GRA · Banking Nexus
Spatial financeWhere capital meets hazard, exposure and transition
Climate and nature risk in banking
Climate change and nature loss reach banks through their borrowers, their collateral and the markets they depend on. Physical damage lowers property values and firm income, the transition to a low-carbon economy reprices emissions-intensive assets, and degraded ecosystems weaken the businesses that rely on them. This page explains how these drivers become credit, market, liquidity and operational risk across short, medium and long horizons, and how scenario analysis is used despite deep uncertainty.
Climate and nature risk in banking on the record.
Climate and nature risk in banking: how the risk develops.
Financial exposure across horizons. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Physical risk hits collateral and income
Floods, storms, heat and drought damage property and disrupt production. Uninsured losses fall on borrowers, raising default probabilities and loss given default, and insurance withdrawal can reprice whole regional mortgage books.
Transition risk reprices sectors
Carbon pricing, regulation, technology and shifting demand can strand assets and squeeze the margins of emissions-intensive borrowers. Timing matters: a late and abrupt transition produces sharper losses than an orderly one.
Nature loss erodes dependencies
Agriculture, fisheries, water-intensive industry and tourism depend on ecosystem services. Degraded soils, water and pollination can weaken borrower cash flows in ways current credit models rarely capture.
Risks compound across horizons
Physical and transition shocks interact with each other and with the macroeconomic cycle. Correlated losses across sectors and regions can coincide with market repricing, testing capital and liquidity together.
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 |
|---|---|---|---|---|
| Financed emissions | tonnes of carbon dioxide equivalent | annual | PCAF methodology applied to loan and investment books | ESTIMATED |
| Exposure to climate-policy-relevant sectors | percent of loan book | annual | Pillar 3 ESG disclosures | REPORTED |
| Collateral in high physical hazard zones | percent of secured lending by hazard class | annual | Hazard maps overlaid on collateral locations | MODELLED |
| Scenario credit loss under NGFS pathways | change in expected loss relative to baseline | per exercise | Climate stress tests and scenario analysis | MODELLED |
| Green asset ratio | percent of covered assets | annual | EU Taxonomy disclosures | DECLARED |
| Energy performance of mortgage collateral | share of collateral by energy performance certificate class | annual | Property registers and certificates | REPORTED |
| Nature dependency and impact materiality | qualitative rating by sector | annual | ENCORE and bank materiality assessments | ESTIMATED |
How a shock to assets becomes a system loss.
The equity the system loses after interbank contagion, against the initial hit, as leverage, interconnection and recovery rates change, the core of a macroprudential stress test. Teaching model with stated illustrative values, not a prediction. The map on this page shows Western Europe; the model itself is not tied to a place.
Controls
- Fall in external assets
- 2.0%
- Leverage (assets over equity)
- 10 times
- Interbank share of assets
- 10%
- Recovery on interbank claims
- 40%
- Banks in the system
- 20
- Core banks
- 20% of banks
Readouts
- Equity lost to the initial shock
- 18.0%
- Equity lost after contagion
- 45.0%
- Added by contagion (DebtRank)
- 27.0 points
- Banks in default
- 0
Numbers, methods and sources
| Round | System equity lost (%) | Banks in default |
|---|---|---|
| 0 | 18.00 | 0 |
| 1 | 28.80 | 0 |
| 2 | 35.28 | 0 |
| 3 | 39.17 | 0 |
| 4 | 41.50 | 0 |
| 5 | 42.90 | 0 |
| 6 | 43.74 | 0 |
| 7 | 44.24 | 0 |
| 8 | 44.55 | 0 |
| 9 | 44.73 | 0 |
| 10 | 44.84 | 0 |
| 11 | 44.90 | 0 |
| 12 | 44.94 | 0 |
| 13 | 44.96 | 0 |
| 14 | 44.98 | 0 |
| 15 | 44.99 | 0 |
| 16 | 44.99 | 0 |
| 17 | 45.00 | 0 |
| 18 | 45.00 | 0 |
| 19 | 45.00 | 0 |
| 20 | 45.00 | 0 |
| 21 | 45.00 | 0 |
| 22 | 45.00 | 0 |
| 23 | 45.00 | 0 |
| 24 | 45.00 | 0 |
| 25 | 45.00 | 0 |
| 26 | 45.00 | 0 |
| 27 | 45.00 | 0 |
| 28 | 45.00 | 0 |
| 29 | 45.00 | 0 |
| 30 | 45.00 | 0 |
Methods
- DebtRank Battiston and others (2012), Scientific Reports 2:541
- Differential DebtRank Bardoscia and others (2015), PLOS ONE 10
- Contagion in financial networks Gai and Kapadia (2010), Proc. R. Soc. A 466
- Clearing in financial networks Eisenberg and Noe (2001), Management Science 47
- 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
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme's own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Banking Nexus in Foundry
- Tested at Banking Nexus in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Banking Nexus in Reports
- Taught in Faculty of Finance and Economics 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 |
|---|---|---|
| NGFS scenarios | Transition and physical risk pathways with macroeconomic variables for scenario analysis | Free, attribution |
| ECB climate stress test and climate-related statistical indicators | Supervisory exercise methodology and experimental indicators of euro area bank exposures | Free, attribution |
| Bank of England Climate Biennial Exploratory Scenario | Design and published results of the exploratory exercise for UK banks and insurers | Free, Bank of England terms of use |
| Pillar 3 ESG disclosures under EBA standards | Bank-reported exposures to carbon-intensive sectors, physical risk and taxonomy alignment | Public, bank terms |
| PCAF Global GHG Accounting and Reporting Standard | Methodology and data quality scores for financed emissions | Free standard; emission factor database by membership |
| Copernicus Climate Change Service and JRC hazard maps | Physical hazard layers for flood, heat and drought | Free and open, attribution |
| ENCORE | Mapping of economic sectors' dependencies and impacts on nature | Free, attribution |
Trace a shock in climate and nature risk in banking to the balance sheet.
Assembled from the Nexus Standards area profile Banking: 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
- Tropical cyclone
- Earthquake
- Ice-jam flood
- Wildfire
- Dam failure
- Sea-ice loss
- Storm surge (set off downstream)
- Intense rainfall (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Dangerous surf (set off downstream)
- Widespread power outage (set off downstream)
- Mass displacement (set off downstream)
- Coastal flood (set off downstream)
- Coastal erosion (set off downstream)
- Aftershock sequence (set off downstream)
- Fault surface rupture (set off downstream)
- Soil liquefaction (set off downstream)
Systems and couplings
- Banking
- Central banks and financial supervisors Coupling
- Insurance and reinsurance Coupling
- Cities and the built environment Coupling
- Digital, telecom and platforms Coupling
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-U2Universe platform profile: Banking NexusNRS-F2Banking climate, nature and transition riskNRS-H113Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-K20Domain pack profile: Climate adaptation (DOMAIN-020)NRS-U8Universe platform profile: Energy NexusNRS-D1Risk-informed development and project screening
Observation
- BIS statistics (banking, credit, debt securities, property prices) (listed for reference)
- IMF data services (IFS, FSI, WEO, GFS) (listed for reference)
- World Development Indicators (listed for reference)
- GLEIF Golden Copy (LEI Level 1 and Level 2 data) (listed for reference)
- Bank capital to assets ratio (%)
- Bank nonperforming loans to total gross loans (%)
- Domestic credit to private sector (% of GDP)
- (a) Number of commercial bank branches per 100,000 adults and (b) number of automated teller machines (ATMs) per 100,000 adults
Example geography
- Western Europe: 152 hexagonal cells
- Rain rate, last half hour (IMERG), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Carbon monoxide, smoke transport (AIRS), NASA GIBS
Who decides, and what the evidence serves.
Banking Nexus 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 |
|---|---|---|
| Bank board and board risk committee | How climate and nature risks enter risk appetite, strategy and transition planning | Materiality assessment, scenario results and sector exposures |
| Credit risk function | How to reflect physical and transition risk in ratings, collateral valuation and IFRS 9 overlays | Borrower-level hazard and transition data, scenario-adjusted loss estimates |
| Prudential supervisor | Whether a bank meets supervisory expectations on climate risk and whether Pillar 2 measures are warranted | Exercise results and gap assessments against the BCBS principles |
| Central banks and supervisors in the NGFS | Which scenarios to publish and use, and how to communicate their uncertainty | Scenario pathways, model comparisons and data gaps |
Outputs this topic produces
- Explainers on the transmission channels from climate and nature to bank risk
- Comparisons of supervisory climate scenario exercises and their design choices
- Guides to collateral hazard mapping with labelled data quality
- Research on combining climate pathways with credit models across horizons
Where exposure in climate and nature risk in banking travels.
The sectors that carry exposure in climate and nature risk in banking and how a loss in one passes to the others, across portfolios and public finances.
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
Shared questions the industry asks.
- 01
Where are physical exposures?
Which loan books and collateral sit in areas exposed to floods, storms, heat, drought and sea-level rise.
- 02
How does transition affect credit?
How policy, technology and market changes affect borrowers in carbon-intensive sectors.
- 03
What are nature dependencies?
Which borrowers depend on water, soils, pollination and other ecosystem services.
- 04
How do horizons differ?
How to analyse risks that unfold beyond typical loan maturities and planning horizons.
What climate and nature risk in banking 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 climate and nature risk in banking 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? |
How the risk arises and transmits.
Physical hazards affect collateral values, borrower income and insurance availability. Where insurance is withdrawn or becomes unaffordable, risk shifts back to borrowers and lenders.
Transition risk arises from changes in policy, technology and preferences. Assets in high-emission sectors may lose value faster than expected, and borrowers may face higher costs or lower demand.
Nature-related risk is harder to measure. Agriculture, food processing, water-intensive industry and tourism depend on ecosystem services whose degradation may be gradual and then abrupt.
Horizons mismatch. Many climate effects unfold over decades, while loans and capital planning run over years. Short-term analysis may understate risks that grow over time, and long-term analysis carries deep uncertainty.
Physical and transition risks interact with each other and with other shocks, so compound scenarios are more informative than single-factor analysis.
How evidence is gathered, and where it stops.
| Method or data | What it shows | Its limit |
|---|---|---|
| Hazard and exposure mapping | Collateral and borrowers in exposed areas | Asset location data often poor |
| Climate scenarios | Exposure under alternative pathways | Deep uncertainty; not forecasts |
| Sector transition analysis | Vulnerable industries and borrowers | Depends on policy assumptions |
| Nature dependency tools | Reliance on ecosystem services | Early-stage methods and data |
How it is studied, and what the methods cannot say.
- 01Financial economics and macro-financeMacro-financial stress scenarios · Credit cycle and buffer calibrationApplied here
- 02Probability and statisticsEstimating default and loss parameters · Operational loss modellingApplied here
- 03Financial and risk engineeringExpected credit loss modelling · Counterparty exposure and valuation adjustmentsApplied here
- 04Network science and contagionInterbank and counterparty contagion · Overlapping portfolios and fire salesApplied here
- 05Artificial intelligence and machine learningCredit decisioning with explainability · Early warning and transaction monitoringApplied here
Where this page leads across the Consortium.
Questions and answers.
Is climate risk a separate risk type?
Why use scenarios rather than forecasts?
What are financed emissions?
Does the page cover nature as well as climate?
Does GRA tell banks which sectors to stop financing?
Are climate scenarios forecasts?
Where does the hazard evidence come from?
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