Others are more sceptical of the need for ‘digital twins’.

“The name can be the lipstick on a pig,” said Dan Travers, co-founder of Open Climate Fix, a non-profit that applies machine learning to reduce greenhouse gas emissions, such as by improving weather forecasts to make solar energy more predictable.

AI tools can augment climate models, especially for understanding weather, Travers noted – but he believes the quality and reliability of data, and how it is used, are more important than ‘digital twin’ visualisations that can be hard to build.

‘Digital twin’ is a “fancy new name” for scientific models that have been around for years, said Josh Hacker, co-founder of Jupiter Intelligence, a Silicon Valley firm that analyses climate risks.

Those models have long fed into research, such as reports from the Intergovernmental Panel on Climate Change (IPCC).

The quest now is to turn the data generated by the models into information that can be used for practical decisions, something governments have been slow to do, he added.

“That gap is where the private sector has always innovated,” said Hacker.

Given the threat of damage to trillions of dollars’ worth of global assets, the market for information that can protect them is expected to become significant – especially as companies are increasingly being required to report on climate risks to their business.

More than 90% of the world’s largest companies will have at least one asset highly exposed to the physical impacts of climate change by the 2050s, according to ratings provider S&P Global.

London-based Cervest is another company that aims to help deal with that threat.

It combines a range of data, including publicly available scientific models, then uses machine learning to analyse climate-related physical risks facing assets like factories, hospitals and dams, from heat stress and flooding to high winds.

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