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Multi-regional technological learning in the energysystems MARKAL model

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  • Leonardo Barreto, Socrates Kypreos

Abstract

This paper describes the implementation of multi-regional endogenous technological learning in the energy optimisation MARKAL model. A mapping procedure is implemented to group learning technologies inside one region or across several regions in a flexible way, in order to allow them to learn together. The approach is described and an illustrative example examining the response of a multi-regional global electricity generation system is presented. The multi-regional learning framework allows the examination of the spatial interactions and mechanisms that affect the technological learning processes in global energy systems. The mutual interactions between the learning and emission trading mechanisms are highlighted. Although emphasis is given to energy modelling, some policy insights can be gained. The results highlight the importance of fostering international cooperation to stimulate the learning process of emerging energy technologies.

Suggested Citation

  • Leonardo Barreto, Socrates Kypreos, 2002. "Multi-regional technological learning in the energysystems MARKAL model," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 17(3), pages 189-213.
    • Handle: RePEc:ids:ijgeni:v:17:y:2002:i:3:p:189-213
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    Citations

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    Cited by:

    1. Chen, Huayi & Ma, Tieju, 2021. "Technology adoption and carbon emissions with dynamic trading among heterogeneous agents," Energy Economics, Elsevier, vol. 99(C).
    2. Przemysław Kaszyński & Jacek Kamiński, 2020. "Coal Demand and Environmental Regulations: A Case Study of the Polish Power Sector," Energies, MDPI, vol. 13(6), pages 1-24, March.
    3. Chen, Huayi & Ma, Tieju, 2017. "Optimizing systematic technology adoption with heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 257(1), pages 287-296.
    4. Strachan, Neil & Kannan, Ramachandran, 2008. "Hybrid modelling of long-term carbon reduction scenarios for the UK," Energy Economics, Elsevier, vol. 30(6), pages 2947-2963, November.
    5. Valentina Bosetti & Enrica De Cian, 2013. "A Good Opening: The Key to Make the Most of Unilateral Climate Action," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 56(2), pages 255-276, October.
    6. Valentina Bosetti, Carlo Carraro and Marzio Galeotti, 2006. "The Dynamics of Carbon and Energy Intensity in a Model of Endogenous Technical Change," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 191-206.
    7. 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.
    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. Spalding-Fecher, Randall & Joyce, Brian & Winkler, Harald, 2017. "Climate change and hydropower in the Southern African Power Pool and Zambezi River Basin: System-wide impacts and policy implications," Energy Policy, Elsevier, vol. 103(C), pages 84-97.
    10. Rafaj, Peter & Kypreos, Socrates, 2007. "Internalisation of external cost in the power generation sector: Analysis with Global Multi-regional MARKAL model," Energy Policy, Elsevier, vol. 35(2), pages 828-843, February.
    11. Thakur, Jagruti & Rauner, Sebastian & Darghouth, Naïm R. & Chakraborty, Basab, 2018. "Exploring the impact of increased solar deployment levels on residential electricity bills in India," Renewable Energy, Elsevier, vol. 120(C), pages 512-523.
    12. Raihan, Selim, 2010. "Implications of the Global Economic Crisis for the Bangladesh Economy," Conference papers 331959, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    13. 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.
    14. 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.
    15. Chicco, Gianfranco & Stephenson, Paule M., 2012. "Effectiveness of setting cumulative carbon dioxide emissions reduction targets," Energy, Elsevier, vol. 42(1), pages 19-31.
    16. Barreto, Leonardo & Kypreos, Socrates, 2004. "Emissions trading and technology deployment in an energy-systems "bottom-up" model with technology learning," European Journal of Operational Research, Elsevier, vol. 158(1), pages 243-261, October.
    17. Michail Chronopoulos, Derek Bunn, and Afzal Siddiqui, 2014. "Optionality and Policymaking in Re-Transforming the British Power Market," Economics of Energy & Environmental Policy, International Association for Energy Economics, vol. 0(Number 2).
    18. 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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