Most transition risk analysis assumes that corporate emissions remain unchanged into the future.

In this episode, we examine how replacing static baselines with probabilistic emissions forecasting changes forward-looking climate risk metrics.

Drawing on the Monte Carlo Momentum (MCM) framework, we discuss how annual Scope-level emissions data from 2020 onward are used to estimate momentum and historical volatility. Each entity-scope is simulated using 1,000 Monte Carlo paths, generating a distribution of plausible futures rather than a single deterministic trajectory. Implausible and highly uncertain outcomes are filtered to ensure decision-useful results.

These probabilistic emissions forecasts are integrated into a scenario-based transition risk framework and applied to Transition Value at Risk (TVaR), temperature alignment and emissions reduction requirements. Comparing the dynamic baseline with a constant-emissions baseline reveals where static assumptions overstate exposure for companies already reducing emissions and understate it for those on an upward path.

For institutional investors assessing portfolio-level exposure, forward-looking emissions are essential inputs to credible climate risk analysis.

Access the full white paper to explore the methodology, validation and analytical applications in detail.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit emmisolutions.substack.com