Present and (Potential) Future of Grassland Methodologies in Voluntary Carbon Markets

The UN expects 24% of NBS credits to come from grasslands by 2050; in reality, grasslands, especially avoidance projects, have fallen victim to egregious oversight and methodological abuse.

Dr. Kebonyethata Dintwe & Mihir Bendre

Abstract

The purpose of this white-paper is to explore ACoGS (Avoided Conversion of Grasslands and Shrublands) project-types within the Nature-Based Solutions grasslands sector of Voluntary Carbon Markets (VCMs), to analyze scientific and logistical concerns in quantifying emissions reductions, while exploring alternative methodological and dMRV (digital Monitoring Reporting and Verification) solutions.

Carbon markets are economic mechanisms designed to reduce greenhouse gas (GHG) emissions, first originating under the Clean Development Mechanism (CDM) in 1997. Since inception, the market branched off into Voluntary and Compliance markets. Within Voluntary Carbon Markets (VCMs), carbon credits can be generated through projects in Nature-Based Solutions (NBS), including forestry, grassland and wetland restoration, and Technology-Based Solutions (TBS) such as renewables and direct-air capture. Grasslands are currently a growing, but underdeveloped sector within Nature-Based Solutions; yet, the United Nations expects 24% of NBS credits to come from grasslands by 2050 (UNEP 2021). While ‘Grassland-based removal’ project types have gained significant interest and investment, the ‘Grassland-based avoidance’ sector has lagged behind, mainly due to challenges in quantification and monitoring. 

While ‘Grassland-based removal’ project types have gained significant interest and investment, the ‘Grassland-based avoidance’ sector has lagged behind mainly due to challenges in quantification and monitoring. 

To address the lack of innovative solutions in grassland-based avoidance projects, this whitepaper serves as an informal review of existing ACoGS (Avoided Conversion of Grasslands and Shrublands) frameworks, and provides recommendations for an improved framework. The whitepaper begins with a review of existing ACoGS frameworks, which can be found in the ACoGS Methodology Review section, which lists out overarching concerns in the ACoGS sector, such as the lack of a scientifically rigorous quantification process and universal applicability to various regions and project scenarios. Furthermore, recommendations on what should go into a framework are outlined in the  Improved ACoGS Framework section. The Conclusions section summarizes our findings, and highlights the role of improved dMRV in this nascent, but underdeveloped sector of Nature-Based Solutions.

Introduction

Regarded as a reliable pathway to mitigating corporate carbon emissions, carbon markets serve as a means to account for corporate carbon emissions until technologies to enable complete carbon neutrality become available. Carbon markets officially took root in the 1997 Clean Development Mechanism (CDM) as part of the Kyoto Protocol, and were restructured in 2015 under Article 6 of UN’s Paris Climate Accord. Today, the United Nations Framework Convention on Climate Change (UNFCCC) has near-universal membership of  198 countries, with the goal of “preventing dangerous anthropogenic (human induced) interference with the climate system.” 

Overview of carbon markets

Carbon markets are largely broken down into two categories: compliance markets and voluntary markets. In simple terms, compliance markets such as California Cap-and-Trade or the European Union’s ETS (Emissions Trading System) are government-regulated, and limit GHG emissions to a certain threshold, while allowing participants to ‘trade’ their remaining allowance. On the other hand, Voluntary Carbon Markets (VCMs) are reliant upon carbon-credits, which can be ‘voluntarily’ purchased to offset corporate emissions, creating financial incentive for organizations and industries to limit their emissions, while investing in projects that mitigate GHG emissions. While carbon-credits have come under criticism several times since inception, VCMs are still a critical pathway to climate-change mitigation, reliably financing climate resiliency through projects that are largely concentrated in the underdeveloped “global south.” As corporations pledge to become carbon-neutral in the coming decades, the global demand for carbon credits is projected to increase by a factor of 100 by 2050, with a valuation of up-to USD fifty billion (Blaufelder et al 2021).

As corporations pledge to become carbon-neutral in the coming decades, the global demand for carbon credits is projected to increase by a factor of 100 by 2050, with a valuation of up-to USD fifty billion

VCMs mainly comprise two credit-types: Nature-Based Solutions (NBS) and Technology-Based Solutions (TBS). According to a Sylvera’s State of Carbon Credits, 2023 report, Nature-Based Solutions are the largest category within VCMs, and continue to receive the most investment, with projects ranging from forest or grassland restoration to increasing agricultural Soil Organic Carbon (SOC) sequestration. Technology-based Solutions also play a substantial role in VCMs along with NBS, with projects involving renewables, energy efficiency, or artificial carbon capture. In both cases, projects are typically categorized as ‘avoidance’ or ‘removal’ credits, each unit representing 1 tCO2e (metric-ton of CO2, equivalent) for the proposed duration of the project.

Nature-Based avoidance

Within NBS, the ‘avoidance’ project category faces challenges in baseline-setting, leakage, and additionality quantification. Nevertheless, avoidance projects represent the largest proportion of NBS credits, largely concentrated in forests. The challenging aspect of NBS avoidance projects is in quantifying project ERs (Emissions Reductions) and additionality, as it requires project developers to accurately hypothesize intangible concepts such as the state of land-use in absence of the project activity, and the incentive for land-conversion. While the agent of conversion is often known, typically deforestation or conversion to cropland, project developers must accurately determine the rate at which this land conversion takes place. Unfortunately, this is a difficult undertaking which requires complicated geospatial imagery analysis, which can be difficult for the average project developer to perform.

Forest-based avoidance sector

To help forest carbon projects monitor baselines and quantify additionality, several technology startups are leading the development of geospatial dMRV (digital Monitoring, Reporting and Verification) solutions. For instance, companies Pachama, Sylvera and Renoster are creating marketplaces, building dMRV solutions, and providing databases and reports to increase the buy-side transparency of NBS markets. On the other hand, players like Cecil, Earthshot Labs, and Nika.eco are focused on assisting project-developers with digital data-tracking, field-collection and LLM-enabled solutions for simplifying the lengthy, multifaceted project creation and management process. Gazelle’s Data Hub aims to bridge the gap between these two types of solutions, by providing an end-to-end project-creation and management platform along with open-sourced dMRV tools for forests and grasslands.

Grassland carbon sequestration

Grasslands are a nascent but growing sector within NBS. Globally, grasslands comprise around 25% of the Earth’s surface and 12% of terrestrial carbon stocks, along with several biodiversity and ecosystem benefits (Lyons et al 2023, Ontl and Janowiak 2017). Although considered more resilient carbon sinks than forests in fire-prone ecosystems according to a study by Kerlin, 2018, grasslands have traditionally comprised a much smaller pool within Nature-Based Solutions (NBS) as compared to forests. However in recent years, there has been a growing interest in carbon-project initiatives on grassland restoration and protection, and the United Nations estimates around 24% of NBS credits to come from grassland or cropland projects by 2050, with the other 62% from forests, and 4 % from wetlands (UNEP 2021). 

Grassland-based removal sector

Within the grassland and cropland category, the ‘removals’ project category has made significant strides in the past decade, contributing to the development of robust quantification and monitoring frameworks. Methodologies applicable to this category are: Verra’s VM0026 (Sustainable Grassland Management), VM0042 (Improved Agricultural Land Management) and VM0032 (Adjustment of Fire and Grazing). Moreover, emerging agriculture-technology startups are actively involved in developing solutions for cropland-based carbon credits, such as Arva Intelligence, Indigo, Boomitra and YardStick, through cropland intelligence, project development, and SOC measurement, modeling and predicting tools.

Figure 1. Savanna in Africa’s Kalahari (SW Botswana) dotted with Acacia Mellifera trees

Grassland-based avoidance sector

However, the grassland-based ‘avoidance’ (i.e. ACoGS) sector has received considerably less attention, and the sector suffers from lack of development, mainly due to challenges in baseline, additionality and leakage quantification, and monitoring concerns. Grassland-based Avoidance projects currently face the absence of scientifically robust quantification frameworks and monitoring protocols for tracking land-conversion events using satellite-based remote-sensing, which is crucial in determining baseline-scenarios and monitoring project activities in avoidance project-types. Unfortunately, most dMRV solutions from the forest-based avoidance  category are often unreliable on grasslands, due to complexity in detecting drivers of conversion. For instance, deforestation is much easier to detect than grassland conversions, as forest land cover is generally impervious to changes induced by weather and precipitation. Moreover the contrast between forest and non-forest is much more prominently picked up by satellite imagery than changes in land-uses or management on grasslands.

ACoGS Methodology Review

The conversion of grassland and shrublands to land-use types impeding on carbon sequestration or introducing ecosystemic imbalance is one of the leading causes of loss of grasslands (IPCC 2022). However, the frameworks regarding Avoided Conversion of Grasslands and Shrublands (ACoGS) lack strong scientific basis and universal applicability, complicating the process of developing carbon projects. Few carbon project frameworks designed to reward ACoGS activities, namely VCS VM0009 (Avoided Ecosystem Conversion), ACR ACoGS and CAR USGP. Of these, the VCS VM0009 and ACR ACoGS frameworks have been thoroughly examined in this section, with commentary provided on areas of improvement.

Frameworks regarding Avoided Conversion of Grasslands and Shrublands (ACoGS) lack strong scientific basis and universal applicability, complicating the process of developing carbon projects

Overview of ACoGS

ACoGS involves keeping well maintained grassland ecosystems intact, and needs separate protocols, as the application of grassland-based removal methodologies often requires an increasing carbon sequestration through project activities, often impractical for already well-managed grassland ecosystems. Existing ACoGS frameworks estimate avoided GHG emission by calculating the amount of emissions-reductions taking place by preventing the conversion (to croplands) of grasslands and shrublands by accounting for baseline scenarios where the conversion agent can either be “identified” or “unidentified,” resulting in “planned” or “unplanned” conversion, respectively. While these frameworks aim to accurately estimate avoided emissions, there are few noteworthy scientific concerns and implementation challenges which are discussed below.

Common Concerns

Some of the common concerns within existing ACoGS frameworks are: (1) inadequate guidance on field data collection and usage, (2) exclusion of grazing from the baseline scenario, (3) lack of explicit inclusion of savanna ecosystems, and (4) biomass burning not taken into account, which are discussed in detail in this subsection:

1. Non-standardized field-data collection:

Many methodologies in the AFOLU (Agriculture, Forestry and Other Land Use) sector rely on field-measurement to accurately quantify carbon stored in above and belowground biomass. However, most existing methodologies differ in their field-sampling guidelines lacking standardized protocols for grassland-sampling. This is partly due to the lack of a grassland-specific field sampling tool by international agencies such as UN, which pioneered many of the initial tools for forest-based field sampling, under its Clean Development Mechanism Tools.

Hence, methodologies such as ACR’s ACoGS do not provide any specific guidelines on field sampling, and instead encourage project developers to use default values for biomass and soil carbon established under credible sources such as scientific or government-affiliated institutions. While other methodologies such as VCS VM0009 suggest project developers perform field measurement, little guidance is provided to ensure that sample measurements are an accurate representation of the entire population being sampled, and sampling is consistent.

2. Grazing is excluded from the baseline-scenario

None of the existing ACoGS frameworks allow grazing/overgrazing on land to be an agent/driver of conversion, which can be problematic for projects located in regions where grazing activities are the most likely alternative land-use scenario and might be the only driver of land conversion. As highlighted by an IPCC report (2022), overgrazing is one of the major causes of desertification in semi-arid grasslands across the world, such as parts of South Asia, the Middle East, Africa and Australia where crop-productivity can be naturally low, and livestock-rearing is the main form of subsistence.

One of the methods of baseline determination under current ACoGS frameworks is a demonstration of the land-use scenario of cropland in the absence of the project activity. This is a challenge because ecosystems such as shrublands and grasslands in some parts of the world are not suitable for crop production (Food and Agriculture Organization, UN). These types of grasslands include western and southern Africa, Australia, the American Southwest, Mongolia, the Tibet Autonomous Region in China, and Patagonia. A combination of factors such as climate and soil fertility render these grasslands non-ideal for the cultivation of crops, making grazing, instead of cropland, the most likely agent of conversion.

Examples of overgrazing leading to significant depreciation of carbon stocks can be given in several global contexts, however they can be most pronounced in South American grasslands, where a vast majority of conversion has occurred fairly recently (within the last century). For example, grasslands in Patagonia have been used for intensive sheep production for little over a century, and the vegetation dynamics has been significantly modified from the presence of livestock, as compared to grazing patterns from native herbivores. The conversion of other South American grasslands such as the Campos, Pampas, and Cerrado to pasture/rangelands was also shown to have impacts on the soil’s hydro-physical properties, reduction in evapotranspiration and increased streamflow (Nóbrega et. al, 2017). While some sources suggest that grassland degradation is in most cases a symptom of mismanaged pastoral systems (UN FAO), others argue that the role of grazing in carbon cycling and fire management is still a debatable topic (Maraseni et al 2016). Nevertheless, it is critical to take into account other grassland conversion scenarios than cropland.

Furthermore, it is important to reiterate that increasing sequestration is not always feasible for projects already performing the best management practices, or in ecosystems with naturally low sequestration capacity, such as semi-arid regions. Furthermore, increase of carbon sequestration is not the sole determiner of healthy ecosystem-functioning, and altering land-management practices to amplify sequestration without knowledge of the native ecosystem can risk disruption of ecological balance.

Increase of carbon sequestration is not the sole determiner of healthy ecosystem-functioning, and altering land-management practices to amplify sequestration without knowledge of the native ecosystem can risk disruption of ecological balance.

3. Savanna ecosystems are unaddressed

Another concern about current ACoGS frameworks is that they do not explicitly address savanna ecosystems in their methodologies, which are significant carbon sinks due to their higher aboveground biomass availability. Savannas are similar to grasslands, but often serve as a transitional zone between forest, grasslands and deserts (Weaver, 2014). While savannas can be classified into various types, such as grass-savannas, shrub-savannas or tree-savannas, their application to ACoGS frameworks is more or less similar (Smith, 2016).

Globally, savannas cover about 30 million km2, which translates to about 20% of the Earth's land surface (Grunow et al. 1980; Ramankutty & Foley 1999; Smit 2004; Shackleton & Scholes 2011). Savannas are found on all continents except for Antarctica, with Africa comprising the largest area, covering about 40 - 50% of the continent’s surface (Fuller 1924; Scholes & Archer 1997; Grace et al. 2006). Home to slightly over 30% of the world's human population, savannas support livestock-rearing, crop production and wildlife-based ecotourism industries (Solbrig et al. 1991). They contain about 15% of global soil organic carbon, making them an important carbon sink and a stronghold for protecting biodiversity (Jobbágy & Jackson 2000; White II et al. 2009). In addition, savannas are remarkably productive ecosystems, with an average net ecosystem productivity of 0.39 Gt C year-1 , translating to roughly 30% of global terrestrial net productivity (Grace et al.2006). Hence, it is important to explicitly include savanna ecosystems in the ACoGS methodologies, especially in the context of conversion to cropland or intense grazing pressures.

In addition, savannas are remarkably productive ecosystems, with an average net ecosystem productivity of 0.39 Gt C year-1 , translating to roughly 30% of global terrestrial net productivity (Grace et al.2006).

In many savanna ecosystems, land-use change tends to be biased towards intensification of livestock production at the expense of plant and animal biodiversity. In southern African countries such as Botswana, for example, local populations do not perceive nature-based ventures such as wildlife conservation or eco-tourism as major sources of income, and largely rear livestock and cultivate land for subsistence. When compared to wildlife-based economic opportunities, livestock-based ventures are relatively less capital-intensive, and are accepted as modern and traditional norms. Furthermore, local parliaments in countries like Botswana are experiencing increasing pressure from locals to convert Wildlife Management Areas (WMAs) into commercial livestock ranching areas, as exemplified by a recent dezoning of an 8,268 km2 WMA (Keeping et al 2019).

In South America, vast stretches of savannas are also being converted to croplands. For instance, over the past few decades, Brazil’s savannas have been cleared to make room for lucrative cash crops such as soybeans, which represent 90% of all agriculture in Brazil’s savannas (Ingizza and Pooler 2022, Almeida de Souza 2020). The risk of converting native savannas to livestock or crop-based ventures is partly due to the lack of precedent, and financial barriers in nature-based ventures. Unfortunately, most well-managed savannas, despite holding significant carbon sinks, are ineligible to take advantage of today’s carbon financing due to inadequate inclusion within current ACoGS protocols.

4. Biomass burning not taken into account

Fire is an integral part of the Earth's systems that have influenced the land-atmosphere for millions of years (Glasspool et al 2004), and fire-prone ecosystems cover 40% of the land surface, including major global biomes such as grasslands, shrublands, savannas, and boreal forests. Collectively, these fire-prone ecosystems are responsible for more than 85% of global fires (Hao et al 1994, Tansey et al 2004). Depending on their frequency and severity, fires can reduce plant biomass and can also replace woodlands with shrublands or grasslands (Bond et al 2005). Moreover, grasslands and savannas are some of the most fire-prone ecosystems, responsible for 65% of the gross global mean fire emissions, where total global fire emissions are 8 Pg CO2eq yr-1 (Lipsett-Moore et al 2018).

In Africa alone, savannas account for 88% of annual total burned area, and grasslands, shrublands, and croplands account for about 5%, 13%, and 6% respectively (Dintwe et al. 2017). In Australia, savanna fires contribute about 3% of annual GHG emissions (Maraseni et al 2016), and changes in land use in these fire-prone ecosystems can significantly alter naturally occuring fire regimes. For instance, the conversion of grassland to cropland might lead to a decrease in fire frequency, but increase in fire severity; therefore, shifting carbon fluxes and dynamics from fire regimes must be adequately accounted for within GHG quantification frameworks.

Moreover, it is important to point out the adaptations undergone by vegetation in fire-prone ecosystems, and its effect on carbon fluxes. As an adaptation mechanism for fire, many plant species preferentially allocate biomass belowground to ensure recovery-capability under fire-events. Furthermore, fire-events may affect root traits and distribution in several indirect ways, including through changes in vegetation structure or soil properties (Le Stradic 2021). There is sufficient evidence from both experimental studies and field observations (Zhou 2023) that fire-prone plant species experiencing frequent fires have a larger root mass fraction than those growing free from fires both at the individual species level and ecosystem level, and increased fire frequency could stimulate vegetation to produce finer roots, which have a high turnover rate. Root turnover is a critical component of ecosystem nutrient dynamics and carbon sequestration and is also an important sink for plant primary productivity (Gill and Jackson 2000). On the contrary, long periods without fire may lead to changes in grass composition or woody encroachment, whereas frequent fire will often favor the dominance of grass species which might lead to an increase in soil organic carbon.

In a recent publication, Tear et al (2021) argue that improved savanna fire management can generate enough carbon revenue to help restore Africa’s rangelands, build ecosystem resilience, and reduce threats to biodiversity. In the absence of fire management, fire return intervals in native savanna ecosystems could be as high as 4 years, and on savannas with high fire-suppression efforts, return intervals could be up to 10 years or more (Machete and Dintwe 2023). Due to the critical role which fire plays on grassland, shrubland and savanna systems, it is imperative to take into account biomass burning when estimating avoided greenhouse gas emissions.

Improved savanna fire management can generate enough carbon revenue to help restore Africa’s rangelands, build ecosystem resilience, and reduce threats to biodiversity

Figure 2. Vegetation and Fire regimes in Sub-Saharan Africa. Source: Dintwe et al. 2017

VCS VM0009

Verra’s VCS VM0009 is one of the few methodologies applicable to the avoided conversion of grasslands. Being the only active Verra methodology allowing for ACoGS (till 27 Nov 2023), it also qualifies under the REDD (Reducing Emissions from Deforestation and forest Degradation) framework¹. There are currently 24 total projects utilizing VM0009 under VCS, with 11 officially registered, 7 under validation, 3 under development, and 2 on-hold as of late 2023. Out of the 24 listed projects, only 2 are performing ACoGS, the rest being REDD (Verra Registry). In January of 2023, Verra’s REDD project framework received criticism from The Guardian for overcrediting due to flawed baseline-determination frameworks, leading Verra on the path to review and replace its five REDD methodologies (including VM0009) with a new Consolidated REDD Methodology by 2025. More specifically, the Kariba REDD+ project which uses the VM0009 methodology recently had its credits suspended by Verra after facing criticism from The New Yorker for over-estimating its ERs. The project was accused of assuming that around 96% of the land’s sequestered carbon would be converted to greenhouse gases in the absence of the project, which might be inaccurate. While Verra’s new methodology aims to fix some of the baselining and monitoring flaws in the existing approaches, it is only focused on forests and does not include grassland baseline types, solidifying the need for revised, more scientifically robust protocols in ACoGS.

While Verra’s new methodology aims to fix some of the baselining and monitoring flaws in the existing approaches, it is only focused on forests and does not include grassland baseline types, solidifying the need for revised, more scientifically robust protocols in ACoGS.

To summarize VM0009’s existing approach, the methodology requires project developers to select a reference area representing the likely Alternate Land-use Scenario (ALS) of the project area in absence of the project’s existence. Then, historical satellite imagery analysis can be performed on the reference area to analyze land conversion rate, on which logistic regression is performed, which is then used to model the future state of biomass on the project area. From here, field measurement is conducted on both the project and proxy areas (representing the ALS) which is plugged into the Biomass Emissions Models along with parameters from the logistic curve. Additionally, project developers must also prove the risk to land conversion using Verra’s VT0001 additionality tool, and perform Non-Permanence risk analysis. Issuable credits are then determined after setting aside buffer pools based on risk-calculations. While VM0009 is sound in some respects, it also has scientific and logistical concerns as outlined below:

1. No safeguards against ‘baseline-gaming’

VM0009 lacks substantial safeguards against baseline-gaming, which involves choosing an unlikely baseline scenario, to artificially increase the volume of credits generated. Under VM0009, emissions reductions mainly depend on two major components: 1) the difference in biomass stocks calculated between the project and proxy areas, and 2) the amount of time it takes for a reference area to undergo conversion.1 Project developers are required to choose reference areas within a certain proximity of the project accounting area, depending on the baseline type; however, project developers ultimately choose reference and proxy areas (“ALS areas”), with few safeguards put in place for avoiding over-crediting².

2. Logistic conversion might not apply to grasslands

The main mechanism for calculating Emissions Reductions within VM0009 are the Biomass Emissions Models (BEMs), which rely on deforestation following a logistic conversion curve. The BEMs take into account various field-measured parameters about biomass and other covariates around the project area such as soil decay rates and information from PRAs (Participatory Rural Appraisals) to model degradation of biomass over time. While projected degradation is modeled on the logistic curve obtained from historical imagery analysis, the idea of modeling ecosystem degradation with a logistic curve may only be accurate on forests, not grasslands. The application of the logistic comes from the economic theory of natural resource consumption, which was initially derived only for forest ecosystems, and there is a paucity of literature suggesting this approach to be accurate on grassland and shrubland ecosystems; rather, given the high variability of vegetation in grassland ecosystems, the logistic pattern can almost certainly inaccurate for ACoGS, warranting the need for exploring new approaches.

3. Grazing not allowed as driver of conversion

It is important to include grazing within the baseline scenario type for ACoGS projects, as grazing is one of the main threats to conservation of grassland ecosystems around the world; overgrazing not only makes ecosystems vulnerable to desertification or shrub-encroachment, but also jeopardizes the land’s future ability to sustain agricultural activities. However, due to the difficulty in observing grazing using remotely sensed imagery, VM0009 excludes grazing as a potential driver of land conversion. The methodology states that “Pasture/grazing lands are highly difficult to identify using nominal remote sensing techniques, and would thus prove impossible to recognize using the BEM model,” thereby eliminating a majority of potential project areas where grazing might be the only major driver of conversion.

Due to the difficulty in observing grazing using remotely sensed imagery, VM0009 excludes grazing as a potential driver of land conversion. The methodology states that “Pasture/grazing lands are highly difficult to identify using nominal remote sensing techniques, and would thus prove impossible to recognize using the BEM model,” thereby eliminating a majority of potential project areas where grazing might be the only major driver of conversion.

4. Field measurement and comparison concerns

Furthermore, predicting the change in carbon stocks which land would undergo in the absence of a project often rely on comparing the biomass-stocks of project areas to other reference areas of similar land-use type and ecological classifications. However, the comparison of carbon stocks on two different areas can be inherently flawed (especially on grasslands, shrublands and savannas with naturally variable aboveground biomass densities), given that different areas might differ significantly in their aboveground carbon sequestration capacities. While the methodology includes discrete requirements for the qualification of proxy areas, such as proximity to the project area, metrics for measuring similarity in land use type, ecosystem type and vegetation cover, VM0009 leaves the selection of proxy areas and field-measurement ultimately up to the project developer’s discretion.

ACR ACoGS

The ACR ACoGS methodology estimates the emissions avoided by preventing the conversion of Grassland and Shrubland Ecosystems (GSE) to annual crop production, under the assumption that GSE may support greater plant biomass than annual cropland, especially belowground. This methodology assumes the primary source of emission reductions to be the protection of soil organic carbon (SOC) stocks. Designed specifically for application in the United States, the methodology relies on three primary datasets that can be used to estimate grassland conversion rates: National Agricultural Statistics (NASS), National Resources Inventory (NRI), and National Land Cover Database (NLCD). Furthermore, since this methodology is only applicable in the U.S., it includes several assumptions and eligibility criteria that tend to limit the eligible baseline scenarios and project activities to North America.

Furthermore, it is also important to point out that there are a quite number of dimensions over which the available datasets vary. For instance, scale, frequency, and land use classes. In this context, scale refers to the physical size of the project area; frequency refers to how often land-use observations are available; and land use observations are provided on annual, five-year, or decadal intervals. Land use classes, on the other hand, refer to the land covers, which in most cases include vegetation types, built-up, and other geophysical features. Below is a discussion of some of the concerns and limitations regarding ACR ACoGS:

1. Conversion to uses other than Cropland is not eligible

While the only acceptable form of land conversion under ACR’s protocol is conversion to cropland, GSEs (Grassland and Shrubland Ecosystems) are often at risk of other forms of conversion which can lead to a loss in soil carbon. For instance, introducing livestock to a natural ecosystem such as grassland may result in increased herbivory and ultimately loss of herbaceous plant species, along with their contributions to organic carbon in the ecosystem.

2. Allows altering natural systems to sequester carbon

Since the baseline scenario within ACR’s ACoGS protocol is conversion to cropland, where artificial irrigation is likely taking place, the restoration of grasslands into their native state with the absence of irrigation taking place might not lead to significant changes in sequestration of above and below-ground carbon. Hence, the methodology allows project developers who would perform irrigation in the alternate land-use scenario to continue irrigation on the project scenario of native grassland. From an ecological point of view, irrigation of native grasslands may pose significant concerns, as irrigating a natural ecosystem automatically changes it into an artificial or human-influenced system. Furthermore, irrigation may lead to unintended consequences such as depletion of groundwater resources and alterations of hydro-biological cycles, even though it supports aboveground carbon sequestration.

Improved ACoGS Framework

After examining some key concerns from a review of some of the existing ACoGS frameworks, below is a list of topics which we have collated, that should be considered in the creation of an improved ACoGS framework, namely: (1) universal applicability and inclusion of grazing; (2) use of remote-sensing based dMRV tools; (3) tracking of ecological parameters relevant to grazing; and (4) allowing for open-sourced SOC models.

Universal applicability, and inclusion of grazing

One of the goals during creation of an improved methodology for ACoGS should be to make it applicable in any grassland/shrubland/savanna ecosystem across the world that is at risk of undergoing degradation from grazing pressures. While it is difficult to estimate what would occur on an area in the absence of an ACoGS project in place, it might be possible to know region-specific parameters such as historical stocking densities of an area, and the hypothetical stocking densities in the future. Under a grazing-based methodology, ecological parameters such as carrying capacity, stocking density, and energy-transfer rates might be important to track, as described in subsequent sections.

One of the goals during creation of an improved methodology for ACoGS should be to make it applicable in any grassland/shrubland/savanna ecosystem across the world that is at risk of undergoing degradation from grazing pressures.

Utilizing remote-sensing based dMRV tools

Spatial and temporal coverage from remote-sensing provides an effective tool for rapidly and accurately assessing vegetation and other surface attributes such as bare soil and burn scars. While remote-sensing based approaches work effectively in homogenous vegetation types such as forests, it becomes more complex to conduct remote sensing in shrublands and savannas because of discontinuous tree canopy, grass patches, and exposed soil patches. The wet and dry seasonal patterns observed in grassland, shrubland and savanna ecosystems can add more complexity to the picture; furthermore, land use type has an impact on the structure of the savannas. For example, livestock production usually leads to the depletion of the herbaceous layer resulting in an increase in shrubs and trees, a phenomenon referred to as bush encroachment (Dougill and Trodd 1999; Bond et al. 2003; Hughes 2003; Archer et al. 2004; Bragg et al. 2013; McGlynn and Okin 2006). It is therefore imperative to take into account an array of factors when assessing land cover land use change using remote sensing techniques.

Ecological parameters relevant to grazing

Monitoring of land management and ancillary ecosystemic variables is not common practice in current ACoGS frameworks which are primarily focused on conversion to croplands. However, if grazing is to be considered as a driver of conversion, it is important to take into account land-management parameters such as carrying capacities and stocking densities, as well as ecosystemic parameters such as trophic energy transfer rates. Brief descriptions of each of these parameters is given below:

1. Carrying capacity: Carrying capacity is the maximum number, density, or biomass of a population that a specific area can support sustainably (Hartvigsen, 2017). For example, the amount of grazing land that should be made available to an individual herbivore so that it can be supported efficiently without deterioration of the natural resources of that area. Carrying capacity is affected by numerous factors such as precipitation (rainfall), evapotranspiration, structure and texture of the soil, vegetation composition and topography. It is generally also described as the correct stocking rate for a given area.

2. Stocking Density: The density at which an area is stocked, usually expressed in terms of hectares per mature stock unit per year, i.e. hectares per livestock unit (ha/LSU). The stocking rate is purely a report on the situation at a given time, and inference about land management requires its comparison to carrying capacity.

3. Energy Transfer Rate: Energy transfer rates represent flow of energy from primary producers to primary consumers, all the way through top predators. Grasslands have relatively high energy transfer rates, typically around 70%, implying that 30% of standing biomass will be left at the end of the growing season, assuming stocking densities are accurately estimated.

4. Open-sourced vegetation/SOC models

Not all of the existing ACoGS frameworks include ecologically-informed SOC models for estimation of carbon sequestration from soil carbon pools. Therefore, reliable SOC models should be used to model plant-soil nutrient cycling and simulate carbon and nutrient dynamics. To illustrate the use of SOC models in an ACoGS context, definitions and examples of two models, CENTURY and DayCent are given below.

The CENTURY ecosystem model includes a submodel about soil organic matter, which includes three soil organic matter pools (active, slow, and passive) with different potential decomposition rates, above and below-ground litter pools, and a surface microbial pool which is associated with decomposing surface litter. To simulate a savanna or shrubland, a combination of these three submodels can be utilized, along with additional submodels simulating nutrient competition and shading effects.

Other submodels include a grassland/crop production model simulating plant production for different herbaceous crops and plant communities, and a forest model simulating the growth of deciduous or evergreen forests in juvenile and mature phases. DayCent is a daily time-step version of the CENTURY model, which can also be used in agroecosystems to simulate fluxes of carbon and nitrogen between the atmosphere, vegetation, and soil. Disturbances such as fire, harvest, grazing, and cultivation can also be simulated with a combination of DayCent and CENTURY’s event-scheduling and land-management functions.

Conclusions

It is evident that carbon markets play a pivotal role in addressing global climate change, and the burgeoning grasslands sector, expected to contribute significantly to Nature-Based Solutions credits, requires more rigorous and universally applicable protocols. Existing evidence indicates that by 2030, Nature-Based Solutions implemented across all ecosystems can deliver emission reductions and removals of at least 5 GtCO2e per year, of a maximum estimate of 11.7 GtCO2e per year (UNEP 2021).

By 2050, this estimate could rise to at least 10 GtCO2e per year, of a maximum estimate of 18 GtCO2e per year. It is further estimated that about 24% of the estimated reduction would come from solutions in grasslands and croplands. By delving into the critical landscape of the NBS ACoGS sector, and undertaking a thorough analysis of the scientific and logistical challenges inherent in existing ACoGS carbon quantification and monitoring frameworks, this white-paper serves as a response to the identified shortcomings in existing frameworks, and presents the groundwork for some of Gazelle’s innovative methodological and dMRV solutions.

Fundamentally, carbon markets and especially the ACoGS sector within Nature-Based Solutions need rapid innovation and protocol development to expedite project development in this sector, and enable more communities and organizations to practice grassland, savanna and shrubland protection efforts. We believe that there is significant potential in improving quantification and monitoring frameworks within this nascent, but critical sector of NBS, and are building dMRV functionalities to enhance the accessibility and transparency of geospatial monitoring tools, facilitating improvement in carbon quantification and monitoring across NBS. The future of carbon markets relies on transparency, and Gazelle’s mission is to advance the tools necessary to measure and monitor nature.

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1. Verra’s VM0009 v3.0 methodology was deactivated 27 November 2023 due to the revised consolidated REDD+ (VM0048) methodology’s debut and expected transition by developers. Further details specifying the requirements for developers with regard to usage of VM0009 v3.0 for existing projects can be found on the Verra website.

2. The proxy area and reference area are both representing the ALS (Alternate Land-use Scenario). However, the proxy area is strictly the area where field measurements for the ALS are conducted, whereas the reference area is strictly for performing LULC analysis (to determine the rate of land-conversion). Further details specifying the requirements for proxy and reference areas can be found in the methodology.