Since the emergence of “Modern Portfolio Theory” and the “Capital Asset Pricing Model” in the late 1960s, institutional investors have taken a quantitatively driven approach to portfolio construction, looking to create portfolio diversification and obtain better risk-adjusted returns by balancing their asset-class exposures. This journey has seen several important advancements in thinking about how to optimally achieve desired results.
Funds that want to take the total portfolio approach first need to get the total portfolio view. To do that they not only need data – and lots of it – but a rock-solid understanding of exactly how they’re going to use it.
Can investors ever trust carbon data?
Net-zero pledges on carbon emissions are everywhere – but you can’t manage what you can’t measure.
We know that the vast majority of the ~37 billion tonnes of industrial carbon dioxide emitted annually comes from companies. But there’s still an enormous data gap at the company level for investors to truly understand the losers and winners as the world transitions to net-zero.
For years, disclosing carbon emissions for companies has been voluntary. This week however, the US SEC is stepping in, following existing regulations in the United Kingdom. US regulators proposed making it mandatory to not only disclose carbon emissions, but also climate-related risk information and how it will impact business. That last part requires much more than just emissions – it requires translating emissions into financial impact and risk.
Despite these regulatory mandates, corporate carbon data will continue to be outdated, poor quality and not cover enough of the market. This leads to lack of trust that ultimately puts the brakes on investors’ understanding of climate risk and opportunities.
Why is carbon data broken and can we fix it?
Coverage
The vast majority of publicly listed companies (~80%) have never reported on carbon emissions, creating a large data gap in the carbon information market. The lack of reporting is particularly evident for small to mid-sized companies and in the developing world.
Timeliness
Carbon data is typically more than 12-18 months old. Information must be timely for any market to function efficiently – and current carbon data fails to provide this to all market participants.
The Scope 3 problem
Scope 3 emissions are indirect value chain emissions from business operations. There are 15 different categories to report. Employee and business travel tends to be well reported, but quantifying emissions from ‘Use of Sold Products’ is more difficult to calculate, so often goes unreported. Only a fraction of companies disclose Scope 3 emissions and of those that do, many are selective about the categories they report on.
Black-box models
To overcome some of the problems listed above, carbon data vendors often predict emissions for Scope 3 as an example – but often the methods are secretive and opaque. It’s extremely difficult to trust any model that lacks transparency.
Blackrock recently disclosed its carbon footprint for its entire global portfolio. The world’s largest fund manager could only estimate its carbon emissions for 65% of its portfolio. The other 35% lacked the data quality and/or methodology to quantify it. This highlights the overall challenges existing today due to sub-optimal carbon data and or models.
Resolving the carbon data chasm
Let’s be honest, carbon data is always going to be uncertain, but so are markets. Investors understand uncertainties and spreads – so fixing the carbon data problem must rely on better models and a robust understanding of how uncertainties in the data predictions impact risk and opportunities across the market. That is how we have approached solving these carbon data challenges for investors at Emmi.
Building the best machine learning predictive models is important since that allows us to estimate and forecast carbon emissions using company financials alone. That allows more timely up-to-date estimates independent of when a company reports emissions.
Aggregating reported and predicted data allows greater understanding of uncertainties and outliers in this data-science framework. Our proprietary method has allowed us to generate timely and transparent Scope 1, 2 and 3 emission predictions and trust scores for over 40,000 publicly listed securities, covering the global market.
Most importantly, by integrating finance and carbon within a data science framework, we have for the first time, propagated carbon data uncertainties that allow investors a data-driven perspective on whether a company or portfolio is truly a carbon risk. That carbon risk is ultimately what the new US SEC mandate is seeking.
We are excited to build a first of a kind investment infrastructure at Emmi – based around better carbon data – to help investors quantitatively understand the risk and opportunities of their portfolios under climate scenarios, now and into the future.
Dr McNeil is a climate data scientist and co-founder at Emmi, a carbon finance investment platform. McNeil has published over fifty scientific articles and has been cited extensively in scientific literature. He has been a reviewer for the IPCC and co-led the Copenhagen Diagnosis in 2009, an in-depth climate change report collectively written by twenty-six climate scientists from eight countries.
