Ortec Finance: Looking under the hood of climate data

Ortec Finance’s Willemijn Verdegaal and Lisa Eichler caution investors to scrutinise the data underpinning their net-zero strategy.

This article is sponsored by Ortec Finance.

As investors pursue a transition to net zero, access to high-quality climate data is essential to map progress toward emission reduction goals. Willemijn Verdegaal and Lisa Eichler, managing directors for climate and ESG solutions at risk-and-return technology specialists Ortec Finance, explain why it’s critical that investors interrogate the scope and limitations of climate data. Understanding the assumptions behind the data is vital, they say, to enable investors to construct realistic transition plans.

How can investors ensure the quality of the data used to track and report on their transition to net zero?

Willemijn Verdegaal of Ortec
Willemijn Verdegaal, Ortec Finance

Willemijn Verdegaal: Independent, well-researched data is vital. There’s a lot of scrutiny of ESG data, which varies significantly in quality.

For investors to make decisions based on correct interpretations of the data, they need to take ownership of the data they use and not take it at face value. They need to understand underlying assumptions and scenarios, as well as the strengths and limitations of the modelling.

It’s not simply the metrics, but the explainers and disclaimers, and the transparency around the data, that ensures quality.

Financial institutions need to get informed. Wherever they are on their transition to net zero, they can’t rely on regulators alone to determine what information is useful or valid. Box-ticking is not good enough.

Lisa Eichler of Ortec Finance
Lisa Eichler, Ortec Finance

Lisa Eichler: Climate data is complex, but net-zero scenarios and climate modelling tend to be oversimplified. We often see the entire transition proxied on one indicator – the carbon price. Instead, investors need to work with scenarios that reflect a variety of realistic policy and technology options across sectors and jurisdictions. For example, with many scenarios, investors count on carbon capture and storage, but it’s not clear that they are modelling appropriate assumptions about technology take-up. The role of government subsidies in changing consumer behaviour, for example around electric vehicles, is something else to take into account.

The net-zero transition is a new and challenging topic for many financial institutions. If an investor is using unrealistic scenarios to steer their strategy, even if they follow it to a T, they’ll still miss the point. Often, we see clients using inappropriate tools for their use case.

For example, some investors frame a carbon footprint as a forward-looking metric to monitor financial climate risk. There is still a lot of capacity building to be done. Climate informed risk-return analytics are more appropriate for measuring risk. For measuring the impact of investments on the climate, an Implied Temperature Rise score may be a good choice. It’s important to pick the right tool for the job.

Does using more sources of data result in more clarity?

LE: Financial institutions need to determine what data points are the best fit for their use-case. The focus shouldn’t be on quantity. A single data set might suffice for TCFD reporting. But an investor that wants to integrate data into a climate strategy encompassing strategic asset allocation down to due diligence on individual deals will require more granular data. They might need to get comfortable working with several providers – but they should do their research first. Switching data vendors absorbs time and resources.

WV: In addition to company disclosures and modelled data, there’s a wealth of public scientific data that some investors might not be aware of. Groups of like-minded financial institutions are pooling resources and data to solve common issues and fill gaps. Open-source climate data is a great way to share knowledge, collectively assess the quality of data sets and decide on the best way to apply it to a use-case. When an institution knows what data it needs, it can prioritise and budget. Data is not cheap, and understanding it is a time investment.

There is some apprehension around modelling. How useful is it?

WV: No model is perfect, but some are useful. Most data sets are based on models and estimates to some extent; even corporate disclosure often relies on modelling and estimation techniques to generate data. Modelling is a useful way to fill information gaps and to sanity-check empirical data.

Time is running out to reach net zero. We can’t wait for perfect modelling or even better disclosure or more capacity building. Quality data is, of course, the goal, but it’s better to make a start with what we have than not do anything at all. Investors should be brave about setting targets, even while acknowledging data limitations.

How well do investors understand what data they need?

WV: Clients are increasingly ambitious and discerning. In addition to the push for scalable data solutions, there’s also a trend among some more forward-thinking institutions to seek alternative, flexible data sets and scenarios. Sophisticated clients understand climate models and have opinions about using different scenarios.

For instance, when you construct an Implied Temperature Rise score, there’s always a certain weighting that balances historical emissions and potential net-zero targets. That weighting, which has a major impact on outcomes, can differ from one data provider to the other. We believe the client should have a voice in deciding what it is. Perhaps they know a certain company very well and are happy to put 100 percent weight on its transition plan. Or it could be the other the way around, or 50:50.

In addition to client input, where else do you see scope for innovation?

LE: There are areas where we need to capture more data, for instance around environmental tipping points, which are hard to quantify and not captured by current models. And we should incorporate linked themes, such as nature and biodiversity, into existing data sets.

Climate scenarios typically model standard physical and transition risk but don’t take market reaction to those risks into account. This could be addressed by overlaying a financial markets narrative to account for the risks the market has already priced in around rising temperatures. From an impact and materiality perspective, by including this information the scenarios become much more impactful.

You’ve described an intricate climate data landscape. How should institutions approach selecting a vendor?

LE: When investors look for a data provider, they should ask what data and methodology choices move the dial on outcomes; whether they agree with the calibration that moves the dial; where are the uncertainties and what is the data not capturing. Clients should have the ability to take a hands-on approach in questioning the data and interrogating the numbers.

With a flexible model, clients can test their assumptions and how sensitive, or not, the results are. That helps them learn – there’s real value in learning by doing. We often hear from clients that they waited too long to begin their transition journey. There are challenges with the data, but that should not deter institutions from making a start.