UN endorses Global Framework to strengthen disaster-related statistics

The 57th Session of the United Nations Statistical Commission has endorsed the Global Disaster-Related Statistics Framework (G-DRSF), marking the first time a comprehensive global statistical framework has been agreed to strengthen how disaster-related statistics are defined, compiled, and used within national statistical systems.

The framework brings together National Statistical Offices (NSOs), National Disaster Management Offices (NDMOs), other data producing agencies and key stakeholders within a shared statistical architecture, helping countries strengthen national data governance and improve the quality, consistency, and comparability of disaster-related statistics.

Strengthening the evidence base for disaster risk reduction

Disaster risk reduction depends on reliable statistics. Understanding who and what are exposed, how vulnerability evolves, how losses accumulate, and how investments reduce impacts requires more than isolated disaster reports. It requires a coherent statistics system that connect risk conditions, disaster impacts, and prevention efforts.

The G-DRSF provides a common statistical foundation that enables countries to better understand risk before, during, and after hazardous events and disasters occur, and to measure impacts. It supports more consistent, timely recording of:

impacts on people, including deaths, injuries, and displacement;
impacts on assets, such as damage in housing, infrastructure, and ecosystems;
impacts on flows, including economic losses and additional costs;
expenditures on disaster risk reduction (DRR) activities, strengthening visibility of investments in prevention and preparedness.

By integrating risk-related statistics, such as exposure, vulnerability, and coping capacity, the framework reinforces the principle that disaster risk should be continuously measured, and different population may experience different exposure, vulnerability, and coping capacity factors during an event. This approach strengthens prevention-focused planning and decision-making and provides the structured data needed to support forward-looking risk modelling.

“Cooperation to prevent disasters relies on having a shared understanding of risks, both within countries and across borders. The Global Disaster-Related Statistics Framework is a major milestone in creating a common language to drive disaster prevention. This is a success for multilateralism, and I am grateful to all partners whose commitment made this possible.” Kamal Kishore, Special Representative of the UN Secretary-General for Disaster Risk Reduction:

A multilateral achievement

The Framework originates from the recommendations of the Open-ended Intergovernmental Expert Working Group on indicators and terminology related to disaster risk reduction (OIEWG), which called for the development of international standards for disaster-related statistics, with active engagement of national disaster management offices (NDMOs) and national statistical offices (NSOs). In response, the 50th Session of the UN Statistical Commission, through its Decision 50/116, established the Inter-Agency and Expert Group on Disaster-Related Statistics (IAEG-DRS), currently co-chaired by UNDRR, UN ESCAP, and the UK Health Security Agency (UKHSA), to take the lead in developing a globally agreed framework to strengthen national disaster-related statistics quality, comparability, and interoperability, and capacity.

The development of the G-DRSF reflects an extensive, multi-year collaboration process, involving the global statistical, DRR, geospatial, climate change, and other key communities. The creation of the G-DRSF is informed by annual expert forums held since 2021, hosted by the UN Regional Commissions on a rotating basis with UNDRR support, as well as a global consultation. This global collaborative effort helped ensure that the G-DRSF aligns with existing statistical standards and frameworks, as well as operational disaster risk management needs, losses and damages tracking methodology, while grounding firmly in the authority and institutional setups of the global and national statistical system.

“The Global Disaster-Related Statistics Framework shows how regional innovation can shape global progress. It consolidates and scales up the Asia-Pacific framework endorsed in 2018 and sets a strong example of a bottom-up pathway toward internationally coherent disaster-related statistics.” Armida Salsiah Alisjahbana, Under-Secretary-General of the United Nations and Executive Secretary of ESCAP

Supporting global frameworks without adding reporting burdens

The G-DRSF strengthens the statistical basis for reporting under the Sendai Framework for Disaster Risk Reduction, the Sustainable Development Goals (SDGs), and climate adaptation agendas, including the Global Goal on Adaptation.

It does not introduce new global indicators or reporting obligations. Instead, it builds on existing national data systems and promotes coherence across frameworks, reducing duplication and improving comparability.

Countries retain flexibility in how they implement the framework, based on national context and capacity.

As disaster risks grow more complex and interconnected, stronger disaster-related statistics are essential for informed policy, financing, and risk modelling. The adoption of the G-DRSF establishes a shared global reference that can evolve over time as methodologies develop and user needs change.

Building the risk data foundations for resilience

Through DELTA Resilience (Disaster & Hazardous Events, Losses and Damages Tracking & Analysis), UNDRR helps translate shared statistical standards into an operational disaster tracking system that:

link hazardous events to recorded human, economic, and environmental impacts;
strengthen data governance and institutional coordination across NSOs, NDMOs, and sector agencies;
enable interoperable and sustainable national platforms aligned with international classifications.
Complementing this work, UNDRR’s Risk to Resilience Metrics initiative advances forward-looking probabilistic risk approaches—such as Average Annual Loss (AAL) and Probable Maximum Loss (PML)—to help bridge the gap between risk measurement and risk-informed policies, plans and investments.