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Endogenous implementation of technology gap in energy optimization models--a systematic analysis within TIMES G5 model

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  • Rout, Ullash K.
  • Fahl, Ulrich
  • Remme, Uwe
  • Blesl, Markus
  • Voß, Alfred

Abstract

Evaluation of global diffusion potential of learning technologies and their timely specific cost development across regions is always a challenging issue for the future technology policy preparation. Further the process of evaluation gains interest especially by endogenous treatment of energy technologies under uncertainty in learning rates with technology gap across the regions in global regional cluster learning approach. This work devised, implemented, and examined new methodologies on technology gaps (a practical problem), using two broad concepts of knowledge deficit and time lag approaches in global learning, applying the floor cost approach methodology. The study was executed in a multi-regional, technology-rich and long horizon bottom-up linear energy system model on The Integrated MARKAL EFOM System (TIMES) framework. Global learning selects highest learning technologies in maximum uncertainty of learning rate scenario, whereas any form of technology gap retards the global learning process and discourages the technologies deployment. Time lag notions of technology gaps prefer heavy utilization of learning technologies in developed economies for early reduction of specific cost. Technology gaps of any kind should be reduced among economies through the promotion and enactment of various policies by governments, in order to utilize the technological resources by mass deployment to combat ongoing climate change.

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  • 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.
  • Handle: RePEc:eee:enepol:v:37:y:2009:i:7:p:2814-2830
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    5. 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.
    6. Rout, Ullash K., 2011. "Prospects of India's energy and emissions for a long time frame," Energy Policy, Elsevier, vol. 39(9), pages 5647-5663, September.
    7. Rout, Ullash K. & Akimoto, Keigo & Sano, Fuminori & Tomoda, Toshimasa, 2010. "Introduction of subsidisation in nascent climate-friendly learning technologies and evaluation of its effectiveness," Energy Policy, Elsevier, vol. 38(1), pages 520-532, January.
    8. Pina, André & Silva, Carlos & Ferrão, Paulo, 2011. "Modeling hourly electricity dynamics for policy making in long-term scenarios," Energy Policy, Elsevier, vol. 39(9), pages 4692-4702, September.
    9. Alam Hossain Mondal, Md. & Mathur, Jyotirmay & Denich, Manfred, 2011. "Impacts of CO2 emission constraints on technology selection and energy resources for power generation in Bangladesh," Energy Policy, Elsevier, vol. 39(4), pages 2043-2050, April.
    10. Rout, Ullash K. & Voβ, Alfred & Singh, Anoop & Fahl, Ulrich & Blesl, Markus & Ó Gallachóir, Brian P., 2011. "Energy and emissions forecast of China over a long-time horizon," Energy, Elsevier, vol. 36(1), pages 1-11.
    11. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2023. "Hindcasting to inform the development of bottom-up electricity system models: The cases of endogenous demand and technology learning," Applied Energy, Elsevier, vol. 340(C).
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