GRA · Climate and Nature Financial Risk
Spatial financeWhere capital meets hazard, exposure and transition
Model and measurement gaps
Climate and nature financial risk is measured with models that leave much out: tipping points, compound and cascading events, nature, social and political responses, and the feedback between finance and the economy. Gaps can lead institutions and supervisors to understate risk. This page sets out where the main gaps are, how they arise, what research is closing them, and who decides how to treat what is unknown.
Model and measurement gaps in seven parts.
- 01The questionModel and measurement gaps: how the risk develops
- 02IndicatorsWhat is measured, and how well
- 03SourcesWhere the evidence comes from
- 04DecisionsWho decides, and what the evidence serves
- 05Core questionsWhat model validators ask
- 06MechanismsWhere errors enter
- 07Methods and dataAddressing the gaps
Model and measurement gaps: how the risk develops.
Geolocation, uncertainty and aggregation. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Tipping points are excluded
Abrupt changes such as ice sheet loss or forest dieback are rarely in economic damage functions, so severe outcomes are underweighted.
Compound events are missed
Simultaneous or sequential hazards, such as drought then fire, and their effects on supply chains are harder to model than single perils.
Second-round effects are thin
Few models capture how financial losses reduce lending, which slows the economy and raises losses further.
False precision
Point estimates from complex chains of models look precise. Without uncertainty ranges, users treat them as more reliable than they are.
From a satellite tile to an exposure update.
How satellite radar turns a flood into an exposure update. Synthetic scene; no real event or place.
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Scenario exercises including tipping points | count | annual | Supervisor and research publications | REPORTED |
| Disagreement between physical risk providers | correlation of scores | per study | Comparison studies | ESTIMATED |
| Models with second-round effects | count in supervisory exercises | annual | Exercise documentation | REPORTED |
| Nature risk metrics in use | count by framework | annual | Framework reviews | REPORTED |
| Uncertainty ranges reported with results | share of published results | per review | Disclosure reviews | OBSERVED |
| Research outputs on climate-financial modelling | count | annual | Bibliographic databases | REPORTED |
Nature risk screening.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Nature risk screening
Model and measurement gaps · Nature-sensitive sectors · LEAP cycle 2
LEAP
- Locateborrower sites
- Evaluatedependencies · impacts
- Assesscredit and reputational risk
- Preparetargets · disclosure
Dependencies by sector
| Agriculture | 3 | High | High | High | High |
| Food processing | 1 | High | Medium | Medium | Medium |
| Mining | 1 | High | Low | Medium | Medium |
| Real estate | 15 | Medium | Low | Low | Low |
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| NGFS technical documentation and research | Scenario limitations and research agenda | Free, NGFS terms |
| IPCC Sixth Assessment Report chapters on tipping points and compound events | Assessed evidence on low-likelihood high-impact outcomes | Free, IPCC terms |
| Institute and Faculty of Actuaries reports on climate scenarios | Critiques of scenario damage estimates | Free to read, terms to check |
| FSB and BCBS reports on climate data and methodologies | Gaps in data and methods | Free, FSB and BIS terms |
| Academic comparisons of physical risk scores | Divergence among providers | Free to read, terms per publisher |
Trace a shock in model and measurement gaps 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
Who decides, and what the evidence serves.
Climate and Nature Financial Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Supervisors and the NGFS | How to incorporate tail risks and second-round effects | Research findings and exercise experience |
| Risk functions | How much judgement overlay to apply to model results | Gap analysis and uncertainty evidence |
| Research funders | Which modelling gaps to prioritise | Gap assessments and user needs |
| Boards | How to use results whose uncertainty is large | Ranges, scenario spreads and limitations |
Outputs this topic produces
- Guide to known gaps in climate-financial models
- Method note on presenting uncertainty with model results
- Brief on tipping points and compound events for financial users
- Questions for boards relying on model output
What model and measurement gaps 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 model and measurement gaps 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? |
What model validators ask.
- How accurately are exposed assets located, and how much does location error change results?
- How is uncertainty in hazards, damage functions and scenarios propagated and reported?
- Does aggregation across assets and portfolios hide concentration or create false precision?
- How are models validated when the future differs from the past?
Where errors enter.
Physical risk depends on precise location: a few metres of elevation can determine whether a building floods. Yet financial data often record only an address, a postcode or a company headquarters rather than the operating assets. Hazard data vary in resolution and may not reflect future climate. Damage functions relating hazard to loss are uncertain and often derived from limited data.
Errors compound as models are chained: climate projections feed hazard models, which feed damage models, which feed financial models. Aggregating results into a single score can conceal both the uncertainty and the concentration that matter most.
Addressing the gaps.
- 01
Geolocation improvement
Linking assets to precise locations, using spatial statistics and geostatistics and Earth observation. Coverage remains incomplete.
- 02
Uncertainty quantification
Reporting ranges and sensitivity rather than single values. Communicating uncertainty clearly is difficult.
- 03
Model validation
Comparing models with observed losses and with each other, using scenario design and stress testing. Validation against the future is impossible.
- 04
Aggregation review
Checking whether aggregation hides concentration or tail risk.
How it is studied, and what the methods cannot say.
- 01Climate and atmospheric scienceHazard projections for portfolios · Tail and compound hazards · Scenario physical variablesApplied here
- 02Scenario design and stress testingTranslating NGFS scenarios · Short-term climate scenarios · Dynamic balance sheetsApplied here
- 03Ecology and biodiversity scienceEcosystem service dependency · Ecosystem condition at asset sites · Thresholds and regime shiftsApplied here
- 04Spatial statistics and geostatisticsGeolocating exposures · Aggregating location risk · Resolution sensitivityApplied here
- 05Financial economics and macro-financeCredit risk transmission · Market pricing studies · Cross-sector transmissionApplied here
Questions and answers.
Why might climate stress tests understate risk?
What is a compound event?
How should uncertain results be used?
Why does asset location matter so much?
What is false precision?
Does the platform validate commercial models?
Can models be validated for future climate?
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