GCRI · Disaster Resilience and Recovery
Risk reduction policy
Disaster risk reduction policy decides whether new risk is built into roads, homes and towns, and whether existing risk is reduced before the next event. Strategies, laws, budgets and land-use rules carry that policy into practice. This page sets out how risk reduction policy works and fails, how progress is measured against the Sendai Framework, and what evidence serves the ministries and local governments that set and fund it.
Risk reduction policy on the record.
Risk reduction policy: how the risk develops.
Prevention and risk-informed development. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Development creates new risk
Most future disaster risk is being built now, as housing, infrastructure and industry expand into hazard-exposed places without risk-informed rules. Preventing new risk is cheaper than reducing it later.
Prevention is underfunded
Budgets favour response, which is visible, over prevention, whose benefits are avoided losses that never appear. Risk reduction competes with immediate needs and often loses.
Responsibility is spread thin
Risk reduction touches planning, finance, environment, health and infrastructure ministries. When a disaster agency holds the mandate without the budget or authority over other sectors, policy stays on paper.
Local capacity limits delivery
National strategies depend on local governments to enforce building codes, plan land use and maintain drainage. Where local revenue and staff are thin, delivery lags behind policy.
People and hazards in the Philippines.
Population density, night lights, flood water and true-colour imagery over the Philippines, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 4
People and hazards, for this page
Keyboard, with the map focused
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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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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.
People and hazards, framed on the Philippines.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · 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 hexagonal 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 hexagonal 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 hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- 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 hexagonal 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 hexagonal 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 hexagonal cells where shown (modified). NASA open data
- 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 hexagonal 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 hexagonal 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 hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal 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.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET 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).
- Optional layers (5 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
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 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
- Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
Risk reduction policy cuts across sector mandates.
- WaterCouplingFlood and drought plans are among the most common sector strategies.What to watchIntegration with national DRR strategy
- HealthCouplingHealth emergency and disaster risk management plans need DRR links.What to watchHealth in national DRR strategies
- BiodiversityCouplingEcosystem-based risk reduction is recognised in the Sendai Framework.What to watchNature-based measures in strategies
Policy analyses are designed for the Country Systems Risk and Resilience series. Sendai Framework target E tracks national and local DRR strategies through the Sendai Framework Monitor.
- National and local DRR strategies with their review dates.
- Evaluations of strategy implementation.
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
Risk reduction policy · Affected area North · Day 113 after event
Damage and loss by sector
local units, millions, estimated
| Housing | 58 | 46 | 191 | 19 |
| Transport | 158 | 44 | 220 | 58 |
| Water and sanitation | 44 | 29 | 54 | 73 |
| Agriculture | 15 | 130 | 90 | 28 |
| Health | 5 | 16 | 45 | 86 |
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
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 |
|---|---|---|---|---|
| National disaster risk reduction strategy in line with Sendai | status as reported | annual | Sendai Framework Monitor target E | Published statistics |
| Local governments with risk reduction strategies | percent of local governments | annual | Sendai Framework Monitor target E | Published statistics |
| Public spending on risk reduction | share of public investment tagged to risk reduction | annual | Budget tagging and expenditure reviews | ESTIMATED |
| Building permits subject to hazard review | percent of permits | annual | Local planning records | REPORTED |
| Disaster risk reduction law in force | status and date of enactment | per change | National legal databases | Published statistics |
| Development in mapped hazard zones | hectares of new built-up area | annual | Satellite built-up area and hazard maps | ESTIMATED |
Stocks, flows, delays and a tipping threshold.
Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Philippines; the model itself is not tied to a place.
Controls
- Growth of pressure
- 6.0% a year
- Efficacy of capacity in reducing pressure
- 4.0% a year
- Investment response to perceived pressure
- 20%
- Perception delay
- 4.0 years
- Tipping threshold of pressure
- 60 index
- Amplification past the threshold
- 2.0 times
- Years simulated
- 50 years
Readouts
- Peak pressure
- 113 index
- Capacity at the end
- 186 index
- Years over the threshold
- 15.8 years
Numbers, methods and sources
| Year | Pressure | Capacity | Perceived |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Methods
- System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
- 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 Disaster Resilience and Recovery in Foundry
- Tested at Disaster Resilience and Recovery in Universe
- Codified in Hazards and Disaster Risk Reduction in Standards
- Published in Disaster Resilience and Recovery in Reports
- Taught in Faculty of Hazards, Disasters and Resilience 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 |
|---|---|---|
| Sendai Framework Monitor | National reporting against the seven targets and indicators | Public, UN terms of use |
| UNDRR Global Assessment Report | Global analysis of disaster risk and progress on risk reduction | Public, UN terms of use |
| IFRC Disaster Law Database | Disaster risk management laws and policies by country | Public, IFRC terms (to check) |
| Global Human Settlement Layer (European Commission JRC) | Built-up area and settlement growth over time | Open, CC BY 4.0 |
| Public expenditure and financial accountability reviews | Assessments of budget processes, including contingency and risk spending | Public records, terms per publisher |
Model risk reduction policy as a system. Run the cascade.
Assembled from the Nexus Standards area profile Disaster risk reduction: 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. To model a place of your own choosing, open the full System Builder.
Hazards
- Tropical cyclone
- Flash flood
- Heatwave
- Ice-jam flood
- HIV epidemic
- Earthquake
- Storm surge (set off downstream)
- Intense rainfall (set off downstream)
- River 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)
- Extreme fire weather (set off downstream)
- Wildfire (set off downstream)
- Heat-driven grid stress (set off downstream)
- Agricultural drought (set off downstream)
- Flash drought (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-C15Loss, damage and impact accountingNRS-H114Hazard family: Macroeconomic and fiscal shocks (NHS-1 soc.macro-fiscal)NRS-K20Domain pack profile: Climate adaptation (DOMAIN-020)NRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-H65Hazard family: Planetary boundary transgressions (NHS-1 planet.boundary)NRS-H119Hazard family: Cultural heritage loss (NHS-1 soc.heritage)
Observation
- DesInventar disaster loss databases (listed for reference)
- INFORM Risk Index (listed for reference)
- Global SDG Indicators Database (listed for reference)
- EM-DAT international disaster database (listed for reference)
- Number of deaths and missing persons attributed to disasters per 100,000 population
- Number of directly affected people attributed to disasters per 100,000 population
- Direct economic loss attributed to disasters in relation to global GDP
- Damage to critical infrastructure attributed to disasters
- Number of countries that adopt and implement national DRR strategies
- Total public international support for national disaster risk reduction actions (Sendai indicator F-1)
- Number of countries that have multi-hazard early warning systems
- Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population
Example geography
- Bangladesh: 134 hexagonal cells
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Surface air temperature, daytime (AIRS), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
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 | Which priorities the national strategy should carry, and how progress is tracked | Policy reviews; Sendai indicators; local strategy coverage |
| Ministries of finance and planning | How much to invest in prevention, and how to tag and protect that spending | Budget tagging evidence; cost of past disasters |
| Local governments | Where to restrict development or require resilient construction | New development in hazard zones; permit review records |
| Legislatures | Whether disaster risk management law gives clear mandates and resources | Comparative law and policy evidence |
Outputs this topic produces
- Policy reviews against Sendai Framework priorities and targets
- Maps of new development in hazard zones
- Risk reduction budget tagging methods
- Briefs for ministries comparing prevention and response spending
How risk reduction policy 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 risk reduction policy 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 experts ask.
Researchers in risk reduction policy ask how prevention can compete with more visible priorities.
- Which policies reduce exposure and vulnerability most reliably, and in which settings?
- How is disaster risk built into land-use plans, building codes and public investment decisions?
- Why does spending tend to favour response over prevention, and what changes that balance?
- How can the effect of prevention, which is a disaster that did not happen, be measured?
- How do national strategies translate into action by local governments with limited capacity?
Why prevention is hard to sustain.
Prevention offers benefits that are uncertain, delayed and invisible when they work, while its costs are immediate and certain. Political and budget cycles reward visible response more than avoided losses. Development pressure pushes building into floodplains, unstable slopes and coastal zones where land is available, and enforcement of codes is often weakest where growth is fastest, particularly in informal settlements.
Effective risk reduction tends to share features: clear legal mandates, risk information that is available to planners, screening of public investment, enforcement capacity at local level and attention to the people least able to protect themselves. It also depends on coherence across sectors, so that a road, a school and a housing programme do not each add risk that another agency must later manage.
How the evidence is built, and where it stops.
- 01
Policy and institutional analysis
Comparative study of laws, mandates and budgets, drawing on social and behavioural science. Formal rules may differ from practice.
- 02
Risk assessment for planning
Hazard, exposure and vulnerability mapping with geospatial information systems. Vulnerability data is often coarse or outdated.
- 03
Cost-benefit and decision analysis
Comparison of options under uncertainty using decision analysis. Results depend on discounting and on which losses are counted.
- 04
Evaluation of avoided losses
Comparing similar events before and after measures. Few natural experiments exist and attribution is uncertain.
How it is studied, and what the methods cannot say.
- 01Decision analysis under uncertaintyCost-benefit of risk reduction · Acting on forecasts · Recovery prioritisationApplied here
- 02Social and behavioural scienceWarning response surveys · Participatory risk assessment · Social vulnerability analysisApplied here
- 03Geospatial information systemsExposure mapping · Rapid damage assessment · Common operational dataApplied 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 disaster risk reduction?
What are the Sendai Framework targets?
Why is prevention underfunded?
Does prevention pay for itself?
Why focus on development decisions?
Does the platform advise governments on policy?
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