The value of BECCS in IAMs: a review
File(s)
Published version
Author(s)
Köberle, Alexandre C
Type
Journal Article
Abstract
Purpose of Review
Integrated assessment model (IAM) scenarios consistent with Paris Agreement targets involve large negative emission technologies (NETs), mostly bioenergy with carbon capture and storage (BECCS). Such reliance on BECCS implies IAMs assign it a high value. Past analyses on the value of BECCS in IAMs have not explicitly addressed the role of model structure and assumptions as value drivers. This paper examines the extent to which the value of BECCS in IAMs is enhanced by model structure constraints and assumptions.
Recent Findings
Predominant use of high discount rates (3.5–5%) means models opt for delayed-action strategies for emissions mitigation that lead to high levels of cumulative net-negative emissions, while lower discount rates lead to reduce reliance on NETs. Until recently in the literature, most models limited NET options to only BECCS and afforestation, but introduction of other CDR options can reduce BECCS deployment. Constraints on grid penetration of variable renewable energy (VRE) is a determining factor on the level of BECCS deployment across models, and more constrained grid penetration of VREs leads to more BECCS in electricity generation.
Summary
This paper concludes BECCS derives significant value not only from the existing structure of IAMs but also from what is not represented in models and by predominant use of high discount rates. Omissions include NETs other than BECCS and deforestation, low-carbon innovation in end-use technologies, grid resilience to intermittent sources, and energy use in agriculture production. As IAMs increasingly endogenize such constraints, the value of BECCS in resulting scenarios is likely to be dampened.
Integrated assessment model (IAM) scenarios consistent with Paris Agreement targets involve large negative emission technologies (NETs), mostly bioenergy with carbon capture and storage (BECCS). Such reliance on BECCS implies IAMs assign it a high value. Past analyses on the value of BECCS in IAMs have not explicitly addressed the role of model structure and assumptions as value drivers. This paper examines the extent to which the value of BECCS in IAMs is enhanced by model structure constraints and assumptions.
Recent Findings
Predominant use of high discount rates (3.5–5%) means models opt for delayed-action strategies for emissions mitigation that lead to high levels of cumulative net-negative emissions, while lower discount rates lead to reduce reliance on NETs. Until recently in the literature, most models limited NET options to only BECCS and afforestation, but introduction of other CDR options can reduce BECCS deployment. Constraints on grid penetration of variable renewable energy (VRE) is a determining factor on the level of BECCS deployment across models, and more constrained grid penetration of VREs leads to more BECCS in electricity generation.
Summary
This paper concludes BECCS derives significant value not only from the existing structure of IAMs but also from what is not represented in models and by predominant use of high discount rates. Omissions include NETs other than BECCS and deforestation, low-carbon innovation in end-use technologies, grid resilience to intermittent sources, and energy use in agriculture production. As IAMs increasingly endogenize such constraints, the value of BECCS in resulting scenarios is likely to be dampened.
Date Issued
2019-12
Date Acceptance
2019-12-01
Citation
Current Sustainable/Renewable Energy Reports, 2019, 6 (4), pp.107-115
ISSN
2196-3010
Publisher
Springer (part of Springer Nature)
Start Page
107
End Page
115
Journal / Book Title
Current Sustainable/Renewable Energy Reports
Volume
6
Issue
4
Copyright Statement
© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Identifier
https://link.springer.com/article/10.1007%2Fs40518-019-00142-3
Publication Status
Published
Date Publish Online
2019-12-02