GRA · Climate and Nature Financial Risk
Spatial financeWhere capital meets hazard, exposure and transition
Disclosure and data
Climate and nature disclosure gives investors, lenders and supervisors information on exposure and management. ISSB IFRS S2 sets a global baseline adopted in many jurisdictions; the Basel Committee has a voluntary bank framework; the TNFD addresses nature. Underneath sit data on emissions, exposures and locations that are often estimated. This page sets out how disclosure and data work, where they fall short, and who decides.
Disclosure and data on the record.
Disclosure and data: how the risk develops.
Source quality and comparability. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Standards set the baseline
IFRS S2 requires governance, strategy, risk management, metrics and targets disclosures, with industry-specific metrics and financed emissions for financial institutions. Jurisdictions adopt with reliefs and timelines.
Counterparty data are estimated
Financial institutions depend on borrowers' and investees' data, which many do not report. Estimates fill gaps, with quality scores that must be disclosed.
Location data are missing
Physical and nature risk need asset locations, which are often absent from loan and investment records. Without them, exposure is guessed.
Assurance lags
Sustainability data receive less assurance than financial statements. Errors in estimates and methods can persist into decisions.
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.
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 |
|---|---|---|---|---|
| Jurisdictions adopting ISSB standards | count by approach | on change | IFRS Foundation jurisdictional profiles | Published statistics |
| Financial institutions disclosing financed emissions | count | annual | PCAF and disclosure records | DECLARED |
| PCAF data quality score | score, one to five | annual | Institution disclosures | DECLARED |
| Share of counterparties with reported emissions | percent | annual | Institution data | REPORTED |
| Exposures with geolocated collateral | share of portfolio | periodic | Bank data | REPORTED |
| Sustainability disclosures with external assurance | share of reports | annual | Company filings | DECLARED |
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
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 |
|---|---|---|
| IFRS Foundation jurisdictional profiles | Adoption of ISSB standards by jurisdiction | Free to read, IFRS Foundation terms |
| BCBS disclosure of climate-related financial risks framework | Voluntary bank disclosure templates | Free, BIS terms |
| PCAF standard and signatory list | Financed emissions methods and adopters | Free to read, PCAF terms |
| NZDPU (Net-Zero Data Public Utility) | Company emissions and targets data | Free, terms to check |
| CDP disclosure data | Self-reported climate, water and forest data | Proprietary; summary results free to read |
Trace a shock in disclosure and data 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 |
|---|---|---|
| Securities regulators and finance ministries | Whether and how to adopt ISSB standards | Jurisdictional comparisons |
| Prudential supervisors | Whether to require the Basel climate disclosure framework | Framework content and peer adoption |
| Bank and insurer audit committees | How climate data are governed and assured | Data quality and assurance practice |
| Data providers and users | Which data to collect and estimate | Coverage and quality evidence |
Outputs this topic produces
- Guide to climate and nature disclosure standards for financial institutions
- Method note on financed emissions data quality
- Brief on geolocation of exposures
- Questions for boards on disclosure assurance
What disclosure and data 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 disclosure and data 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 data specialists ask.
- Where does each data point come from: reported, estimated or modelled?
- How comparable are disclosures across firms, sectors and jurisdictions?
- How reliable are third-party estimates where companies do not report?
- Which data gaps matter most for risk analysis?
Why data quality is the binding constraint.
Disclosure frameworks, such as the standards of the International Sustainability Standards Board, the recommendations of the Task Force on Climate-related Financial Disclosures and the framework of the Taskforce on Nature-related Financial Disclosures, have improved consistency. But coverage is uneven, especially for smaller and private companies, and value-chain emissions are often estimated.
Where data are missing, providers estimate them using models, and different providers' estimates can differ substantially. Without clear labelling, estimated data can be mistaken for reported data, giving false precision.
Assessing data quality.
- 01
Data provenance labelling
Distinguishing reported, estimated and modelled values. Labelling is inconsistent across providers.
- 02
Comparability review
Assessing consistency of definitions, scope and boundaries.
- 03
Estimation methods review
Understanding how missing data are estimated, using financial economics and macro-finance. Methods are often proprietary.
- 04
Location data quality
Assessing asset location data, with spatial statistics and geostatistics. See Model and measurement gaps.
How it is studied, and what the methods cannot say.
- 01Financial economics and macro-financeCredit risk transmission · Market pricing studies · Cross-sector transmissionApplied here
- 02Spatial statistics and geostatisticsGeolocating exposures · Aggregating location risk · Resolution sensitivityApplied here
- 03Climate and atmospheric scienceHazard projections for portfolios · Tail and compound hazards · Scenario physical variablesApplied here
Questions and answers.
What does IFRS S2 require of banks?
Is the Basel climate disclosure framework mandatory?
What is a PCAF data quality score?
Why do estimates from different providers differ?
What are value-chain emissions?
Does the platform provide or validate data?
Is more disclosure always better?
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