Geolocating exposures
Addresses and asset records are geocoded and matched to hazard and ecosystem layers, with match rates and positional error reported.
GRA · Climate and Nature Financial Risk
Spatial financeWhere capital meets hazard, exposure and transition
Spatial statistics and geostatistics for climate and nature financial risk
Spatial statistics links hazards and ecosystems to the locations of assets and collateral. On this platform it supports physical and nature risk screening, aggregation of location-specific risk to portfolios and treatment of spatial uncertainty.
01Register
Spatial statistics and geostatistics for climate and nature financial risk on the record.
Method recordSTEM method
- MethodSpatial statistics and geostatistics
- StewardSpace and Earth Observation
- Applied inClimate and Nature Financial Risk
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What spatial statistics and geostatistics does here.
Spatial statistics and geostatistics analyse data that vary across space, accounting for spatial dependence. They estimate values at unsampled locations and test for spatial patterns. In climate and hazards, biodiversity and nature and capital markets and investment, spatial statistics produce continuous hazard and exposure surfaces with uncertainty. They reveal geographic clustering of risks and vulnerabilities across sectors. The canonical description is on the STEM Commons.
Aggregating location risk
Site-level risks are aggregated to portfolio level with spatial correlation, so concentration in one region is visible.
Resolution sensitivity
Results are compared across hazard map resolutions to show how the spatial scale chosen changes exposure estimates.
03Spatial finance
Assets on the grid, and the portfolio they make.
Where a portfolio's assets sit, and how much of its value is most exposed. Hypothetical assets, not an assessment.
04Data
What the method consumes.
Datasets and records used by spatial statistics and geostatistics on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Hazard maps | Physical exposure | MODELLED |
| Asset location records | Exposure geography | REPORTED |
| Biodiversity layers | Nature sensitivity | Published statistics |
05Monitor
Spatial model.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
06Validation
How results are checked.
Predictions are tested using spatial cross-validation that respects dependence. Uncertainty maps are compared with independent observations.
| Check | How it is done |
|---|---|
| Geocoding accuracy | Match rates and errors are reported |
| Spatial correlation | Aggregation accounts for correlated losses |
| Scale effects | Results are tested across resolutions |
Results depend on the scale and zoning of analysis. Sparse or clustered sampling limits prediction in unsampled areas.
07Scenario model
Transition and physical risk on a portfolio.
Three scenario families after the NGFS framework move a sector portfolio through carbon costs and warming damages; the transmission view follows the losses to lenders, investors, public finances and households. Scenario: named assumptions about the future, not a forecast. The map on this page shows Southeast Asia; the model itself is not tied to a place.
Controls
- Carbon price at the horizon, orderly path
- 200 per tCO2
- Delay before a disorderly transition
- 50% of horizon
- Cost passed to customers
- 50%
- Warming today above pre-industrial
- 1.30°C
- Warming per decade, current policies
- 0.25°C
- Damage coefficient
- 0.24% per °C²
- Years ahead
- 25 years
Readouts
- Worst scenario at the horizon
- −33.6% of value
Numbers, methods and sources
| Year | Orderly transition (% value) | Delayed, disorderly transition (% value) | Current policies (% value) |
|---|---|---|---|
| 0 | −0.38 | −0.38 | −0.38 |
| 3 | −2.87 | −0.53 | −0.67 |
| 6 | −5.36 | −0.68 | −0.97 |
| 9 | −7.84 | −0.84 | −1.27 |
| 12 | −10.33 | −0.99 | −1.57 |
| 15 | −12.82 | −7.12 | −1.87 |
| 18 | −15.31 | −15.05 | −2.17 |
| 21 | −17.80 | −23.00 | −2.48 |
| 24 | −20.29 | −30.94 | −2.79 |
Methods
- Climate scenario families NGFS (2023), Climate Scenarios for central banks and supervisors, Phase IV
- Climate stress test of the financial system Battiston and others (2017), Nature Climate Change 7
- Damage function Nordhaus (2017), Revisiting the social cost of carbon, PNAS 114
- Transmission channels BCBS (2021), Climate-related risk drivers and their transmission channels
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Urban extents Natural Earth
- Seaports Natural Earth
- Past year, daily temperature (ERA5 via Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus ERA5 (C3S/ECMWF) · register open-meteo.public-pages
- Surface air temperature, daytime (AIRS) NASA AIRS · register nasa.gibs.context-live-layers
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Climate and Nature Financial Risk in Foundry
- Tested at Climate and Nature Financial Risk in Universe
- Codified in Climate and Earth Systems in Standards
- Published in Climate and Nature Financial Risk in Reports
- Taught in Faculty of Earth and Environmental Sciences in Academy
08Connections
Topics that rely on this method.
09Twin and cascade simulator
Trace a shock to the balance sheet.
Assembled from the Nexus Standards area profile Climate risk standards: the shocks it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the full System Builder.
Hazards
- Sea-level rise
- Coastal flood (set off downstream)
- Storm surge (set off downstream)
- High-tide flooding (set off downstream)
- Coastal erosion (set off downstream)
- Saltwater intrusion (set off downstream)
- Estuary flood (set off downstream)
- Coastal habitat squeeze (set off downstream)
- Wetland loss (set off downstream)
- Mangrove loss (set off downstream)
- Groundwater flood (set off downstream)
- Climate-driven migration and relocation (set off downstream)
- Mass displacement (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-1The UNI and the artefact catalogue (grammar v0.2: domain, sector, region and environment segments)NS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-3bDataset and product metadata profile (STAC, DCAT, PROV-O)NS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-H65Hazard family: Planetary boundary transgressions (NHS-1 planet.boundary)NRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-H71Hazard family: Deep-time and cosmic catastrophes (NHS-1 planet.deep-time)NRS-H79Hazard family: Mental health crises (NHS-1 health.mental)NRS-H75Hazard family: Climate-sensitive illness (NHS-1 health.climate-sensitive)NRS-H7Hazard family: Large-scale circulation anomalies (NHS-1 atmo.climate-mode)
Observation
- ERA5 reanalysis (Copernicus Climate Data Store) (listed for reference)
- CMIP6 climate projections (Copernicus Climate Data Store) (listed for reference)
- NGFS Scenario Explorer (listed for reference)
- INFORM Risk Index (listed for reference)
- Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population
- Total greenhouse gas emissions per year
- Direct economic loss attributed to disasters in relation to global GDP
Example geography
- Pacific Island states: 150 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
10Stewardship
Who maintains the method.
Space and Earth Observation stewards spatial statistics and geostatistics for the Consortium and reviews how it is applied across platforms.
11Sector signature
Where exposure in spatial statistics and geostatistics for climate and nature financial risk travels.
The sectors that carry exposure in spatial statistics and geostatistics for climate and nature financial risk and how a loss in one passes to the others, across portfolios and public finances.
Select corners · hover lines6 / 15 relations
Signature
Biodiversity and its partners
Biodiversity gives pollination and soils to food (0.6), watershed regulation to water (0.5) and zoonotic spillover and medicines to health (0.4).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Strongest relations in play
- Food → Biodiversity · land conversion0.6
- Water → Food · irrigation0.8
- Water → Biodiversity · environmental flows0.6
- Food → Health · nutrition0.7
12Connected
Where this page leads across the Consortium.
13Exposure, loss and capital
What it means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Scenario pathways
How spatial statistics and geostatistics for climate and nature financial risk could be stressed under the scenario families central banks and supervisors use.
| Scenario family | Transition risk | Physical risk | What a stress test asks |
|---|---|---|---|
| Orderly | Early, gradual policy | Lower | Can the book absorb a steady repricing? |
| Disorderly | Late, abrupt policy | Lower to medium | What breaks under a sudden repricing and credit migration? |
| Hot house world | Limited policy | High and rising | Where do physical losses concentrate in collateral and insurance? |
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
14Questions
Questions and answers.
What are the limits of spatial statistics and geostatistics in this field?
Results depend on the scale and zoning of analysis. Sparse or clustered sampling limits prediction in unsampled areas.
How are results checked?
Predictions are tested using spatial cross-validation that respects dependence. Uncertainty maps are compared with independent observations.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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Global Risks AllianceClimate and Nature Financial RiskFinance and Insurance ReadinessScenario and Stress-Test
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