emission constraints to 2030, as well as their impacts on marginal and total abatement costs for the main world regions. As a last step, the consequences of changes in public R&D are examined in Section 5. This exercise shows that the performance and diffusion of the technologies benefiting from the shift in public R&D are largely improved, in spite of noticeable "crowding out" effects - of private research by public research - for these technologies.

Suggested Citation

  • Nikolaos Kouvaritakis & Antonio Soria & Stephane Isoard & Claude Thonet, 2000. "Endogenous learning in world post-Kyoto scenarios: application of the POLES model under adaptive expectations," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 222-248.
    • Handle: RePEc:ids:ijgeni:v:14:y:2000:i:1/2/3/4:p:222-248
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    Cited by:

    1. Valentina Bosetti & David G. Victor, 2011. "Politics and Economics of Second-Best Regulation of Greenhouse Gases: The Importance of Regulatory Credibility," The Energy Journal, , vol. 32(1), pages 1-24, January.
    2. Bosetti, Valentina & Carraro, Carlo & Duval, Romain & Tavoni, Massimo, 2011. "What should we expect from innovation? A model-based assessment of the environmental and mitigation cost implications of climate-related R&D," Energy Economics, Elsevier, vol. 33(6), pages 1313-1320.
    3. Rout, Ullash K. & Fahl, Ulrich & Remme, Uwe & Blesl, Markus & Voß, Alfred, 2009. "Endogenous implementation of technology gap in energy optimization models--a systematic analysis within TIMES G5 model," Energy Policy, Elsevier, vol. 37(7), pages 2814-2830, July.
    4. Carraro, Carlo & Duval, Romain & Bosetti, Valentina & Tavoni, Massimo, 2010. "What Should we Expect from Innovation? A Model-Based Assessment of the Environmental and Mitigation Cost Implications of Climat," CEPR Discussion Papers 7751, C.E.P.R. Discussion Papers.
    5. Grubler, Arnulf, 2010. "The costs of the French nuclear scale-up: A case of negative learning by doing," Energy Policy, Elsevier, vol. 38(9), pages 5174-5188, September.
    6. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    7. Giacomo Marangoni & Massimo Tavoni, 2014. "The Clean Energy R&D Strategy For 2°C," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-23.
    8. Rout, Ullash K. & Blesl, Markus & Fahl, Ulrich & Remme, Uwe & Voß, Alfred, 2009. "Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model," Energy Policy, Elsevier, vol. 37(11), pages 4927-4942, November.
    9. Sue Wing, Ian, 2006. "Representing induced technological change in models for climate policy analysis," Energy Economics, Elsevier, vol. 28(5-6), pages 539-562, November.
    10. Enrica De Cian & Valentina Bosetti & Alessandra Sgobbi & Massimo Tavoni, 2009. "The 2008 WITCH Model: New Model Features and Baseline," Working Papers 2009.85, Fondazione Eni Enrico Mattei.
    11. 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.
    12. Lohwasser, Richard & Madlener, Reinhard, 2013. "Relating R&D and investment policies to CCS market diffusion through two-factor learning," Energy Policy, Elsevier, vol. 52(C), pages 439-452.
    13. Aliaga Lordemann, Javier & Herrerra Jiménez, Alejandro, 2014. "Energy-Mix Scenarios for Bolivia," Documentos de trabajo 8/2014, Instituto de Investigaciones Socio-Económicas (IISEC), Universidad Católica Boliviana.
    14. Wüstemeyer, Christoph & Bunn, Derek & Madlener, Reinhard, 2012. "Bridging the Gap between Onshore and Offshore Innovations by the European Wind Power Supply Industry: A Survey-based Analysis," FCN Working Papers 19/2012, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    15. Enrica Cian & Valentina Bosetti & Massimo Tavoni, 2012. "Technology innovation and diffusion in “less than ideal” climate policies: An assessment with the WITCH model," Climatic Change, Springer, vol. 114(1), pages 121-143, September.
    16. Aliaga Lordemann, Javier & Herrera Jiménez, Alejandro, 2014. "Escenarios de la matriz energética para Bolivia," Revista Latinoamericana de Desarrollo Economico, Carrera de Economía de la Universidad Católica Boliviana (UCB) "San Pablo", issue 22, pages 135-160, Noviembre.
    17. Berglund, Christer & Soderholm, Patrik, 2006. "Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models," Energy Policy, Elsevier, vol. 34(12), pages 1344-1356, August.
    18. Heinrich, G. & Howells, M. & Basson, L. & Petrie, J., 2007. "Electricity supply industry modelling for multiple objectives under demand growth uncertainty," Energy, Elsevier, vol. 32(11), pages 2210-2229.
    19. Bosetti, Valentina & Carraro, Carlo & Massetti, Emanuele & Sgobbi, Alessandra & Tavoni, Massimo, 2009. "Optimal energy investment and R&D strategies to stabilize atmospheric greenhouse gas concentrations," Resource and Energy Economics, Elsevier, vol. 31(2), pages 123-137, May.
    20. Kahouli-Brahmi, Sondes, 2009. "Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach," Ecological Economics, Elsevier, vol. 68(4), pages 1195-1212, February.
    21. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    22. Söderholm, Patrik & Sundqvist, Thomas, 2007. "Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies," Renewable Energy, Elsevier, vol. 32(15), pages 2559-2578.
    23. Mathias Mier & Jacqueline Adelowo & Valeriya Azarova, 2022. "Endogenous Technological Change in Power Markets," ifo Working Paper Series 373, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

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