Most climate risk models assume corporate emissions remain unchanged over time.

In this episode, we examine how incorporating Science-Based Targets (SBTi) into forward-looking emissions trajectories changes projected transition risk.

Drawing on new research, we discuss how more than 4,000 matched absolute targets are converted into emissions glide paths and integrated into a scenario-based risk framework. By recalculating Transition Value at Risk (TVaR), emissions reduction requirements, and temperature alignment under both constant-emissions and target-achieved pathways, the analysis quantifies how much risk may be reduced if companies deliver on their commitments.

The results generally show reduced projected TVaR and improved temperature outcomes, while also highlighting where additional decarbonisation is required under more ambitious IPCC and NGFS scenarios.

For institutional investors, this provides a structured way to compare business-as-usual assumptions with conditional target achievement and to assess how portfolio-level transition risk evolves when corporate commitments are incorporated.

Access the full white paper for detailed methodology and analytical applications.

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.

As outlined in Carbon Credits – Climate 101, global policy momentum is rapidly converting carbon risk from an ESG talking point into a core driver of asset valuations. Emissions-trading systems now cover nearly 28% of global greenhouse-gas output, with EU, UK, China, and Australia strengthening caps, tightening baselines, and embedding carbon costs into trade and capital flows.

This audio brief breaks down what these developments mean for institutional investors. We examine the mechanics of compliance carbon markets, how carbon prices flow through earnings and valuations, and how Potential Carbon Liability (PCL) can quantify exposure across asset classes.

Listeners will learn why carbon credits, when treated as financial hedging instruments rather than reputational offsets, can reduce downside risk, enhance transition readiness, and improve portfolio temperature alignment. With clear explanations and practical examples, this brief equips investment committees and fiduciaries to evaluate carbon-credit strategies with confidence and precision.

Our latest research reveals how extreme weather events are reshaping financial markets. By analysing 45 major U.S. storms over 24 years, we uncover clear patterns of storm sensitivity, showing which industries consistently lose value when disasters strike. The findings expose material vulnerabilities and the hidden supply-chain and market-wide shocks that traditional physical-risk tools fail to capture.

Click here to download the full white paper.

Our podcast examines why today’s dominant physical climate risk tools fail to meet investor needs. Originally designed for property insurers, these models calculate building repair costs, but miss the broader business disruptions, supply chain shocks, and market ripple effects that drive enterprise value.

The podcast highlights this mismatch: investors are paying premium prices for asset-level precision that adds little value to portfolio management, while the systematic risks that truly move markets go unmeasured.

Emmi proposes a different path. By integrating market signals with climate science, we outline an approach that captures how physical risk actually affects portfolios. Rather than underwriting data repackaged for finance, investors gain clarity on where climate events reshape sectors, shift market share, and reveal resilience.

Emmi’s latest research forecasts a likely global peak in fossil CO₂ emissions, based on 24 years of data from major economies. The analysis identifies a 2027 plateau at 36.9 GtCO₂, with annual declines to 2030, and highlights structural shifts in China, the US, and the EU that are reshaping global emissions trends, carbon pricing signals, and transition risk exposure for institutional investors.

Speaking on Ausbiz, Dr Ben McNeil discusses his new research and market-leading methodology that has revealed that private equity markets account for 3 billion tonnes of emissions globally. He also explains why this is important for investors in managing their transition risk and meeting their reporting responsibilities.

Our latest research uncovers the emissions footprint of private equity, an $8.2 trillion asset class largely overlooked in climate risk reporting. We show how our machine learning model outperforms traditional estimation methods by up to 7x. This enables more accurate and consistent emissions insights across unlisted assets.

In this podcast we breakdown climate transition risk within the global corporate bond market via a new research paper from Emmi here. Covering $24 trillion of outstanding debt, we examine the impact of various climate scenarios on this debt, revealing substantial risk even under moderate Paris-aligned targets.

A key finding is that "green bonds," while intended to support environmentally friendly projects, do not inherently reduce climate transition risk for investors. The analysis uses a proprietary machine learning model to quantify financed emissions and potential carbon liabilities, offering insights into which industries and companies face the greatest financial exposure. Ultimately, the paper emphasises the need for investors to focus on companies actively reducing emissions to mitigate climate transition risk.

Podcast produced via Google Notebook LM

Emmi's latest analysis reveals the first decline in global listed company emissions, dropping 2.5%. Speaking on Ausbiz, Dr Ben McNeil discusses this milestone shift and what it means for investors navigating the transition to a low-carbon economy.

Learn more in our white paper, ‘Estimating The Corporate Carbon Footprint: A Meta-Model Machine Learning Approach’: www.emmi.io/newsroom/white-paper-nov-2024