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Deferred Harvest on Smallholder Lands
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This Module provides the requirements and procedures for the calculation of net carbon dioxide equivalent (CO2e) removal from the atmosphere through improved forest management (IFM) practices implemented by small forest landowners — i.e, smallholders.
Small forest landowners represent a significant portion of the United States' forest ownership, controlling approximately 36% of total forestland1. These landowners can substantially contribute to climate change mitigation through IFM practices. However, small landowners face disproportionate barriers to participating in carbon markets due to high transaction costs, complex verification requirements, and limited technical resources.
This Module addresses these systemic barriers by establishing a comprehensive approach to crediting specifically designed for small landowner participation in carbon markets with a design that incentivizes improved ecological and economic outcomes for small private landowners while leveraging technological innovations to reduce participation barriers. The Module focuses on deferred timber harvest strategies that allow forests to accumulate additional carbon beyond baseline management scenarios while maintaining the economic viability of forest ownership for participating landowners.
Carbon accumulation is quantified through a dynamic matching baseline approach that compares project forests to similar reference forests within the same region, accounting for variations in forest type, management history, and site conditions. This Module ensures accurate quantification of additional carbon benefits while reducing the technical burden on individual landowners through continuous change detection owned by Isometric and the Project Proponent. The Module employs advanced remote sensing technology, including LiDAR-enhanced forest inventories, to monitor carbon accumulation with high precision across multiple small properties while maintaining rigorous standards for measurement, reporting, and verification and reduced field-based verification costs.
To ensure ecological integrity and biodiversity conservation, this Module requires a high proportion of native species composition within project areas and prohibits enrollment of industrial plantation forests and monoculture systems. The Module includes provisions for invasive species management to enhance ecosystem function and resilience throughout the Crediting Period.
Further, this Module ensures the economic sustainability of project activities for participating landowners through guaranteed revenue sharing arrangements, with landowners receiving a large share of Credit revenue. Contract durations are established based on the biological rotation ages specific to forest types, with a conservative 50-year net-impact deduction to ensure meaningful carbon storage duration.
This Module accounts for the quantification of the gross amount of CO2e removed via enhanced carbon storage in forest biomass, as well as all cradle-to-grave life-cycle Greenhouse Gas (GHG)emissions associated with the implementation process. This Module is developed to adhere to the requirements of ISO 14064-2: 2019 – Greenhouse Gasses – Part 2: Specification with guidance at the Project level for quantification, monitoring, and reporting of greenhouse gas emission reductions or removal enhancements.
The Module ensures:
Throughout this Module, the use of "must" indicates a requirement, whereas "should" indicates a recommendation.
This Module relies on and is intended to be compliant with the following standards and protocols:
Additional reference standards that inform the requirements and overall practices incorporated in this Module include:
Additional principles that were considered in the development of this Module and aligned with, where feasible, include:
This Module was developed based on the current state of the art, publicly available science regarding IFM activities and long-term monitoring of forest carbon projects. This Module aims to be scientifically stringent and robust. We recognize that some requirements may exceed the status quo in the market and that there are numerous opportunities to improve the rigor of this Module as new approaches and techniques enter the Voluntary Carbon Market.
Additionally, this Module will be reviewed when there is an update to published scientific literature, government policies, or legal requirements which would affect net CO2e removal quantification or the monitoring guidelines outlined in this Module, or at a minimum of every 2 years.
In addition to the requirements outlined in Section 4 of the Improved Forest Management Protocol, Projects are subject to the following applicability requirements, which must be demonstrated in the Project Design Document (PDD).
In order to maintain ecological integrity and ecosystem function, demonstrate additionality, and ensure trust and transparency, it is incumbent upon Projects to adhere to the requirements below, which must be demonstrated and reported in the Project Design Document.
Given the nature of this Module, it is anticipated that a Project, facilitated by the Project Proponent, may enroll many smallholder landowners within a Project.
To ensure accurate carbon sequestration claims and facilitate Validation and Verification, this Module requires that a Project be registered for a singular or grouped Site(s) — sets of land parcels subject to similar bioclimatic, regulatory, and geographic conditions.
[/R-4NGB-0]Sites must consist of enrolled land parcels — discrete areas of land as defined by town, county, or regional tax maps — and/or all eligibile portions of enrolled mixed-use land parcels (Figure 1). Hereafter, the use of the term parcel shall mean both whole, single-use land parcels and all elibile portions of mixed-use land parcels, for simplicity.
[/G-Carbon quantification, as set out in Section 9, will be calculated at the Site level. Thus, each Site — and every constituent parcel — within a Project must independently meet all applicable eligibility criteria within this Module, which must be evidenced in the PDD.
[/G-The Project Proponent(s) and enrolled landowner(s) must adhere to the following additional requirements for parcel and Site enrollment:
All parcels must have road or navigable waterway access.
To ensure the credibility of the climate impact of project activities, non-additional areas of the Project must be excluded, as set out by various requirements in Section 4.1 of the Improved Forest Management Protocol and Section 4.1.2 of this Module.
It is vital to the integrity of the Project that no other areas within each parcel may be excluded from project activities. Smallholders participating in the Project must enroll all eligible areas of a parcel. This requirement prevents the selective enrollment of areas that are less desirable for harvest. Land can only be excluded for reasons stated within this Module or the Improved Forest Management Protocol, and these excluded areas must be documented and mapped within the PDD in accordance with and to fulfill the cumulative impact assessment requirements of the IFM Protocol (see Section 4.1.2) — with sufficient justification of the legal, financial, or operational restrictions on harvest.
For the purposes of this Module, parcel classes will be used for dynamic baselining and carbon quantification — i.e., distributed reference regions used as a point of comparison between behaviors observed in the enrolled Site(s) and those observed in areas with similar landowners (Figure 1).
Figure 1. Representative breakdown of land enrolled in a Project. All potential parcels (polygons) are shaded by parcel class, with eligible parcels outlined. These eligible parcles are then sorted into Sites. Areas within the parcels, and thus Sites, ineligible for Crediting are excluded.
The Project Proponent must generate between 10 and 50 parcel classes for the Project by clustering parcels in the ecoregion(s) of the country of the Site(s) into groupings with shared socio-economic, topographic, bioclimatic, historical, and structural attributes in relation to forest management.
[/R-RAC0-0]The Project Proponent must assign any parcel(s) enrolled prior to Validation to the respective parcel class(es) at Validation, and report these assignments.
[/G-9K0T-0]Upon enrollment of any additional Site(s), the Project Proponent must assign the constituent parcel(s) of each Site to the closest existing parcel class, and report these parcel class assignments to Isometric at the next Verification.
Table 1. Parcel Class Clustering Attributes
| Cluster Category | Variable | Potential Datasets |
|---|---|---|
| Economic | Land value | Nolte, et. al. 2020; Local property tax assessments; Real estate market data |
| Building Presence | Microsoft Global ML Building Footprints; Cadastral records; Zoning data; Building permits database | |
| Building footprint area | Microsoft Global ML Building Footprints; High-resolution satellite imagery; Municipal building databases; OpenStreetMap building footprints | |
| Distance to highways and/or navigable waterways | OpenStreetMap road networks; National highway databases; Transportation department GIS data | |
| Distances to mills | Forest industry facility databases; Manufacturing facility registries; Economic census data | |
| Distances to urbanized areas | Census urban area boundaries; Land use/land cover datasets; Municipal boundary files | |
| Distances to urban expansion | Historical land use maps; Urban growth models; Development planning documents | |
| Parcel size | Cadastral/parcel boundary datasets; Property records; Land registry data | |
| Topographic | Slope | Digital Elevation Models (DEMs); SRTM data; National topographic databases |
| Topographic roughness index | DEM-derived metrics; Terrain analysis from USGS; ASTER Global DEM | |
| Elevation | DEM-derived metrics; Terrain analysis from USGS; ASTER Global DEM | |
| Bioclimatic | Index of Productivity (e.g., BFI, NPP) | MODIS NPP data; Forest productivity indices; Site index databases |
| Precipitation (seasonality and annual average) | WorldClim data; National weather station records; PRISM climate data | |
| Temperature (seasonality and annual average) | WorldClim temperature data; Climate normals; Meteorological databases | |
| Historical | Time since last commercial forest harvest | Forest management records; Harvest permit databases; Silvicultural treatment histories |
| Structural | Carbon stock (LiDAR-derived) | Airborne LiDAR surveys; Forest inventory plots; Biomass estimation models |
| Merchantable volume (LiDAR-derived; e.g., Leboeuf et al., (2022)^[15]) | LiDAR forest metrics; Timber cruising data; Forest inventory databases | |
| Stem Density (LiDAR-derived; e.g., Maltamo et al., (2004)3) | LiDAR point cloud analysis; Forest stand data; Tree count surveys | |
| Species groupings | Forest type maps; Vegetation surveys; Species composition databases |
Project activities must demonstrate additionality by accounting for Site-specific constraints that limit harvesting feasibility under baseline conditions. If ecological and Site conditions would render harvesting unlikely under baseline conditions, these areas within the Project Boundary cannot be claimed as additional and must be excluded from the Project Boundary. Soil saturation, permanent water bodies, slope gradients, and proximity to dwellings and other structures create natural and regulatory limitations on smallholder harvesting activities that must be evaluated when determining eligible project areas.
Project Proponents must identify and exclude from Crediting (see Section 4.1.1.1) any areas within the Project Boundary where economic, physical or regulatory constraints make harvesting highly unlikely under baseline management scenarios based on the guidance and requirements below.
Historically, harvesting operations in waterlogged soils occurred primarily during periods of prolonged freezing temperatures, which provided ground stability for heavy timber equipment. Climate change has reduced the predictability and duration of these freezing periods, particularly in temperate and polar regions, making winter harvesting increasingly unreliable. Consequently, harvest avoidance in these soil types does not constitute additional management activity beyond what would occur under baseline conditions.
Project Proponents must identify and exclude from Crediting any areas within the Project Boundary with soils classified as "very poorly drained" or "inundated" according to the U.S. Soil Taxonomy classification system, or similarly classified soils in equivalent authoritative sources if a Project occurs outside of the United States of America.
[/R-BZ7N-0]Project Proponents may include areas with soils classified as "very poorly drained" or "inundated" within the Project Boundary where evidence demonstrates that baseline harvesting operations remain economically viable despite soil drainage limitations.
Areas with waterlogged soils may be included when the Project Proponent demonstrates:
To include these areas, the Project must also demonstrate additionality of these waterlogged soils through financial analysis showing that baseline harvesting remains economically feasible despite drainage-related operational constraints, and that the carbon revenue represents a necessary incentive for the proposed management changes.
Project Proponents must identify and exclude from the Project any areas within the Project Boundary that fall within management buffers around permanent water bodies as defined by applicable governmental regulations or voluntary Best Management Practices, i.e., BMPs (see Appendix B of the Improved Forest Management Protocol).
[/R-ZM3A-0]Project Proponents must provide evidence of common harvesting practices and equipment availability within the relevant wood basket to demonstrate whether steep-slope operations are feasible under baseline management scenarios.
[/R-K8YZ-0]Research on non-industrial private forest owner preferences demonstrates that the majority of forest owners will not permit harvesting activities in the immediate vicinity of residential dwellings and other permanent structures6. Additionally, regulations or BMPs may prohibit harvesting within a certain proximity of built structures.
Project Proponents must exclude from crediting calculations any areas within 50 feet of permanent structures, or within larger buffers if required by applicable regulations or BMPs (see Appendix B of the Improved Forest Management Protocol).
[/R-6XTM-0]Project Proponents must demonstrate that each parcel contains sufficient merchantable stocking in a market where harvests would be economically viable in the baseline scenario. This merchantable stocking determination will establish which areas of each parcel contain timber that would be economically attractive for harvest under common practice, thereby demonstrating the additionality achieved through deferred harvest activities.
Project Proponents must exclude all areas from the Project which do not meet the requirements in Sections 4.1.2.5.1 and 4.1.2.5.2.
For each enrolled parcel, the Project Proponent must demonstrate economic viability through a parcel-specific financial analysis accounting for all revenues and costs associated with timber harvest operations under baseline management scenarios.
[/R-FZ3R-0]A parcel shall be classified as economically viable for harvest in the baseline scenario if:
Parcels which fail to meet one of these thresholds must be excluded from the Project.
Beyond parcel-level economic analysis, Project Proponents must evaluate whether sufficient regional market capacity exists to absorb timber volumes from enrolled parcels under baseline harvest scenarios.
[/R-E7HY-0]The Project Proponent must assess whether aggregate baseline harvest volumes from all economically viable project parcels would exceed regional processing capacity by:
Project Proponents must account for existing non-Project timber supply as part of this analysis.
Where appropriate, Project Proponents may consider temporal market dynamics to justify the additionality of the Project, such as:
Where significant market changes are documented or reasonably anticipated, the Project Proponent should adjust merchantability classifications or apply appropriate discount factors to baseline projections.
Project Proponents must demonstrate that landowners — who have owned the land for more than 5 years — enrolling into the Project do not possess merchantable stocking significantly above the regional average of the parcel class. Such stocks of merchantable carbon would indicate a pre-existing intent to conserve or defer harvest in violation of the requirements set out in the IFM Protocol (see Section 7.7).
[/R-1WDK-0]Project Proponents must first develop and report growth curves for each Site (Figures 2 & 3), which describe the average growth trajectory of the forest.
[/G-DJWX-0]Using historical disturbance history (e.g., Hansen Global Forest Change Product) to find the average time, [math: \bar{RA}], and standard deviation, [math: RA_{SD}], between harvest events (e.g., average rotational age) within each parcel class, the Project Proponent must then determine and report the baseline average carbon stock, [math: \bar{RS}], and standard deviation, [math: RS_{SD}], of harvest within each Site (Figure 2).
[/G-Figure 2 Growth curve used to determine the baseline carbon stock at average rotation within a site. (a) Is the average rotational age of the Site's parcel class, [math: \pm] one standard deviation. These values are then used to find (b) the intersection of the average rotation age, [math: \bar{RA}], and the growth curve, which yields the baseline average carbon stocks for that Site, [math: \bar{RS}], [math: \pm] one standard deviation.
Project Proponents must then calculate and report the dynamic extension period, [math: EP], for each Site, which is [math: \frac{1}{6}] of the relevant [math: \bar{RA}].
[/G-The Project Proponent must then report the initial standing aboveground carbon stock, [math: M_{AGB}(t_0)], of each Site eligible for enrollment through a field inventory or with LiDAR according to one of the following Capture & Conversion Modules:
[/G-Finally, the Project Proponent must plot [math: M_{AGB}(t_0)] for each Site against its growth curve to determine the current stand age of the Site, [math: CSA]. Project Proponents must include these plots in the PDD.
[/G-If a Site’s carbon stocking exceeds one standard deviation, [math: RA_{SD}], of that of the average harvest date, [math: \bar{RA}], plus the dynamic extension period, [math: EP] — to allow for growth into later size-classes — it shall be deemed ineligible under this Module and must be removed from the Project (Figure 3).
[/G-Figure 3. Average growth trajectory plot of a Site. (a) The average rotational time ([math: \bar{RA}]) and stocking level ([math: \bar{RS}]) at harvest within the parcel class and Site, respectively. One standard deviation above the average stocking level of the Site, [math: RS_{SD}], intersects the growth curve at (b), which is also one standard deviation above the average rotational time ([math: \bar{RA}]) of the parcel class. (c) The carbon stock limit at which sites are not eligible for enrollment, determined by taking (b) one standard deviation above the mean [math: \bar{RA}] plus [math: \frac{1}{6}] of the [math: \bar{RA}], or the [math: EP], and finding its intersection on the growth curve. (d) and (e) represent the estimated initial carbon stock of potential Sites. (f) The current stand age, [math: CSA], of an eligible Site, with an estimated stand age less than the carbon stock limit. (g) The current stand age, [math: CSA], of an ineligible Site, with an estimated stand age greater than the carbon stock limit.
Under deferred harvest, the planting of new trees cannot be the primary method to achieve forest cover. Project Proponents must limit new planting only to fill in gaps in existing patches of degraded forests or to regenerate small areas — less than 1 hectare — of otherwise forested land.
[/R-Enrolled landowners under this Module are afforded a three-cord-per-year ([math: \approx] 10 cubic meters) per household harvesting allowance for subsistence activities (e.g., firewood) within their parcels, which does not need to be reported to Isometric.
[/R-964X-0]The length of the Crediting Period of the Project shall be determined by the cumulative temporal maximum of the contract lengths of the enrolled landowner(s) and their site(s) or 40 years, whichever is longer.
[/R-The contract length for parcels enrolled in the Project is determined by the biological and economic trajectory of the forest stand, and set by an assessment of key forest characteristics measured using this Module’s approved remote sensing technologies, ideally LiDAR. These contracts must meet the requirements set forth in the IFM Protocol (see Section 5.1).
Project Proponents must assess three critical parameters — current forest size, estimated biological rotation age, and current stand age — to determine contract lengths for enrolled landowners, and provide due diligence that each contract was determined accordingly.
[/R-GFW7-0]The initial contract length for each enrolled landowner, [math: Contract\ Length_{landowner}], must be calculated using the following equation that takes these parameters into account:
[math: Contract\ Length_{landowner} = (\bar{RA} + RA_{SD} + EP) - CSA]
(Equation 1)
[/G-Where:
The Crediting Period will commence either when the Project Proponent commences activities on a Site or on the date of the first enrolled landowner(s) contract signature within the Project, with project activities required to commence immediately. No Credits shall be issued for activities that occurred prior to the contract signature date.
[/R-Projects seeking to extend the initial Crediting period must demonstrate continued additionality in line with all requirements set forth in this Module, the Improved Forest Management Protocol, and the Isometric Standard.
The first Reporting Period shall end at a date set by the Project Proponent and approved by Isometric at Validation. This first Reporting Period must occur within 5 years of the initiation of the Crediting Period.
As Projects enroll any additional landowner(s), the Site(s) from the newly enrolled contract(s), [math: i], must take a deduction of Credits based on the time between the date of enrollment and the end of the Reporting Period according to the following Equation 2:
[math: ARPF_i= \frac{Days\space to\space end\space of\space Reporting\space Period}{Total\space Days\space of\space Reporting\space Period} ]
(Equation 2)
Where [math: ARPF_i] is the Adjusted Reporting Period Factor discount rate applied in Equation 7 to the calculation of CO2e stored for the parcel(s) under each individual contract, [math: i], weighted to ensure only growth since contract initiation is included in the Credit issuance for the Reporting Period (see Section 9.1).
In addition to the requirements set forth in the IFM Protocol (Section 6.4) of the Improved Forest Management Protocol, Projects must not resemble industrial commercial or monoculture plantations.
[/R-QSKH-0]While a forest management plan is not required for landowner enrollment into the Project, Project Proponents should work with enrolled landowner(s) to develop parcel- and site-specific plans. Project Proponents should facilitate expert forester consultation with enrolled landowners as part of enrollment into the Project, and disclose any such past or planned activity in the Project Design Document.
Certain forest management activities must be disclosed by the Project Proponent, such as Stand Improvement Activities and Harvesting Plans. These activities are subject to the requirements set forth by Isometric in this Module, the IFM Protocol (Section 6.5), and the Isometric Standard.
By providing advanced notice of intended forest management activities, enrolled landowners can assist the Project Proponent in managing the expected issuance of Credits, enabling greater flexibility in forest management activities and harvesting. Enrolled landowners who intend to harvest or actively manage their forest should disclose these plans to the Project Proponent with enough advance notice to ensure compliance with this Module, the Improved Forest Management Protocol, and the Isometric Standard.
Enrolled landowners may engage in Isometric-approved stand-improving activities in consultation with the Project Proponent. These activities must follow specific restrictions set initially by the Project Proponent and approved in consultation with Isometric to ensure that the volume of harvested wood does not exceed the volume of issued Credits.
The Project Proponent must disclose all planned stand-improvement activities in the Project Design Document prior to Validation and at each subsequent Verification, and must inform Isometric of any changes to these plans immediately and report these changes prior to the next Verification.
[/R-BEF6-0]Credits will not be issued until the forest biomass within the Site recovers above the Site’s most recent pre-stand-improving-activity carbon stock baseline.
Project emissions associated with stand improvement activities must be reported according to the requirements in the IFM Protocol (see Section 8).
To ensure that the Project’s carbon stock does not dip below a reasonable threshold of the levels at which Credits were previously issued — which would trigger a Reversal — Project Proponents should work closely with enrolled landowners to plan all non-intervention (i.e., deferred harvest) harvest activities.
Project Proponents must work with enrolled landowners and certified foresters to determine any such anticipated non-intervention harvesting schedule and pre-emptively plan reporting to ensure Credit issuance occurs when enough carbon has been sequestered to prevent the triggering of an avoidable reversal. This non-intervention harvesting is additionally subject to the following requirements:
[/G-To ensure this compliance, the Project Proponent must describe all non-intervention harvesting plans and timelines for the enrolled site(s) prior to Validation and at each subsequent Verification. It is anticipated that these will be informed by the forest management plan of each enrolled landowner.
[/R-Credits will not be issued until the forest biomass within each Site recovers above the Site’s most recent pre-non-intervention-harvesting carbon stock baseline.
Project emissions associated with harvesting activities must be reported according to the requirements in the IFM Protocol (see Section 8).
Project Proponents must disclose all pre-existing forest management or traditional stewardship plans for any enrolled Site(s).
These plans must specify likely or planned harvest(s), or traditional forest stewardship activity, during the Crediting Period.
[/G-In addition to any Indigenous Peoples stakeholder engagement and Free, Prior, and Informed Consent (FPIC) process that may be required for the Project as proscribed in the IFM Protocol (see Section 6.6), Project Proponents must follow the same stakeholder engagement plan and principles of FPIC described in the IFM Protocol (see Section 6.6.1) for all enrolled landowners.
It is vital that enrolled landowners understand the contracts they are entering into as part of the Project. As part of this stakeholder engagement and FPIC process, Project Proponents must make publicly available and disclose to enrolled landowner(s), and ensure that these landowner(s) understand the following, during the process:
[/R-6D25-0]Given the inherent economic nature of forest management activities, the impacts of the Project extend beyond enrolled landowner(s) to local economies. Giving these stakeholders early notice of planned changes in timber harvest enables market adaptation and prevents job losses in any local timber-based economy.
Project Proponents must provide enrolled landowners an estimate of the likely change in the volume of their future harvest and, if applicable, any quality changes in their forests during the Project that should also be disclosed to loggers and local resource management agencies to inform broader resource and timber planning.
[/R-T1XT-0]The Project Proponent or enrolled landowners must notify local authorities and loggers, mills, or other parties with whom the enrolled landowner(s) has existing formal or informal offtake agreements of the contracted change in harvest volume and/or schedule of timber upon enrollment into the Project.
[/R-DPSF-0]In addition to the requirements set forth in the IFM Protocol (Section 6.6.2), the Project Proponent must disburse to enrolled landowners a minimum of 40% of the revenues generated from Credits issued to the Project. Eligible forms of revenue sharing include:
[/R-1BN4-0]If any of revenue sharing is contingent upon the landowner meeting particular requirements (e.g., adherence to a harvesting plan), these requirements must be clearly communicated to the landowners and disclosed to Isometric.
[/G-Project Proponents must also provide details of the systems which will be used for the disbursement of revenue and how the disbursement will be tracked (e.g., digital payment records, other documentation in absence of formal financial systems).
Revenue sharing percentages should also be made public, including percent revenue or Credits divided among each party (e.g., Project Proponent, enrolled landowner(s), insurance provider(s), and other documented parties listed in the PDD).
[/G-Project Proponents must provide an anticipated timeline of revenue sharing disbursement over the duration of each contract.
[/G-In order to ensure additionality, the Project Proponent must demonstrate a pre-existing intent to harvest trees within the Project Boundary and prove a demonstrable history of commercial harvesting and/or traditional forest stewardship within the parcel(s) of any enrolled landowner(s). These requirements must be robustly evidenced by data of the following types.
[/R-The carbon pools within the scope of this Module are aboveground and belowground woody biomass, as these are the main carbon pools affected by the intervention of deferred harvest. Soil carbon is excluded [math: CO_2e_{Stored, RP}] as the literature suggests minimal change under low-intensity deferred harvests7. For the remainder of the Module, the use of AGB and BGB refers to only the living aboveground and belowground woody biomass, respectively, unless otherwise noted.
This Module requires Projects to account for leakage through the Global Timber Model (GTM), which captures both direct landowner behavioral responses (activity-shifting) and broader market-mediated effects (market leakage) within a unified economic modeling approach. This integration reflects the reality that in improved forest management, activity-shifting behaviors are often driven by the same market signals and economic incentives that drive broader market leakage in response to timber supply restrictions. In this Module, leakage is calculated for distinct species groups for each regional wood basket, which vary by geographic location and forest composition.
Recent scientific literature has highlighted significant challenges in accurately quantifying leakage from forest carbon projects. Studies by Badgley et al. (2021)8, Haya et al. (2023)9, and others have found systematic over-crediting in forest carbon offset programs, largely due to inadequate accounting for leakage impacts10. This research indicates that traditional approaches to leakage assessment for forest carbon offset programs often underestimate both the magnitude and geographic scope of displacement effects.
The complexity of global timber markets means that reduced harvest in one location can trigger responses across multiple regions and through various economic pathways. Market-mediated leakage can occur through price signals that incentivize increased harvesting in distant locations, while activity-shifting often occurs through landowner responses that extend to other land owned by the Project Proponent or enrolled landowners, but not included in the Project.
This methodology adopts a conservative modeling approach with a minimum 10% leakage deduction, using the Global Timber Model (GTM) to prevent well-documented overcrediting that has resulted from other improved forest management methodologies.
Any enrolled landowner(s) who own or manage forested land outside of the enrolled parcel(s) of the Project must provide an attestation that they will not alter forest management on this land outside of the enrolled parcel(s) as a result of project activities.
[/R-8NCE-0]Additionally, for all non-primary residence non-enrolled land parcel(s) greater than 10 hectares, the attestation document must also include, in order of preference:
The enrolled landowner(s) should provide documentation of their annual harvest volume(s) for all non-enrolled lands for every year that the landowner(s) remain enrolled in the Project to the Project Proponent, who shall report all available annual harvest volume(s) to Isometric prior to each Verification.
Isometric reserves the right to audit future harvests on non-enrolled land(s), inclduing via earth observation remote sensing systems, and compare these harvests against the submitted attestation.
The Global Timber Model (GTM) was conceptualized by Sohngen et al. (1999)11, and it seeks to re-balance supply and demand in response to market shocks over decadal time periods for forest-type regions.
The GTM has been adopted for carbon projects through an update led by the University of Maine, in collaboration with The Nature Conservancy and the Global Forest Lab at The Ohio State University, as discussed in Daigneault et al. (2025)12. This updated GTM estimates how forest carbon projects in aggregate change trade balances regionally and globally, as a result of both direct and indirect leakage.
Isometric will quantify the leakage deduction for each Reporting Period for Projects based on global runs of leakage rates (Equation 3) for the forest-type using the Global Timber Model (GTM), annualized and weighted by the Project’s basal area. As changes to timber harvest through project activities directly and indirectly impact the balance of supply and demand for decades, and the GTM initiates this predicted response within the same decadal time period as the start of the Crediting Period, the leakage deduction schedule for the percent deduction of a Reporting Period, [math: L_{project, RP}] will be set at project initiation, and updated as needed by Isometric with the inclusion of any new site(s).
Further information on how Isometric implements the GTM and determines leakage is set forth in Appendix D, which contains additional information on the technical requirements of model implementation and the data sources used for modeling.
The average leakage rate, LR, for each forest type-region (see Appendix D3) in 10-year time periods is calculated as:
[math: LR_{f,r,t} = \rho_t \left[ \frac{(PCS_{project,f,r,t} - PCS_{counterfactual,f,r,t}) - (GCS_{project,f,r,t} - GCS_{counterfactual,f,r,t})}{(PCS_{project,f,r,t} - PCS_{counterfactual,f,r,t})} \right]]
(Equation 3)
Where:
The Project’s leakage for each Reporting Period, [math: CO_2e_{leakage, RP}] is then calculated using the following equations:
[math: L_{project,t} = \frac{\sum B_{f,r,t} \times LR_{f,r,t}}{B_{project,t}}]
(Equation 4)
Where:
And, [math: B_{project,t}] must equal [math: B_{f,r,t}]
As the GTM outputs leakage estimates on decadal timesteps, Isometric will then annualize the project leakage, applying a linear interpolation between decadal estimates to derive the leakage deduction for each year within a decade:
[math: L_{project, RP} = L_{project, 0} + \frac{t}{10}\left( L_{10}-L_0 \right) ]
(Equation 5)
Where:
The final leakage deduction for each Reporting Period, [math: CO_2e_{leakage, RP}] can then be calculated as a percentage of the sequestered carbon in a Reporting Period, [math: CO_2e_{Stored, RP}] (see Section 9.1).
[math: CO_2e_{leakage, RP} = L_{project, RP} \times CO_2e_{Stored, RP} ]
(Equation 6)
If the leakage rate, [math: L_{project, RP}], is <10%, the minimum annual leakage deduction of 10% will be applied by Isometric. When positive leakage is predicted by a negative leakage rate ([math: L_{project, RP} < 0]%), it will be subject to the minimum annual leakage deduction of 10%. Positive leakage indicates a model which predicts that restricting supply in the region could increase timber prices, and incentivize more considered forest management or land to be converted to forest.
Given the distributed nature of improved forest management projects under this Module, the Project Proponent may have project sites with variable contract lengths for the intervention of deferred harvest and forest management practices, often with multiple enrolled landowners. As described in Section 10.1, this creates challenges for the permanence claim and issuance of Credits with verifiable additionality.
Projects crediting under this Module must apply the permanence factor, [math: PF], calculated in Section 10.1.1 to the stored carbon for each Reporting Period, [math: RP], to derive the proper value of [math: CO_2e_{Stored, RP}] for Credit issuance under the Improved Forest Management Protocol following Equation 7.
[math: CO_2e_{stored,RP} = \sum_{i=1}^{n} ARPF_i \times PF_i \times \left( CO_2 e_{AGB,RP,i} + CO_2 e_{BGB,RP,i} \right)]
(Equation 7)
Where:
To derive [math: CO_2e_{AGB, RP, i}] and [math: CO_2e_{BGB, RP, i}], Project Proponents must calculate [math: CO_2e_{AGB, RP}] and [math: CO_2e_{BGB, RP}] for the parcel(s) under each individual contract, [math: i], according to the guidance and requirements found in Sections 9.3.2 and 9.3.4 of the IFM Protocol, respectively.
Isometric encourages Project Proponents to quantify the total above-ground biomass, [math: M_{AGB}], in the project area at time point, [math: t], through one of the Capture & Conversion Modules listed in the IFM Protocol (see Section 9.3.3).
[/R-WA83-0]Alternatively, subject to meeting all the requirements set forth in Section 9.2.1, Projects may elect to quantify [math: M_{AGB}(t)] through an alternative growth-disturbance model. Projects electing to use a growth-disturbance model must obtain approval from Isometric at Validation based on a comprehensive evaluation of the methodology in accordance with the requirements in Sections 9.2.1.1 and 9.5.
[/G-The total aboveground woody biomass, [math: M_{AGB}], at the current timepoint, [math: t], can be derived via modeling of the change in biomass due to growth and disturbance (loss) since the most recent direct measurement of biomass within the project area (Figure 4). The model must be developed using national or sub-national forest inventory (NFI), or other publicly-available inventory (e.g., research, industry) plot data from the region, which may be derived from field plot measurements, LiDAR measurements, or some combination thereof. Hereafter these will be collectively referred to as NFI data for simplicity, but eligible datasets are broader as noted.
Figure 4. Visualization of growth-disturbance model for no-disturbance (left) and disturbance (right) scenarios. In the distrubance scenario, the biomass credited (green) is derived by imputing the loss from a disturbance event (grey) from the measured biomass.
To apply the model across the project area, the project area should be subdivided in a manner than corresponds to the NFI dataset which is being used for model development. For example, if rasterized NFI data are being used, the project area should be divided into pixels of equivalent or greater (up to 100%) size. If NFI field plots are being used, sampled field plots of equivalent size should be used within the project area as the basis for assessment.
Under this approach, the Project’s total aboveground woody biomass, [math: M_{AGB}], at the current time point, [math: t], can be calculated as:
[math: M_{AGB,i}(t) = M_{AGB,i}(t_m) + \int_{t_m}^t G_i \, dt + \int_{t_m}^t D_i \, dt]
(Equation 8)
Where:
To determine the project-level aboveground biomass, the mean across the project area sub-units should be used:
[math: M_{AGB}(t) = \frac{\sum_{i=1}^{n} M_{AGB,i}(t)}{n}]
(Equation 9)
Where:
To apply the growth-disturbance model described in Equation 8, Projects must meet all of the applicability requirements in Sections 9.2.1.2 and 9.2.1.3 for the respective model components.
Project Proponents demonstrate that the growth-disturbance model development and performance meets the following requirements:
Growth in the project area can be assessed through the development of models which estimate growth based on observed growth measured in NFI data for similar areas.
Project Proponents must meet all of the following requirements for the development and application of such growth model(s):
If using NFI data with spatially disjoint sub-plots, Project Proponents should aggregate these sub-plots according to the guidance and design of the NFI dataset.
Absolute change is then imputed on an annual basis for each sub-area plot matches, and those annualized growth estimates are added to the latest direct measurement-derived estimate of the pixel (Equation 8).
Disturbance models may be used to estimate forest carbon loss stocks since the last field survey or LiDAR acquisition. Disturbance models both detect the occurrence of a loss within the project area, and estimate the carbon stock changes associated with the loss. The loss model is developed based on relevant observations of post-disturbance biomass change from NFI data or other publicly available datasets.
Project Proponents must meet all of the following requirements for model development and performance to use any disturbance model(s):
Project Proponents are encouraged to leverage commercial high-resolution imagery, spaceborne multi-spectral imagery (e.g., Sentinel-2), and/or synthetic-aperture radar SAR (e.g., ALOS or NISAR) in the development of their disturbance model(s).
[/G-In the event of a disturbance, the corresponding carbon stock must then be subtracted from the sub-unit biomass, and sub-unit plot matches must be remapped for subsequent years (Equation 8).
At the initial enrollment of a parcel, and at a minimum frequency of every 10 years thereafter throughout the Crediting Period, the Project Proponent will collect field-based or LiDAR-derived estimates of carbon stock on the parcel to establish [math: M_{AGB,i} (t_m)]. Further, if the modelled losses over the entire project area are greater than 60% of the modeled growth over the entire project area for a given Reporting Period, direct measurements must be made for that Reporting Period. Project Proponents must quantify this carbon stock through one of the following two approved Capture & Conversion Modules:
The following section outlines the workflow that Isometric will take for the calculation of [math: CO_2e_{Counterfactual, RP}] via a dynamic baseline approach; the Project Proponent is not responsible for carrying out the steps in this section. Project Proponents may suggest areas that could constitute suitable control pixels or features for matching based on their expert knowledge of their unique system. However, the ultimate determination of control pixels will be done by Isometric following the procedure and criteria below.
At Validation, the control plot zone must meet the following eligibility criteria:
If possible, other features should also be matched between the control plot zone and project area, such as:
Initially, the zone for eligible control plots should be limited to the same state or province, or a 100 km band around the project area — whichever is smaller. However, if suitable matches (see Section 9.3.3) are not found in this zone, additional step-outs in 10 km increments may occur to find appropriate control plots, assuming they meet the criteria above. The final area determined for suitable control plots is hereafter referred to as the Donor Zone.
Once the boundaries of the Donor Zone are determined, Isometric will generate high-resolution (≤30 m) pixel maps representing forest carbon stocks or a suitable proxy for forest carbon stocks. These layers must cover the entire project area and Donor Zone at the same resolution for at least five historical time points relative to the start of the Project. Each historical time point must be separated by at least 1 year.
Isometric will select a suitable proxy that meets the following criteria:
Project and control plot pixels will be matched by Isometric at Validation, and these matches will be retained and tracked over the Crediting Period. In this procedure a representative random sample of project pixels are matched to control plot pixels based on a mix of current and historical time series of environmental, socioeconomic, management, and forest structure data — including the selected forest carbon proxy, [math: C_{proxy}], of each pixel — using k-nearest neighbors without replacement, or an alternative justified algorithm. Isometric shall use, at minimum and where possible, the datasets in Table 2 for matching.
Project and control plot pixels will be matched within the same parcel classes (see Section 4.1.1.2). To reduce spatial autocorrelation, a maximum of 15 control plot pixels are permitted within individual non-project parcels. Additionally, a minimum of one project pixel will be selected for each hectare of project area.
This matching will use, at a minimum, five historical time points capturing at least the five years prior to project initiation. Each selected project pixel must be matched to a minimum of 5 different control pixels, and the mean of each dataset over the group of control pixels is used — with the mean forest carbon proxy over the group of control pixels derived from the map product created using the procedure described in Section 9.3.2.
Table 2. Dynamic baseline pixel matching Criteria and clustering.
| Cluster Category | Variable | Potential Datasets |
|---|---|---|
| Forest Structure | Carbon proxy | LiDAR-derived biomass estimates; Forest inventory carbon models; Remote sensing carbon proxies (see Section 9.2) |
| Stem density | LiDAR point cloud analysis; Forest inventory plot data; Airborne laser scanning metrics | |
| Species composition | Forest type classification maps; Vegetation surveys; Hyperspectral imagery analysis | |
| Number of years since last disturbance | Forest management records; Disturbance history databases; Landsat time series analysis | |
| Environmental | Elevation (≤30m resolution) | SRTM 30m DEM; ASTER Global DEM; National elevation datasets |
| Slope (≤30m resolution) | DEM-derived slope calculations; USGS National Elevation Dataset; Terrain analysis products | |
| Climate Zone | Köppen climate classification maps; World climate zone datasets; Regional climate atlases | |
| Site productivity | USGS SSURGO soil classification; Forest site index databases; Soil productivity indices | |
| Socioeconomic | Land value estimates | Nolte, et. al. 2020; Property tax assessments; Real estate valuation models |
| Distance to timber mills | Forest industry facility databases; Mill location registries; Economic facility datasets | |
| Distance from roads and/or navigable waterways | OpenStreetMap road networks; National transportation databases; Road density maps | |
| Distance from urban development | Urban boundary datasets; Land use/land cover maps; Census urbanized area files | |
| Geographic distance | Coordinate-based distance calculations; Spatial proximity analysis; GIS distance tools | |
| Parcel size | Cadastral boundary datasets; Property parcel databases; Land registry records |
All dynamic remotely sensed variables must be captured within 18 months of each Verification.
For individual project pixels, the change in carbon stock over the Reporting Period is calculated both for the project pixel and for the collection of corresponding control pixels (taking the mean across the group) using the values from the carbon proxy map:
[math: \Delta C_{proxy}=C_{proxy}(t_2)- C_{proxy}(t_1) ]
(Equation 10)
Where:
The estimated carbon removal of the counterfactual scenario is found by scaling the quantified carbon removal in the project area by the ratio of the mean differences of the proxy change between the project and control pixels:
[math: CO_2e_{Counterfactual,RP} = CO_2e_{Stored,RP} \times \frac{\mu_{\Delta C_{proxy,project}} - \mu_{\Delta C_{proxy,difference}}}{\mu_{\Delta C_{proxy,project}}}]
(Equation 11)
Where:
To meet the additionality condition, the change in proxy value in the project area, [math: \Delta C_{proxy, project}], must be statistically greater (p < 0.05, inclusive of uncertainty) than the mean change in proxy value for the matched control pixels, [math: \Delta C_{proxy, control}]. If the mean proxy change in the control pixels is negative such that the resulting product of Equation 11 is negative, the counterfactual carbon storage, [math: CO_2e_{Counterfactual,RP}], will be assumed to be 0 in order to ensure the accounting of carbon storage is limited to removals above the harvest counterfactual.
The counterfactual carbon storage is then used to calculate a performance benchmark for the project area, [math: PB_{RP}]:
[math: PB_{RP}= \frac{CO_2e_{Removed, RP}}{CO_2e_{Counterfactual, RP}}]
(Equation 12)
If the additionality requirement is met, the performance benchmark will be greater than 1, with larger magnitudes indicating a greater difference between storage in the Project and control areas.
At each Verification, the control pixels are reviewed to determine continued eligibility around similar regulatory regimes within control plots as outlined in Section 9.3.1. In the event that control pixel matches are no longer suitable, replacements will be selected for the impacted project pixels. For example, a change in regulations around IFM projects which leads to variance in the legality of such projects between control and project pixels would lead to rematching of control and project pixels.
Isometric will account for uncertainty in the dynamic baseline to obtain a conservative estimate of [math: CO_2e_{Counterfactual, RP}] in Equation 11. This will include an evaluation of at least the following sources of uncertainty:
[math: CO_2e_{Emissions, RP}] is the total GHG emissions associated with a given Reporting Period, RP.
Equations and emissions calculation requirements for [math: CO_2e_{Emissions, RP}] are set out in the relevant Protocol and are not repeated in this Module.
As part of [math: CO_2e_{Emissions, RP}], [math: CO_2e_{Leakage, RP}] must be quantified for the Reporting Period. [math: CO_2e_{Leakage, RP}] is quantified following the approach described in Section 8.2.
Any models used under this Protocol which contribute to the quantification of net CDR must be well-validated and skillful for the purpose that they were used for. Proof of model validation can be achieved through either:
Any models developed or used for the purpose of AGB quantification using the methodology described in Section 9.2.1 must also be evaluated and approved by Isometric at Validation in line with the requirements set forth in Section 9.2.1.1.
Harvest deferral activities require customized contract commitments to achieve additionality. Contract durations must be calibrated to avoid the issuance of Credits for Projects where similar forest growth would have occurred under baseline conditions. Often, short-term harvest deferrals result in the crediting of young forest growth that would have occurred within the existing forest management practice.
Conversely, contract periods that significantly exceed the original harvest timeline may fail to demonstrate financial additionality. Extended commitments may only attract landowners who have already incorporated conservation objectives independent of carbon market incentives into their financial and forest management plans15, 16, 17, 18.
This Module’s additionality framework therefore requires customized contract durations that defer planned harvest activities and incentivize enhanced forest management practices in line with the additionality requirements set forth in the Isometric Standard and Improved Forest Management Protocol.
Within aggregated smallholder sites, individual parcels must demonstrate varying deferral periods aligned with their specific baseline management scenarios. This variability in contract lengths, ranging from 20 to 50 years within a single Project, presents challenges for the application of a uniform durability claim and for Credit standardization for IFM through deferred harvest.
To ensure consistent durability across all Credits issued under this Module, a conservative discount factor will be applied to normalize permanence to a standardized 50-year time horizon. Credits issued from contracts with durations shorter than 50 years will receive proportional deductions through this approach to meet an equivalent 50-year durability claim.
The permanence factor, PF, is calculated as the contract length divided by 50 years.
[math: PF_i = \frac{contract\ length_i}{50}]
(Equation 13)
Where:
The permanence factor approach assumes a linear relationship for normalizing different contract lengths representing removals of different lengths. This linear relationship is grounded in the proportional relationship between cumulative emissions and temperature increase which has been well-demonstrated in climate literature and which is distinct from equivalency approaches used elsewhere in the voluntary carbon market to align short-term removals with long-term impacts of emissions19.
This durability framework ensures that all Credits achieve equivalent permanence regardless of individual contract variations, while maintaining scientific rigor and conservative assessment consistent with the Isometric Standard and Improved Forest Management Protocol requirements for additionality and long-term carbon removal and storage. While shorter contract lengths will not result in 50 years of carbon storage, this approach ensures the crediting will reflect the proportional, putative physical climate impact that is derived from storage time and amount.
The Risk Assessment is used to assess the overall delivery and storage risk associated with improved forest management through deferred harvest and may inform the Buffer Pool contribution during Credit delivery (see IFM Protocol Section 10.4 of the Improved Forest Management Protocol). The assessment must first be filled in by the Project Proponent and must be validated by a VVB. During project Validation, discrepancies between the Project Proponent’s self reported score and VVB may result in monitoring or risk mitigation activities, or project ineligibility. Eligible projects must have an initial risk score ≤ 16 and initial risk category scores at or below the following thresholds:
All risk categories shall have a minimum score of 0, regardless of the outcome of the Risk Assessment.
If Project Proponents choose to forgo a flat 20% Buffer Pool contribution, this Risk Assessment will inform Buffer Pool contributions for the Project according to the process outlined in Appendix C for each Reporting Period and in accordance with the requirements in the IFM Protocol (see Section 10.4.1).
Table A1. Risk Assessment for Improved Forest Management projects applying deferred harvest on smallholder land, with the score to be filled out for each question.
| Risk Category | Risk Indicator | Evidence | Scoring Guidelines | Score |
|---|---|---|---|---|
| Project Proponent Capacity Risk | Does the Project Proponent maintain staff with domain expertise relevant for forest carbon projects? (e.g., forest ecology, forest measurement, carbon accounting) | Project's team structure | If no, describe how gaps in relevant expertise will be filled, +1. | |
| Does the Project Proponent maintain a staff presence in the local vicinity (within one day of travel) of the project site? | Project's team structure | If no, +2. | ||
| Was the Project Proponent established more than 12 months ago? | Project Proponent declaration | If no, +1. | ||
| Does the Project Proponent have prior experience in improved forest management, carbon projects or planting? | Review of Project Proponent provided evidence and independent research | If yes, -1. | ||
| Has the Project Proponent abandoned or failed previous projects? | Review of projects on other registries | If yes, +3. | ||
| What proportion of the project area requires active enforcement against external threats (e.g., illegal logging, agricultural encroachment, unauthorized grazing) to protect carbon stocks? | Peer-reviewed publications, local or national government databases, NGO reports and assessments, site security assessment, satellite data, data on enforcement from other reforestation projects in the same region, local or national reports on environmental crimes or violations | If > 50% of project area, +2. If 25 to 50% of project area, +1. If no active enforcement required, -1. | ||
| Has the Project obtained non-delivery risk insurance | Project Proponent declaration | If yes, -1. | ||
| Financial Viability Risk | Has the Project secured funding to cover all activities required before carbon revenue accrues? | Project financial plan | If > 90%, -2. If > 80%, -1. If < 50%, +1. If < 30%, +2. If < 10%, fail. | |
| What is the projected time to reach financial breakeven? | Project financial plan | If > 20 years, fail. If 15 to 20 years, +3. If 10 to 15 years, +2. If 5 to 10 years, +1. | ||
| Is the budget reasonable given the proposed project activities and ex-ante estimates for forest growth? Budget should at minimum include: personnel, equipment and supplies, infrastructure, travel and certification fees. | Project financial plan | If no, +2. | ||
| Does the Project financial plan rely on future increases in market price for Carbon Credits? | Project financial plan | If yes, +1. | ||
| Has the Project obtained non-delivery risk insurance | Project Proponent declaration | If yes, -2. | ||
| Social Governance Risk | Are there currently or have there been disputes over land ownership over the last 20 years? | Jurisdictional history | If yes, + 2. | |
| Does the government have a history of revoking legal agreements regarding land ownership, access, and usage? | Jurisdictional history | If yes, + 2. | ||
| Does the Project host country score below the 40th percentile on 3+ of the Worldwide Governance Indicators over the last 10 years? | Worldwide Governance Indicators | If yes, +2. | ||
| Does the government have an NDC in place that addresses corresponding adjustments/prevents of project Credits and NDC contributions? | National registries | If no, +1. | ||
| Does the Project have a detailed benefit-sharing plan that includes: clear distribution mechanisms, transparent criteria for beneficiary selection, a grievance resolution process, monitoring and reporting procedures? | Project financial plan | If no, +2. If missing elements, +1. If legally binding with all elements, -1. If audited by 3rd party with all elements, -1 | ||
| Does the Project Proponent have a presence on human rights, environmental or labor infraction lists? | National registries | If yes, fail. | ||
| Does the Project Proponent have ongoing legal disputes? | National registries | If yes, +1. | ||
| Have projects on Indigenous or Community Lands been identified? | Cross reference project documentation with Global Forest Watch | If no, fail. | ||
| Has the Project Proponent developed and/or procured a forest management plan for enrolled landowners | Project Design Document and/or Project Proponent Declaration | If no, +1. | ||
| Are opportunity cost risk mitigations in place? | Legal agreements protecting carbon stocks, Non-profit status documentation, grant/funding agreements | Legally protected for Crediting Period, -1. Legally protected for ≥ 100 years, -2. Non-profit status or secured additional funding, -1. | ||
| Disturbance Risk | Fire risk | Mean daily Global Fire Weather Index over the prior two years | If > 10, +1. If > 30, +2. If > 50, +3. If > 75, fail. | |
| Pest and disease outbreak risk | Regional third-party maps, if available. | If high, +2. If medium, +1. If low, 0. | ||
| Extreme weather (temperature - heat and cold) | IPCC AR620 - See Appendix B for scoring | If high, +2. If medium, +1. If low, 0. | ||
| Extreme weather (hydrologic - flood and drought) | IPCC AR620 - See Appendix B for scoring | If high, +2. If medium, +1. If low, 0. | ||
| Windfall risk | Regional third-party maps, if available. | If high, +2. If medium, +1. If low, 0. | ||
| Icestorm risk | Regional third-party maps, if available. | If high, +2. If medium, +1. If low, 0. | ||
| Coastal risks (sea level rise, storm surge, tropical cyclones, salinity intrusion) | Regional third-party maps, if available. | If high, +2. If medium, +1. If low, 0. | ||
| Geologic risks (earthquakes, tsunami, volcanoes) | NOAA NCEI Natural Hazards viewer | If historical hazards in area, +1. | ||
| Illegal timber risk | Country IDAT risk score | If high, +2. If medium, +1. If low, 0. | ||
| Surrounding anthropogenic activities pose environmental risk (e.g., toxic pollution, industrial farming, new developments etc.) | Satellite imagery, site visit | If yes, +1. | ||
| Ecological Resilience | Project Design Document | If > 80% of trees heat/drought tolerant, -1. |
The following section outlines how Isometric calculates the indicator scores for climate-related extreme weather disturbance risks to carbon permanence within a project’s region (Figure B1). Project Proponents should use Table B1 to lookup the Isometric-calculated values for their project's region and include those scores in their Risk Assessment (see Appendix A).
Extreme weather risks are assessed via two indicators: a temperature indicator (extreme heat and/or cold) and a hydrologic indicator (flooding and/or drought). Each indicator includes both historical data (1961-2015) and projected future extreme events. Historical data indicate the likelihood of extreme events based on past climate patterns, e.g., projects in regions with extended dry periods are expected to experience increased water stress as part of their typical climate. Climate model projections describe how changing climate conditions, relative to historical patterns, might present an increased risk of disturbances. Areas where there is a larger shift towards extreme conditions under future climate relative to their historical baseline have a greater disturbance risk (e.g., drier conditions relative to historical averages increases risk for drought-driven mortality).
To calculate the scores in Table B1, Isometric uses values from the Intergovernmental Panel on Climate Change AR6 report21. The temperature indicator, IndicatorTemperature, is calculated using data describing the annual number of frost days (FD, minimum temperature below 0°C) and annual number of days with a maximum temperature above 40°C (TX40) to capture extreme cold and extreme heat risks, respectively. The hydrologic indicator, IndicatorHydrological, is calculated using data describing the maximum 5-day precipitation (RX5Day) and annual maximum number of consecutive dry days (CDD) to capture risks of flooding and drought, respectively. All values come from the CMIP6 climate models. Future projections use the SSP2-4.5 medium term (2041-2060) scenario and are assessed as the change in value relative to a historical baseline (1961-1990).
For each indicator subcomponent, the region’s terrestrial median value is compared with the global terrestrial distribution of the same variable (Table B2). To convert the regional value into a subscore, regional values below the global 75th percentile are considered Low Risk, regional values equal to or greater than the global 75th percentile but below the 90th percentile are Medium Risk, and any regional values equal to or greater than the global 90th percentile are High Risk. Low Risks are given a subscore of 0, Medium Risks are 0.25, and High Risks are 0.5. The overall score for each of the indicators is calculated by summing the corresponding subscores, as described below:
Projected change in the number of frost days is not included as a subcomponent since it is projected that they will decline under future climate across the globe, representing a low risk of future extreme cold events.
Figure B1. Map and lookup table for IPCC regional codes
Table B1. Regional Lookup Table of Disturbance Risk
| Region | Indicator | Variable | Time | Value | Risk | Score | Total |
|---|---|---|---|---|---|---|---|
| NW North America (NWN) | Temperature | Frost Days | Historical | 224.2 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0 | Low | 0 | ||||
| Hydrological | CDD | Historical | 23.5 | Low | 0 | 0.5 | |
| Change (Days) | -1.4 | Low | 0 | ||||
| 5-Day Precip | Historical | 54.4 | Low | 0 | |||
| Change (%) | 11.9 | High | 0.5 | ||||
| NE North America (NEN) | Temperature | Frost Days | Historical | 242.9 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0 | Low | 0 | ||||
| Hydrological | CDD | Historical | 25.1 | Low | 0 | 0.5 | |
| Change (Days) | -2.7 | Low | 0 | ||||
| 5-Day Precip | Historical | 50.6 | Low | 0 | |||
| Change (%) | 11.8 | High | 0.5 | ||||
| Western North America (WNA) | Temperature | Frost Days | Historical | 128.4 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 0.9 | Low | 0 | |||
| Change (Days) | 2 | Low | 0 | ||||
| Hydrological | CDD | Historical | 40.6 | Low | 0 | 0 | |
| Change (Days) | -0.2 | Low | 0 | ||||
| 5-Day Precip | Historical | 67.3 | Low | 0 | |||
| Change (%) | 5.5 | Low | 0 | ||||
| Central North America (CNA) | Temperature | Frost Days | Historical | 104.9 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 4.7 | Low | 0 | |||
| Change (Days) | 8.9 | Low | 0 | ||||
| Hydrological | CDD | Historical | 23.7 | Low | 0 | 0.25 | |
| Change (Days) | -0.4 | Low | 0 | ||||
| 5-Day Precip | Historical | 84.3 | Medium | 0.25 | |||
| Change (%) | 6.7 | Low | 0 | ||||
| Eastern North America (ENA) | Temperature | Frost Days | Historical | 116 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 0.1 | Low | 0 | |||
| Change (Days) | 0.6 | Low | 0 | ||||
| Hydrological | CDD | Historical | 15.5 | Low | 0 | 0.75 | |
| Change (Days) | -0.3 | Low | 0 | ||||
| 5-Day Precip | Historical | 89.4 | High | 0.5 | |||
| Change (%) | 9 | Medium | 0.25 | ||||
| Northern Central America (NCA) | Temperature | Frost Days | Historical | 15.9 | Low | 0 | 0 |
| Days > 40°C | Historical | 4.7 | Low | 0 | |||
| Change (Days) | 8.7 | Low | 0 | ||||
| Hydrological | CDD | Historical | 51.6 | Low | 0 | 0.5 | |
| Change (Days) | -0.2 | Low | 0 | ||||
| 5-Day Precip | Historical | 92.9 | High | 0.5 | |||
| Change (%) | 6 | Low | 0 | ||||
| Southern Central America (SCA) | Temperature | Frost Days | Historical | 0.1 | Low | 0 | 0 |
| Days > 40°C | Historical | 0.5 | Low | 0 | |||
| Change (Days) | 1.7 | Low | 0 | ||||
| Hydrological | CDD | Historical | 39.7 | Low | 0 | 0.5 | |
| Change (Days) | -1.6 | Low | 0 | ||||
| 5-Day Precip | Historical | 134.3 | High | 0.5 | |||
| Change (%) | 4.3 | Low | 0 | ||||
| Caribbean (CAR) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0 | Low | 0 | ||||
| Hydrological | CDD | Historical | 24.3 | Low | 0 | 0.5 | |
| Change (Days) | 0.1 | Low | 0 | ||||
| 5-Day Precip | Historical | 99.9 | High | 0.5 | |||
| Change (%) | 0.8 | Low | 0 | ||||
| NW South America (NWS) | Temperature | Frost Days | Historical | 0.6 | Low | 0 | 0 |
| Days > 40°C | Historical | 0.1 | Low | 0 | |||
| Change (Days) | 0.8 | Low | 0 | ||||
| Hydrological | CDD | Historical | 28.6 | Low | 0 | 0.5 | |
| Change (Days) | -0.2 | Low | 0 | ||||
| 5-Day Precip | Historical | 133 | High | 0.5 | |||
| Change (%) | 7.5 | Low | 0 | ||||
| Northern South America (NSA) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
| Days > 40°C | Historical | 0.5 | Low | 0 | |||
| Change (Days) | 9.4 | Low | 0 | ||||
| Hydrological | CDD | Historical | 46.7 | Low | 0 | 1 | |
| Change (Days) | 9.7 | High | 0.5 | ||||
| 5-Day Precip | Historical | 111.6 | High | 0.5 | |||
| Change (%) | 5.5 | Low | 0 | ||||
| NE South America (NES) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
| Days > 40°C | Historical | 0.3 | Low | 0 | |||
| Change (Days) | 4.4 | Low | 0 | ||||
| Hydrological | CDD | Historical | 95 | High | 0.5 | 1.5 | |
| Change (Days) | 6.3 | High | 0.5 | ||||
| 5-Day Precip | Historical | 144.3 | High | 0.5 | |||
| Change (%) | 5.7 | Low | 0 | ||||
| South America-Monsoon (SAM) | Temperature | Frost Days | Historical | 7.5 | Low | 0 | 0.25 |
| Days > 40°C | Historical | 2.2 | Low | 0 | |||
| Change (Days) | 11.5 | Medium | 0.25 | ||||
| Hydrological | CDD | Historical | 64.8 | Medium | 0.25 | 1.25 | |
| Change (Days) | 13.7 | High | 0.5 | ||||
| 5-Day Precip | Historical | 133.7 | High | 0.5 | |||
| Change (%) | 6 | Low | 0 | ||||
| SW South America (SWS) | Temperature | Frost Days | Historical | 35.8 | Low | 0 | 0 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0 | Low | 0 | ||||
| Hydrological | CDD | Historical | 65.9 | Medium | 0.25 | 0.5 | |
| Change (Days) | -4.2 | Low | 0 | ||||
| 5-Day Precip | Historical | 83 | Medium | 0.25 | |||
| Change (%) | -0.9 | Low | 0 | ||||
| SE South America (SES) | Temperature | Frost Days | Historical | 16.6 | Low | 0 | 0 |
| Days > 40°C | Historical | 2.8 | Low | 0 | |||
| Change (Days) | 5.5 | Low | 0 | ||||
| Hydrological | CDD | Historical | 36.3 | Low | 0 | 0.75 | |
| Change (Days) | 0.4 | Medium | 0.25 | ||||
| 5-Day Precip | Historical | 105.1 | High | 0.5 | |||
| Change (%) | 8.4 | Low | 0 | ||||
| Southern South America (SSA) | Temperature | Frost Days | Historical | 75.4 | Low | 0 | 0 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0.1 | Low | 0 | ||||
| Hydrological | CDD | Historical | 20.5 | Low | 0 | 0.5 | |
| Change (Days) | 1.6 | High | 0.5 | ||||
| 5-Day Precip | Historical | 57.5 | Low | 0 | |||
| Change (%) | 3.4 | Low | 0 | ||||
| Northern Europe (NEU) | Temperature | Frost Days | Historical | 150.3 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0 | Low | 0 | ||||
| Hydrological | CDD | Historical | 18.5 | Low | 0 | 0.25 | |
| Change (Days) | 0 | Low | 0 | ||||
| 5-Day Precip | Historical | 52.8 | Low | 0 | |||
| Change (%) | 10 | Medium | 0.25 | ||||
| Western & Central Europe (WCE) | Temperature | Frost Days | Historical | 109.7 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 0.1 | Low | 0 | |||
| Change (Days) | 0.7 | Low | 0 | ||||
| Hydrological | CDD | Historical | 22.8 | Low | 0 | 0.5 | |
| Change (Days) | 1.3 | High | 0.5 | ||||
| 5-Day Precip | Historical | 55 | Low | 0 | |||
| Change (%) | 8.5 | Low | 0 | ||||
| Eastern Europe (EEU) | Temperature | Frost Days | Historical | 171.4 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 1 | Low | 0 | |||
| Change (Days) | 2.7 | Low | 0 | ||||
| Hydrological | CDD | Historical | 27.6 | Low | 0 | 0.5 | |
| Change (Days) | 1.1 | Medium | 0.25 | ||||
| 5-Day Precip | Historical | 42.9 | Low | 0 | |||
| Change (%) | 10 | Medium | 0.25 | ||||
| Mediterranean (MED) | Temperature | Frost Days | Historical | 27.6 | Low | 0 | 0.25 |
| Days > 40°C | Historical | 6 | Low | 0 | |||
| Change (Days) | 11.8 | Medium | 0.25 | ||||
| Hydrological | CDD | Historical | 75 | High | 0.5 | 1 | |
| Change (Days) | 6.7 | High | 0.5 | ||||
| 5-Day Precip | Historical | 49.5 | Low | 0 | |||
| Change (%) | 3.9 | Low | 0 | ||||
| Western Africa (WAF) | Temperature | Frost Days | Historical | 0 | Low | 0 | 1 |
| Days > 40°C | Historical | 24 | High | 0.5 | |||
| Change (Days) | 26.4 | High | 0.5 | ||||
| Hydrological | CDD | Historical | 83.5 | High | 0.5 | 1.25 | |
| Change (Days) | -0.4 | Low | 0 | ||||
| 5-Day Precip | Historical | 85.2 | Medium | 0.25 | |||
| Change (%) | 19.6 | High | 0.5 | ||||
| Central Africa (CAF) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0.25 |
| Days > 40°C | Historical | 11.1 | Medium | 0.25 | |||
| Change (Days) | 9.2 | Low | 0 | ||||
| Hydrological | CDD | Historical | 61.8 | Low | 0 | 0.75 | |
| Change (Days) | -0.1 | Low | 0 | ||||
| 5-Day Precip | Historical | 84.4 | Medium | 0.25 | |||
| Change (%) | 14.9 | High | 0.5 | ||||
| North Eastern Africa (NEAF) | Temperature | Frost Days | Historical | 0 | Low | 0 | 1 |
| Days > 40°C | Historical | 16.1 | High | 0.5 | |||
| Change (Days) | 15.4 | High | 0.5 | ||||
| Hydrological | CDD | Historical | 80.4 | High | 0.5 | 1 | |
| Change (Days) | -2.1 | Low | 0 | ||||
| 5-Day Precip | Historical | 64.6 | Low | 0 | |||
| Change (%) | 15.4 | High | 0.5 | ||||
| South Eastern Africa (SEAF) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0.3 | Low | 0 | ||||
| Hydrological | CDD | Historical | 78.5 | High | 0.5 | 1.5 | |
| Change (Days) | 0.4 | Medium | 0.25 | ||||
| 5-Day Precip | Historical | 91.9 | High | 0.5 | |||
| Change (%) | 9.7 | Medium | 0.25 | ||||
| West Southern Africa (WSAF) | Temperature | Frost Days | Historical | 1.2 | Low | 0 | 0 |
| Days > 40°C | Historical | 0.1 | Low | 0 | |||
| Change (Days) | 3 | Low | 0 | ||||
| Hydrological | CDD | Historical | 108.7 | High | 0.5 | 1.5 | |
| Change (Days) | 10.5 | High | 0.5 | ||||
| 5-Day Precip | Historical | 87.6 | High | 0.5 | |||
| Change (%) | 2 | Low | 0 | ||||
| East Southern Africa (ESAF) | Temperature | Frost Days | Historical | 2.7 | Low | 0 | 0 |
| Days > 40°C | Historical | 0.7 | Low | 0 | |||
| Change (Days) | 2.8 | Low | 0 | ||||
| Hydrological | CDD | Historical | 68.7 | Medium | 0.25 | 1.25 | |
| Change (Days) | 4.3 | High | 0.5 | ||||
| 5-Day Precip | Historical | 127.5 | High | 0.5 | |||
| Change (%) | 6 | Low | 0 | ||||
| Madagascar (MDG) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0.2 | Low | 0 | ||||
| Hydrological | CDD | Historical | 46.7 | Low | 0 | 0.5 | |
| Change (Days) | -1.2 | Low | 0 | ||||
| 5-Day Precip | Historical | 175.7 | High | 0.5 | |||
| Change (%) | 5.8 | Low | 0 | ||||
| Russian-Arctic (RAR) | Temperature | Frost Days | Historical | 271.3 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0 | Low | 0 | ||||
| Hydrological | CDD | Historical | 31.5 | Low | 0 | 0.5 | |
| Change (Days) | -4.3 | Low | 0 | ||||
| 5-Day Precip | Historical | 39.7 | Low | 0 | |||
| Change (%) | 16.7 | High | 0.5 | ||||
| West Siberia (WSB) | Temperature | Frost Days | Historical | 203.1 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 0.7 | Low | 0 | |||
| Change (Days) | 2 | Low | 0 | ||||
| Hydrological | CDD | Historical | 31.4 | Low | 0 | 0.25 | |
| Change (Days) | -0.4 | Low | 0 | ||||
| 5-Day Precip | Historical | 37.5 | Low | 0 | |||
| Change (%) | 10.8 | Medium | 0.25 | ||||
| East Siberia (ESB) | Temperature | Frost Days | Historical | 233.9 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0.1 | Low | 0 | ||||
| Hydrological | CDD | Historical | 34.1 | Low | 0 | 0.25 | |
| Change (Days) | -3.7 | Low | 0 | ||||
| 5-Day Precip | Historical | 54.5 | Low | 0 | |||
| Change (%) | 11.5 | Medium | 0.25 | ||||
| Russian-Far-East (RFE) | Temperature | Frost Days | Historical | 238.1 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0 | Low | 0 | ||||
| Hydrological | CDD | Historical | 28.8 | Low | 0 | 0.5 | |
| Change (Days) | -3.2 | Low | 0 | ||||
| 5-Day Precip | Historical | 65.1 | Low | 0 | |||
| Change (%) | 14.4 | High | 0.5 | ||||
| West Central Asia (WCA) | Temperature | Frost Days | Historical | 95.8 | High | 0.5 | 1.5 |
| Days > 40°C | Historical | 22 | High | 0.5 | |||
| Change (Days) | 17.5 | High | 0.5 | ||||
| Hydrological | CDD | Historical | 113.6 | High | 0.5 | 0.75 | |
| Change (Days) | -0.3 | Low | 0 | ||||
| 5-Day Precip | Historical | 42.5 | Low | 0 | |||
| Change (%) | 10 | Medium | 0.25 | ||||
| East Central Asia (ECA) | Temperature | Frost Days | Historical | 195.6 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 0.5 | Low | 0 | |||
| Change (Days) | 2.5 | Low | 0 | ||||
| Hydrological | CDD | Historical | 75.1 | High | 0.5 | 1 | |
| Change (Days) | -6.2 | Low | 0 | ||||
| 5-Day Precip | Historical | 30.5 | Low | 0 | |||
| Change (%) | 12.9 | High | 0.5 | ||||
| Tibetan-Plateau (TIB) | Temperature | Frost Days | Historical | 258.6 | High | 0.5 | 0.5 |
| Days > 40°C | Historical | 1.6 | Low | 0 | |||
| Change (Days) | 0.4 | Low | 0 | ||||
| Hydrological | CDD | Historical | 42.3 | Low | 0 | 0.75 | |
| Change (Days) | -2.6 | Low | 0 | ||||
| 5-Day Precip | Historical | 80.9 | Medium | 0.25 | |||
| Change (%) | 11.6 | High | 0.5 | ||||
| East Asia (EAS) | Temperature | Frost Days | Historical | 91.7 | Medium | 0.25 | 0.25 |
| Days > 40°C | Historical | 0.3 | Low | 0 | |||
| Change (Days) | 0.7 | Low | 0 | ||||
| Hydrological | CDD | Historical | 29.1 | Low | 0 | 0.75 | |
| Change (Days) | 0.1 | Low | 0 | ||||
| 5-Day Precip | Historical | 132 | High | 0.5 | |||
| Change (%) | 9.6 | Medium | 0.25 | ||||
| South Asia (SAS) | Temperature | Frost Days | Historical | 7.9 | Low | 0 | 1 |
| Days > 40°C | Historical | 33.1 | High | 0.5 | |||
| Change (Days) | 14.5 | High | 0.5 | ||||
| Hydrological | CDD | Historical | 93.9 | High | 0.5 | 1.5 | |
| Change (Days) | -3.3 | Low | 0 | ||||
| 5-Day Precip | Historical | 132.2 | High | 0.5 | |||
| Change (%) | 12 | High | 0.5 | ||||
| Southeast Asia (SEA) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
| Days > 40°C | Historical | 0.3 | Low | 0 | |||
| Change (Days) | 1.1 | Low | 0 | ||||
| Hydrological | CDD | Historical | 26.8 | Low | 0 | 0.75 | |
| Change (Days) | 0.8 | Medium | 0.25 | ||||
| 5-Day Precip | Historical | 168.4 | High | 0.5 | |||
| Change (%) | 7.3 | Low | 0 | ||||
| Northern Australia (NAU) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0.75 |
| Days > 40°C | Historical | 11.8 | Medium | 0.25 | |||
| Change (Days) | 20.4 | High | 0.5 | ||||
| Hydrological | CDD | Historical | 95.7 | High | 0.5 | 1.25 | |
| Change (Days) | 0.7 | Medium | 0.25 | ||||
| 5-Day Precip | Historical | 163.7 | High | 0.5 | |||
| Change (%) | 7.7 | Low | 0 | ||||
| Central Australia (CAU) | Temperature | Frost Days | Historical | 0.1 | Low | 0 | 1 |
| Days > 40°C | Historical | 27.8 | High | 0.5 | |||
| Change (Days) | 75.8 | High | 0.5 | ||||
| Hydrological | CDD | Historical | 27.3 | Low | 0 | 1 | |
| Change (Days) | 3.5 | High | 0.5 | ||||
| 5-Day Precip | Historical | 86.5 | High | 0.5 | |||
| Change (%) | 4.7 | Low | 0 | ||||
| Eastern Australia (AU) | Temperature | Frost Days | Historical | 1.4 | Low | 0 | 0 |
| Days > 40°C | Historical | 2.7 | Low | 0 | |||
| Change (Days) | 4.2 | Low | 0 | ||||
| Hydrological | CDD | Historical | 35.8 | Low | 0 | 0.5 | |
| Change (Days) | -0.5 | Low | 0 | ||||
| 5-Day Precip | Historical | 120.6 | High | 0.5 | |||
| Change (%) | 5.6 | Low | 0 | ||||
| Southern Australia (SAU) | Temperature | Frost Days | Historical | 1.3 | Low | 0 | 0 |
| Days > 40°C | Historical | 7.1 | Low | 0 | |||
| Change (Days) | 6.9 | Low | 0 | ||||
| Hydrological | CDD | Historical | 40.2 | Low | 0 | 0.5 | |
| Change (Days) | 2 | High | 0.5 | ||||
| 5-Day Precip | Historical | 60.3 | Low | 0 | |||
| Change (%) | 2.8 | Low | 0 | ||||
| New Zealand (NZ) | Temperature | Frost Days | Historical | 5.9 | Low | 0 | 0 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0 | Low | 0 | ||||
| Hydrological | CDD | Historical | 13.8 | Low | 0 | 0.75 | |
| Change (Days) | 0.5 | Medium | 0.25 | ||||
| 5-Day Precip | Historical | 92.3 | High | 0.5 | |||
| Change (%) | 5.6 | Low | 0 | ||||
| South Pacific Ocean (SPO) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
| Days > 40°C | Historical | 0 | Low | 0 | |||
| Change (Days) | 0 | Low | 0 | ||||
| Hydrological | CDD | Historical | 19.4 | Low | 0 | 0.5 | |
| Change (Days) | -0.5 | Low | 0 | ||||
| 5-Day Precip | Historical | 183.3 | High | 0.5 | |||
| Change (%) | 3.8 | Low | 0 |
Table B2. Global Benchmark Values for Extreme Weather Risks.
| Time Frame | Variable | Median | 75th% | 90th% |
|---|---|---|---|---|
| Historical | Frost Days | 89.8 | 94.1 | 97.5 |
| Days Max Temp > 40°C | 9.9 | 15.1 | 21.9 | |
| Consecutive Dry Days | 63.7 | 71 | 76.2 | |
| Maximum 5-day Precipitation (mm) | 79.5 | 86 | 90.4 | |
| Projected Future | Frost Days | - | - | - |
| Days Max Temp > 40°C | 9.9 | 11.8 | 14.6 | |
| Consecutive Dry Days | 0.3 | 1.1 | 1.8 | |
| Maximum 5-day Precipitation (%) | 8.9 | 11.5 | 14.1 |
By default, Projects are subject to a flat 20% Buffer Pool contribution as outlined in the IFM Protocol (see Section 10.4.1) of the Improved Forest Management Protocol. Project Proponents may opt to calculate a project-specific Buffer Pool contribution based on the outputs of their Risk Assessment (Appendix A) for each Reporting Period.
The following steps are used to convert the outputs of the Risk Assessment into a Buffer Pool contribution:
Table C1. Risk score to Buffer Pool contribution conversion for each risk category.
| Risk Category | Cumulative Risk Score | Buffer Pool Contribution |
|---|---|---|
| Project Proponent Capacity Risk | 0 | 2.0 |
| 1 | 2.2 | |
| 2 | 2.7 | |
| 3 | 4.0 | |
| 4 | 6.0 | |
| 5 | 7.3 | |
| 6 | 7.8 | |
| 7 | 8.0 | |
| Financial Viability Risk | 0 | 2.0 |
| 1 | 2.2 | |
| 2 | 2.7 | |
| 3 | 4.0 | |
| 4 | 6.0 | |
| 5 | 7.3 | |
| 6 | 7.8 | |
| 7 | 8.0 | |
| Social Governance Risk | 0 | 2.0 |
| 1 | 2.2 | |
| 2 | 2.5 | |
| 3 | 3.1 | |
| 4 | 4.3 | |
| 5 | 5.7 | |
| 6 | 6.9 | |
| 7 | 7.5 | |
| 8 | 7.8 | |
| 9 | 8.0 | |
| Disturbance Risk | 0 | 2.0 |
| 1 | 2.1 | |
| 2 | 2.3 | |
| 3 | 2.5 | |
| 4 | 2.8 | |
| 5 | 3.4 | |
| 6 | 4.1 | |
| 7 | 5.0 | |
| 8 | 5.9 | |
| 9 | 6.6 | |
| 10 | 7.2 | |
| 11 | 7.5 | |
| 12 | 7.7 | |
| 13 | 7.9 | |
| 14 | 8.0 |
The Buffer Pool contribution for each risk category is determined using a sigmoid function described by Equation C1. The Buffer Pool contribution for each risk category ranges from 2% to 8%.
[math: BP_{risk}=\frac{L}{1+e^{-k(x-x_0)}}+2]
(Equation C1)
Where:
The sigmoid function, Equation C1, applied to each risk category can also be visualized in Figure C1.
Figure C1. Buffer Pool contribution based on risk score for each risk category.
The Project has completed the Risk Assessment and obtained the following risk scores in a Reporting Period:
Mapping these risk scores to Table C1, the total Buffer Pool contribution for the Project is:
2.7% + 6.0% + 3.1% + 2.5% = 14.3%
This appendix provides detailed information on how Isometric implements the Global Timber Model (GTM) for leakage assessment in this Module, including technical requirements, scalar values, and forest type-regional classifications used in the modeling process.
Isometric implements the Global Timber Model (GTM) to quantify leakage deductions for each Reporting Period based on global runs of leakage rates for forest-type regions, annualized and weighted by the Project's basal area (see Section 8.2). Currently, Isometric uses a 2025 version of the GTM published by Dr. Adam Daigneault at the University of Maine, Dr. Brent Sohngen at Ohio State University, and collaborators from The Nature Conservancy.
Isometric currently applies the scalar values and parameters found below in Table D1 in its GTM implementation.
Table D1. Summary Table of Key Scalars in GTM for Protocol Leakage Assessment
| Scalar | Value | Same as used in Daigneault et al 2025 | Explanation |
|---|---|---|---|
| Annual discount rate | 5.00% | Yes | Commonly assumed to represent the average expected rate of return in the global forest sector |
| Global carbon project implementation rate | 4.00% | No | A conservative accounting of forest area unavailable for commercial harvest due to carbon projects; see detailed explanation below |
| Elasticity of management intensity to timber price | Variable | Yes | Set for each region |
| Elasticity of timber demand to timber price | -1.0 | Yes | Substitutions occur over decade-long timesteps in GTM, and in aggregate are more likely than single substitutions in single years. See Sohngen 2024 for additional explanation. |
| Elasticity of land area in forest to timber price | 0.3 | Yes | Based on Lubowski et al. 2006, Sohngen et al. 2019 |
To date, the GTM uses a global implementation rate representing the proportion of all forest land globally available for harvest that is enrolled in forest carbon projects. Isometric determines harvest eligibility by accessibility, i.e., all accessible forests are eligible. However, Isometric has excluded forest area reported in the FAO Forest Resources Assessment as "protected".
As of the certification of this Module, the total global area of land enrolled in forest carbon projects is 77.599 Mha, based on an analysis of all forest-based carbon projects recorded by Renoster as of June 3, 2024. This represents approximately 8% of the total available forest area, which is 982.5 Mha as of the certification of this Module.
Research by Haya et al. (2023)9, 22, Stapp et al. (2023)23, and others suggests that the majority of forest carbon projects greatly overestimate emission reductions and that enrollment in carbon projects has historically been unlikely to result in near-term reductions in harvesting or deforestation. Thus, Isometric applies a conservative estimate that the actual amount of carbon project land on which all harvest is prevented is 50% of the calculated implementation rate of 8%. Thus, Isometric applies a global carbon project implementation rate of 4% (Table D1).
Isometric will match the species present in the project area to a GTM forest type and region (Tables D2, D3). The forest types are based on the 2010 United Nations Food and Agriculture Organization (UN FAO) Global Ecological Zones (GEZs), with enhanced resolution for United States forest types based on USFS FIA Regional Groupings. If species or genus are not explicitly listed in the GTM forest types, consult authoritative sources including local foresters and Wood Database to determine similar commercial use and ecological characteristics for matching.
Table D2. Forest type and region classification table for the United States
| Forest Type | Region | Forest Type Description | Region Description |
|---|---|---|---|
| Pine | Southeast | Pine species grown naturally or commercially, including loblolly pine (Pinus taeda), slash pine (P. elliottii), longleaf pine (P. palustris), and shortleaf pine (P. echinata) | Virginia, North Carolina, South Carolina, Georgia, Florida, Alabama, Mississippi, Louisiana |
| EHW | Eastern non-oak hardwood species grown naturally or commercially, including sweetgum (Liquidambar styraciflua), yellow-poplar (Liriodendron tulipifera), and red maple (Acer rubrum) | ||
| Oak | Oak species (Quercus spp.) including white oak (Q. alba), northern red oak (Q. rubra), southern red oak (Q. falcata), and post oak (Q. stellata) | ||
| Pine | South-Central | Pine species including shortleaf pine (Pinus echinata), loblolly pine (P. taeda), and pitch pine (P. rigida) | Kentucky, Tennessee, Arkansas, Oklahoma |
| EHW | Eastern non-oak hardwood species including tulip tree (Liriodendron tulipifera), sweetgum (Liquidambar styraciflua), and black walnut (Juglans nigra) | ||
| Oak | Oak species including white oak (Quercus alba), black oak (Q. velutina), and chestnut oak (Q. prinus) | ||
| SF | Northeast | Spruce-fir forest types including red spruce (Picea rubens), balsam fir (Abies balsamea), and eastern hemlock (Tsuga canadensis) | Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York, Pennsylvania |
| Pine | Northern pine species including eastern white pine (Pinus strobus), red pine (P. resinosa), and jack pine (P. banksiana) | ||
| EHW | Eastern hardwood species including sugar maple (Acer saccharum), American beech (Fagus grandifolia), and yellow birch (Betula alleghaniensis) | ||
| Oak | Oak species including northern red oak (Quercus rubra), white oak (Q. alba), and chestnut oak (Q. prinus) | ||
| SF | North-Central | Spruce-fir complexes including white spruce (Picea glauca), balsam fir (Abies balsamea), and northern white-cedar (Thuja occidentalis) | Minnesota, Wisconsin, Michigan, Iowa, Missouri, Illinois, Indiana, Ohio |
| Pine | Northern pine species including red pine (Pinus resinosa), jack pine (P. banksiana), and eastern white pine (P. strobus) | ||
| EHW | Hardwood species including sugar maple (Acer saccharum), American basswood (Tilia americana), and northern red oak (Quercus rubra) | ||
| Oak | Oak species including bur oak (Quercus macrocarpa), white oak (Q. alba), and northern red oak (Q. rubra) | ||
| DF | West | Douglas-fir (Pseudotsuga menziesii) and associated coniferous species including western hemlock (Tsuga heterophylla) and grand fir (Abies grandis) | California, Oregon, Washington |
| Pine | Western pine species including ponderosa pine (Pinus ponderosa), sugar pine (P. lambertiana), and lodgepole pine (P. contorta) | ||
| HW | Western hardwood species including red alder (Alnus rubra), bigleaf maple (Acer macrophyllum), and Oregon white oak (Quercus garryana) | ||
| SW | Softwood species other than Douglas-fir and pine, including western redcedar (Thuja plicata), western hemlock (Tsuga heterophylla), and true fir species (Abies spp.) | ||
| HW | Southwest | Southwestern hardwood species including Fremont cottonwood (Populus fremontii), Arizona sycamore (Platanus wrightii), and Gambel oak (Quercus gambelii) | Texas, New Mexico, Arizona |
| DF | Douglas-fir (Pseudotsuga menziesii) adapted to southwestern growing conditions | ||
| Pine | Mountain | Mountain pine species including ponderosa pine (Pinus ponderosa), lodgepole pine (P. contorta), and limber pine (P. flexilis) | Montana, Wyoming, Colorado, Idaho, Nevada, Utah |
| HW | Mountain hardwood species including quaking aspen (Populus tremuloides), Rocky Mountain maple (Acer glabrum), and paper birch (Betula papyrifera) |
Table D3. Forest type and region classification table for Countries and regions excluding the United States.
| Forest Type | Region | Forest Type Description | Region Description |
|---|---|---|---|
| Plantation | China | Fast-growing plantation species including eucalyptus (Eucalyptus spp.), poplar (Populus spp.), and Chinese fir (Cunninghamia lanceolata) under intensive management | All administrative regions with forest plantation development |
| Natural | Natural forest ecosystems including mixed temperate forests, boreal forests, and subtropical forests with native species composition | ||
| S. Plt. | Brazil | Short-rotation plantation forestry focused on eucalyptus (Eucalyptus spp.) and pine (Pinus spp.) for industrial wood production | All states with plantation forestry operations |
| Trop. Evergreen | Tropical evergreen rainforest including Amazon rainforest with diverse hardwood species | Primarily Amazon Basin states | |
| Trop. Mixed | Tropical mixed forests including Atlantic Forest remnants and Cerrado formations | Atlantic coast regions and central Cerrado | |
| Atlantic/MW HW | Canada | Atlantic and Maritime hardwood forests including sugar maple (Acer saccharum), yellow birch (Betula alleghaniensis), and American beech (Fagus grandifolia) | Atlantic Maritime provinces |
| Atlantic/MW SW | Atlantic and Maritime softwood forests dominated by red spruce (Picea rubens), balsam fir (Abies balsamea), and eastern hemlock (Tsuga canadensis) | ||
| Atlantic/MW Mx | Atlantic and Maritime mixed forests combining hardwood and softwood species | ||
| Boreal/Hudson HW | Boreal hardwood forests including paper birch (Betula papyrifera), trembling aspen (Populus tremuloides), and balsam poplar (P. balsamifera) | Hudson Bay lowlands region | |
| Boreal/Hudson SW | Boreal softwood forests including black spruce (Picea mariana), white spruce (P. glauca), and jack pine (Pinus banksiana) | ||
| Boreal/Hudson MX | Boreal mixed forests combining deciduous and coniferous species in the Hudson Bay region | ||
| Taiga HW | Northern taiga hardwood species including paper birch (Betula papyrifera) and trembling aspen (Populus tremuloides) | Northern territories and provinces | |
| Taiga SW | Taiga softwood forests dominated by black spruce (Picea mariana) and tamarack (Larix laricina) | ||
| Taiga Mx | Taiga mixed forests combining hardwood and softwood species in extreme northern climates | ||
| Boreal West HW | Western boreal hardwood forests including trembling aspen (Populus tremuloides), balsam poplar (P. balsamifera), and paper birch (Betula papyrifera) | Western boreal regions | |
| Boreal West SW | Western boreal softwood forests including white spruce (Picea glauca), black spruce (P. mariana), and lodgepole pine (Pinus contorta) | ||
| Boreal West MX | Western boreal mixed forests representing transitions between hardwood and softwood stands | ||
| Boreal Montane HW | Montane boreal hardwood forests including trembling aspen (Populus tremuloides) and paper birch (Betula papyrifera) | Rocky Mountain and cordilleran regions | |
| Boreal Montane SW | Montane boreal softwood forests including subalpine fir (Abies lasiocarpa), Engelmann spruce (Picea engelmannii), and lodgepole pine (Pinus contorta) | ||
| Boreal Montane Mx | Mixed montane boreal forests combining elevation-adapted hardwood and softwood species | ||
| Pacific HW | Pacific coast hardwood forests including red alder (Alnus rubra) and bigleaf maple (Acer macrophyllum) | British Columbia coastal regions | |
| Pacific SW | Pacific coast softwood forests dominated by western hemlock (Tsuga heterophylla), western redcedar (Thuja plicata), and Douglas-fir (Pseudotsuga menziesii) | ||
| Pacific Mx | Pacific coast mixed forests combining coastal hardwood and softwood species | ||
| Conifer | Russia | Russian coniferous forests including Siberian pine (Pinus sibirica), Siberian spruce (Picea obovata), and Siberian fir (Abies sibirica) | Siberian and Far East regions |
| Temp HW | Temperate hardwood forests including birch (Betula spp.), aspen (Populus spp.), and oak species (Quercus spp.) | European Russia and southern regions | |
| Bor HW | Boreal hardwood forests dominated by birch (Betula spp.) and aspen (Populus spp.) | Northern boreal zone | |
| HW | General hardwood forest classification for mixed deciduous species | Various regions with hardwood forest presence | |
| Nord. Plt. | European Union | Nordic plantation forestry including Norway spruce (Picea abies), Scots pine (Pinus sylvestris), and managed species | Scandinavia and northern European countries |
| Ce.S. Plt. | Central European softwood plantations including Norway spruce (Picea abies) and silver fir (Abies alba) | Germany, Austria, Czech Republic, and surrounding regions | |
| Ce.H.Plt. | Central European hardwood plantations including European beech (Fagus sylvatica) and oak species (Quercus spp.) | ||
| So.S.Plt. | Southern European softwood plantations including maritime pine (Pinus pinaster) and stone pine (P. pinea) | Mediterranean countries | |
| So.H.Plt. | Southern European hardwood plantations including cork oak (Quercus suber), holm oak (Q. ilex), and eucalyptus (Eucalyptus spp.) | ||
| E. SW | Eastern European softwood forests including Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) | Eastern European member states | |
| E. HW | Eastern European hardwood forests including European beech (Fagus sylvatica), oak species (Quercus spp.), and hornbeam (Carpinus betulus) | ||
| Temp HW | Europe (Non-EU) | Temperate hardwood forests in non-EU European countries including beech (Fagus spp.) and oak (Quercus spp.) | Non-EU European countries including Switzerland, Norway, and Balkans |
| Temp SW | Temperate softwood forests including Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) | ||
| Temperate Mixed | Mixed temperate forests combining hardwood and softwood species | ||
| LR PLT | South Asia | Large-rotation plantation forestry including teak (Tectona grandis) and sal (Shorea robusta) | India, Pakistan, Bangladesh, and surrounding countries |
| Tropical | Tropical forests including monsoon forests, dry deciduous forests, and wet evergreen forests | ||
| S PLT | Central America | Small-scale plantation forestry including coffee shade trees and mahogany (Swietenia spp.) | Mexico, Guatemala, Belize, Honduras, El Salvador, Nicaragua, Costa Rica, Panama |
| TMP EVER | Temperate evergreen forests including cloud forests and pine-oak forests | ||
| TMP MX | Temperate mixed forests combining coniferous and deciduous species | ||
| TMP DC | Temperate deciduous forests including oak forests and mixed deciduous formations | ||
| S PLT | Rest of South America | Short-rotation plantation forestry including eucalyptus (Eucalyptus spp.) and pine (Pinus spp.) | Countries other than Brazil |
| TR EVER | Tropical evergreen rainforests including Amazon basin forests | ||
| TR MIX | Tropical mixed forests including seasonal dry forests and forest-savanna mosaics | ||
| TEMP MX | Temperate mixed forests including Valdivian rainforest and Andean mixed forests | ||
| S PLT | Sub-Saharan Africa | Short-rotation plantation forestry including eucalyptus (Eucalyptus spp.) and wattle (Acacia spp.) | African countries south of the Sahara Desert |
| TR EV | Tropical evergreen forests including Congo Basin rainforests and East African montane forests | ||
| TR MIX | Tropical mixed forests including miombo woodlands and moist forests | ||
| Dipt | Southeast Asia | Dipterocarp forests dominated by Dipterocarpaceae family species characteristic of Southeast Asian rainforests | Indonesia, Malaysia, Thailand, Philippines, Vietnam, and surrounding countries |
| Trop. Dry | Tropical dry forests including monsoon forests and deciduous forests | ||
| Trop. Moist | Tropical moist forests including lowland rainforests and wet evergreen formations | ||
| S. Plt. | Short-rotation plantation forestry including oil palm (Elaeis guineensis), rubber (Hevea brasiliensis), and timber species | ||
| Temp | Oceania | Temperate forests including sclerophyll forests and wet temperate rainforests | Australia, New Zealand, and Pacific Islands |
| Trop. Dry | Tropical dry forests and woodlands adapted to seasonal rainfall | ||
| Trop. Moist | Tropical moist forests including wet tropical rainforests | ||
| S. Plt. | Short-rotation plantation forestry including eucalyptus (Eucalyptus spp.) and pine (Pinus spp.) | ||
| Temperate | Japan | Temperate forests including deciduous broad-leaved forests and mixed forests | All prefectures |
| S PLT | Short-rotation plantation forestry including Japanese cedar (Cryptomeria japonica) and other managed species | ||
| TR EVER | Africa and Middle East | Tropical evergreen forests in limited areas with suitable tropical climate | Limited tropical forest areas |
| TR MIX | Tropical mixed forests and woodlands including dry forests and savanna-forest transitions | Various countries in Africa and Middle East |
Isometric would like to thank Renoster, for their extensive feedback during this Module's development.
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