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Learning lessons from over-crediting to ensure additionality in forest carbon credits

Tom Swinfield et al · Nature Portfolio · 2026

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Abstract Independent evaluations have shown substantial over-issuance of REDD+ (Reducing Emissions from Deforestation and Degradation) credits traded on the voluntary carbon market. We synthesise these evaluations to estimate the additional forest conservation achieved by first-generation REDD+ projects and to identify mechanisms underlying over-crediting. We combine six independent ex post evaluations of avoided deforestation covering 44 REDD+ projects. These evaluations show that most projects reduced deforestation, but that they claimed an aggregate of 10.7 times more avoided deforestation than is justified by independent estimates. This discrepancy is not driven by the choice of forest cover data, but by selection bias in projects’ control areas and modelling approaches. Although recent initiatives that transfer assessment to unconflicted parties and restrict methodological flexibility are critical, they are insufficient. Ex post certification against credible counterfactuals is necessary if carbon markets are to represent causal reductions in deforestation.

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APA 7

al, T. S. E. (2026). Learning lessons from over-crediting to ensure additionality in forest carbon credits. https://doi.org/10.1038/s41467-026-71552-3

MLA

al, Tom Swinfield et. "Learning lessons from over-crediting to ensure additionality in forest carbon credits." 2026. https://doi.org/10.1038/s41467-026-71552-3.

Chicago

al, Tom Swinfield et. 2026. "Learning lessons from over-crediting to ensure additionality in forest carbon credits.". https://doi.org/10.1038/s41467-026-71552-3.

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al, T. S. E. 2026, Learning lessons from over-crediting to ensure additionality in forest carbon credits, Nature Portfolio, available at: https://doi.org/10.1038/s41467-026-71552-3 [Accessed 8 Aug. 2026].

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Title
Learning lessons from over-crediting to ensure additionality in forest carbon credits
Author / contributors
Tom Swinfield et al
Publisher
Nature Portfolio
Publication year
2026
ISSN
2041-1723
ISSN
2041-1723
Language
English
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