Biodiversity in the Digital Age Part 4B

Welcome back!

This is the second part of the fourth article in the Biodiversity in the Digital Age series. This section of the article focuses on the principles behind biodiversity credit design for the supply side of the system and should be read in conjunction with an overview of the function for the supply side components provided in Part 4A.

Before recommencing it is worth re-visiting definitions for various concepts in this fourth article, specifically the terms ‘system’, ‘function’ and ‘design’.

· ‘system’ relates to the overarching objective of linking the natural and digital worlds through mutually beneficial transfers of value.

· ‘function’ relates to the mechanics and principles by which the system operates (i.e. foundations of a high integrity market).

· ‘design’ relates to the components and principles by which units of value in the function are constructed (i.e. considerations for creating high integrity and high utility credits in the system).

Within the system, biodiversity credits represent the atomic unit of value on the supply side, capturing measured and evidence based positive biodiversity outcomes. Although commonalities exist in the form and structure of biodiversity credits (see here, here and here) given the nascency and exploratory stage of the market it is not surprising to find a variety of different schemes each with a different approach to describing and capturing value.

Beyond a measure of biodiversity outcomes, however, it is worth considering that a credit (as a unit) can also be a container for additional measures. As mentioned above a credit can offer a representation of the governance and integrity of the system that creates it, whilst also being a record of the participants and management actions involved.

There is also consideration in the system for the end use of the credit, particularly for distinct (i.e. ‘value transfer to the digital world’) versus broad (i.e. ‘delivering credits to the market’) applications. As such, consideration for the utility and versatility of the credit on the demand side should also be incorporated into the design.

The following section outlines a series of principles relating to the design of biodiversity credits to enable high integrity value capture alongside their optimal use in the system.

The first principle to consider is that a biodiversity credit needs to be consistent yet adaptable. Consistency is required to ensure that the system it is portable and translatable to many locations whilst adaptability is required to ensure that important site level variability is captured. Many schemes seek to address the consistency component by adopting both a standardised area and standardised time period for credit unitisation. Whilst this is approach offers convenience from a market point of view there are some potential drawbacks.

The first is that different ecosystems exhibit varying relationships with time, particularly as relates to credits generated via ecosystem restoration. For example, an old growth forest may take centuries to reach peak biodiversity and stability, while other ecosystems like grasslands, might respond more rapidly to restoration efforts due to differing succession dynamics, and reproductive and life history traits.

The second is that standardising area has implications for the suitability of applying the same approach to ecosystems of varying complexity, species diversity and heterogeneity. For example, a naturally sparse ecosystem like a rangeland is structurally very different to a denser ecosystem like a tropical rainforest, with the composition of the former being better captured and described at larger rather than finer scales.

On top of this, natural variability between ecosystems is further complicated by the types and degrees of disturbances (across impacts and threatening processes), climate and the types and magnitudes of human interventions and management activities being undertaken. All of these are locally specific.

Beyond technical considerations, credit area standardisation also has implications for the supply side market dynamics with standardisation potentially incentivising the pursuit of more achievable and lower cost outcomes in areas of convenience (i.e. sparser ecosystems) rather than more complex and more costly areas of need. For example, in the Australian context the cost of actively restoring 1ha of rainforest was identified as being five times the cost of actively restoring 1 hectare of grassland.

A risk therefore exists that the market conflates size and time standardisation with a consistent amount of ecological benefit or conservation effort being attained, further driving activities towards commercial rather than ecological outcomes.

One approach to counteract this is to assign grades to credits, indicating the relative importance of a project location to biodiversity contribution. For example, Savimbo use public data on biodiversity density and ecosystem threat to classify their credits into 4 tiers (Platinum, Gold, Silver, Bronze) whilst Greencollar use an Environmental Significance Classification (ESC) based on conservation criteria to define a three tier grading system (Tier 1, Tier 2, Tier 3).

These credit grading systems are geared to incentivise nature restoration and conservation in areas where it is most needed by placing greater recognition on biodiversity credit projects occurring in areas of higher priority. Grades are also likely to offer benefits to market adoption by facilitating greater levels of product standardisation, interoperability and comparability between different biodiversity credit projects.

However, whilst aligning with publicly recognised priorities for biodiversity is desirable it is important to also recognise the constraints that come with incomplete datasets and the coarse scale generalisations within most of these layers. To that end using these as the sole criteria for defining a credit class would more closely align with pursuing modelled outcomes rather than monitored outcomes (a topic I covered in more detail in Article 3). Specifically, there is risk with this type of approach that the market ends up rewarding perceptions of biodiversity (eg. restored koala habitat in areas of potential koala occurrence) instead of the desired goal (i.e. koalas).

Plan Vivo have sought to address this by integrating a rarity-weighed richness value (threatened birds and mammals only) amongst other public data when assigning site-level significance labels for terrestrial sites. They have also aimed to reduce any potential methodology gaming or grading level generalisations by eliminating ranking both within and between their significance label types.

However, a potentially more robust approach is to account for the actual composition of an ecosystem and its biodiversity when considering the overall ‘value’. Savimbo do this by incorporating a measure of Integrity into the calculation of a biodiversity credit, with full integrity being defined as an ecosystem with every ecological niche available to, and occupied by, native species. In other schemes the concept of Integrity is sometimes captured by the similar concept of Condition. Importantly Integrity/Condition in this context is calculated based on measurement as defined by an approved methodology, with changes in integrity over time being used to define the outcome that has been achieved.

This then leads to the final bit of standardisation which is the type of biodiversity being measured. Given the complexity of natural ecosystems it is difficult to measure everything that constitutes biodiversity, so a practical and convenient approach is to focus on specific environmental asset classes. As an example, Accounting for Nature currently identify six distinct asset classes, Vegetation, Soil and Sediment, Fauna, Water, Ecosystem and Microorganism, with expectation that this be expanded upon over time (i.e. inclusion of Marine etc). Each of these asset classes can be described and measured through a range of certified methods, with each method being assigned an expected accuracy for which the asset will be measured (Moderate, High, Very High).

Within this in mind, the structure of a single asset credit expands beyond the previously considered measures of outcome, area and time to include consideration for the asset, method, accuracy and grade (Figure 1).

However, for any given spatial extent there are bound to be multiple, overlapping environmental assets present. So, although the structure offered in Figure 1 offers a convenience for market adoption, as well as a one biodiversity asset per area safeguard against potential over-crediting, it could be argued that a more representative biodiversity credit contains multiple environmental assets classes. With this in mind Figure 2 offers a potential structure for considering multiple assets within a single credit, with asset outcomes being stacked spatially and aggregated as a group outcome for a given period of time. Based on current approaches to grading for other schemes in the market, it is possible that this consideration be attached to either the asset (Figure 2A) or the location (Figure 2B).

Looking at the credit structures given in Figures 1 and 2 it is obvious that despite moves to standardise individual components of a biodiversity credit there will continue to be high degrees of variability based on the combination of variables measured. Therefore, although aspirations to standardise area for all circumstance as a basis of equivalence is understandable, some aspects of the approach, when so many other measures and outcomes within that area are possible, seem questionable.

With this in mind, one of the aspects I am keen to explore in the system is whether it is possible to somewhat normalise the outcome of a biodiversity credit by allowing for variability in size. This could be done through use of a hierarchical geospatial data structure like a quadtree to encapsulate biodiversity data at different levels of spatial resolution and with multiple levels of detail.

A quadtree is a hierarchical data structure that recursively subdivides a two-dimensional space into progressively smaller quadrants or cells. Each cell can then be further divided into smaller quadrants if necessary, forming a nested hierarchy of spatial partitions. The result is a standardised parent-child relationship that allows for consistent referencing and efficient querying and indexing.

The value of this approach is that offers a consistent structure that is adaptive to containing and representing spatial data at varying levels of granularity, making it suitable for accommodating the unique biodiversity attributes and structures of different environments.

Although contrary to commentary on the importance of area standardisation for market adoption, aggregating and synthesising biodiversity outcomes into variable sized credits could be more suitable to the ecosystem and structure of ecological benefit that has been attained. Of course, the suitability of using this approach when considering single asset versus multi asset biodiversity credits would need to be explored. Nevertheless, allowing for flexibility and adaptability on the credit area based on ecological conditions is an interesting concept, particularly as relates to variability in another key market factor, price!

As outlined above a biodiversity credit is primarily structured to capture and represent measured and evidence based positive biodiversity outcomes. However, due to the spatial and temporal nature of a credit there is a wealth of additional data and metadata that can also be recorded in a digital biodiversity credit, largely relating to the structure of biodiversity protection and restoration projects being undertaken.

This includes details of the credit archetype (eg. protection, restoration, stewardship, adaptation) and the objectives of the local conservation plans governing and directing the management activities (more details in article 3). It also offers an opportunity at the credit level to categorise the degree of engagement and participation for Indigenous People in the design and implementation of biodiversity credit projects. This can be done by recording whether programs are designed and led by IP and inclusive of IP values for success.

A credit can also contain a record of all on-ground management activities undertaken within the project. Management activities form a critical part of the information set used to evaluate the effectiveness of conservation programs, and from a management perspective are essential in planning the future deployment of effective and efficient activities. Management activities have a spatial footprint associated with them, and from a social perspective a link to the stakeholders involved with implementing them. Both aspects can be encapsulated within the spatial framework of a credit.

Capturing a record of management activities also offers opportunities for considering those circumstances where credit issuance for conservation actions might be appropriate. This includes ecosystems where long duration timeframes for recovery are expected (i.e. deserts, polar etc) or circumstance where a strong evidence-based relationship between intervention and outcome exists yet the upfront expense for intervention is prohibitively high compared to the timeframe for anticipated recovery.

To maintain high levels of integrity in this circumstance it is important to ensure that management activities remain additional to measured outcomes as the base structure of a credit, with a credit at the very least including a record of the baseline condition of the ecosystem.

A credit can also include records of physical conditions of habitat that support biodiversity, particularly where interventions are geared towards the removal of non-biological threats (i.e. reductions in erosion, removal of contamination, improvements in water pH etc). Once again interventions to achieve these outcomes can be costly to implement so mechanisms of credit issuance, whilst balancing against the maintenance of high integrity outcomes for biodiversity, or potentially a separate pool of funding for such activities, need to be considered.

Connectivity in ecosystems is crucial for maintaining biodiversity, ecosystem stability, and the resilience of natural communities. It allows for the free movement of species, is essential for many species’ life cycles, and supports ecological processes such as pollination, seed dispersal, and nutrient cycling. Well-connected ecosystems are also more resilient, demonstrating a greater ability to withstand and recover from disturbances and natural disasters.

Ecological connectivity is increasingly recognised in global policy as a key element for biodiversity conservation and ecosystem resilience. International frameworks such as the Convention on Biological Diversity (CBD) and the United Nations’ Sustainable Development Goals (SDGs) emphasise the importance of creating and maintaining ecological corridors and networks (for example here and here).

Ecological connectivity is therefore a value worth considering within the framework of a biodiversity credit. Connectivity is a key component in ensuring that biodiversity protection and restoration activities are resulting in long-term positive impacts, with failure to account for connectivity potentially misrepresenting the overall value and effectiveness of any given project (note — size and configuration is an important consideration here but that is a whole other topic beyond the scope of this discussion).

Connectivity is commonly recognised across two components. Structural connectivity refers to the physical arrangement and continuity of habitat patches within a landscape. It is concerned with the spatial configuration and the presence of physical links, such as corridors, that connect different habitat areas, allowing for the potential movement of species. Functional connectivity goes beyond physical layout to consider how landscape features facilitate or impede the actual movement and flow of organisms and ecological processes. It focuses on the behavioural responses of species to the landscape structure, including their ability to move, disperse, and interact within the ecosystem.

Whilst not comprehensive to all facets of connectivity there are a range of numerical indices offering quantitative descriptions of the spatial context and connectivity of ecosystems. These include measures like patch density, size distribution, edge density, connectivity index, landscape shape index, core area proportion, habitat diversity index, and effective mesh size.

A key component of our design is to therefore incorporate quantitative measures of connectivity into the design of the credit, not only for the purpose of ensuring high integrity outcomes but also for the opportunity of centering outcomes around nature itself.

Given that the concept of connectivity is deeply embedded in the idea of nature providing value for nature, rather than the anthropogenic view of nature providing value for humans, the incorporation of connectivity offers a means of including nature as a key stakeholder in the credit via measures indicative of its intrinsic right to exist.

It is anticipated that this process will be aided by the adoption of the quadtree structure in the function of the system where the relationship between cells is clearly understood, and the richness of outcomes can be used to define the value of linkages at patch and landscape scales.

Once a measured biodiversity outcome has been achieved there are opportunities to provide verification of that outcome by cross-verifying across a range of data and metadata components within the credit and the project. This includes looking at the intersection of measured outcomes with the spatial footprint of the management activities, comparison with other ecological measures and indicators, and the degree of change recorded at the credit level versus the whole of project level. For some environmental assets there is also an opportunity to adopt a consensus-based approach. This can be done by comparing outcomes recorded at the project and credit levels against external public information sources such as Sentinel satellite data, with a reference to the source data being recorded in the credit.

Third party verification frameworks such as Accounting for Nature framework also require comparisons to be made between the measured outcome and the integrity or condition of a similar ecosystem in a pristine state. Typically, this is done using data from a reference site obtained via the same method used in the project area. With remote sensing data, however, it is possible to aggregate measures of ecosystem condition for multiple reference sites across several locations to provide an additional layer of comparison with the outcome that has been achieved.

By leveraging diverse data inputs to obtain a more comprehensive and accurate assessment of biodiversity outcomes there is an opportunity to reduce some of the bias and uncertainty associated with relying on a single method or data source. Additionally, by incorporating cross-verification checks into the pre-issuance process there is an opportunity to enhance the credibility, reliability and efficiency of credit issuance.

This can be achieved by using data rules to highlight any anomalous outcomes and recording the results of the cross-verification process, including the location of any reference sites and external data sources, in the metadata of the credit.

Over time there is also an opportunity to bring more automation and transparency to credit issuance. This can be done by moving progressively from a pre-issuance flagging approach with a high degree of manual oversight to a more automated process using smart contracts once reliable outcomes have been clearly demonstrated.

The final principle to consider for the design is the use of credit data as a means of dynamic credit benchmarking and as a source of continuous improvement. The concept of reflexive biodiversity credits involves continuously grading the value and integrity of each biodiversity credit by comparing it to the entire pool of historical biodiversity credits generated within the system. This approach creates a dynamic feedback system where current credits are contextualised within the performance and impact metrics of all previous credits. By embedding this comparative grade into the credit’s metadata, stakeholders can gain immediate insights into its relative effectiveness and reliability. This reflexive mechanism also ensures that the evaluation of biodiversity credits remains dynamic rather than static, continually evolving and improving over time based on the set of accumulated data.

At scale, the reflexive approach to biodiversity credits becomes significantly more valuable by leveraging the vast amount of data generated from all biodiversity credits ever issued. As the dataset grows, the system’s ability to discern patterns, identify best practices, and pinpoint areas for improvement becomes increasingly sophisticated. This large-scale data aggregation allows for more nuanced and accurate assessments of biodiversity outcomes, enhancing the overall credibility and utility of the credits. Also, by analysing trends and variations across a broad spectrum of projects, the system can better account for ecological complexities and regional differences, leading to generally better informed and more effective conservation strategies.

Furthermore, growth of an extensive data pool offers an opportunity to enhance trust and transparency in the system. With access to comprehensive historical data, stakeholders can make better-informed decisions, confident in the robustness of the grading system. This transparency also fosters greater accountability, as credits are continuously benchmarked against a growing body of evidence.

Ultimately, a reflexive biodiversity credit system, powered by large-scale data, should facilitate a more responsive and resilient approach to biodiversity conservation, by ensuring that efforts are consistently aligned with the evolving ecological landscape and societal needs, and by promoting a continuous drive towards high integrity outcomes.

Congratulations on making it to the end of Part 4B of the Biodiversity in the Digital Age series. The next part of this article will explore the demand side aspects of the system and focus on how the whole system links together.

In the meantime, if you believe that memes can help us build the world’s best biodiversity market then jump over to the EcoMemes project.

Mint this article as an NFT!