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Back to the basic: toward improvement of technoeconomic representation in integrated assessment models

Author

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  • Hiroto Shiraki

    (The University of Shiga Prefecture)

  • Masahiro Sugiyama

    (The University of Tokyo)

Abstract

With the shift of climate debate from understanding to actions, the use of integrated assessment models (IAMs) is gradually expanding. Since IAMs produce least-cost pathways, technoeconomic parameters constitute one of the basic parameters. Traditionally, IAMs dealt with technologies with slowly-changing, relatively homogeneous manner. Since technologies are rapidly evolving, and the pattern of technological development is regionally heterogeneous, the IAM community must embrace a new strategy to treat their underlying technoeconomic parameters. Here we illustrate such challenges by reviewing the treatment and performance of IAMs with respect to some of the rapidly changing technologies (e.g., solar, wind, and batteries). Our review shows that IAMs have difficulty in updating the cost of the rapidly changing technologies. We then articulate a new strategy, drawing upon the lesson from the current model intercomparison projects and climate sciences. We argue that a loose network of modeling groups across the globe should create a database of technological parameters in a standardized format and standard evaluation tool, perhaps to be facilitated by the IAM Consortium. Such a framework would contribute to the review of the progress toward the Paris Agreement goals.

Suggested Citation

  • Hiroto Shiraki & Masahiro Sugiyama, 2020. "Back to the basic: toward improvement of technoeconomic representation in integrated assessment models," Climatic Change, Springer, vol. 162(1), pages 13-24, September.
  • Handle: RePEc:spr:climat:v:162:y:2020:i:1:d:10.1007_s10584-020-02731-4
    DOI: 10.1007/s10584-020-02731-4
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    Cited by:

    1. Pei-Hao Li & Steve Pye & Ilkka Keppo & Marc Jaxa-Rozen & Evelina Trutnevyte, 2023. "Revealing effective regional decarbonisation measures to limit global temperature increase in uncertain transition scenarios with machine learning techniques," Climatic Change, Springer, vol. 176(7), pages 1-23, July.
    2. Heiko Balzter & Mateus Macul & Beth Delaney & Kevin Tansey & Fernando Espirito-Santo & Chidiebere Ofoegbu & Sergei Petrovskii & Bernhard Forchtner & Nicholes Nicholes & Emilio Payo & Pat Heslop-Harris, 2023. "Loss and Damage from Climate Change: Knowledge Gaps and Interdisciplinary Approaches," Sustainability, MDPI, vol. 15(15), pages 1-14, August.
    3. Nikas, A. & Gambhir, A. & Trutnevyte, E. & Koasidis, K. & Lund, H. & Thellufsen, J.Z. & Mayer, D. & Zachmann, G. & Miguel, L.J. & Ferreras-Alonso, N. & Sognnaes, I. & Peters, G.P. & Colombo, E. & Howe, 2021. "Perspective of comprehensive and comprehensible multi-model energy and climate science in Europe," Energy, Elsevier, vol. 215(PA).
    4. Bretschger, Lucas, 2024. "Energy transition and climate change abatement: A macroeconomic analysis," Resource and Energy Economics, Elsevier, vol. 76(C).
    5. Robert J. Brecha & Gaurav Ganti & Robin D. Lamboll & Zebedee Nicholls & Bill Hare & Jared Lewis & Malte Meinshausen & Michiel Schaeffer & Christopher J. Smith & Matthew J. Gidden, 2022. "Institutional decarbonization scenarios evaluated against the Paris Agreement 1.5 °C goal," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    6. Gardian, H. & Beck, J.-P. & Koch, M. & Kunze, R. & Muschner, C. & Hülk, L. & Bucksteeg, M., 2022. "Data harmonisation for energy system analysis – Example of multi-model experiments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    7. Barbrook-Johnson, Peter & Tankwa, Brendon, 2023. "Increasing inequality between countries in key renewable energy costs," INET Oxford Working Papers 2023-20, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.

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