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1 year ago NCX response: Our business as usual model is a hierarchical statistical model that predicts one-year harvest risk and intensity based on FIA training data and a suite of covariates that include geographic, biological, economic, and sociological factors. Partial pooling across forest types ensures that the model is able to leverage the similarity and ubiquity of covariate relationships across the forests of the continental U.S. while still allowing for regionally specific differences. Predicting behavior of any type, which is the basis for any forest carbon program, is not straightforward, and depends on models whose performance can be measured. Our revised methodology requires the propagation of model uncertainty through to calcation of final credits, as well as reporting of benchmarking for all models.
Commenter Organization: The Lyme Timber Company
Commenter: Jim Hourdequin
2021 Deferred Harvest Methodology Section: Additionality
Comment: Higher Additionality Risk from Assumption that Adequate Baseline Models can be developed across all Forest Types: The assertion is made that it’s possible to create baseline models in a range of forest types. While I agree with the assertion that, aside from the elements described in #2 above, the establishment of a baseline in plantation should be possible, I am not convinced that there are good and thoroughly tested models that do not rely on historical activity to predict landowner behavior on non-plantation lands in the US. The heterogeneity of forests, terrain, markets, access & road costs, landowner objectives, contractor availability, etc. on non-plantation forests is vastly greater than on plantation forests. I believe that any predictive model on these lands has to take into account historical practice on relatively large subject properties (>5,000 acres) over a period of years, and thus I am skeptical of the broad statement that predictive models can be developed across forest types. At a minimum, there should be a requirement to demonstrate that the predictive models are calibrated with historical practice on similarly situated subject properties. In general, I am not convinced that there has been sufficient study and/or academic support for the use of predictive models to forecast baseline activity over a 1-year period. Experience in the compliance market has demonstrated that forecasting baseline activity can be fraught over 100-year periods on large ownerships which ceteris paribus should be far easier given higher likelihood of activity over long timeframes and larger areas. The baseline models described in Section 6 and Appendices A and B strike me as highly theoretical and stylized, with little basis in empirical data and potentially little opportunity for calibration with empirical data.
Proposed Change: No Proposed Change