ncx-co / ifm_deferred_harvest

Documents, Data, and Code. The NCX Methodology For Improved Forest Management (IFM) Through Short-Term Harvest Deferral.
Apache License 2.0
11 stars 1 forks source link

Public Comment: 282 (Jessica Orrego) #282

Closed ncx-gitbot closed 1 year ago

ncx-gitbot commented 1 year ago

Commenter Organization: Mercuria Energy Trading

Commenter: Jessica Orrego

2021 Deferred Harvest Methodology Section: Baseline

Comment: Appendix A describes an approach for developing a hierarchical model to estimate the probability that an area of forest would be harvested during the specified commitment period. However, the appendix only provides a very broad and theoretical description of how a model should be developed. This ambiguity will almost certainly result in a great deal of variability in the application of this approach across projects. Without detailed information about how to model the baseline, this methodology does not provide a framework that is verifiable. To address this the methodology indicates that the baseline models are “subject to review by an expert panel”. However, important details are missing that describe how this expert panel is selected and what criteria the panel must use to assess the baseline. It is also unclear if the results of this expert panel’s assessment will be publicly available. Furthermore, criteria and guidance related to validations of the baseline are also missing, which raises concerns about the resulting assurance of third party audits. We recommend that significant additional details, criteria and guidance are included in appendix A. This is necessary to enable transparent and consistent application of the methodology, and credibility of validations, verifications and expert panel reviews.

Proposed Change: No Proposed Change

ncx-gitbot commented 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. We will soon be releasing our empirical benchmarks that demonstrate adequate performance for predicting business as usual behavior. Furthermore, we require landowner attestations that affirm the intent to harvest.