GRA · Climate and Nature Financial Risk
Spatial financeWhere capital meets hazard, exposure and transition
Scenario analysis
Climate scenario analysis asks how financial institutions would fare under different climate and policy pathways. Supervisors use it in exploratory exercises; firms use it to test strategy and meet disclosure rules. Scenarios translate emissions, temperatures and policy into macroeconomic and sector variables, then into losses. This page sets out how climate scenario analysis works, where it falls short, and who decides how it is used.
Scenario analysis on the record.
Scenario analysis: how the risk develops.
Horizon, assumptions and limitations. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Scenarios set narratives
Reference scenarios such as those from the NGFS describe orderly, disorderly and hot-house paths. Each implies different carbon prices, damages and growth.
Translation adds uncertainty
Scenario variables must be translated into sector output, firm revenues, property values and default probabilities. Each step adds model uncertainty.
Horizons outrun balance sheets
Climate effects unfold over decades; balance sheets turn over in years. Static balance sheet assumptions make long-horizon results illustrative.
Damage functions understate tails
Many scenario damage estimates omit tipping points, compound events and indirect effects, which can understate severe outcomes.
A loan book under scenario families.
How physical and transition risk could reach a loan book under different scenarios. Illustrative design values.
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 |
|---|---|---|---|---|
| Credit loss under climate scenarios | currency or basis points of exposure | per exercise | Supervisory and internal exercises | MODELLED |
| Change in probability of default by sector | percentage points | per exercise | Scenario models | MODELLED |
| Insurance losses under physical scenarios | currency | per exercise | Insurer scenario analysis | MODELLED |
| Portfolio value change under transition scenarios | percent | per exercise | Investor scenario analysis | MODELLED |
| Supervisory climate exercises completed | count by authority | annual | Supervisor publications | Published statistics |
| Scenario variables published | count of variables and regions | per vintage | NGFS scenario database | Published statistics |
Climate scenario analysis.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Climate scenario analysis
Scenario analysis · Bank loan book · Exercise 2
Scenario pipeline
- Select scenariosorderly · disorderly · hot house
- Pathwayscarbon price · GDP
- Sector impactsmargins
- Physical hazardsassets
- Credit parametersPD · LGD
- Portfolio losscapital
- Discloselimits
PD uplift by sector and scenario
basis points, illustrative
Expected loss path
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 Scenarios Portal and database | Long-term and short-term climate scenarios with variables | Free, NGFS terms |
| ECB economy-wide climate stress test and supervisory exercise reports | Methods and results of euro area exercises | Free, ECB terms |
| Bank of England Climate Biennial Exploratory Scenario report | Exploratory scenario exercise for banks and insurers | Free, Bank of England terms |
| Federal Reserve pilot climate scenario analysis report | Exploratory exercise with large United States banks | Public domain |
| IPCC AR6 scenario database | Emissions and climate pathways | Free, terms per dataset |
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
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 central banks | Which scenarios to use and how results inform supervision | Scenario design, exercise findings and limits |
| Bank, insurer and investor boards | How scenario results inform strategy and risk appetite | Internal and supervisory scenario results |
| Disclosure preparers | How to report scenario analysis under IFRS S2 | Standard requirements and peer practice |
| The NGFS | How scenarios are updated | User feedback and research |
Outputs this topic produces
- Guide to climate scenario families and their assumptions
- Method note on translating scenarios to credit and market risk
- Brief on the limits of damage functions
- Questions for boards using scenario results
Trace a shock in scenario analysis 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
What scenario designers ask.
- What question is the scenario meant to answer, and over what horizon?
- Which pathways, such as orderly, disorderly or hot-house futures, are relevant?
- How are physical and transition effects combined?
- How sensitive are results to assumptions, and how should they be communicated?
What scenario analysis 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 scenario analysis 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? |
Why scenarios rather than forecasts.
Climate and nature risks unfold over decades under deep uncertainty about policy, technology and the climate system. History is a poor guide, and probabilities for different futures are not well defined. Scenarios address this by exploring a range of internally consistent futures, often based on reference sets such as those published by the Network for Greening the Financial System.
Scenario results depend heavily on their assumptions and on the models used to translate them into economic and financial terms. Many models have historically understated physical damages, omitted tipping points and assumed smooth adjustment, which may make severe outcomes look milder than they could be.
Building and using scenarios.
- 01
Reference scenarios
Using shared pathways for comparability, with scenario design and stress testing. Reference scenarios may not fit every institution's question.
- 02
Integrated assessment and macro models
Linking emissions, policy and the economy, with financial economics and macro-finance. Damage functions are a known weakness.
- 03
Sector and asset translation
Mapping macro paths to sectors and assets. Each translation step adds uncertainty.
- 04
Sensitivity and reverse stress testing
Testing which assumptions matter most and what would cause severe outcomes.
How it is studied, and what the methods cannot say.
- 01Scenario design and stress testingTranslating NGFS scenarios · Short-term climate scenarios · Dynamic balance sheetsApplied here
- 02Climate and atmospheric scienceHazard projections for portfolios · Tail and compound hazards · Scenario physical variablesApplied here
- 03Financial economics and macro-financeCredit risk transmission · Market pricing studies · Cross-sector transmissionApplied here
- 04Spatial statistics and geostatisticsGeolocating exposures · Aggregating location risk · Resolution sensitivityApplied here
Questions and answers.
Are climate scenarios forecasts?
What are the NGFS scenarios?
Why do climate stress tests show small losses?
Is a climate scenario a forecast?
Why might scenarios understate risk?
Does the platform run scenarios for individual firms?
What is reverse stress testing?
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