Since 2015, the Global Earthquake Model (GEM) Foundation and its partners have been supporting regional programs and bilateral collaborations to develop an open global earthquake risk model. These efforts led to the development of a repository of probabilistic seismic hazard models, a global exposure dataset comprising structural and occupancy information regarding the residential, commercial and industrial buildings, and a comprehensive set of fragility and vulnerability functions for the most common building classes. These components were used to estimate probabilistic earthquake risk globally using the OpenQuake-engine, an open-source software for seismic hazard and risk analysis. This model allows estimating a number of risk metrics such as annualized average losses or aggregated losses for particular return periods, which are fundamental to the development and implementation of earthquake risk mitigation measures.

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The spatial resolution of exposure data has a substantial impact on the accuracy and reliability of seismic risk estimates.While several studies have investigated the influence of the geographical detail of urban exposure data in earthquake loss models, there is also a need to understand its implications at the regional scale. This study investigates the effects of exposure resolution on the European loss model and its influence on the resulting loss estimates by simulating dozens of exposure and site models (630 models) representing a wide range of assumptions related to the geo-resolution of the exposed asset locations and the associated site conditions. Losses are examined in terms of portfolio average annual loss (AAL) and return period losses at national and sub-national levels. The results indicate that neglecting the uncertainty related to asset locations and their associated site conditions within an exposure model can introduce significant bias to the risk results. The results also demonstrate that disaggregating exposure to a grid or weighting/relocating exposure locations and site properties using a density map of the built areas can improve the accuracy of the estimated losses.

A uniform and comprehensive classification system, often referred to as taxonomy, is fundamental for the characterization of building portfolios for natural hazard risk assessment. A building taxonomy characterizes assets according to attributes that can influence the likelihood of damage due to the effects of natural hazards. Within the scope of the Global Earthquake Model (GEM) initiative, a building taxonomy (GEM Building Taxonomy V2.0) was developed with the goal of classifying buildings according to their seismic vulnerability. This taxonomy contained 13 building attributes, including the main material of construction, lateral load-resisting system, date of construction and number of stories. Since its release in 2012, the taxonomy has been used by hundreds of experts working on exposure and risk modeling efforts. These applications allowed the identification of several limitations, which led to the improvement and expansion of this taxonomy into a new classification system compatible with multi-hazard risk assessment. This expanded taxonomy (named GED4ALL) includes more attributes and several details relevant for buildings exposed to natural hazards beyond earthquakes. GED4ALL has been applied in several international initiatives, enabling the identification of the most common building classes in the world, and facilitating compatibility between exposure models and databases of vulnerability and damage databases.