Geography

Forest carbon protocols underestimate climate-driven carbon loss risks

Document Type

Article

Abstract

Although the reduction of fossil fuel emissions remains of the utmost importance to mitigate climate change, maintaining and enhancing carbon sinks in forests have been widely promoted as nature-based climate solutions1, 2, 3–4. However, disturbances that could result in losses of forest carbon stocks are poorly accounted for when estimating the potential role of forests in climate mitigation5, 6–7. This makes it difficult to appropriately size ‘buffer pools’: a mechanism designed to compensate for unintended carbon losses in carbon crediting projects8,9. Here we use forest inventory, satellite data, disturbance modelling and machine learning to map reversal (carbon loss) risk in the contiguous United States (CONUS) from natural disturbance. Across CONUS forests, we show that climate change increases the 100-year risk of carbon losses from natural disturbance, particularly in California and the Intermountain West. The current buffer pool of the largest CONUS forest climate mitigation programme is likely too small by an average factor of 6.3, and this could range from 2.2- to 8.0-fold too small when considering uncertainties around future climate scenarios, disturbance severity and other carbon pools. We provide spatially explicit maps of the long-term risks to forest carbon losses from natural disturbances, which highlight that current methodologies used for constructing carbon offset buffer pools require revisions to succeed under climate change. © The Author(s), under exclusive licence to Springer Nature Limited 2026.

Publication Title

Nature

Publication Date

6-2026

Volume

654

Issue

8117

First Page

107

Last Page

113

ISSN

0028-0836

DOI

10.1038/s41586-026-10571-y

Keywords

carbon sequestration; climate change, forest, machine learning, metabolism, United States

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