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Mitigating risk of credit reversal in nature-based climate solutions by optimally anticipating carbon release
Univ Cambridge, Conservat Res Inst, Cambridge, England.;Univ Cambridge, Dept Plant Sci, Cambridge, England..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0001-6682-6559
Univ Cambridge, Conservat Res Inst, Cambridge, England.;Univ Cambridge, Dept Plant Sci, Cambridge, England..
Univ Cambridge, Conservat Res Inst, Cambridge, England.;Univ Cambridge, Dept Zool, Cambridge, England..
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2024 (English)In: Carbon Management, ISSN 1758-3004, E-ISSN 1758-3012, Vol. 15, no 1, article id 2390854Article in journal (Refereed) Published
Abstract [en]

Nature-based climate solutions supply carbon credits generated from net carbon drawdown in exchange for project funding, but their credibility is challenged by the inherent variability and impermanence of drawdown. By evaluating drawdown benefits from a social cost of carbon perspective, project developers can enhance credibility and estimate impermanence by conservatively anticipating drawdowns to be eventually released following a release schedule, issuing additional credits when actual release is less severe than anticipated. We demonstrate how we can use ex post observations of drawdowns to construct optimal release schedules that limit the risk of credit reversals (when net drawdown is negative). We simulate both theoretical and real-life projects to examine how this approach balances the trade-off between generating credits evaluated as more permanent and limiting reversal risk. We discuss how this approach incentivizes project performance and provides a pragmatic solution to challenges facing larger-scale implementation of nature-based climate solutions.

Place, publisher, year, edition, pages
Informa UK Limited , 2024. Vol. 15, no 1, article id 2390854
Keywords [en]
Carbon credits, temporary carbon storage, REDD plus, deforestation, climate action, life on land
National Category
Other Computer and Information Science
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URN: urn:nbn:se:kth:diva-353131DOI: 10.1080/17583004.2024.2390854ISI: 001302374700001Scopus ID: 2-s2.0-85202862058OAI: oai:DiVA.org:kth-353131DiVA, id: diva2:1897347
Note

QC 20240912

Available from: 2024-09-12 Created: 2024-09-12 Last updated: 2024-09-12Bibliographically approved

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Gross, James

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Citation style
  • apa
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  • vancouver
  • Other style
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
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Output format
  • html
  • text
  • asciidoc
  • rtf