University of Exeter researchers model Carbon at Risk metric for CO2 storage

Researchers from the University of Exeter have introduced a probabilistic metric called Carbon at Risk (CaR) to quantify the permanence risks of carbon dioxide removal projects. Inspired by the financial sector's value-at-risk metric, the model defines the gap between contracted CO2 removal and the amount actually retained over a specified time horizon and confidence level, according to a study published via doi.org doi.orgdoi.orgdoi.orgOpen source ↗. The researchers demonstrated the framework using Monte Carlo simulations for forest projects alongside geological storage evaluations based on the Storage Security Calculator.

In proof-of-concept modeling evaluated at a 95% confidence level over a 200-year horizon, simulated single-project forest carbon in California showed a CaR exceeding 70% of contracted storage doi.orgdoi.orgdoi.orgOpen source ↗. This calculation integrated satellite-derived fire disturbance data from 2002 to 2023 across 5,000 simulated pathways. By contrast, simulated geological storage sites exhibited 95% CaR values ranging between 0.15% for well-regulated offshore reservoirs and 17% for poorly regulated onshore formations.

Quantitative comparison across technologies

Traditional carbon removal markets have historically relied on qualitative labels or arbitrary buffer pools that do not capture the magnitude, timing, or likelihood of reversal risks. Forests can release carbon during wildfires, while geological formations present variable risks of physical leakage doi.orgdoi.orgdoi.orgOpen source ↗. The researchers noted that while California and Mato Grosso in Brazil exhibited comparable average burned areas in empirical data, faster modeled forest regeneration in Mato Grosso reduced its overall shortfall distribution compared to fire-prone California.

Buffer design and portfolio diversification

The CaR framework establishes a physical rather than monetary unit to standardize insurance pricing, buffer-pool sizing, and reserve fund design across policy-relevant horizons doi.orgdoi.orgdoi.orgOpen source ↗. The team showed that combining biological and geological technologies within a diversified portfolio helps manage tail risks at minimal overall cost. The minimum cost necessary to meet a targeted permanence threshold depends directly on the magnitude and correlation structure of the respective operational risk factors.