Improved assessment of baseline and additionality for forest carbon crediting

In the California compliance cap-and-trade carbon market, improved forest management (IFM) projects generate carbon credits in the initial reporting period if their initial carbon stocks are greater than a baseline. This baseline is informed by a "common practice" stocking value, which represents the average carbon stocks of surveyed privately owned forests that are classified into the same general forest type by the California Air Resources Board. Recent work has called attention to the need for more ecologically informed common practice carbon stocking values for IFM projects, particularly those in areas with sharp ecological gradients. Current methods for estimating common practice produce biases in baseline carbon values that lead to a clustering of IFM projects in geographical areas and ecosystem types that in fact support much greater forest carbon stocks than reflected in the common practice. This phenomenon compromises additionality, or the increases in carbon sequestration or decreases in carbon emissions that would not have occurred in the absence of carbon crediting. This study seeks to expand upon recent work on this topic and establish unbiased common practice estimates along sharp ecological gradients using methods that do not rely upon discrete forest classification. We generated common practice values for credited IFM projects in the Southern Cascades using a principal components analysis on species composition over an extensive forest inventory to determine the ecological similarity between inventoried forests and IFM project sites. Our findings strengthen the results of recent research suggesting common practice bias and adverse selection. At several sites, even after controlling for private ownership, 100% of the initial carbon stocks could be explained by ecological variables. This result means that improved management did not preserve or increase carbon stocks above what was typical, suggesting that no carbon offsets should have been issued for these sites. This result reveals greater bias than that been found at project sites in this region by research that has used discrete forest categorization.