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METHODS article

Front. For. Glob. Change
Sec. Forest Management
Volume 8 - 2025 | doi: 10.3389/ffgc.2025.1501303

Quantifying and mitigating bias and increased variability when using large-scale estimates of forests for subdomains

Provisionally accepted
Jordan Golinkoff Jordan Golinkoff *Mauricio Zapata-Cuartas Mauricio Zapata-Cuartas Emily Witt Emily Witt Adam Bausch Adam Bausch Donal O'Leary Donal O'Leary Reza Khatemi Reza Khatemi Ma Wu Ma Wu
  • Finite Carbon, Portland, OR, United States

The final, formatted version of the article will be published soon.

    This paper presents an empirical method to calculate a conservative discount factor when applying a large-scale estimate to an internal subset of areas (subdomains) that accounts for both the precision (variability) and potential bias of the estimate of the subset (i.e. -the small area estimated within the large-scale framework). This method is presented in the context of forest carbon o set quantification and therefore considers how to conservatively adjust a large-scale estimate when applied to a subdomain within the original estimation domain. The approach outlined can be used for individual or aggregated carbon projects and allows large-scale estimates of forest stocks to be scaled down to project and stand-level results by discounting estimates to account for the potential variability and bias of the estimates. The conceptual basis for this approach is built upon a method described in Nee (2021) 1 and has been adopted 2 by the American Carbon Registry for use in the Small Non-Industrial Private Forestlands 3 (SNIPF).Though this publication uses an example dataset from the Southeastern United States and is specific to the ACR SNIPF Improved Forest Management (IFM) protocol, the intent of this work is to introduce a method that can be applied in any forest type or geography using any forest carbon o set protocol where there exist independent estimates of forest carbon stocks that overlap with the large-scale estimates. The application of this method relies on user-defined levels of risk and inventory confidence combined with the distribution of observed error. This approach allows remote sensing estimates of carbon stocks to be applied to forest carbon o set quantification. By doing so, this approach can reduce the costs for forest landowners and can therefore help to increase the impact of these market-based forest carbon o set programs on forest conservation and climate change mitigation.

    Keywords: Forest carbon accounting, small area estimation (SAE), Carbon offset in forest projects, uncertainty, Climate Change, Forest loss and degradation, Forest inventory

    Received: 24 Sep 2024; Accepted: 06 Jan 2025.

    Copyright: © 2025 Golinkoff, Zapata-Cuartas, Witt, Bausch, O'Leary, Khatemi and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jordan Golinkoff, Finite Carbon, Portland, OR, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.