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Global energy sector emission reductions and bioenergy use: overview of the bioenergy demand phase of the EMF-33 model comparison

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Abstract

We present an overview of results from 11 integrated assessment models (IAMs) that participated in the 33rd study of the Stanford Energy Modeling Forum (EMF-33) on the viability of large-scale deployment of bioenergy for achieving long-run climate goals. The study explores future bioenergy use across models under harmonized scenarios for future climate policies, availability of bioenergy technologies, and constraints on biomass supply. This paper provides a more transparent description of IAMs that span a broad range of assumptions regarding model structures, energy sectors, and bioenergy conversion chains. Without emission constraints, we find vastly different CO2 emission and bioenergy deployment patterns across models due to differences in competition with fossil fuels, the possibility to produce large-scale bio-liquids, and the flexibility of energy systems. Imposing increasingly stringent carbon budgets mostly increases bioenergy use. A diverse set of available bioenergy technology portfolios provides flexibility to allocate bioenergy to supply different final energy as well as remove carbon dioxide from the atmosphere by combining bioenergy with carbon capture and sequestration (BECCS). Sector and regional bioenergy allocation varies dramatically across models mainly due to bioenergy technology availability and costs, final energy patterns, and availability of alternative decarbonization options. Although much bioenergy is used in combination with CCS, BECCS is not necessarily the driver of bioenergy use. We find that the flexibility to use biomass feedstocks in different energy sub-sectors makes large-scale bioenergy deployment a robust strategy in mitigation scenarios that is surprisingly insensitive with respect to reduced technology availability. However, the achievability of stringent carbon budgets and associated carbon prices is sensitive. Constraints on biomass feedstock supply increase the carbon price less significantly than excluding BECCS because carbon removals are still realized and valued. Incremental sensitivity tests find that delayed readiness of bioenergy technologies until 2050 is more important than potentially higher investment costs.

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Acknowledgements

The views expressed in this paper are those of the individual authors and do not necessarily reflect those of the author’s institutions or funders. All errors are the responsibility of the authors. NB and JS received funding from the German Research Foundation (DFG) Priority Programme (SPP) 1689 (CEMICS). SR was supported by the Electric Power Research Institute (EPRI); however, the views expressed here are solely those of the authors and do not necessarily represent those of EPRI or its funders. SF and TH were supported by the Environment Research and Technology Development Fund (2-1702) of the Environmental Restoration and Conservation Agency, Japan.

Author information

Authors and Affiliations

  1. Potsdam Institute for Climate Impact Research (PIK), Leibniz Association, 601203, 14412, Potsdam, Germany

    Nico Bauer, Jessica Strefler & David Klein

  2. Energy and Environmental Analysis Research Group, Electric Power Research Institute, Washington, DC, USA

    Steven K. Rose

  3. Department of Environmental Engineering, Kyoto University, Kyoto, Japan

    Shinichiro Fujimori

  4. National Institute for Environmental Studies (NIES), Tsukuba, Japan

    Shinichiro Fujimori & Tomoko Hasegawa

  5. Netherlands Environmental Assessment Agency (PBL), The Hague, The Netherlands

    Detlef P. van Vuuren & Vassilis Daioglou

  6. Copernicus Institute for Sustainable Development, Utrecht University, Utrecht, The Netherlands

    Detlef P. van Vuuren

  7. Stanford University, Palo Alto, CA, USA

    John Weyant

  8. Pacific Northwest National Laboratory (PNNL), College Park, MD, USA

    Marshall Wise & Yiyun Cui

  9. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

    Matthew J. Gidden, Etsushi Kato & Oliver Fricko

  10. The Institute of Applied Energy, Minato, Tokyo, 105-0003, Japan

    Atsushi Kurosawa

  11. Joint Research Center (JRC), Seville, Spain

    Alban Kitous

  12. Centre International de Recherche sur l’Environnement et le Développement, Paris, France

    Florian Leblanc & Ruben Bibas

  13. U.S. Department of Agriculture, Economic Research Service, Washington, DC, USA

    Ronald Sands

  14. Research Institute of Innovative Technology for the Earth (RITE), Kyoto, Japan

    Fuminori Sano

  15. Environmental Science Laboratory, Central Research Institute of Electric Power Industry (CRIEPI), Abiko, Chiba, Japan

    Junichi Tsutsui

  16. Grenoble Applied Economics Lab (GAEL), University Grenoble Alpes, CNRS, INRA, Grenoble INP, Grenoble, France

    Silvana Mima

  17. National Renewable Energy Laboratory (NREL), Golden, CO, USA

    Matteo Muratori

Authors
  1. Nico Bauer
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  2. Steven K. Rose
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  3. Shinichiro Fujimori
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  4. Detlef P. van Vuuren
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  5. John Weyant
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  6. Marshall Wise
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  7. Yiyun Cui
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  8. Vassilis Daioglou
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  9. Matthew J. Gidden
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  10. Etsushi Kato
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  11. Alban Kitous
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  12. Florian Leblanc
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  13. Ronald Sands
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  14. Fuminori Sano
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  15. Jessica Strefler
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  16. Junichi Tsutsui
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  17. Ruben Bibas
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  18. Oliver Fricko
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  19. Tomoko Hasegawa
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  20. David Klein
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  21. Atsushi Kurosawa
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  22. Silvana Mima
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  23. Matteo Muratori
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Corresponding author

Correspondence to Nico Bauer.

Additional information

This article is part of the special issue “Assessing Large-scale Global Bioenergy Deployment for Managing Climate Change (EMF-33)” edited by Steven Rose, John Weyant, Nico Bauer, Shinichiro Fuminori, Petr Havlik, Alexander Popp, Detlef van Vuuren, and Marshall Wise.

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Bauer, N., Rose, S.K., Fujimori, S. et al. Global energy sector emission reductions and bioenergy use: overview of the bioenergy demand phase of the EMF-33 model comparison. Climatic Change 163, 1553–1568 (2020). https://doi.org/10.1007/s10584-018-2226-y

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  • DOI: https://doi.org/10.1007/s10584-018-2226-y

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