Methodology
Global Wildfire | A Spectra Hazard Model
Asset-level wildfire risk intelligence for financial institutions
Wildfire losses are climbing worldwide, reaching over $106bn in global economic losses between 2014 and 2023 and far exceeding the previous decade.
Many wildfire models rely on aggregate historical proxies such as total area burned, but forward-looking wildfire risk is more accurately quantified by modelling fire location, spread dynamics, intensity and exposure at the asset level.
Covered in the in-depth guide:
- How the model works and how it's calibrated
- Asset-level burn probability and Average Annual Loss (AAL) explained
- How the model compares to statistical, catastrophe, and index-based approaches
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The wildfire risk landscape
The importance of forward-looking models.
Most models still rely on historical proxies like total area burned. Asset-level physicas-based modelling gives you a clear indication of which assets are actually at risk and the liklihood they will burn.
Under the hood
From ignition to loss, modelled end to end.
We breakdown how Climate X combines machine learning, physical fire-spread simulation, satellite observations, climate projections and asset-level vulnerability into one chain.
Where we go further
Absolute probability, not a relative score.
We outline how Global Wildfire provides financial institutions with the metrics they need for strageic decison making.
