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

Our new research suggests corporate emissions peaked in 2022. In this 10-minute AI-enhanced breakdown, we explore the machine-learning approach behind our findings, examining how we analyse emissions across 45,000 listed companies. Using Google's NotebookLM, we distil complex methodology into key technical insights about corporate decarbonisation trends and their implications for investors.