GCRI · Disaster Resilience and Recovery
Disaster loss accounting
Disaster loss accounting records what disasters cost: deaths, injuries, people affected, damage to assets and losses to economies and services. Consistent records are the basis for tracking Sendai targets, pricing risk and learning what works. This page sets out how losses are recorded, why small and slow disasters are undercounted, and what evidence serves the agencies that keep and use loss data.
Disaster loss accounting in seven parts.
- 01The questionDisaster loss accounting: how the risk develops
- 02Across the domainsLoss accounts show how losses fall on each domain
- 03IndicatorsWhat is measured, and how well
- 04SourcesWhere the evidence comes from
- 05DecisionsWho decides, and what the evidence serves
- 06Core questionsWhat experts ask
- 07Mechanisms and driversWhy losses are hard to count
Disaster loss accounting: how the risk develops.
Direct and indirect loss methods. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Large events are counted, small ones are not
International databases use thresholds that capture major disasters. Frequent small events, which together cause large losses, appear only in national databases where they exist.
Damage and loss are different
Damage is the destruction of physical assets; loss is the change in economic flows, such as lost production or extra costs. Mixing them or counting one twice distorts totals.
Indirect and slow losses are missed
Deaths from heat, health effects of interrupted care, and losses that unfold over months after drought are hard to attribute, so they are often left out of disaster records.
Records change after the event
Early figures are revised as assessments progress, and methods change over time. Without versioned records, trends reflect changes in counting as well as in disasters.
Recovery and needs tracker.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Recovery and needs tracker
Disaster loss accounting · Affected area North · Day 68 after event
Damage and loss by sector
local units, millions, estimated
| Housing | 110 | 44 | 268 | 38 |
| Transport | 79 | 34 | 204 | 65 |
| Water and sanitation | 24 | 22 | 51 | 70 |
| Agriculture | 25 | 46 | 157 | 37 |
| Health | 20 | 25 | 14 | 88 |
Build back better
- Standard
- raised floor levels, hazard map zoning
- Safeguard
- no relocation without consent
- Owner
- Municipality
- Label
- ESTIMATED
Sendai targets, national
progress vs baseline, illustrative
Loss accounts show how losses fall on each domain.
- FoodCouplingAgricultural loss is a Sendai indicator and often the largest share in droughts.What to watchDirect agricultural loss
- HealthCouplingDamage to health facilities is reported under Sendai target D.What to watchFacilities damaged
- WaterCouplingWater service disruptions are counted as basic service disruptions.What to watchService disruption counts
Loss accounts are designed for the Country Quarterly series. National loss databases built on DesInventar Sendai and global records such as EM-DAT under-record small events and indirect losses; the page says so with every figure, and records would carry NS-3 identifiers under the proposed Nexus Standards.
- Event-level loss records with sources.
- Methods for estimating indirect losses.
How disaster loss accounting is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Uncertainty bands
How far the outlook for disaster loss accounting could spread, shown as ranges rather than a single line.
- Central estimate
- median of the ensemble
- Ranges
- likely and very likely, in IPCC calibrated language
- Ensemble
- members, scenarios and initial conditions stated
- Read with
- the sensitivity of the result to each input
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 |
|---|---|---|---|---|
| Deaths and missing persons attributed to disasters | count per hundred thousand population | annual | Sendai Framework Monitor target A | Published statistics |
| People directly affected | count per hundred thousand population | annual | Sendai Framework Monitor target B | Published statistics |
| Direct economic loss | share of gross domestic product | annual | Sendai Framework Monitor target C | Published statistics |
| Damage to critical infrastructure and disruption of basic services | count of facilities and service disruptions | annual | Sendai Framework Monitor target D | Published statistics |
| Events recorded below international thresholds | count in national databases | annual | National disaster loss databases | REPORTED |
| Records disaggregated by sex, age and disability | percent of records | annual | National disaster loss databases | REPORTED |
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 |
|---|---|---|
| DesInventar Sendai (UNDRR) | National disaster loss databases at local resolution | Open, terms per national database (to check) |
| Sendai Framework Monitor | Published national reporting of losses against targets A to D | Public, UN terms of use |
| EM-DAT international disaster database (CRED, UCLouvain) | Events above thresholds with human and economic impacts | Free for non-commercial use with registration |
| IRDR Peril Classification and Hazard Glossary | Standard hazard classes for loss recording | Public, IRDR terms (to check) |
| Post-Disaster Needs Assessment reports | Detailed damage and loss estimates for major events | Public, terms per publisher |
Who decides, and what the evidence serves.
Disaster Resilience and Recovery 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 |
|---|---|---|
| National disaster management authorities and statistics offices | How to organise and maintain a national loss database | Quality reviews; disaggregation guidance |
| Ministries of finance | How past losses should inform contingency budgets and risk financing | Loss series with method changes marked |
| UNDRR and regional organisations | How to support consistent reporting across countries | Crosswalks; completeness evidence |
Outputs this topic produces
- Loss database quality reviews covering completeness and thresholds
- Crosswalks between national databases and international records
- Guidance on disaggregated loss recording
- Versioned loss series with method changes marked
What experts ask.
Loss accounting specialists ask what is counted, by whom and with what consistency.
- How are deaths, injuries, displacement, damage and economic losses defined and recorded?
- How are indirect losses, such as business interruption and supply-chain effects, estimated?
- Why are small, frequent disasters and losses to poorer people systematically undercounted?
- How comparable are loss records across countries, hazards and time?
- How can loss data be linked to hazard and exposure data to explain what drives losses?
Why losses are hard to count.
Direct losses, the physical damage to buildings, infrastructure, crops and stock, are the most often recorded, usually in replacement or repair terms. Indirect losses arise from disrupted flows: businesses that cannot operate, services that stop, supply chains that break. They can extend far beyond the affected area and last for years, but are harder to attribute to a single event.
Loss records are shaped by who collects them and why. Insured losses are well documented where insurance is common and nearly absent where it is not. Damage to informal housing and livelihoods is easily missed. Small, frequent events rarely make national databases, yet together they can account for a large share of loss to poor households. Without attention to these biases, loss data can understate risk where it is highest.
How the evidence is built, and where it stops.
- 01
Disaster loss databases
Event-by-event records of impacts at local level. Coverage and definitions vary between countries and over time.
- 02
Damage and needs assessment
Structured post-event assessment of damage, losses and recovery needs. Methods are standardised but quality depends on access and time.
- 03
Economic modelling
Input-output and general equilibrium models for indirect losses, with operations research methods. Results depend on assumptions about substitution and recovery.
- 04
Remote sensing of damage
Satellite and aerial assessment using geospatial information systems. It shows physical damage, not economic or human loss.
How it is studied, and what the methods cannot say.
- 01Social and behavioural scienceWarning response surveys · Participatory risk assessment · Social vulnerability analysisApplied here
- 02Geospatial information systemsExposure mapping · Rapid damage assessment · Common operational dataApplied here
- 03Decision analysis under uncertaintyCost-benefit of risk reduction · Acting on forecasts · Recovery prioritisationApplied here
- 04Forecasting and time-series analysisTrigger verification · Flood forecast lead time · Seasonal outlooks for drought preparednessApplied here
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.
Questions and answers.
What is the difference between damage and loss?
Why do disaster databases disagree?
What is DesInventar?
Why do insured losses not show the full picture?
Are losses rising because disasters are worse?
Does the platform publish loss figures?
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