Digital twin of the earth helps firms assess climate risk

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Staying ahead of the competition isn’t the only concern for firms. Global weather can serve up challenging conditions for businesses too. Banks, insurers, property developers, telecoms providers, and others, need to stay on top of climate risks such as flooding, landslides, wildfires, extreme heat, storms, and hurricanes to secure their long-term prospects. Just last year, the US Federal Reserve announced that, “Six of the nation’s largest banks will participate in a pilot climate scenario analysis exercise designed to enhance the ability of supervisors and firms to measure and manage climate-related financial risks.” The European Central Bank is also keen to know more and has a framework to look ahead 30 years to examine the resilience of financial organizations in the EU to changes in weather patterns. And the Bank of England’s key work on understanding climate-related risks kicked off in 2015. But crunching all of those numbers is no easy task, even for central banks. So, it’s clear that companies will need a helping hand, and Climate X’s digital twin of the earth provides a giant one.

The company – co-founded by Lukky Ahmed and Kamil Kluza, who have backgrounds in banking and loss and valuation modelling – has been busy for 18 months building a climate risk data analytics platform dubbed Spectra. Featuring digital twin simulations, the tool allows users to simulate extreme weather events under different climate scenarios to see how business-critical locations and assets could be impacted. “We’re recreating cities, block-by-block, to create the perfect sandbox,” Alexandre Crépault, Director of Commercial Strategy and Operations at Climate X, told TechHQ.

To picture a digital twin of the earth in action, imagine a cross between Google Earth and SimCity. Users can select from a range of risk filters, such as river flooding, surface flooding, coastal flooding, subsidence, landslide, wildfire, extreme heat, storm, and hurricane, and zoom in on a map to determine how different sites are affected. An ensemble of climate models that run behind the scenes projects the impact of climate change worldwide up to 2100.

NewSpace contributions

“The majority of the data that we use is from remote sensing,” Crépault explains, noting the value that sources such as synthetic-aperture radar (SAR), LiDAR, and other satellite information bring to the simulation. “They fill in the data gaps.” For example, SAR – which bounces energy in the microwave and radio portions of the electromagnetic spectrum off the earth’s surface – can distinguish between light and heavy flooding, based on surface reflection. Organizations such as Planet Watchers use SAR data to dynamically map the world’s food resources, as the information can highlight fields that have been recently ploughed or where crop harvesting is taking place.

A team from US-based Lawrence Berkeley National Laboratory (LBNL) has used machine learning tools to trawl through RGB, thermal, and LiDAR imagery acquired by satellites, aircraft and drones, to provide a measure of buildings efficiency. In the US, buildings are thought to consume as much as 40% of primary energy, hence the interest in energy performance. And it’s a similar picture elsewhere too.

Using a digital twin of the earth, companies can drill down on energy performance certificate (EPC) data – Climate X’s Spectra tool has 100% coverage of EPC ratings for the UK, to highlight just one of the countries featured – and benchmark their performance region-by-region. Firms such as property developers are likely to have more detailed information too about building assets, and this can be uploaded and added to the simulation to enhance the results.

Opening the front-end portal, users can access the climate risk data by simply entering a street address, or a variety of other coordinates, including unique property reference numbers (UPRNs) as well as longitude and latitude. The tool also accepts CSV files, which makes it possible for firms to upload the locations of all building assets and operating concerns in one go. Alternatively, users may wish to interact with a digital twin of the earth via an API. And the results of the climate risk assessment can be exported as a report.

Decision-making tool

A big strength of the portal is its visualization capability. Maps are intensity-graded to indicate which regions face the greatest climate threats. And assets are grouped into low, medium and high risk. The tool can also total up potential losses in terms of replacing buildings facing climate damage, and estimate transition costs of upgrading assets to meet new targets, helping companies budget for compliance. Settings also allow users to simulate undefended or defended climate scenarios.

One of the benefits of computer models in general is that they can be updated as new and improved measurements become available, and the same goes for digital twins of the earth. But there are other upgrades too, as Crépault points out – for example, to include regulatory updates as policy wheels turn. Climate risk assessments can put some tough decisions and big numbers in front of business leaders, and Climate X includes methodology descriptions to accompany its analytics for transparency. “Clients need to understand how we get to those outputs,” said Crépault.