The Shape of Things to Come

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