Study highlights misleading nature of raw climate data

A lack of standardisation and the complexity of the science behind climate model data are compounding uncertainties for financial markets participants, leading to unintended misuse in the context of financial decision-making and disclosures, such as material misstatements in financial reports and greenwashing.

This is the warning from S&P Global Ratings' Sustainable Finance Scientific Council, whose latest whitepaper explores the rapid uptake of climate risk analytics among financial market participants.

According to the paper, these risks are particularly problematic in the case of long-term capital investments in public infrastructure, which often have a multi-decade operational lifetime.

Translating this data into specific potential impacts is particularly complex when considering the financial impact of climate events.

Paul Munday, associate director at S&P Global Ratings Sustainable Finance and lead author of the white paper, says: "Climate risk analytics is an increasingly important tool the finance industry is using to improve its preparedness for the physical impacts of climate change. But the lack of consistency between various related-technologies, as well as the complex nature of climate science, is hampering its utility."

While there is no one-size-fits-all solution, the paper argues that the standardisation of terminologies and data quality thresholds, as well as establishing appropriate use cases, will improve the relevance of climate risk analytics to financial market participants. In addition, supplementing climate model outputs with entity-specific data will help to rationalise information and enhance dialogue with entities to understand how the risks are being managed, monitored, and mitigated.

The paper also determines that using multiple scenarios may help decision-makers consider a broader range of outcomes, bringing greater clarity to the type of interventions that may be required in response to climate risk.

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