Accounting for Albedo in Carbon Market Protocols

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Accounting for Albedo in Carbon Market Protocols

https://doi.org/10.21203/rs.3.rs-5248564/v1

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Although natural climate solutions (NCS) are increasingly deployed within carbon markets and beyond, their climate benefits may be over-estimated due to a lack of accounting for changes in albedo. Here we analyze 172 Afforestation, Reforestation, and Revegetation projects within the voluntary carbon market and find more than 10% occur in places where albedo entirely negates the climate mitigation benefit and a quarter occur in places where albedo halves the mitigation benefit. Yet, the majority of projects are concentrated where albedo changes are expected to be minimal, and 9% of projects occur where albedo would augment the mitigation benefit. New data make albedo accounting possible for the first time, and we outline an iterative approach for incorporating albedo considerations into carbon crediting protocols. Doing so will enable prioritization of projects with greater climate benefit and more accurately quantify credits that may be used to address unabated emissions.

Earth and environmental sciences/Environmental social sciences/Climate-change mitigation

Scientific community and society/Forestry

Natural climate solutions (NCS)—conservation, improved management, and restoration of ecosystems that results in climate benefits—can provide cost-effective climate mitigation (Griscom et al, 2017; Busch et al, 2024) and are essential to achieving global climate goals (Buma et al, 2024; Rogelj et al, 2018). Carbon markets are a prominent mechanism for funding NCS, providing marketplaces for trading verified metric tons of carbon dioxide equivalent (CO₂e) emissions that have been avoided or removed (i.e., carbon credits) from the atmosphere. Carbon credits are produced through established protocols to ensure that each credit corresponds to an actual climate benefit. Although NCS can be employed for a variety of goals, use of NCS to abate emissions requires very high standards to ensure reliability and durability (Zickfield et al, 2023). To date, carbon crediting protocols solely account for the biochemical (i.e., greenhouse gas) benefits of projects, but largely omit the biophysical (non-greenhouse gas) factors that also can substantially influence climate (Novick et al, 2024; Walker et al, 2022; Windisch et al, 2021; Zickfeld et al, 2023; Graf et al., 2023).

One biophysical factor that has received recent attention is surface albedo (i.e., the amount of shortwave radiation reflected to space relative to what is absorbed by the surface and converted to heat in the Earth system; Fig. 1). Changes in the albedo of Earth’s surface can significantly contribute to the net climate effect—warming or cooling—attributable to NCS projects (Hasler et al, 2024, Williams et al, 2021). For example, while reforestation sequesters atmospheric CO₂ through forest growth, it also tends to decrease surface albedo, which can partially (or fully) cancel out the climate benefit of CO₂ removal (Graf et al, 2023; Fig. 1). Ignoring albedo can lead to projects with inflated estimates of climate mitigation, particularly over decadal timeframes, or even projects that exacerbate rather than mitigate climate change (Hasler et al, 2024; Williams et al, 2021).

Although albedo has been difficult to include in NCS accounting, new, open-source maps provide spatially-explicit albedo changes expected from reforestation (Hasler et al, 2024). The data estimate the expected albedo change in CO₂e for a suite of non-forest to forest land cover transitions (“single transition maps”). It also boils the information down into the percentage of maximum CO₂ removal that would be negated by changes in albedo for the “most likely” reforestation transition (Hasler et al, 2024). Here, we refer to the latter as the “albedo deduction/benefit.” For example, a 50% albedo deduction on the map indicates that 50% of the maximum CO₂ removal from reforestation is negated by albedo changes. Conversely, in some situations, reforestation can create an albedo benefit by brightening the surface, meaning that the resulting net cooling is greater than what would occur from CO₂ removal alone. For example, this can occur in some places in which darker shrubs are reforested to woody savannas. However, albedo deductions are much more common than albedo benefits when tree cover increases given the low albedo of forests relative to most other land cover types. While the potential for albedo to strongly alter the climate benefit of NCS projects is well known, awareness of how much albedo change matters for carbon market projects is only starting to emerge, and guidelines for how to best incorporate this evolving science are urgently needed.

Here, we quantify the potential impact of albedo on 172 existing Afforestation, Reforestation, and Revegetation (ARR) projects and present an iterative approach for incorporating albedo into project prioritization, design, and accounting. To do so, we coupled the new Hasler et al, 2024 data with publicly available data on the location and expected carbon credit production of ARR projects in the Voluntary Carbon Market (VCM)—one of the largest and most prominent carbon markets. We focused on ARR over other types of NCS for three reasons. First, we see increasing interest in CO₂ removal credits (which ARR projects produce) (Jones 2024); second, datasets that quantify albedo effects from ARR are now available (e.g., Hasler et al, 2024); and third, albedo change from ARR is expected to be consequential, as expanding tree cover often involves converting more reflective surfaces to darker surfaces with lower albedo (Bonan et al, 2008).

We assessed 172 ARR projects—all VCM projects with accessible geospatial and project documentation available—distributed across five continents (Fig. 2) and amounting to nearly 800 million metric tons CO₂e of projected climate mitigation across the next century. We reviewed the five registry standards and accompanying protocols used by these projects to confirm that none account for changes in albedo, though one standard, the Verified Carbon Standard, signaled interest in biophysical accounting in future innovations through its ABACUS label (ABACUS 2023).

We find that the median albedo deduction across all projects is 18%. However, a quarter of projects are expected to face albedo deductions of 50% or more, including some where the entire CO₂ benefit is negated by changes in albedo. In contrast, 9% of projects are estimated to have an albedo benefit (Fig. 2). Projects with the largest albedo deductions occur in a variety of contexts: they occur on four continents (Africa, Asia, Australia, and South America), within 12 biomes, are diverse across project start dates (from decades-old projects to projects that began development in the past year), use seven protocols, and have a range of geographic sizes (from hundreds to hundreds of thousands of hectares) (Fig. 3; Tables 1, S1, and S2). Most credits occur in tropical and subtropical moist broadleaf forests where the albedo deduction is moderate (12% median). However, other biomes like montane grasslands also have substantial credits and much higher albedo deductions (39% median) (Table S1).

Tiered Approach for Albedo Incorporation

Our assessment suggests that albedo changes are relevant for many VCM projects and that a quarter of projects produce half (or less) of the climate mitigation expected. Twenty-one projects may even exacerbate climate change. Based on this, there is a strong need to account for albedo in VCM protocols, especially when credits are sold to address emissions elsewhere. Here, we present a tiered approach that progressively integrates albedo change into VCM accounting. Additionally, we depict the impact of each tier of accounting ambition within the 172 projects assessed (Table 1, Fig. 4), asking, what would the albedo-informed climate mitigation benefit be under each tier, as compared to today’s VCM accounting? This framework is designed to facilitate a phased transition to address the albedo accounting gap and identifies opportunities where more intensive accounting changes, research, communication, data, and/or tools are needed.

Table 1

Impacts of different tiers of ambition to account for albedo deductions and benefits. Tier 1 represents the current projected credits, in million metric tons CO₂, expected across all 172 projects assessed from self-reported projections found within each project’s project description. Tier 2 introduces a threshold of albedo deduction beyond which projects would be ineligible (an “ineligibility threshold”), and shows two scenarios of ineligibility thresholds: 50% and 100%. These thresholds refer to places where there is a 50% or 100% albedo deduction to the climate benefit. For example, a 100% threshold would eliminate projects where the potential CO₂ removal would be entirely negated by changes in albedo. A 50% threshold would eliminate projects where half of the projected CO₂ removal would be negated by changes in albedo. Tier 3 builds on Tier 2 and discounts credits by the albedo deduction after removing ineligible projects based on the inelgibility threshold. Tier 4 builds on Tier 3 and allows crediting for projects with a median albedo benefit such that there is an increase in climate mitigation due to changes in albedo.
Tier	Ineligibility Threshold	Discounting	Benefits	Projected Credits with Albedo Accounting (million metric tons CO₂)	Difference from Current Projected Credits
1	--	--	--	795	N/A
2	100% Median Albedo	--	--	556	30.0% fewer credits
2	50% Median Albedo	--	--	484	39.1% fewer credits
3	100% Median Albedo	All other albedo deductions	--	425	46.5% fewer credits
3	50% Median Albedo	All other albedo deductions	--	405	49.0% fewer credits
4	100% Median Albedo	All other albedo deductions	Albedo benefits accounted	429	46.0% fewer credits
4	50% Median Albedo	All other albedo deductions	Albedo benefits accounted	408	48.6% fewer credits

For Tier 1, new projects incorporate albedo into project design by prioritizing project sites or forest types where albedo is anticipated to have a lower deduction or a benefit. Project funders could ask about this in due diligence processes. Project ratings agencies could add it to their assessment methodologies. The Hasler et al, 2024 dataset allows projects to demonstrate they are in areas with lower potential albedo deductions. As this would not impact existing projects, the projected credits for this tier match what is reported by projects today.

For Tier 2, albedo could be incorporated into standards’ “no net harm” guidelines for environmental impacts using the maps and data in Hasler et al, 2024. Where an albedo deduction is large, additional follow up could ensure that, when assessed at higher resolution, the effect was insignificant. Where this cannot be demonstrated, the project could be deemed ineligible if the albedo deduction was past a specified threshold. For example, a 100% threshold would eliminate projects where the potential CO₂ removal would be entirely negated by changes in albedo. A 50% threshold would eliminate projects where half of the projected CO₂ removal would be negated by changes in albedo. For the 172 projects assessed, a 100% albedo deduction threshold would make 21 (12%) projects ineligible, lowering the total projected credit production by 30.0% (Table 1). A 50% albedo deduction threshold would make 43 projects (25%) ineligible, lowering the total projected credit production by 39.1% (Table 1). We note that a relatively small reduction in eligible projects would lead to a relatively larger reduction in projected credits (e.g., 25% of projects versus 39.1% of credits). This suggests that the high-albedo-deduction projects appear to be disproportionately projecting higher per-project credit productions and/or be of larger geographic size.

For Tier 3, projects that are eligible could be required to either discount total CO₂ removals by the median albedo deduction provided by the Hasler et al, 2024 maps for the project area or, ideally, a refined value that leverages the single transition maps from Hasler et al, 2024. The Hasler et al, 2024 data were constructed from a global albedo atlas that sampled the albedo of pixels that are fully encompassed by a single land cover type, and then expanded with a hierarchical nested grid to estimate the albedo for all locations worldwide with nearby samples. This means that the single transition maps can be applied to smaller project areas where the starting and ending conditions directly match the land cover transitions (e.g., crop to evergreen broadleaf forest). For the 172 projects assessed here and using the median albedo deduction for each project area, a 100% ineligibility threshold would reduce projected credits across all projects by 46.5% (Table 1). A 50% ineligibility threshold would reduce projected climate mitigation by 49.0% (Table 1). This includes projects eliminated because they surpass the respective ineligibility threshold for albedo deduction, and credits reduced due to discounting remaining projects that have albedo deductions.

For Tier 4, projects could monitor, verify, and report albedo benefits in addition to deductions where they occur, and these benefits could become valued for their direct contribution to climate change mitigation. Sixteen projects (9.3%) in our analysis showed the potential for albedo benefits, indicating that their net climate benefit could be greater than expected from CO₂-only estimates. These projects could have more than 3.8 million metric tons CO₂e of estimated benefit unaccounted for due to albedo benefits (Table 1), which would translate to more than $59 million in additional income to support project scaling, project longevity, and local communities if albedo benefits were valued similarly to ARR CO₂ removals at $15.60 per credit (Donofrio, Procton 2023). In comparison, under the same assumptions for credit price, Tier 3 with a 50% ineligibility threshold (Table 1) would result in more than $6 billion less going to projects in places with albedo deductions. We place this tier of ambition fourth because higher-resolution datasets and deeper protocol development would be needed to justify an albedo benefit increase while erring on the side of more conservative estimates (Ellis et al, 2024; Fig. 4).

Sources of Uncertainty

Incorporating new datasets into protocols that guide implementation on the ground begs questions of how the additional data contributes to overall uncertainty. To provide context around the reliability of this dataset for the uses we propose, we situate the uncertainty associated with the albedo data within the current uncertainty requirements of existing protocols.

Existing ARR protocols primarily focus on uncertainty surrounding the number of carbon credits produced. Upon credit issuance, protocols typically require reporting of sampling error, but inconsistently require propagation of other error sources such as measurement error (e.g., measurements of stem diameter at different locations over time) or model error (e.g., selection of an allometric model poorly suited to the site) (Chave et al, 2004; Haya et al, 2023). For our analysis, we used publicly available project boundaries and reported projections of carbon credits. Most projects do not report robust uncertainty around projected (ex-ante) estimates of credit production, so we could not directly quantify uncertainty associated with the projected carbon credits. The ex-ante estimates used here are also likely to be refined before third-party verified credits are issued and will vary depending on ARR success, models used, or shifts in the anticipated boundaries of the project.

In many instances, the carbon data may have greater uncertainty than the albedo data (Hasler et al, 2024, Walker et al, 2022). Albedo data benefits from being directly quantifiable from satellite data, whereas carbon sequestration and storage flux are more difficult to identify remotely (Novick et al, 2024). However, there are several sources of uncertainty around the estimates of albedo change under land cover transitions used here. The non-forest to forest transitions occurring in ARR projects may differ from the transitions assumed within the albedo deduction/benefit layer from Hasler et al, 2024. Different baseline approaches may alter the albedo change attributable to the project. The albedo deduction/benefit layer is based on the ratio of maximum albedo change to maximum carbon storage (Walker et al, 2022), and our use of that layer assumes that ratio holds over a project’s lifetime. However, that ratio will shift over time because albedo change occurs more rapidly than maximum CO₂ removal is achieved (Fig. 1), and/or because of management decisions that alter carbon storage and/or land cover. The underlying estimates of maximum carbon storage and surface albedo changes are also subject to uncertainties, such as uncertainty in land cover classification; however, these may improve with future advances (e.g., in remote sensing or spatial analysis).

Incorporating albedo in face of uncertainty

For the uses of albedo data proposed here, reliability is most critical where new information could change project eligibility or estimates of how many carbon credits will be generated. Thus, Tier 1 is the most immediately implementable because albedo information is being used to steer projects towards places that are more likely to be climate positive but does not alter eligibility or carbon credits. Incorporating Tier 1 considerations into project design and assessment is immediately available to project developers, reviewers, and purchasers of carbon credits pursuing the highest quality of ARR credits, regardless of whether protocols call for its use.

Tier 2 requires the next level of reliability in albedo information, especially for projects that occur along the threshold of ineligibility. In those locations, higher resolution data or more complete accounting for other biophysical factors could shift projects in or out of eligibility. However, Hasler et al, 2024 found that accounting for variation in the albedo data did not substantially shift albedo deductions/benefits. Notably, there are reasons why lands with albedo deductions may be preferred. For example, they may be lower cost and/or come with important co-benefits beyond climate mitigation. A strict threshold could exclude these locations, whereas a less strict threshold could allow these locations to remain with a more complete estimate of their climate mitigation potential (this paper’s Tier 3).

The need for reliable albedo CO₂e may be most important for Tiers 3 and 4 because its inclusion would alter the carbon credits for many projects. Reliability may be improved by ensuring that albedo deduction and benefit estimates are derived from the non-forest and forest transitions best representing a particular project, made possible with the detailed suite of single transition maps provided by Hasler et al, 2024.

Our analysis suggests that more than 10% of current VCM ARR projects occur in places where albedo entirely negates the mitigation benefit and a quarter occur in places where albedo halves the mitigation benefit. Yet, the majority of projects are concentrated in places where albedo changes from albedo are expected to be minimal, and 9% of projects occur in places where albedo would augment the mitigation benefit. While our analysis has uncertainties – including projected carbon accumulation, timing of albedo change, and starting and ending conditions at the site – it clearly shows the importance of incorporating albedo in carbon credits from ARR, and we provide a tiered approach for doing so.

All tiers will require collaborative communication and education with market stakeholders who may not be familiar with biophysical factors since climate mitigation activities historically have focused on biogeochemical considerations—specifically CO₂e. Additionally, deeper tiers of ambition will benefit from and in some cases require additional research, datasets, tools, and policy to become feasible. Development of an operational Sentinel-2 and Landsat albedo product (Li et al, 2018, Franch et al, 2019, Claverie et al, 2018, Franch et al, 2014) at 20- to 30-m resolution with global, seasonal coverage has the potential to enhance the Hasler et al, 2024 dataset and provide albedo deduction/benefit information at spatial scales more directly aligned with ARR projects.

To be clear, we view these tiers of albedo accounting as progressively ambitious iterations of applied science within carbon markets. Participants in the carbon markets should be held accountable to the best available science without invalidating previous efforts that used the best science available at that time. This rapid iteration is happening, for example, with the uptake of projects using VM0047 to incorporate dynamic baselining after academic studies demonstrated gaps in traditional baselining approaches (Cook-Patton et al, 2021; West et al, 2023). Similarly, tools that calculate non-permanence risk have been updated in light of scientific discourse on the need to incorporate future climate change impacts (NPRT 2023). In this vein, we acknowledge that adoption of higher tiers may be limited at this point, but we include them because of their potential value and to encourage continued research and policy development. Notably, it appears that implementing Tier 2—making ineligible the portion of projects at greatest risk for especially large albedo deductions—may resolve much of the accounting gap, given the smaller credit reductions as the tiers advance (Table 1; Fig. 4). Nevertheless, the holistic accounting unlocked by Tiers 3 and 4 will be important to prioritizing climate finance and accurately valuing NCS. Market labels such as ABACUS (ABACUS 2023) exemplify efforts to differentiate credits that meet higher standards without preventing projects that may provide other benefits but not meet eligibility thresholds for albedo. Given that the albedo change may dominate the climate impact of a project in the early years, considering albedo through our tiers supports avoiding projects that would be most affected in the near-term, when we urgently need to prevent warming, while not preventing those that can effectively remove CO₂ in the mid- to long-term.

We focused on incorporating changes in albedo because it is now readily quantifiable and likely to be the most influential biophysical factor for ARR projects (Weber et al, 2024). However, other biophysical factors can substantially impact net climate impacts of NCS projects either augmenting or further diminishing the benefits (Ellison et al, 2024; Zelinka et al, 2020). These include enhancement of cloud cover, local changes in surface cooling from altered evapotranspiration rates, vegetation moisture storage changes, changes in surface roughness with effects on heat transfers, and changes in snow cover duration (Nabuurs et al, 2022; Zickfield et al, 2023; Cerasoli et al, 2021). Moreover, albedo benefits do not directly reduce ocean acidification the way atmospheric CO₂ removal does, highlighting a distinction between climate change mitigation approaches that target holistic atmospheric restoration that goes beyond just climate impacts. These other factors involve complex land-atmosphere-climate system feedbacks that can be strongly scale dependent. As information at sufficient spatial and temporal resolution becomes available, these should also be iteratively added to protocol guidance on project accounting. Rather than wait for all unknowns to be resolved, we believe that carbon market protocols should be iteratively updated to incorporate the best available science and data products.

Inclusion of albedo accounting within carbon markets will be most effective where protocols implement consistent, standardized processes for its inclusion. The Hasler et al, 2024 dataset was generated by an independent third-party; is publicly available and globally consistent; and was created with reproducible and transparent methods. It also comes in a format familiar to many project developers, and thus presents an opportunity for use by all VCM participants, including use cases beyond ARR projects. As with ARR, albedo and other biophysical factors are likely to be relevant to Improved Forest Management, Reduced Deforestation and Degradation, and biochar projects (Genesio et al, 2012; Sjølie et al, 2013). Research for all these cases is needed, including reliable, accurate assessments that are extended to practice with maps and tools. It will be important for these datasets to be dynamically updated, as climate change is likely to continue to influence albedo values, for example, through reduced snow cover (Hasler et al, 2024).

Albedo accounting presents a significant opportunity for NCS to carry out critically needed climate change mitigation, allowing climate finance to flow toward project activities, locations, and communities with the greatest potential to meet the goals of that funding and global targets. Our results show that albedo deductions are currently more prevalent than albedo benefits and that we now have tools to support albedo-informed projects going forward. As carbon markets are poised to scale, our tiers of albedo accounting will enable climate finance to support the highest-potential projects; avoid the systemic failure of isolating variables of interest that adversely affect others; and allow us to more efficiently and effectively realize the potential of NCS.

Competing Interests

The lead author’s organization (American Forest Foundation) is a participant within the Voluntary Carbon Market and has projects implementing reforestation. The remaining authors have no conflicts of interest to declare.

Data Availability

All input data are publicly available. Project descriptions were sourced from the registries listed in Table M1. Project boundaries sourced from Karnik et al, 2024 were retrieved from https://doi.org/10.7910/DVN/G17RXL. Project boundaries sourced from projects using VM0047 were sourced from .kml files on the Verra Registry at https://registry.verra.org/app/search/VCS. The albedo deduction/benefit raster layer used to calculate project median albedo was sourced from Hasler et al, 2024, at https://dataverse.harvard.edu/api/access/datafile/8550241.

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Searching protocols for current handling of albedo

We reviewed current ARR protocols to identify any existing guidance of handling albedo effects. To do so, we first identified ARR protocols that correspond to all ARR projects on the VCM (see below). We then performed a word search in both the protocols and their parent standard documents ; for example, the word search was conducted on both VM0047 and the Verified Carbon Standard, v4.7. The terms used in the search were: “albedo,” “biophysical,” “physical,” “biogeophysical,” “reflectance,” and “radiative forcing.” Some instances of “physical” and “biophysical” occurred but were not in relation to albedo. Otherwise, no mentions or mechanisms for albedo accounting were found in any of the protocols, nor their parent standards.

Selecting projects for assessment

The VCM ARR projects assessed were all those available as of June 2024 on the open-access database of nature-based carbon offset project boundaries published in Karnik et al, 2024. This database provides a public, open-source, global set of VCM project boundaries, including checks for alignment of project geospatial boundaries against project documentation (Karnik et al, 2024). From this database, all projects with a “Project Type” of “ARR” (Afforestation, Reforestation, and Revegetation) were selected for a total of 190 projects. Then, 24 that only provided geospatial data in point format—rather than polygons—were filtered out, bringing the total to 166 projects. Upon discussion with the authors of the database, we used the “Project Accounting Area” rather than the “Project Area” when the former was available. Karnik et al, 2024 describe the “Project Accounting Area” as the “geographical area of the project that was used to calculate carbon credit issuance,” though in most cases, the “Project Accounting Area” and the “Project Area” are the same (Karnik et al, 2024).

Of the 166 projects remaining, 16 projects were dropped from the assessment where they could not produce a quantified median albedo deduction or where ex-ante projected credits could not be found within corresponding project documentation available publicly on the carbon registries on which these projects are listed (Table S2). This brought the total projects for assessment to 150.

An additional 22 projects were added to those 150 projects to include projects using Verra Methodology 0047 (VM0047), which was published in late 2023 and had not yet been added to the Karnik et al, 2024 project database. This was done in anticipation of VM0047 becoming increasingly used within the VCM as it replaces other protocols due to its advancement of the widely-recognized dynamic baseline approach to enhancing causal attribution and additionality of a project intervention relative to traditional baseline approaches (Verra 2023; Cook-Patton et al, 2021). This brought the total projects assessed to 172 (Table M1). We sourced geospatial boundaries for the VM0047 projects from the Verra Registry. These were converted from .kml files to polygon files for the assessment using ArcGIS Pro v3.3.0.

All data was last accessed for this assessment in June 2024.

Albedo Deduction Data

Albedo deductions and albedo benefits were procured from the raster dataset (“AlbedoOffset_005.tif”) published by Hasler et al, 2024. Positive values in the dataset indicate albedo deductions whereas negative values indicate albedo benefits. The albedo dataset caps values at +/- 10,000% to avoid +/- infinity (Hasler et al, 2024). We use the term “albedo deduction” rather than “albedo offset” to avoid confusion with emissions offsets associated with carbon credits.

Summarizing by Biome and Protocol

We examined how project-level albedo deductions and benefits interact with biomes and the protocols that projects are registered under. To examine whether albedo deductions and benefits were more likely to occur within a given biome, we overlaid the project boundaries on a global dataset of biomes and summarized the project hectares located in the biome, the expected credits, and the minimum, maximum, and median albedo offset (Table S1). Biomes were sourced from the RESOLVE ecoregions dataset (Dinerstein et al, 2017). Of the 14 biomes in the dataset, 10 had more than one project in our assessment, and the other 4 biomes were excluded from this analysis. Where projects spanned multiple biomes, we only included the portion of projects’ hectares that intersected with each biome (Table S1).

For projects that spanned multiple biomes, we attributed projected credits to each biome by assuming the proportion of projects’ area in each biome aligns with the proportion of projected credits in that biome. To arrive at this, we calculated the proportion of the project hectares within each biome relative to the total hectares of those projects. For example, if project X has 100 hectares and 75 of those intersected with Biome A, we included 75% of project X’s projected credits to Biome A’s projected credits, and the other 25% in the other biome(s) project X intersected.

For protocols, we classified each project by the protocol under which carbon credits were generated. We similarly summarized the project extent, expected credits, and median, maximum, and minimum albedo deductions for each project (Table S2).

Projects’ Ex-Ante Credit Projections

Project documentation for the 172 projects was downloaded from the respective registries to retrieve the reported ex-ante projected credits. Where multiple project documents were available with ex-ante projected credits reported, the most recent document published was used. Caution should be noted with the use of ex-ante credit projections, as these projections from a project are often refined before credits are issued ex-post (i.e., after the project activity has occurred, appropriate protocols have been used to quantify successful carbon sequestration, and quantification is validated by an accredited third-party). Our assessment was performed using these ex-ante projections rather than ex-post issued credits, as in a majority of cases, projects have not yet issued credits. In most instances, ex-ante estimates of credit production employ locally-parameterized predictive models and are used to garner early-stage project investment, and thus we assume that—although uncertain—they are reasonable proxies of the ex-post credits to be produced.

Applying the Albedo Deduction

We used the zonal statistics function to calculate the median albedo deduction/benefit within each project boundary for all ARR projects. Where the median albedo deduction for a project was null, the project was dropped from this analysis. This is a potential source of under-reporting albedo deductions, as areas within the albedo deduction raster dataset with null data are often deserts, which would have high albedo deductions if reforested (Hasler et al, 2024).

The median albedo deduction for each project was applied as a percent deduction on the ex-ante projected credit production sourced from project documentation (see above). For example, where the median albedo deduction for a project’s area was 60%, the project’s total estimated CO₂e climate benefit was multiplied by 0.4 to derive an albedo-adjusted estimate of that project’s total estimated CO₂e climate benefit.

Yes there is potential Competing Interest. The lead author’s organization (American Forest Foundation) is a participant within the Voluntary Carbon Market and has projects implementing reforestation. The remaining authors have no conflicts of interest to declare.

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Accounting for Albedo in Carbon Market Protocols

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Abstract

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Introduction

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Tiered Approach for Albedo Incorporation

Sources of Uncertainty

Incorporating albedo in face of uncertainty

Discussion

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Competing Interests

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References

Methods

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