5.9.5 — Climate Risk Intelligence — maturity: live
Asset-Level Climate Exposure
Quantifying physical climate hazard at the level of individual infrastructure assets—power plants, ports, roads, hospitals—using satellite-derived observational data rather than modelled proxies.
Pinpointing exactly which factories, ports, pipelines and farms sit inside tomorrow's flood plains, wildfire corridors and heat-stress zones — before regulators or insurers do it for you.
National regulators, finance ministries and infrastructure owners are under mounting pressure to disclose how specific physical assets will perform under intensifying climate hazards. The problem is that most risk frameworks rely on coarse, globally averaged model grids that cannot resolve a single dam, transformer yard or coastal highway. When a foreign data vendor fills that gap, the underlying hazard layers, confidence intervals and update schedules are opaque—and can be withdrawn at contract renewal.
A sovereign constellation combining optical imagery, synthetic aperture radar and multispectral thermal sensors can observe every significant national asset on a sub-weekly cadence. Satellite-derived land surface temperature, inundation extent, ground subsidence (via InSAR), vegetation health and coastal erosion rates are ingested against a national asset register to produce per-asset exposure scores tied to observed reality rather than interpolated models. The result is a living ledger: when a cyclone crosses a power corridor or a heatwave depresses a river level below a cooling-water intake, the system flags affected assets within hours.
Operationally, this changes how governments allocate adaptation capital. Infrastructure owners can prioritise hardening budgets with actuarial precision. Sovereign regulators can mandate disclosure standards anchored to nationally verified data rather than accepting a commercial vendor's proprietary score. Central banks conducting climate stress tests stop depending on information they cannot audit. The feedback loop closes: satellite observation drives investment, investment is tracked by the same satellites, and outcomes are verifiable.
Frequently asked
What exactly is 'asset-level' climate exposure, and why does it matter more than portfolio-level averages?
Asset-level exposure pins a specific physical hazard — flood inundation depth, wildfire probability, chronic heat stress — to a named, geolocated facility rather than spreading risk across a country or sector average. A factory 400 metres from a river faces categorically different flood risk than its neighbour on higher ground; portfolio averages obscure that entirely. Regulators under IFRS S2 and ESRS E1 are now requiring site-specific disclosure precisely because aggregated figures have proven useless for pricing and hedging decisions.
Why should a government operate its own observation satellites for this rather than buying data from Planet or ICEYE?
Commercial providers set their own access terms, pricing and data retention policies, and can withdraw service, impose embargo clauses or be acquired overnight. A sovereign constellation ensures that critical national infrastructure — power grids, water systems, ports — is assessed with data that cannot be withheld during a crisis or geopolitical dispute. It also means the nation retains the raw imagery and derived hazard layers as a permanent national asset, rather than leasing a view that expires with a contract.
Which satellite sensors are most useful for asset-level climate exposure work?
Optical multispectral imagery (Sentinel-2, Planet SuperDove) provides land-cover, vegetation stress and flood extent mapping at 3–10 m resolution. SAR (Sentinel-1, ICEYE, Capella) penetrates cloud for flood and subsidence detection. Thermal infrared (Landsat 8/9, ECOSTRESS) captures urban heat island effects and wildfire fronts. LiDAR-derived DEMs underpin flood depth modelling. A sovereign programme should plan for all three modalities to avoid single-sensor blind spots.
How does satellite data feed into a climate risk score that a bank or insurer can actually use?
The pipeline typically runs: (1) satellite imagery → land-cover and hazard-extent maps; (2) DEM + hydrological model → flood return-period probability; (3) asset registry geolocation → spatial intersection with hazard layers; (4) climate scenario (RCP 2.6/4.5/8.5 or SSP equivalents) → forward projection of hazard probability; (5) damage function → financial loss estimate at each asset. The satellite data anchors step 1 and validates step 2; everything downstream depends on the quality of that geospatial foundation.
How often does imagery need refreshing to keep exposure scores current?
For slow-moving hazards like land subsidence or long-term coastline change, quarterly or annual updates may suffice. For dynamic hazards — active flood seasons, wildfire spreads, post-storm damage — daily or sub-daily revisit is needed. This argues for a tiered architecture: a microsatellite constellation providing routine monitoring at 3–5 day revisit, augmented by tasked high-resolution passes and SAR for event response.
Can existing free data (Copernicus, Landsat) do the job without a national constellation?
Free archives are invaluable for baseline work and historical trend analysis, and ESA's Copernicus programme provides Sentinel-2 at 10 m resolution with 5-day revisit globally. However, free data carries no SLA guarantees, has limited tasking priority for specific national assets, and lacks the 1 m or sub-metre resolution needed to assess individual buildings, transmission towers or port infrastructure with confidence. A national constellation complements rather than replaces free-tier data.
What is the role of the TCFD and ISSB frameworks in driving demand for this capability?
The TCFD (Task Force on Climate-related Financial Disclosures), now absorbed into the ISSB's IFRS S2 standard, requires organisations to disclose material physical climate risks using recognised climate scenarios. IFRS S2 was published in June 2023 and is being adopted across the EU (under CSRD/ESRS), UK, Australia, Singapore and Canada. This creates hard regulatory deadlines for tens of thousands of companies to produce asset-level exposure data — driving both commercial demand and national interest in controlling the underlying observation infrastructure.
How do you handle assets that span multiple hazard zones — a pipeline running 800 km across three climate regions?
Linear infrastructure requires segment-level analysis: the pipeline is discretised into nodes (typically every 1–5 km), each assigned its own hazard profile for flood, landslide, wildfire and permafrost thaw. The resulting risk profile identifies the most exposed segments, informing maintenance prioritisation, insurance structuring and rerouting decisions. This is computationally intensive but entirely tractable with modern geospatial APIs (OGC 17-069r3) and cloud processing environments.