ncx-co / ifm_deferred_harvest

Documents, Data, and Code. The NCX Methodology For Improved Forest Management (IFM) Through Short-Term Harvest Deferral.
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Public Comment: 186 (Ben Parkhurst) #186

Closed ncx-gitbot closed 1 year ago

ncx-gitbot commented 1 year ago

Commenter Organization: Bluesource LLC

Commenter: Ben Parkhurst

2021 Deferred Harvest Methodology Section: 11

Comment: While Appendix A provides a theoretical overview of the baseline model, it is nowhere near specific enough in clarifying how it is to be applied consistently among project proponents. Other IFM methodologies make it very clear how projects are to be implemented and provide clear language and default factors on how project emissions reductions are to be quantified and verified. This ensures integrity and consistency among projects that are developed by different entities. This methodology would be nearly impossible to apply consistently among project proponents, and different approaches could easily lead to situations where emissions reductions from different projects are simply not equivalent due to varying assumptions and methodologies. We recommend Verra make all information from the panel review process publicly available to allow for additional stakeholders to evaluate the integrity of baseline quantification model and the ability for project proponents to utilize the methodology consistently across projects.

Proposed Change: No Proposed Change

ncx-gitbot commented 1 year ago

NCX response: The 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. We appreciate comments noting that the structure and performance of the baseline model used within this methodology is strongly influential on the predicted and realized climate impact of projects. Our revised methodology increases transparency rather than following an expert review process. This includes both detailed documentation of particular models used, as well as sharing benchmarking and performance information for baseline models. Finally, the revised approach to uncertainty explicitly accounts for imprecision in the baseline model in calculating the final number of credits generated from projects developed under this methodology.