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

  • Nico Bauer
  • Steven K. Rose
  • Shinichiro Fujimori
  • Detlef P. van Vuuren
  • John Weyant
  • Marshall Wise
  • Yiyun Cui
  • Vassilis Daioglou
  • Matthew J. Gidden
  • Etsushi Kato
  • Alban Kitous
  • Florian Leblanc
  • Ronald Sands
  • Fuminori Sano
  • Jessica Strefler
  • Junichi Tsutsui
  • Ruben Bibas
  • Oliver Fricko
  • Tomoko Hasegawa
  • David Klein
  • Atsushi Kurosawa
  • Silvana Mima
  • Matteo Muratori
Article

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.

Notes

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.

Supplementary material

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References

  1. Anderson K, Peters G (2016) The trouble with negative emissions. Science 354:182–183CrossRefGoogle Scholar
  2. Bauer N et al (2017) Shared socio-economic pathways of the energy sector—quantifying the narratives. Glob. Environ Change 42:316–330CrossRefGoogle Scholar
  3. Bhave A et al (2017) Screening and techno-economic assessment of biomass-based power generation with CCS technologies to meet 2050 CO2 targets. Appl Energy 190:481–489CrossRefGoogle Scholar
  4. Buck HJ (2016) Rapid scale-up of negative emissions technologies: social barriers and social implications. Clim Chang 139:1–13CrossRefGoogle Scholar
  5. Calvin K et al (2017) The SSP4: a world of deepening inequality. Glob. Environ Change 42:284–296CrossRefGoogle Scholar
  6. Chum H et al (2011) Bioenergy, in: IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation (SRREN), Chapter 2. Cambridge University Press, Cambridge and New YorkGoogle Scholar
  7. Clarke L et al (2014) Assessing transformation pathways. In: Climate Change 2014: Mitigation of Climate Change. Chapter 6. Contribution of working group III to AR5 of the IPCC. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  8. Creutzig F et al (2013) Integrating place-specific livelihood and equity outcomes into global assessments of bioenergy deployment. Environ Res Lett 8:035047CrossRefGoogle Scholar
  9. Dooley JJ (2013) Estimating the supply and demand for deep geologic CO2 storage capacity over the course of the 21st century. Energy Procedia 37:5141–5150CrossRefGoogle Scholar
  10. Fargione JE et al (2010) The ecological impact of biofuels. Annu Rev Ecol Evol Syst 41:351–377CrossRefGoogle Scholar
  11. Field CB, Mach KJ (2017) Rightsizing carbon dioxide removal. Science 356:706–707CrossRefGoogle Scholar
  12. Fricko O et al (2017) The marker quantification of the shared socioeconomic pathway 2. Glob Environ Change 42:251–267CrossRefGoogle Scholar
  13. Fujimori S et al (2017) SSP3: AIM implementation of shared socioeconomic pathways. Glob Environ Change 42:268–283CrossRefGoogle Scholar
  14. Fuss S et al (2014) Betting on negative emissions. Nat Clim Chang 4:850–853CrossRefGoogle Scholar
  15. Grahn M et al (2007) Biomass for heat or as transportation fuel? Biomass Bioenergy 31:747–758CrossRefGoogle Scholar
  16. Kato E et al (2017) A sustainable pathway of bioenergy with carbon capture and storage deployment. Energy Procedia 114:6115–6123CrossRefGoogle Scholar
  17. Kaya A et al (2017) Constant elasticity of substitution functions for energy modeling in general equilibrium integrated assessment models: a critical review and recommendations. Clim Chang 145:27–40CrossRefGoogle Scholar
  18. Keramidas K et al (2017) POLES-JRC model documentation (JRC Technical Report No. EUR 28728 EN). Seville, SpainGoogle Scholar
  19. Klein D et al (2014) The value of bioenergy in low stabilization scenarios: an assessment using REMIND-MAgPIE. Clim Chang 123:705–718CrossRefGoogle Scholar
  20. Koelbl BS et al (2014) Uncertainty in carbon capture and storage (CCS) deployment projections: a cross-model comparison exercise. Clim Chang 123:461–476CrossRefGoogle Scholar
  21. Kriegler E et al (2014) The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. Clim Chang 123:353–367CrossRefGoogle Scholar
  22. Kriegler E et al (2017) Fossil-fueled development (SSP5): an energy and resource intensive scenario for the 21st century. Glob. Environ. Change 42:297–315CrossRefGoogle Scholar
  23. Laurens L (2017) State of technology review—algae bioenergy (Task No. Task 39). IEA Bioenergy, Golden, COGoogle Scholar
  24. Lomax G et al (2015) Reframing the policy approach to greenhouse gas removal technologies. Energy Policy 78:125–136CrossRefGoogle Scholar
  25. Otto SAC et al (2015) Impact of fragmented emission reduction regimes on the energy market and on CO2 emissions related to land use. Technol. Forecast Soc. Change Part A 90:220–229CrossRefGoogle Scholar
  26. Riahi K et al (2017) The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ Change 42:153–168CrossRefGoogle Scholar
  27. Rogelj J et al (2015) Energy system transformations for limiting end-of-century warming to below 1.5 °C. Nat Clim Chang 5:519–527CrossRefGoogle Scholar
  28. Rose SK et al (2014) Bioenergy in energy transformation and climate management. Clim Chang 123:477–493CrossRefGoogle Scholar
  29. Sands R et al (2017) Dedicated energy crops and competition for agricultural land. Economic Research Report 223, U.S. Department of Agriculture, Economic Research Service, Washington DCGoogle Scholar
  30. Sano F et al (2015) Assessments of GHG emission reduction scenarios of different levels and different short-term pledges through macro- and sectoral decomposition analyses. Technol. Forecast. Soc. Change Part A 90:153–165CrossRefGoogle Scholar
  31. Scott V et al (2015) Fossil fuels in a trillion tonne world. Nat Clim Chang 5:419–423CrossRefGoogle Scholar
  32. Smith P et al (2015) Biophysical and economic limits to negative CO2 emissions. Nat Clim Chang 6:42–50CrossRefGoogle Scholar
  33. van Vuuren DP et al (2010) Bio-energy use and low stabilization scenarios. Energy J 31:193–221Google Scholar
  34. van Vuuren DP et al (2017) Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm. Glob. Environ Change 42:237–250CrossRefGoogle Scholar
  35. Waisman H et al (2012) The Imaclim-R model: infrastructures, technical inertia and the costs of low carbon futures under imperfect foresight. Clim Chang 114:101–120CrossRefGoogle Scholar
  36. Wilson C et al (2012) Marginalization of end-use technologies in energy innovation for climate protection. Nat Clim Chang 2:780–788CrossRefGoogle Scholar
  37. Yamamoto H et al (2014) Role of end-use technologies in long-term GHG reduction scenarios developed with the BET model. Clim Chang 123:583–596CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Nico Bauer
    • 1
  • Steven K. Rose
    • 2
  • Shinichiro Fujimori
    • 3
    • 4
  • Detlef P. van Vuuren
    • 5
    • 6
  • John Weyant
    • 7
  • Marshall Wise
    • 8
  • Yiyun Cui
    • 8
  • Vassilis Daioglou
    • 5
  • Matthew J. Gidden
    • 9
  • Etsushi Kato
    • 9
  • Alban Kitous
    • 11
  • Florian Leblanc
    • 12
  • Ronald Sands
    • 13
  • Fuminori Sano
    • 14
  • Jessica Strefler
    • 1
  • Junichi Tsutsui
    • 15
  • Ruben Bibas
    • 12
  • Oliver Fricko
    • 9
  • Tomoko Hasegawa
    • 4
  • David Klein
    • 1
  • Atsushi Kurosawa
    • 10
  • Silvana Mima
    • 16
  • Matteo Muratori
    • 17
  1. 1.Potsdam Institute for Climate Impact Research (PIK), Leibniz AssociationPotsdamGermany
  2. 2.Energy and Environmental Analysis Research GroupElectric Power Research InstituteWashingtonUSA
  3. 3.Department of Environmental EngineeringKyoto UniversityKyotoJapan
  4. 4.National Institute for Environmental Studies (NIES)TsukubaJapan
  5. 5.Netherlands Environmental Assessment Agency (PBL)The HagueThe Netherlands
  6. 6.Copernicus Institute for Sustainable DevelopmentUtrecht UniversityUtrechtThe Netherlands
  7. 7.Stanford UniversityPalo AltoUSA
  8. 8.Pacific Northwest National Laboratory (PNNL)College ParkUSA
  9. 9.International Institute for Applied Systems Analysis (IIASA)LaxenburgAustria
  10. 10.The Institute of Applied EnergyTokyoJapan
  11. 11.Joint Research Center (JRC)SevilleSpain
  12. 12.Centre International de Recherche sur l’Environnement et le DéveloppementParisFrance
  13. 13.U.S. Department of Agriculture, Economic Research ServiceWashingtonUSA
  14. 14.Research Institute of Innovative Technology for the Earth (RITE)KyotoJapan
  15. 15.Environmental Science Laboratory, Central Research Institute of Electric Power Industry (CRIEPI)Abiko, ChibaJapan
  16. 16.Grenoble Applied Economics Lab (GAEL)University Grenoble Alpes, CNRS, INRA, Grenoble INPGrenobleFrance
  17. 17.National Renewable Energy Laboratory (NREL)GoldenUSA

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