# The paradigms To measure ecosystems for financial markets, we must first negotiate paradigms between Nature (best represented in Indigenous sciences) and financial markets (one of our most optimized industrial-world sciences). We’ll quickly review and explain the multidisciplinary insights we have adopted, which took years to build, so as to cover the emerging science in the next section, but encourage readers to review the logical frameworks we operate in independently. #### Nature was complex, now its also chaotic Nature has always been inherently complex (Bak and Paczuski 1993), and in recent decades its dynamics have become increasingly chaotic (Bernardini et al. 2025). We are now in ‘black swan’ territory (Sornette 2009), with chaotic weather events increasingly likely (Hardin 1968). AI might give us some clue of what is coming, but it will not save us. Machine learning (ML) (pattern recognition AI) is particularly error-prone in this context, as long-term prediction may fail catastrophically regardless of the data available (Fan et al. 2020). Therefore, predictions based on the past, no matter how expensive or detailed, are increasingly unreliable. Attempting to measure everything in an ecosystem is an inherently flawed endeavor. It is particularly unfortunate, in the context of severe financial shortfalls for Nature, that many developers are expending unnecessary costs in quantification methods that are inherently expensive and ultimately illogical (Dinerstein et al. 2019; United Nations Environment Program 2022). #### Nature markets are emerging markets Humanity is only beginning to understand and value the limits of growth (Strauss 2012). Our economic sciences operated under the assumptions that the commons had no owner, no regulation, and no limit (Hardin 1968). Now valuing natural commons, the associated risks of its destruction to a nation, a planet, and its food supply, are completely unoptimized processes and science (Constanza et al. 1997). Full of human hubris, errors of cognitive horizon, and, like any emerging market, rife with speculation and misrepresentation (Taleb 2007). Ironically, one of the world's most optimized markets is agriculture, transacted as a commodity. Perhaps the planet’s least optimized one is planetary sciences, which should be tangible but transacts in assets, commodities, charities, greenwashing claims, and fiat and blockchain Over-the-counter (OTC) exchanges with wide heterogeneity and enthusiastic grassroots abandon. The collision of the two is a source of unending confusion for anyone who works in either of them these days. Early markets typically go through a predictable cycle, called the Gartner Hype Cycle. A period of heightened expectations, followed by disillusionment, then standardization (Fig. 2).
\ Figure 2. Phases of the Gartner-Hype cycle. Reproduced from Savimbo was born from a consulting group specializing in frontier markets, hard science, and high-tech bubbles. As such, we know from years of experience in several frontier markets that the way to survive the ups and downs is to keep focused on tangibility, value, standardization, and interoperability. Serious project developers are aware of this and tend to sell climate credits with third-party verifiers and aim at credibility while communicating uncertainties clearly. We keep an eye on emerging science that clarifies our thinking, and one orienting concept has been orthogonal ecosystem dimensions. #### Orthogonal ecosystem dimensions Nature does not live in databases. Nor is it neatly divisible. Certainly, our linear computing systems and statistics do poor justice to natural patterns like fractals. Indeed, many of our Indigenous friends bemoan the deconstructionist nature of industrial world analytics in the natural sciences (Forestiero 2022). However, and pragmatically, the current economics of Nature are becoming increasingly computerized. Making databases or metrics that are overly simplified gives measurement noise, fails to account for harmful effects, and wastes time in confusion (Muller 2018). But its also true that simplifying data can lead to elegant solutions in complex systems that cannot be accurately characterized (Mitchell 2009). Additionally, multidimensional measurements can reduce harm by measuring unintended effects (Scott 1998). Just like complex three-dimensional mathematical shapes can be described in an x-y-z axis, ecosystems can be better characterized in clean orthogonal dimensions. Orthogonality implies linear independence between dimensions (Szabo 2015), such that variation in one dimension (e.g., carbon) does not determine variation in another (e.g., biodiversity). Orthogonal structures are essential for describing ecosystems such as forests exhibiting the ‘empty-forest syndrome’ (Redford 1992) or high-carbon monoculture agricultural systems where eucalyptus damages non-native water tables, or trees are planted in native grasslands (Villalba-Martínez et al. 2025). In all of these cases, carbon, biodiversity, and water show orthogonality (See [Fig. B](#figure-b.-worlds-shittiest-graphic-explaining-the-carbon-and-biodiversity-markets)). #### **Figure B.** World's shittiest graphic explaining the carbon and biodiversity markets
Four-panel diagram contrasting carbon and biodiversity markets using apples as ecosystems. Carbon data alone rates a healthy forest and a non-native eucalyptus plantation as equally "awesome." Stacking biodiversity data on top exposes the plantation as an ecological disaster. Illustrates the orthogonal-data principle behind Savimbo's SexyTrees reforestation methodology.
We think ecosystems are best characterized in six orthogonal dimensions, as defined in the Ecological Benefits Framework (EBF): soil, air, water, biodiversity, carbon, and equity. The framework was not developed through a top-down academic approach but through three sequential grassroots practitioner workgroups who collectively defined the dimensions based on their applied experience. The workgroups ran from peer expert networks recruited from ocean sustainable fishing, organic farming, and regenerative agriculture and converged reliably on the same dimensions. Three of the EBF dimensions neatly align with the Rio Conventions, and have emerging or established markets with some standardization [(](https://docs.google.com/document/d/1Z907mScq_zQJyqts29ganDMpyMYYJ7QGLfTPfakuNU0/edit?tab=t.0#heading=h.uzgkzqo64ccv)see section [The Protocol)](https://docs.google.com/document/d/1Z907mScq_zQJyqts29ganDMpyMYYJ7QGLfTPfakuNU0/edit?tab=t.0#heading=h.uzgkzqo64ccv). Of these, only carbon has an easily agreed-upon unit, although the IBU biodiversity unit proposed later in this chapter does have market traction. The six dimensions each face independently emerging science and markets, and their interactions remain poorly characterized — a fragmentation that our stacking framework is specifically designed to navigate. But as a basic framework on which to orient, we think EBF is the most reliable place for anyone working with Nature from an industrial world context to start. Figure 3. Illustrating how orthogonal dimensions enable more precise characterization of ecosystems.
#### Stacking vs bundling Working with six interconnected dimensions does have the potential for double-counting (paying for the same action twice). But conversely, it allows simple, holistic actions to get rewards for multiple beneficial effects. We strongly recommend that developers are explicit about their choices on stacking vs bundling so all parties are clear on this before any ecological credits are listed or sold. Stacking refers to the practice of separating ecosystem dimensions into separate crediting layers. A topology could issue carbon credits and biodiversity credits under separate data layers, even separate project developers or certifiers, and be paid for those actions separately. Stacking ecological data layers is a common practice in GIS information systems like Google Earth Engine. Bundling is the practice of claiming more than one ecosystem dimension in the same area-based credit. For instance, a hectare of ‘Nature’ with associated carbon, biodiversity, and water claims, metrics, or storytelling. Bundled credits widely vary in their standardization, and to our knowledge, do not have third-party certification pipelines as yet. But they have the benefit of making more sense to local populations, and are more aligned with Indigenous cosmologies that treat Nature as a living, interconnected whole rather than a set of separable measurable dimensions. We recommend data “stacking” for integrity in data science and because of the different rates of development of the markets corresponding to the Rio Conventions. This is mirrored in science, finance, and public opinion. It does impose a Western analytical decomposition onto systems that resist it, but we believe this can be resolved through co-design with Indigenous rights-holders for each dimension, as demonstrated in the IBU negotiations [(Paynter et al. 2024)](https://sciwheel.com/work/citation?ids=18207076\&pre=\&suf=\&sa=0). In our community negotiations, leaders have been pragmatic about the need to access biodiversity and carbon markets independently — including through different partners — as a deliberate strategy for diversifying risk. Stacking orthogonal dimensions prevents gaming because you can't optimize for all six simultaneously — improving one at the expense of another becomes visible. Single-dimension carbon crediting has demonstrably enabled perverse outcomes — monoculture plantations that sequester carbon while collapsing biodiversity, depleting water, and undermining food sovereignty [(Martello et al. 2024)](https://sciwheel.com/work/citation?ids=18626925\&pre=\&suf=\&sa=0). Large-scale restoration projects financed primarily through pre-certification carbon offtake agreements face structural incentives to optimize for biomass accumulation over genuine ecological complexity — the financial equivalent of a monoculture, regardless of species diversity on paper. Orthogonal stacking constrains this: a system that performs well on carbon but poorly on biodiversity, water, and equity dimensions cannot claim holistic ecological integrity. Stacking also makes pragmatic sense now because the dimensions are at different maturity stages — carbon is post-hype, biodiversity and water are pre-standardization — so a diversified stack hedges against any single market collapsing or stagnating (See Fig. 2). Market development lags, but does reflect, scientific standardization, especially in refining real-world translation from academia. In our projects, the science of the carbon dimension and the science of the biodiversity and water dimensions are radically different in both character and convergence, as are the calculations for each. We also suffer the market variances between these three, and it has been better for us to diversify our market access as we cannot control global trends in the markets. So,while we truly sympathize with the unquantifiable chaotic glory of an intact natural system, we find it simpler, more practical, and more actionable to stack the financial rewards associated with it. Humbly admitting it's a poor translation overall to arrive at a database, or a dollar for something we admire so greatly. #### Interoperable Biodiversity Unit (IBU) We'd like to focus this chapter on the tangible gains achieved by adding a standardized unit to the biodiversity dimension — a meaningful milestone, and the subject of the conceptual methodology and protocol that follows. But first, a brief review of the innovation and its implications. A brief reminder that units, methodologies (protocols), and metrics are not the same thing. They differ in their interoperability and generality. A metric is a direct representation of a physical state. A methodology is often restricted in scope and standardized for bias, sampling error, and conclusions in a particular context. A unit is an output format that is interoperable with other contexts. Assessing absolute changes (as opposed to percentage shifts) is essential for ensuring comparability of biodiversity credits across projects and ecosystems, and to prevent “baseline gaming” [(Bull et al. 2014)](https://sciwheel.com/work/citation?ids=11958580\&pre=\&suf=\&sa=0). It also allows access to credits through commodities exchanges. The only certified credits in the world for biodiversity at this moment use Savimbo’s Indicator Species Biodiversity Methodology (ISBM) [(Savimbo 2024; Carbon Pulse 2025)](https://sciwheel.com/work/citation?ids=17980270,18207323\&pre=\&pre=\&suf=\&suf=\&sa=0,0). This protocol is restricted to area-based conservation sites and uses indicator species observations as a metric. For its final output, it issues IBUs, which are interoperable with other types of actions like avoided loss, impact reporting, and uplift (IBUs; [(Paynter et al. 2024)](https://sciwheel.com/work/citation?ids=18207076\&pre=\&suf=\&sa=0). Interoperable Biodiversity Units are issued based on time, area, and ecosystem integrity; one unit representing one hectare for one month at full integrity, i.e., an intact ecosystem with all ecological niches occupied (Fig. 4). In a typical restoration scenario, IBUs represent the absolute change in integrity between the pre-intervention state and the crediting point per month and hectare (i.e., the total integrity gained from t₀ to t₁) and it is derived by multiplying the time units by the percent increase in integrity (Fig. 4). Figure 4. Example IBU calculations for a 1 ha platinum ecosystem. a) A restoration project. The integrity is observed to increase from I=0.25 to I=0.75 over a 6-month period, resulting in 3 platinum credits awarded. b) A restoration project. This project only credits the difference in integrity, thus 1.5 platinum credits are awarded for this project. c) A conservation project. This project has full integrity over each 1-month interval, resulting in 6 platinum credits awarded. Adapted from [(Paynter et al. 2024)](https://sciwheel.com/work/citation?ids=18207076\&pre=\&suf=\&sa=0). The insights above have helped us to narrow the scope of agroforestry crediting by simplifying targets and defining what should not be attempted. In the next section, we will review what the next steps in science likely are and how we are meaningfully advancing it.
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