G-P01 · TOPIC · cascades-and-interdependenceGCRI dossier · service level R0
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Topic dossier · Systemic Risk Management

Cascades and interdependence

Systemic risk arises when systems depend on one another, so that a failure in one travels into others: a power outage stops water pumping, a port closure stalls manufacturing, a cyber incident halts payments. Cascades can amplify a modest shock into a large loss, or be absorbed by buffers and substitutes. This page sets out how cascades develop and propagate, how dependencies are mapped, and what the evidence serves for those responsible for connected systems.

01In brief

The short answer.

Cascading failures are failures that spread from one system to others that depend on it, as when a power outage stops water pumping or a port closure stalls manufacturing. Buffers set their timing, concentration creates single points of failure, and feedback can amplify losses. Dependency maps and scenarios show where cascades are likely to travel.

02Geography

Hazards and exposure in Asia.

For cascades and interdependence, in GCRI's spatial-intelligence reading, the map opens on night lights, true-colour imagery and population density with relief in 3D, framed on Asia; every other NASA layer, live feed and forecast waits in the layer list.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Hazards and exposure, for this page

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

For cascades and interdependence, in GCRI's spatial-intelligence reading, the map opens on night lights, true-colour imagery and population density with relief in 3D, framed on Asia; every other NASA layer, live feed and forecast waits in the layer list.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 · GDACS · Copernicus Sentinel-2 L2ASources 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 Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. 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). CC BY 4.0
  • 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
  • GHRSST Level 4 MUR analyses, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.DAAC); 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). 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 official warnings.
  • Live: GDACS (EC JRC / UN OCHA) Events are indicative, not official warnings.
  • Observed (EO): Copernicus Sentinel-2 L2A (contains modified Copernicus Sentinel data 2026), Microsoft Planetary Computer
  • Optional layers (35 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

  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
  • Sea surface temperature anomaly: GHRSST MUR25, NASA JPL PO.DAAC · 12 Oct 2026
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026

03The question

Cascades and interdependence: how the risk develops.

Network, common-cause and correlated failures. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.

Mechanism

Failure propagates through dependency

When one system supplies another with power, data, materials or services, its failure removes an input the second system needs. Each link adds a path, and paths combine into chains that cross sectors and borders.

Mechanism

Buffers set the timing

Stocks, backup generation, redundant routes and spare staff delay the effect of a lost input. The cascade arrives when the first buffer runs out, which can be hours or months after the trigger.

Mechanism

Concentration creates single points

Where many systems rely on the same supplier, hub, cloud region or chokepoint, one failure reaches all of them at once. Concentration often grows unseen because each dependent made a reasonable choice alone.

Mechanism

Feedback amplifies the loss

Losses can feed back: an outage reduces revenue, which cuts maintenance, which raises the chance of the next outage. Reinforcing loops turn a contained event into a longer and wider one.

04Key terms

Terms used on this page.

Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.

TermDefinition
InterdependencyA two-way relationship in which the state of each system affects the state of the other.
Single point of failureA component or supplier whose loss stops a function because no substitute exists.
BufferA stock, reserve or redundant capacity that delays or absorbs the effect of a lost input.
05Indicators

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.

IndicatorUnitCadenceSource typeLabel
Mapped critical dependenciescount of supplier and service dependencies recordedannualDependency inventoriesDECLARED
Single points of failurecount of dependencies without a substituteannualDependency analysisESTIMATED
Buffer endurancehours or days of operation after loss of an inputper assetContinuity plans and asset recordsDECLARED
Supplier concentrationHerfindahl-Hirschman index of spend or volumeannualProcurement recordsOBSERVED
Cascade depth in scenariosnumber of systems affected downstreamper scenarioInterdependency modelsMODELLED
Restoration sequence timehours to restore dependent servicesper eventIncident and restoration recordsOBSERVED
06Canvas

From sources to decisions.

The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.

Pipeline canvas · Cascades and interdependence as a run graphDesign, not a record
Cascades and interdependence: a cascade across interdependent systemsA hazard hits one system; dependencies carry the failure into others; impact grows until buffers, redundancy or restoration priorities stop it; the analysis ranks the links to strengthen. Design of the process, not a record of a run.

Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.

07Sources

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.

SourceWhat it givesLicence class
Incident reports from infrastructure operators and regulatorsDocumented failures with their causes and downstream effectsPublic records, terms per publisher
UNDRR Global Assessment ReportAnalysis of systemic and cascading risk across sectors and scalesPublic, UN terms of use
OECD national supply and use tables and Inter-Country Input-Output tablesEconomic dependencies between industries and countriesOpen, OECD terms of use
Grid and network operator outage databasesRecords of outages, duration and customers affectedPublic summaries; full data by agreement (to check)
Academic interdependency case librariesReconstructed cascades from past events, with methods describedLicence per publication
08Decisions

Who decides, and what the evidence serves.

Systemic Risk Management prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.

Who decidesThe questionEvidence this page serves
Infrastructure operatorsWhich upstream dependencies need backup, diversification or contractual protectionDependency maps; single points of failure; buffer endurance
Emergency management agenciesWhich services must be restored first after a widespread outageRestoration sequences; cascade scenarios with timing
Sector regulators and competition authoritiesWhether concentration in a shared provider needs oversightConcentration analysis with its data gaps stated
Treasury and planning ministriesWhere public investment in redundancy reduces the most shared exposureCascade depth in scenarios; restoration evidence

Outputs this topic produces

  • Dependency maps across energy, water, communications, transport and finance
  • Cascade scenarios with buffers and timing stated
  • Concentration analysis of shared suppliers, hubs and providers
  • Restoration sequencing notes for interdependent services
09Core questions

What cascade analysts ask.

  • Which components, if they failed, would cause the widest loss across the system?
  • Which apparently independent risks are in fact correlated through a common cause?
  • Where are the feedback loops that could amplify a shock rather than damp it?
  • How close is the system to a threshold beyond which recovery becomes much harder?
10Mechanisms

How cascades work.

In a network, each node carries load or provides service to others. When a node fails, its load shifts to neighbours. If they are close to capacity, they fail too, and the cascade continues until the remaining system can carry the load or the network fragments. Highly connected hubs make networks robust to random failures but vulnerable to targeted or hub failures.

Common-cause failure defeats redundancy. Two backup systems that share a power supply, a software version, a supplier or a location may fail together. Correlation often rises under stress: assets, markets and infrastructures that move independently in normal times move together in a crisis. Feedback loops, such as forced selling or panic buying, can turn a moderate shock into a severe one.

11Methods and limits

How cascades are analysed.

  1. 01

    Network models

    Network science and contagion models simulate how failures spread across a mapped network. They depend on knowing the network, which is often partial.

  2. 02

    Fault and causal networks

    Fault trees and Bayesian networks represent how combinations of failures lead to outcomes. They capture common causes when those causes are known.

  3. 03

    Dependence modelling

    Statistical models of joint extremes, from probability and statistics, capture tail dependence. Data on joint extremes is scarce by nature.

  4. 04

    Agent-based models

    Simulate behaviour of many interacting actors. They are useful for insight but hard to validate.

12Methods

How it is studied, and what the methods cannot say.

14Questions

Questions and answers.

What is a cascading failure?
A failure in one part of a system, or in one system, that causes failures in other parts or other systems that depend on it, sometimes repeatedly and across sectors.
What is the difference between systemic and cascading risk?
Cascading risk describes the chain of effects from one failure. Systemic risk is broader: it arises from the structure of a whole system, including its dependencies, concentration and feedbacks, and can threaten the functioning of the system itself.
Can cascades be predicted?
Their paths can be explored with dependency maps and scenarios, which shows where they are likely to travel. The timing and size of a specific cascade remain uncertain.
Is redundancy enough to prevent cascades?
Not if redundant components share a cause of failure. Diversity and separation matter as much as duplication.
Why do correlations rise in a crisis?
Because common drivers, such as liquidity needs, shared hazards or panic, dominate under stress and push many components in the same direction.
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