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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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    Citations

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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, International Association for Energy Economics, vol. 0(Number 1), pages 1-24.
    2. 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.
    3. Giacomo Marangoni & Massimo Tavoni, 2013. "The Clean Energy R&D Strategy for 2°C," Working Papers 2013.93, Fondazione Eni Enrico Mattei.
    4. 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.
    5. repec:eee:appene:v:202:y:2017:i:c:p:447-458 is not listed on IDEAS
    6. 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.
    7. 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.
    8. 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.
    9. Aliaga Lordemann, Javier & Herrera Jiménez, Alejandro, 2014. "Escenarios de la matriz energética para Bolivia," Revista Latinoamericana de Desarrollo Economico, Instituto de Investigaciones Socio-Económicas (IISEC), Universidad Católica Boliviana, issue 22, pages 135-160, Noviembre.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    15. 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.

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