Carbon Removal Delivery Confidence Calculator

Evaluate the robustness of your carbon removal procurement plan by combining reliability estimates with diversification and buffer tonnes.

Tonnes of CO2e you must take delivery of under the portfolio contract horizon.
Tonnes of CO2e contracted across all suppliers, including any buffer capacity.
Due diligence estimate of on-time delivery probability averaged across the portfolio.
Herfindahl-adjusted supplier count capturing diversification (e.g., 4 equal suppliers ⇒ 4).

Scenario modelling aid for carbon removal procurement teams; complement with supplier-specific diligence and contractual risk analysis before reporting figures externally.

Examples

  • Target 8,000 tCO2e, contracted 9,000 tCO2e, 75% success probability, six effective suppliers ⇒ 17.80% chance of meeting the target and a 95% floor of 4,500 tCO2e.
  • Target 12,000 tCO2e, contracted 15,000 tCO2e, 85% probability, eight effective suppliers ⇒ 65.72% chance of success and a 95% floor of 9,375 tCO2e.

FAQ

How do I estimate effective suppliers?

Compute the inverse Herfindahl index of supplier shares (1 / Σ share²). For example, four equally sized suppliers yield an effective count of 4, while a dominant supplier reduces the figure.

Can I include correlated supplier risks?

This model assumes independence. Reduce the effective supplier count to reflect correlated failure modes or run sensitivity cases with lower success probabilities.

What if I over-contract substantially above the target?

The calculator will report a high buffer percentage and improved confidence, but remember to assess storage, registry, and financing costs associated with the excess.

Additional Information

  • Result unit: percentage probability of achieving the target removals plus supporting tonnage metrics.
  • Effective supplier count approximates diversification by converting fractional supplier exposures into an integer trial count.
  • The 95% confidence floor is the tonne volume exceeded in 95% of simulated delivery outcomes given the binomial assumption.

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