How to Calculate Carbon Removal Delivery Confidence
Corporate buyers and climate funds increasingly contract multi-supplier carbon removal portfolios to achieve net-zero milestones. The challenge is delivery risk: emerging technologies, permitting hurdles, and logistics delays can all reduce tonnes delivered relative to the contracted quantity. A rigorous delivery confidence calculation converts diverse supplier intelligence into defensible probability estimates that integrate with financial provisioning such as the buffer pool workflow.
This guide outlines a probabilistic framework grounded in binomial statistics. By defining effective supplier counts, weighted success probabilities, and contractual buffers, we can compute the probability of hitting a removal target, expected delivered tonnes, and conservative delivery floors. The method aligns with portfolio analytics practices and complements procurement economics covered in the virtual PPA analysis and data quality assessments in the data clean room guide.
Definition and modelling scope
Delivery confidence expresses the probability that a carbon removal portfolio delivers at least a target amount of verified tonnes within the contract horizon. It depends on three pillars: the success probability of each supplier, the diversification of the portfolio, and any contracted buffer capacity. The model treats suppliers as Bernoulli trials that either deliver their allocation or fail entirely; partial deliveries can be handled by scaling probabilities accordingly.
Scope decisions include defining the portfolio boundary (technology types, geographies, registry standards) and clarifying which failure modes are captured. Financing or policy risks that affect all suppliers simultaneously should be reflected by adjusting the diversification input downward, while idiosyncratic risks remain in the individual success probabilities.
Variables, notation, and units
Use consistent units to make the probability logic transparent:
- T – Target removals required (tCO2e).
- C – Contracted removals including buffer (tCO2e).
- p – Weighted average success probability of suppliers (0–1). Derived from due diligence scoring.
- n – Effective number of independent suppliers (dimensionless). Calculated via inverse Herfindahl index or scenario analysis.
- S – Tonnes allocated to each effective supplier (tCO2e) = C / n.
- K – Minimum number of suppliers that must deliver to reach T, defined as K = ceil(T ÷ S).
The effective supplier count translates a diversified but uneven portfolio into equivalent equal-weighted suppliers. If one counterparty delivers 60% of tonnes, n might be close to 1 despite nominally contracting five projects. This adjustment ensures the binomial model captures concentration risk accurately.
Computing delivery probability and risk metrics
The binomial distribution underpins the delivery probability calculation. Let X be the number of suppliers that successfully deliver. Then
P(X = k) = C(n, k) × pk × (1 − p)n−k
P(X ≥ K) = Σk=Kn C(n, k) × pk × (1 − p)n−k
Expected delivered tonnes = C × p
Besides the probability of meeting the target, stakeholders need conservative delivery estimates. The 95% confidence delivery floor corresponds to the 5th percentile of the distribution: the smallest tonne volume exceeded in 95% of scenarios. Practically, identify the smallest k where the cumulative distribution exceeds 5%, then multiply by S.
The buffer ratio (C − T) ÷ T contextualises over-contracting. A higher buffer increases both the probability of success and the conservative floor, but it also ties up capital. Communicate this trade-off explicitly in portfolio governance discussions.
Step-by-step workflow
1. Consolidate supplier diligence
Gather technical, commercial, and policy assessments for each project. Convert qualitative ratings into probabilistic success scores, ensuring the methodology is consistent across suppliers.
2. Compute effective supplier count
Calculate the inverse Herfindahl index of contracted shares to derive n. Adjust downward to reflect correlated risks such as reliance on a single sequestration hub or common permitting authority.
3. Determine buffer strategy
Compare target tonnes with contracted tonnes to calculate the buffer ratio. Align the buffer with risk tolerance, financing covenants, and the availability of make-good provisions.
4. Run delivery probability calculations
Use the formulas above—or the embedded calculator—to compute P(X ≥ K), expected deliveries, and the 95% floor. Document assumptions about independence and revisit them quarterly.
5. Integrate outputs into governance
Feed delivery confidence metrics into portfolio dashboards, credit committees, and sustainability disclosures. Highlight how changes in supplier mix or buffer strategy alter the probability curve.
Validation and stress testing
Validate success probabilities using historical analogues or pilot data. Compare calculated probabilities with independent models from third-party auditors or insurance providers. Significant discrepancies warrant revisiting diligence assumptions.
Perform sensitivity analyses by varying p, n, and buffer ratios. For example, reducing p by 10 percentage points or halving n simulates simultaneous technology setbacks. Record how confidence metrics shift so executives can appreciate downside exposure.
Where data allows, complement the binomial approach with scenario simulation that mixes partial deliveries and timing delays. The calculator’s deterministic output should be treated as a baseline that more complex Monte Carlo models can refine.
Limits and practical considerations
The binomial assumption implies binary success per supplier and independence between suppliers. In reality, projects may deliver partially, and correlated risks (policy, supply chains, verification bottlenecks) create dependencies. Adjust inputs conservatively or adopt copula-based models when correlations dominate.
The framework also omits price risk and contract optionality. Over-contracting improves delivery probability but may leave surplus tonnes that must be banked or resold. Align delivery confidence targets with financial strategies, including voluntary market resale plans.
Finally, monitor verification and registry data quality. If suppliers rely on novel measurement protocols, success probabilities should be haircut to account for potential invalidations. Integrate ongoing monitoring results to refresh p and n routinely.
Embed: Carbon removal delivery confidence calculator
Input your target tonnes, contracted tonnes, weighted success probability, and effective supplier count to calculate the probability of meeting targets, expected deliveries, and a 95% delivery floor.