Nature-based Solutions (NbS) carbon-credit projects hold immense promise for corporate investors due to their combination of measurable climate impacts along with a boosted reputation, increased financial returns, and social benefits. However, without independent due diligence and expert carbon-modelling risk assessment, many projects fall short of expectations. At Xilva, we specialise in helping investors and corporate buyers assess and mitigate these risks effectively. As the NbS market grows, one key question keeps surfacing: how can we make sure that the carbon credits these projects generate are as reliable as possible?

Recent studies reveal that many projects overestimate their carbon sequestration potential. As a consequence, this can make it harder for investors to accurately predict returns, and for developers to deliver on expectations. The good news is that these challenges are well understood. There are clear ways to manage such challenges, not only with better data and models, but also via thoughtful project design.

Let’s explore why uncertainty happens and what solutions might be out there to reconcile with it. 

Between Models and Reality

Before a single tree is planted, projects rely on ex-ante carbon models to forecast how much carbon they will capture over time. These models are built on factors like tree growth rates, species selection, and land-use assumptions. They are essential for estimating both climate and financial outcomes. Yet in practice, they are often prone to uncertainty, incomplete baselines, and methodological gaps. From Xilva’s due-diligence reviews of nature-based carbon projects, we frequently observe (and have documented this in our report [1]) that 70% of projects assessed face issues tied to carbon models, with 21% of identified risks stemming directly from them.

Compounding this are uninformed market expectations. Many newcomers to the sector underestimate the natural variability of ecosystems, where tree growth can differ dramatically due to genetics, micro-climates, or management practices. Even where long-term forestry data exist, the uncertainty of future climate conditions makes accurate forecasting difficult.

Equally problematic are inflated profitability assumptions. Promises of returns above 20% IRR or carbon priced at just a few dollars per tonne rarely reflect the true biological and operational complexity of sequestration.

Additionally, reliance on remote sensing can create a false sense of precision. While valuable, these tools depend on robust local ground data to remain reliable. Without ground-truthing, sensor-based models risk reinforcing overconfidence rather than reducing uncertainty.

Common Causes of Uncertainty 

Several recurring issues explain why carbon credits are frequently inflated or don’t quite match up to real-world outcomes:

• The ‘emperor’s new clothes’ effect: Stakeholders may take model outputs at face value without questioning their ecological or financial plausibility. This creates a feedback loop where overconfidence is rewarded, and inconvenient truths about natural variability, unrealistic profitability assumptions, or the true costs of sequestration are overlooked.

• Fundraising pressure: Early-stage projects usually need to demonstrate strong impact potential in order to secure investment. This introduces a temptation to showcase optimistic carbon scenarios that deliver a minimum number of credits within a given timeline, otherwise developers risk losing out on funding. 

• Early-stage projections: Many projects rely on preliminary models that lack baseline integrations, risk buffers, and site-specific data. These gaps create unreliable projections of sequestration potential.

• Eligibility and compliance challenges: Some models assume credits can be sold under certain carbon standards without fully demonstrating eligibility or compliance. This creates uncertainty in financial projections.

• Double counting: Projects face risks when sequestration is also claimed by national governments under their Nationally Determined Contributions (NDCs).

• Species and site discrepancies: A lack of known tree growth rates, especially for native species, leads to assumptions about growth rates or local conditions that fail to match reality. This can be compounded by another common pitfall, which is to use the growth rate of one single tree multiplied against the total number of trees, without considering the effect of competition or density. This generates an unrealistic estimation of biomass at maturity.

• Additionality gaps: Projects may claim credits for outcomes that would have occurred anyway, especially when subsidies or pre-existing land-use practices already contribute to sequestration. In some cases, this extends to when the project generates high returns from timber (e.g. fast-growing species with harvesting) or high-value agroforestry systems (e.g. coffee, cocoa, etc.).

• Permanence concerns: Even when sequestration is achieved, long-term sustainability may be threatened by wildfire, disease, or socio-economic pressures such as poverty-driven deforestation, which lead to harvesting after the end of the crediting period due to a lack of proper safeguards.

Ripple Effect of Overestimation

For investors, inflated carbon models erode financial projections and introduce hidden risks. If a project anticipates revenue from 1 million credits but generates only 400,000, the gap will obviously directly affect IRR and cash flow. 

But, inaccurate carbon models go beyond the financial impacts. One such example is a breakdown in community trust. When promised revenue streams fail to materialize, local stakeholders lose faith in the project, leading to disengagement or even conflict. Another impact is the loss of credibility; investors and corporations associated with overestimated credits face accusations of “greenwashing,” undermining ESG commitments. Additionally, the market value of credits goes on the decline, which limits future growth potential of this and other projects. Projects linked to overcrediting controversies are harder to scale, attract less co-investment, and risk regulatory or public backlash.

In sum, unreliable models can weaken the entire market for NbS investments if they are not carefully screened for in a thorough due diligence process. When carbon estimates align with reality, everyone benefits: investors gain confidence in returns, communities see consistent value from projects, and the broader NbS market earns credibility. By engaging a specialist due diligence consultant such as Xilva’s expert team early, stakeholders can mitigate reputational, financial and operational risks in nature-based carbon projects.

Recent Discoveries

• The Haya Study [2]: Found that standards systematically failed to ensure carbon credits represented real, additional sequestration. Many REDD projects were credited for “business-as-usual” practices, eroding credibility.

• Xilva’s internal review: Identified repeated gaps in baselines, lack of methodological validation, and poor governance as key drivers of carbon overestimation in early-stage projects.

• Independent auditing [3]: Highlighted that third-party verifiers themselves often overvalue project credits, raising concerns about systemic bias in the validation process.

These cases reinforce the need to incorporate rigorous due diligence into the investment process. Due diligence providers have the in-house expertise to spot fundamental gaps in carbon modeling approaches and help minimize the risk of carbon credit overestimation and grapple with persistent uncertainties.

Signs of Robust Modeling

To minimize the inaccuracy of carbon models, investors should look for projects that implement robust and transparent practices. Below are a few examples of what our expert analysts at Xilva scrutinize when evaluating carbon model projections:

1. Ex-ante model quality control

   • Verify that project documentation aligns with ex-ante models (e.g., forest management plans reflected in sequestration estimates).

   • Confirm conservative estimates, including buffer discounts and uncertainty, with the latter judged according to the appropriate level of uncertainty:

     1. Level 1: Lack of allometric equations, density values, expansion factors, root to shoot ratios, and growth models– all adapted to species, local context and management practices, especially stand density management.

     2. Level 2: Sample design, which is typically addressed by the chosen carbon standard and set at a level of maximum error expected under a level of confidence.

     3. Level 3: Measurement operations and data processing, which requires training and best practices in QA/QC.

2. Cross-validation with local data

   • Ensure species- and location-specific parameters from peer-reviewed literature are applied.

3. Baseline integrity

   • Scrutinize whether “business as usual” assumptions align with local historical practices.

4. Clear eligibility and compliance 

   • Projects should demonstrate compliance with selected carbon standards and anticipate regulatory evolution.

5. Transparent governance and expertise

   • Look for teams with proven experience in large-scale forestry and carbon methodology validation.

   • Assess whether additional technical support is factored into project design.

6. Additionality and permanence safeguards

   • Ensure financial and environmental additionality are clearly demonstrated.

   • Evaluate long-term permanence strategies, including monitoring, community incentives, and adaptive management.

We integrate these steps into our full due-diligence service offering - Xilva GRADE - for nature-based carbon projects.

Additional Paths Forward

Try as we might, uncertainty will always be present in any NbS project. While minimizing overestimation in carbon models is one approach, there are alternative pathways to deal with uncertainty head-on. In our advisory work at Xilva, we recommend these advanced design mechanisms for NbS carbon project due diligence and structuring.

Scenario-Based Flexibility Framework

Every NbS project operates in a dynamic environment, from changing weather patterns to shifting community priorities. To navigate this variability, a scenario-based flexibility framework can help investors and developers stay aligned while keeping expectations realistic.

Projects can explore a range of potential outcomes, from baseline to optimistic or constrained scenarios, and design agreements that flex with real-world conditions rather than fight against them.

Here’s how this works in practice:

• Scenario development: The project team defines a few plausible implementation scenarios, each reflecting different environmental, operational, and financial conditions.

• Linked terms: The credit purchase agreement is structured to adjust key elements (e.g. volume, pricing, or delivery timelines) depending on which scenario unfolds, guided by clear, pre-agreed indicators.

• Regular check-ins: Built-in review points give both parties a chance to assess progress, align expectations, and make thoughtful adjustments where needed.

This framework encourages transparency, collaboration, and resilience, altogether helping projects adapt gracefully to change.

Minimum Performance Threshold with Optionality

Even with strong modeling and flexible design, some level of uncertainty will always remain in NbS projects. To protect both performance and partnership confidence, a minimum performance threshold paired with buyer optionality can offer a balanced safeguard.

This approach sets a clear, agreed-upon baseline for delivery while maintaining flexibility if results fall short for an extended period.

Key elements include:

• Minimum guarantee: Define a lower performance threshold for credit volume or delivery, grounded in realistic expectations from the project model.

• Buyer option: Include a conditional option that allows the buyer to adjust, defer, or restructure commitments if performance consistently falls below this level.

• Trigger definition: Set clear, jointly agreed triggers and timeframes for when and how this option could be activated.

By integrating this mechanism, projects create space for honest conversations and proactive adjustments. These help to maintain trust, protect value, and ensure that long-term collaboration remains both fair and forward-looking.

Continuous Data Validation and Sector Learning

Reliable long-term monitoring should also strengthen the scientific foundations of carbon models. Each project should incorporate, as part of its monitoring plan, a structured approach to data collection and validation appropriate to its scale. This includes verifying whether the allometric equations, density values, expansion factors, and other parameters used are truly representative of local conditions.

Where necessary, this may involve destructive sampling to calibrate or confirm model assumptions. If, after validation, results prove consistent, the selected equations and parameters can be considered robust. However, if discrepancies are significant, additional sampling should be planned and executed as part of the project’s risk mitigation strategy.

At a broader level, the sector should encourage the publication and sharing of such validation results. Contributing this data to growing global databases, such as GlobAllomeTree and similar initiatives, would help refine models across regions and project types. Over time, this collective learning would reduce uncertainty, enhance model reliability, and strengthen market confidence in NbS carbon modeling.

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[1] (2024). Navigating Risks and Realising Opportunities in Forest & Nature-Based Investments. Xilva AG

[2] Natasha White (2023). Bogus Carbon Credits are a ‘Pervasive’ Problem, Scientists Warn. TIME.

[3] Carla Raus (2025). ‘Independent’ auditors overvalue credits of carbon projects, study finds. Mongabay.