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Systemic and cascading risks
Systemic risk is the risk that a shock in one part of a connected system spreads, amplifies and disrupts the whole. A drought travels through water, energy, food prices, budgets and health. A financial failure halts payments that hospitals and utilities rely on. The Global Centre for Risk and Innovation (GCRI) traces where shocks cross systems, where they amplify and where they can be stopped. Part of global challenges.
Systemic and cascading risks on the record.
- 01InstitutionThe Global Centre for Risk and Innovation (GCRI)
- 02Key ideasFour features make a risk systemic
- 03Terms and measuresWhat systemic risk means in practice, and what can be measured
- 04Methods and their limitsThe models experts use for connected risk, and where each breaks down
- 05Data and its statusThe evidence on connected risk is thinner than the evidence on single hazards
- 06GCRI's partWhat GCRI does here, how it reaches Nexus Reports, and how you can help
- 07How GCRI approaches itMethods for connected risk
Four features make a risk systemic.
Systemic risk is not a separate hazard. It is a property of how systems are connected, which is why it is often invisible to assessments made one sector at a time.
Tight connections
Systems linked by flows of power, water, goods, data and money pass stress along quickly.
Feedback loops
Price spikes, hoarding, panic and forced sales can make a shock larger as it spreads.
Shared points of failure
One asset, supplier or assumption underneath many services can fail them all at once.
Thresholds and tipping points
Systems can absorb stress up to a point and then change state abruptly.
Hazards and exposure worldwide.
Flood water, population density, active fires and true-colour imagery across the world, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 4
Hazards and exposure, for this page
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Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
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Hazards and exposure, framed on World. Layers as NASA publishes them, each with its own date.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · U.S. Geological Survey · NASA HLS S30Sources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
- Live: NASA EONET Events are indicative, not authority warnings.
- Live: U.S. Geological Survey Events are indicative, not authority warnings.
- Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (11 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Active fires and thermal anomalies, last day: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
What systemic risk means in practice, and what can be measured.
Two definitions anchor the work. The International Risk Governance Center describes systemic risks as risks to the functioning of a system that arise from the interactions of its parts. Their effects reach well beyond the point of origin. The UNDRR Global Assessment Report of 2019 adds that such risk is often embedded in a system without being recognised as a risk, until a shock reveals it.
| Concept | What it describes | How it is measured |
|---|---|---|
| Interdependency | A two-way state relation between systems; Rinaldi and colleagues distinguish physical, cyber, geographic and logical links | Dependency matrix of services and assets; share of a service's inputs that come from each other system |
| Cascade | A failure that triggers failures elsewhere, possibly in sequence over hours, weeks or seasons | Fraction of nodes or services failed after propagation; time from first failure to last; time to recover in days |
| Amplification | A feedback that makes the shock larger as it spreads: fire sales, hoarding, price spikes, loss of confidence | Ratio of total to initial loss in a model run, reported with its range |
| Concentration | Many functions resting on one asset, supplier, route or assumption | Supplier or route shares; Herfindahl-Hirschman concentration index; count of single points of failure |
| Critical transition | An abrupt shift between states once a threshold is crossed | Rising variance and lag-one autocorrelation in long series, read as a possible warning, not a forecast |
Model systemic and cascading risks as a system. Run the cascade.
Assembled from the Nexus Standards area profile Global and systemic risk: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification.
Hazards
- Dam failure
- Financial contagion
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Sovereign debt crisis (set off downstream)
- Currency crisis (set off downstream)
Systems and couplings
- Global and systemic risk
- Compound and cascading hazard standards Mechanism
- Financial stability and prudential risk Mechanism
- Supply chain and trade risk Cascade
- Future generations and long-term risk Common driver
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-H120Hazard family: Structural fragility of interconnected systems (NHS-1 sys.fragility)NRS-C6Systemic and cascading riskNRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)
Observation
- INFORM Risk Index (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- IMF data services (WEO, IFS, FSI via SDMX) (listed for reference)
- Direct economic loss attributed to disasters in relation to global GDP
- Macroeconomic Dashboard
Example geography
- Bangladesh: 134 H3 hexagons at resolution 4
- 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
- Surface soil moisture (SMAP), NASA GIBS
The models experts use for connected risk, and where each breaks down.
| Method | What it answers | Where it breaks down |
|---|---|---|
| Network contagion models, including DebtRank (Battiston and others, 2012) | Which institutions or assets carry a shock furthest through a web of exposures | Exposure networks are rarely complete or public; missing links change who looks central |
| Interdependent network percolation (Buldyrev and others, 2010) | Why coupled networks can collapse abruptly where a single network would degrade gradually | Stylised: real operators reroute, shed load and repair, which the theory leaves out |
| Input-output and inoperability models | How lost output in one sector reduces output in sectors that buy from it | Fixed technical coefficients allow no substitution; results overstate losses over longer horizons |
| Agent-based models | How behaviour such as hoarding, panic or switching suppliers shapes spread | Many parameters and few validation data; results are explorations, published with their sensitivity |
| Storylines and compound stress scenarios | What a physically and economically coherent bad sequence looks like, step by step | Carry no probability; their value depends on whether experts in each system test each step |
Haldane and May showed in 2011 how homogeneity and tight coupling made a banking system fragile in ways no single bank's risk model could see. The same lesson holds for water, power and food systems: the property that matters belongs to the network. Every model result here carries its assumptions, its range and the label Modelled or Scenario.
The evidence on connected risk is thinner than the evidence on single hazards.
| Evidence | What it gives | Honest status |
|---|---|---|
| National supply and use tables, and inter-country input-output tables (OECD ICIO) | Flows between sectors and countries for propagation models | Published with a lag of several years; sector detail too coarse for many infrastructure questions |
| Disaster loss databases (EM-DAT, DesInventar Sendai) | Recorded direct losses by event and place | Indirect and cascading losses are rarely recorded; small events are under-reported |
| Infrastructure dependency data held by operators | Which assets serve which services | Mostly restricted for security reasons; public studies work with permitted or synthetic topology |
| Supervisory financial exposure data | Bilateral exposures between institutions | Held by supervisors; public work relies on aggregates and reconstructed networks |
Where the data cannot support a claim, the page says the claim cannot yet be made. A gap is shown as a gap, never filled with a plausible number.
What GCRI does here, how it reaches Nexus Reports, and how you can help.
GCRI is a research, development and policy institution. On systemic risk it develops and tests methods, helps national teams map their own dependencies on their own data, and publishes what review supports. It does not regulate, supervise, fund or approve projects, and every decision on a system stays with the authority or operator that runs it. Work is organised through the Systemic Risk Management platform and the Systems Science and Modelling platform, both in development.
Findings are designed to reach the Global Systems Risk and Resilience series and the Scenario and Stress-Test series. Under the proposed Nexus Standards, each dependency record and indicator series would carry a record identifier (NS-3) and each model a model card (NS-12). A reader could then trace a result back to its inputs.
- A documented cascade with a source for each step, including the steps where attribution is uncertain.
- A critique of a propagation method, with the case where it failed.
- Permitted dependency data for a city or sector, offered under stated terms through the Secretariat.
Methods for connected risk.
- 01
Map the system
Dependencies across sectors and borders with network science and contagion.
- 02
Simulate propagation
Complex systems and agent-based models that show how behaviour shapes spread, with assumptions stated.
- 03
Stress-test
Compound scenarios using scenario design and stress testing, labelled as scenario evidence.
- 04
Find points of control
Interventions that cut or buffer transmission, with their trade-offs and limits.
Platforms, fields and counterparts.
Field work runs through the Systemic Risk Management platform and the Systems Science and Modelling platform, both in development, and the risk management and systemic risk field gateway. The Global Risk Forum (GRF) examines all-hazards governance. The Global Risk Alliance (GRA) examines systemic stability in finance. Worked illustrations: risk transmission examples.
A model of systemic risk is a tool for understanding, not a prediction. Every result states its assumptions and uncertainty.
Where this page's references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
- IRGC Guidelines for the Governance of Systemic Risks (EPFL International Risk Governance Center, 2018)
- Global Assessment Report on Disaster Risk Reduction 2019, the systemic risk edition (UNDRR, 2019)
- OECD Recommendation of the Council on the Governance of Critical Risks (OECD, 2014)
- Haldane and May, Systemic risk in banking ecosystems, Nature 469 (2011)
- Battiston and others, DebtRank: too central to fail?, Scientific Reports 2 (2012)
- Buldyrev and others, Catastrophic cascade of failures in interdependent networks, Nature 464 (2010)
- Rinaldi, Peerenboom and Kelly, Identifying, understanding and analyzing critical infrastructure interdependencies, IEEE Control Systems Magazine 21 (2001)
- Scheffer and others, Early-warning signals for critical transitions, Nature 461 (2009)
- OECD Inter-Country Input-Output tables and Trade in Value Added database
- NS-3, GRIx record UNIs, a proposed Nexus Standard
- NS-12, Software, model and AI/ML artifacts, a proposed Nexus Standard
Questions and answers.
How is systemic risk different from ordinary risk?
Can systemic risk be measured with one number?
Who uses this work?
How do countries take part?
Can systemic risk be eliminated?
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The same subject across the Consortium.
- GRFglobalriskforum.comGovernance NexusMandates, commitments and accountability
- GRAglobalriskalliance.comSystemic interconnectednessExposure, loss and capital
- Nexus Standardsnexustandards.comSystemic RisksStandards that apply
- Risk Academyrisksacademy.comSystemic, compound and cascading riskWhere it is taught
- Nexus Reportstherisk.globalSystemic Risk Management in Nexus ReportsWhere it is reported
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