Asset owners look to artificial intelligence for better climate reporting | ESG | AsianInvestor

As investors continue to grapple with the complexities of carbon emissions reporting, it is becoming clear that artificial intelligence has a crucial role to play in improving the quality of the core data.

The problem of reliable and consistent emissions data was highlighted in the move last month by the $330-billion US pension fund Calstrs to delay publication of its 2023 carbon footprint report, after discovering significant data issues.

In Asia Pacific (APAC), New Zealand Super is already considered to be using one of the more advanced climate management approaches, but even it is forced to qualify its reporting thus: “Due to our reliance on external data, and external data providers’ controls in producing the data, there are risks regarding the lack of completeness of data, unverified data sources, and complexity and judgement involved when the emissions data is sourced.”

Australian climate scientist Ben McNeil told AsianInvestor the first challenge for any company’s emissions analysis is to establish if they are using a good dataset for Scope one, two, and three reporting.

“The reality is we don’t yet have satisfactory datasets. It’s hard for corporates to invest the time and money to understand their carbon footprint. Even today, 80% of listed companies around the world — that’s 50,000 companies — don’t report anything.”

When it comes to Scope three, which relates to companies’ supply chain emissions, only around 1% are reporting in any meaningful way, said McNeil. “Then when you look at private markets, obviously no one does — so there’s a massive gap.”

NZ Super reports that in 2022 and 2023, it was able to collect data from entities representing approximately 8.4% of the fund’s unlisted holdings by asset value.

DATA ANALYSIS

The potential for artificial intelligence to process complex datasets on emissions and their climate effects, thereby helping to model portfolio climate risk, is capturing the attention of large asset owners in the region.

Five years ago, McNeil co-founded Emmi Solutions, a technology firm focused on helping financial institutions understand how future carbon constraints will translate to carbon transition risk.

“There are ways to assess each aspect of emissions data and using the different techniques of machine learning, we can optimise which carbon footprint models work best,” he said.

Michael Wyrsch
Vision Super

Australia’s Spirit Super uses Emmi to analyse their private markets exposures, including infrastructure assets and real estate.

“We analyse how that translates into portfolio risk, taking the financial data from their investments and propagating future climate scenarios,” said McNeil.

“We can give them a range of different outcomes, for a 1.5-degree world, or 2 degrees, or 4. That will allow them to take action to protect their portfolio.”

REPORTING GOALS

Vision Super’s CIO Michael Wyrsch told AsianInvestor that his investment team is currently investigating the use of AI to improve their carbon footprint reporting.

“One issue that is very hard to deal with is systemic under-reporting of emissions, and this is a problem,” said Wyrsch.

A Singaporean technology firm, STACS, has established a platform that powers, among other things, the Monetary Authority of Singapore’s Greenprint ESG Registry. According to managing director Benjamin Soh, advancements in AI have played a pivotal role in enhancing ESG management in the last year, enabling companies to comply with looming ESG regulations.

Data quality remains the biggest challenge, said Soh.

“When we set up, we realised there were a lot of gaps in Asia. Rather than assign estimates or proxies, we have sought to deal with the companies upstream, to collect the data directly at source and attribute data to disclosures made by the companies themselves. Every single data point that comes to us is from a legitimate source.”

One of the ways these technology firms are aiming to capture more data points is to partner with AI specialist solution providers. One such, Eugenie.ai, based in California, has developed an emissions-tracking platform that combines satellite imagery with data from machines and processes. AI then analyses this data to help companies track, trace, and reduce their emissions by up to 30%.

The application of AI in sustainability efforts is expected to grow substantially, said Soh.

“The magnitude of the problem is such that we need technology to help us, because we are talking about literally thousands of companies needing to do something they have never done before.”

NZ Super is keeping a close watch on developments, stating, “We will consider new methodologies as part of reviewing and updating our next set of carbon reduction targets.”