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Article . 2020 . Peer-reviewed
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EMF-33 insights on bioenergy with carbon capture and storage (BECCS)

Authors: Ronald D. Sands; Detlef P. van Vuuren; Detlef P. van Vuuren; Steven K. Rose; Jessica Strefler; Matthew Gidden; Matteo Muratori; +9 Authors

EMF-33 insights on bioenergy with carbon capture and storage (BECCS)

Abstract

This paper explores the potential role of bioenergy coupled to carbon dioxide (CO2) capture and storage (BECCS) in long-term global scenarios. We first validate past insights regarding the potential use of BECCS in achieving climate goals based on results from 11 integrated assessment models (IAMs) that participated in the 33rd study of the Stanford Energy Modeling Forum (EMF-33). As found in previous studies, our results consistently project large-scale cost-effective BECCS deployment. However, we also find a strong synergistic nexus between CCS and biomass, with bioenergy the preferred fuel for CCS as the climate constraint increases. Specifically, the share of bioenergy that is coupled to CCS technologies increases since CCS effectively enhances the emissions mitigation capacity of bioenergy. For the models that include BECCS technologies across multiple sectors, there is significant deployment in conjunction with liquid fuel or hydrogen production to decarbonize the transportation sector. Using a wide set of scenarios, we find carbon removal to be crucial to achieving goals consistent with 1.5 °C warming. However, we find earlier BECCS deployment but not necessarily greater use in the long-term since ultimately deployment is limited by economic competition with other carbon-free technologies, especially in the electricity sector, by land-use competition (especially with food) affecting biomass feedstock availability and price, and by carbon storage limitations. The extent of BECCS deployment varies based on model assumptions, with BECCS deployment competitive in some models below carbon prices of 100 US$/tCO2. Without carbon removal, 2 °C is infeasible in some models, while those that solve find similar levels of bioenergy use but substantially greater mitigation costs. Overall, the paper provides needed transparency regarding BECCS’ role, and results highlight a strong nexus between bioenergy and CCS, and a large reliance on not-yet-commercial BECCS technologies for achieving climate goals.

Countries
Germany, Netherlands, Netherlands
Keywords

Global and Planetary Change, Atmospheric Science, 330, Model comparison, 333, CCS, EMF, Carbon capture and storage, Negative emissions, Taverne, BECCS, Carbon dioxide removal, SDG 13 - Climate Action, Bioenergy, Integrated assessment, SDG 7 - Affordable and Clean Energy, SDG 15 - Life on Land

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    44
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
44
Top 10%
Top 10%
Top 1%
Green
bronze