Net Positive Labs x Blue Guardian
About the authors
This article is the result of a joint effort by Net Positive Labs and Blue Guardian Consulting, two organisations with deep expertise and a strong commitment to advancing carbon markets. Together, they offer their insights on the current state of these markets and explore what the future may hold.
Benno Guenther, CEO and Founder of Blue Guardian Consulting is a specialised environmental and behavioural economics consulting firm based in London. Providing pricing, structuring and risk management solutions for international carbon offset projects and the energy transition, with a mission to contribute towards a more sustainable future.
Frank van Beuzekom, COO of Net Positive Labs. A venture-building and strategy firm focused on creating scalable, market-based solutions in sustainability. The team has been actively engaged in the carbon markets domain across multiple fronts — from strategic advisory to venture development and ecosystem design. This includes work on initiatives such as Cambium, as well as collaborations with corporates, investors, and market participants seeking to navigate — and shape — the next phase of carbon markets.
Carbon markets rely on a deceptively simple premise: one credit equals one tonne of CO₂(-equivalent) reduced or removed. In reality, this “tonne” is often an estimate, shaped by assumptions, sampling methods, and imperfect data.
This article explores why measurement is a major bottleneck of carbon markets — and why improving it is both a technical and systemic challenge. From low sampling rates and static baselines to unmeasured co-benefits and misaligned incentives, the current system struggles to deliver consistent, verifiable outcomes.
At the same time, advances in data, AI, and remote sensing are creating the foundations for a new paradigm: one where measurement becomes continuous, transparent, and multidimensional.
The future of carbon markets will depend not on how much is traded, but on how well impact is measured — and trusted.
At first glance, carbon markets appear highly precise.
A credit represents one tonne of CO₂(-equivalent).
A number that suggests scientific rigor, comparability, and clarity.
But beneath that simplicity lies a more complex reality.
Most carbon credits are not directly measured. They are modelled. They rely on assumptions about what would have happened without a project, how systems behave over time, and how impacts scale across space. In other words, they are constructed representations of reality — not direct observations.
This does not make them invalid.
But it does make them uncertain.
And in a market built on trust, uncertainty is not neutral.
Consider a typical forestry project.
A small sample of trees is selected and measured — their height, diameter, and biomass carefully recorded. In some cases, trees are even cut down to refine estimates. These measurements are then extrapolated across a much larger area to estimate total carbon storage.
It is a process grounded in established scientific methods. But it is also inherently limited.
Sampling introduces uncertainty.
Extrapolation amplifies it.
And time — changes in weather, land use, and ecological dynamics — adds another layer of complexity.
As outlined in the underlying work, this approach is still largely manual, periodic, and assumption-heavy.
It was sufficient for early market development.
It is no longer sufficient for scale.
The challenges of measurement are not new. But they are becoming more visible — and more consequential.
Low sampling rates can lead to significant deviations between estimated and actual impact. Static baselines struggle to reflect evolving realities, making it difficult to determine whether a project is truly additional. Co-benefits — from biodiversity gains to social improvements — are often asserted rather than quantified.
And perhaps most critically, measurement is not isolated from incentives.
If the system rewards the issuance of credits, there is a subtle but persistent pressure to interpret data in ways that support issuance. Not necessarily through bad intent, but through structural design.
The result is a system that can appear robust on paper, while remaining fragile in practice.
What has changed in recent years is not just technology, but expectation.
Stakeholders — from investors to regulators to the public — are demanding a higher level of confidence. It is no longer enough for a credit to be “reasonable.” It must be defensible.
This shift is driving a transformation in how measurement is approached.
Satellite imagery now allows for large-scale monitoring of land use and forest cover. Remote sensing technologies can provide more frequent and consistent data. AI models can process vast datasets, identifying patterns and anomalies that would previously go unnoticed.
At the same time, digital infrastructure is improving traceability. Credits can be tracked, compared, and analysed in ways that were not possible before.
Together, these developments are moving the market from periodic estimation to continuous verification.
As measurement improves, another reality becomes clear: carbon alone is not enough.
Projects operate within ecosystems and communities. They affect water systems, biodiversity, livelihoods, and local economies. Yet these dimensions are still largely absent from formal measurement frameworks.
This creates a gap between what is valued and what is measured.
A project that delivers significant biodiversity benefits may not be recognised for it. Another that performs well on carbon but poorly on social outcomes may still receive equal treatment in the market.
Closing this gap will require a shift toward multidimensional measurement — one that captures not just carbon, but broader system impacts.
For most of its history, the carbon market has relied on estimation.
Sampling, modelling, extrapolation — all necessary tools, but tools that carry uncertainty. That was acceptable in a market finding its footing. It becomes far less acceptable in a market expected to underpin financial decisions at scale.
What is now emerging is a gradual but decisive shift.
Measurement is becoming more continuous, drawing on real-time or near-real-time data rather than periodic snapshots. It is becoming more data-driven, fuelled by satellite imagery, remote sensing, and increasingly sophisticated analytical models. It is also becoming more transparent and comparable, allowing projects to be assessed not just individually, but relative to one another.
But this shift comes with trade-offs.
As measurement improves, it also becomes more complex. Systems need to integrate multiple data sources, reconcile inconsistencies, and operate across geographies and methodologies. The bar is rising — not just for accuracy, but for coherence.
This is not a marginal improvement.
It is a redefinition of what counts as credible.
In the future, a carbon credit will not be judged solely by what it claims to represent, but by how convincingly — and consistently — that claim can be demonstrated
Carbon markets do not fail because they attempt to measure impact.
They fail when the measurement is not strong enough to support the claims being made.
That distinction matters.
Because the next phase of the market will not be shaped by those who can produce the most credits, but by those who can reduce uncertainty — who can bring measurement closer to observable reality, and make that reality transparent to others.
At its core, a carbon credit is not just a unit in a registry or a line in a balance sheet.
It is a statement about the world: that something has changed, somewhere, in a measurable way.
If that statement cannot be trusted, the system that depends on it cannot hold.
The core challenge going forward is thus the reliable measurement of impact which not only determines the ultimate trust in the effectiveness of carbon projects but also creates a wealth of opportunity to get involved.
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