Quantifying and mitigating bias and increased variability when using large-scale estimates of forests for subdomains
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 wit...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
|
| Series: | Frontiers in Forests and Global Change |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/ffgc.2025.1501303/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849721216140574720 |
|---|---|
| author | Jordan Golinkoff Mauricio Zapata-Cuartas Emily Witt Adam Bausch Donal O’Leary Reza Khatami Wu Ma |
| author_facet | Jordan Golinkoff Mauricio Zapata-Cuartas Emily Witt Adam Bausch Donal O’Leary Reza Khatami Wu Ma |
| author_sort | Jordan Golinkoff |
| collection | DOAJ |
| description | 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 offset 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 Neeff’s 2021 publication and in 2024 was adopted by the American Carbon Registry for use in the Small Non-Industrial Private Forestlands (SNIPF) methodology. Although 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 study is to introduce a method that can be applied in any forest type or geography using any forest carbon offset 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 method allows remote sensing estimates of carbon stocks to be applied to forest carbon offset 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 offset programs on forest conservation and climate change mitigation. |
| format | Article |
| id | doaj-art-91b99b5d781b4f9fa2a56c320301caac |
| institution | DOAJ |
| issn | 2624-893X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Forests and Global Change |
| spelling | doaj-art-91b99b5d781b4f9fa2a56c320301caac2025-08-20T03:11:43ZengFrontiers Media S.A.Frontiers in Forests and Global Change2624-893X2025-02-01810.3389/ffgc.2025.15013031501303Quantifying and mitigating bias and increased variability when using large-scale estimates of forests for subdomainsJordan GolinkoffMauricio Zapata-CuartasEmily WittAdam BauschDonal O’LearyReza KhatamiWu MaThis 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 offset 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 Neeff’s 2021 publication and in 2024 was adopted by the American Carbon Registry for use in the Small Non-Industrial Private Forestlands (SNIPF) methodology. Although 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 study is to introduce a method that can be applied in any forest type or geography using any forest carbon offset 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 method allows remote sensing estimates of carbon stocks to be applied to forest carbon offset 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 offset programs on forest conservation and climate change mitigation.https://www.frontiersin.org/articles/10.3389/ffgc.2025.1501303/fullforest carbon accountingsmall area estimationcarbon offsets from forest projectsuncertaintyclimate changeforest loss and degradation |
| spellingShingle | Jordan Golinkoff Mauricio Zapata-Cuartas Emily Witt Adam Bausch Donal O’Leary Reza Khatami Wu Ma Quantifying and mitigating bias and increased variability when using large-scale estimates of forests for subdomains Frontiers in Forests and Global Change forest carbon accounting small area estimation carbon offsets from forest projects uncertainty climate change forest loss and degradation |
| title | Quantifying and mitigating bias and increased variability when using large-scale estimates of forests for subdomains |
| title_full | Quantifying and mitigating bias and increased variability when using large-scale estimates of forests for subdomains |
| title_fullStr | Quantifying and mitigating bias and increased variability when using large-scale estimates of forests for subdomains |
| title_full_unstemmed | Quantifying and mitigating bias and increased variability when using large-scale estimates of forests for subdomains |
| title_short | Quantifying and mitigating bias and increased variability when using large-scale estimates of forests for subdomains |
| title_sort | quantifying and mitigating bias and increased variability when using large scale estimates of forests for subdomains |
| topic | forest carbon accounting small area estimation carbon offsets from forest projects uncertainty climate change forest loss and degradation |
| url | https://www.frontiersin.org/articles/10.3389/ffgc.2025.1501303/full |
| work_keys_str_mv | AT jordangolinkoff quantifyingandmitigatingbiasandincreasedvariabilitywhenusinglargescaleestimatesofforestsforsubdomains AT mauriciozapatacuartas quantifyingandmitigatingbiasandincreasedvariabilitywhenusinglargescaleestimatesofforestsforsubdomains AT emilywitt quantifyingandmitigatingbiasandincreasedvariabilitywhenusinglargescaleestimatesofforestsforsubdomains AT adambausch quantifyingandmitigatingbiasandincreasedvariabilitywhenusinglargescaleestimatesofforestsforsubdomains AT donaloleary quantifyingandmitigatingbiasandincreasedvariabilitywhenusinglargescaleestimatesofforestsforsubdomains AT rezakhatami quantifyingandmitigatingbiasandincreasedvariabilitywhenusinglargescaleestimatesofforestsforsubdomains AT wuma quantifyingandmitigatingbiasandincreasedvariabilitywhenusinglargescaleestimatesofforestsforsubdomains |