Large scale offshore wind farms are relatively new infrastructures and are being deployed in regions prone to earthquakes. Offshore wind farms comprise of both offshore wind turbines (OWTs) and balance of plants (BOP) facilities, such as inter-array and export cables, grid connection etc. An OWT structure can be either grounded systems (rigidly anchored to the seabed) or floating systems (with tension legs or catenary cables). OWTs are dynamically-sensitive structures made of a long slender tower with a top-heavy mass, known as Nacelle, to which a heavy rotating mass (hub and blades) is attached. These structures, apart from the variable environmental wind and wave loads, may also be subjected to earthquake related hazards in seismic zones. The earthquake hazards that can affect offshore wind farm are fault displacement, seismic shaking, subsurface liquefaction, submarine landslides, tsunami effects and a combination thereof. Procedures for seismic designing OWTs are not explicitly mentioned in current codes of practice. The aim of the paper is to discuss the seismic related challenges in the analysis and design of offshore wind farms and wind turbine structures. Different types of grounded and floating systems are considered to evaluate the seismic related effects. However, emphasis is provided on Tension Leg Platform (TLP) type floating wind turbine. Future research needs are also identified.

Catastrophe risk-based bonds are used by governments, financial institutions and (re)insurers to transfer the financial risk associated to the occurrence of catastrophic events, such as earthquakes, to the capital market. In this study, we show how municipalities prone to earthquakes can use this type of insurance-linked security to protect their building stock and communities from economic losses, and ultimately increase their earthquake resilience. We consider Benevento, a middle-sized historical town in southern Italy, as a case study, although the same approach is applicable to other urban areas in seismically active regions. One of the crucial steps in pricing catastrophe bonds is the computation of aggregate losses. We compute direct economic losses for each exposed asset based on high spatial resolution hazard and exposure models. Finally, we use the simulated loss data to price two types of catastrophe bonds (zero-coupon and coupon bonds) for different thresholds and maturity times. Although the present application focuses on earthquakes, the framework can potentially be applied to other natural disasters, such as hurricanes, floods, and other extreme weather events.

Risk-based catastrophe bonds require the estimation of losses from the convolution of hazard, exposure and vulnerability models. These models are affected by different uncertainties that arise from the definition of their input parameters. In this paper, we propose a stochastic approach to treat the uncertainty in the asset location and attributes of the exposure model. The proposed method uses the Monte Carlo sampling approach to generate a stochastic exposure database, where each asset location is generated randomly within the geometric bound of the administration, while the asset attributes (i.e. construction type and material, number of storey, building activity type and number of dwelling) are sampled from distributions built from census data. Finally, a sensitivity analysis is performed to investigate the influence of the spatial resolution of the exposure model on the average annual losses (AAL) and catastrophe bond prices. To this end, we implement four exposure models, with spatial resolution at the asset, municipality and province levels, on a study region comprising ten provinces in southern Italy. Compared to the proposed model, the exposure model where assets are relocated and aggregated at the geometric centroid of the municipality underestimates AAL by $$8\%$$8 % , while a higher difference (up to $$20\%$$20 % ) is observed for the exposure model where assets are relocated and aggregated at the geometric centroid of the province. We also consider an exposure model whose asset locations are extracted from publicly available building footprints. Yet, this latter model was incomplete for some provinces resulting in underestimation of AAL up to $$90\%$$90 % . Differences in catastrophe bond prices obtained from the four exposure models are less evident, with the exposure model built based on the building footprints showing a difference up to $$9.5\%$$9.5 % .

Catastrophe bonds (cat bond in short) are an alternative risk‐transfer instrument used to transfer peril‐specific financial risk from governments, financial institutions, or (re)insurers, to the capital market. Current approaches for cat bond pricing are calibrated on seismic mainshocks, and thus do not account for potential effects induced by earthquake sequences. This simplifying assumption implies that damage arises from mainshocks only, while aftershocks yield no damage. Postearthquake field surveys reveal that this assumption is inaccurate. For example, in the 2011 Christchurch Earthquake sequence and 2016–2017 Central Italy Earthquake sequence, aftershocks were responsible for higher economic losses when compared to those caused by mainshocks. This article proposes a time‐dependent aggregate loss model that takes into account seismicity clustering and damage accumulation effects in the computation of damage. The model is calibrated on the seismic events recorded during the recent 2016–2017 Central Italy Earthquake sequence. Furthermore, the effects of earthquake sequence on cat bond pricing is explored by implementing the proposed model on five Italian municipalities. The investigation showed that neglecting time‐dependency may lead to higher difference (up to 45%) in the cat bond price when compared to standard approaches.

Hailstorms represent a significant weather-related threats to the French insurance industry, as evidenced by the destructive 2022 hail season, which caused insured losses of approximately EUR 5 billion (FA, 2024). Two major outbreaks, linked to low-pressure systems Maya (early June) and Qiara (mid to late June), alone accounted for an estimated EUR 4.5 billion in property and automobile losses. This event ranks among the costliest hailstorms in French history and has reinforced the need for better understanding of hail severity and its impact on insurance portfolios. Given the heterogeneous and localised nature of this peril, accurately capturing hazard intensity and its spatial distribution remains a key challenge in catastrophe modelling.Traditionally, hailstone diameter reports from the European Severe Weather Database (ESWD) have been used to characterize hail intensity. While valuable, these observations present several limitations when attempting to characterize hail footprints for a catastrophe model. One significant concern is the population bias, as hail reports tend to cluster in more densely populated regions, leading to incomplete and spatially uneven representation of the event footprint (Púčik et al., 2019). Furthermore, maximum hailstone diameter—commonly used as a proxy for severity—reflects only isolated peak values and fails to represent impact from full distribution of hail sizes across a region (Allen et al., 2015). From an insurance perspective, understanding the overall impact to the built environment is more relevant than just the largest hailstone observed. Additionally, hail mainly impacts the roofs of the structure, but wind-driven hail can pose a significant threat to windows and building cladding. Therefore, at Verisk we leverage range of datasets including radar reflectivity, hail observations and client claims data to develop a historical hail footprint that uses total impact energy as the intensity measure, which captures the observed damage patterns more realistically. This study outlines the approach used for developing the 2022 French hail event footprints and discusses several challenges encountered during this process. We also show how well these footprints captures the spatial severity of the event, thereby, enabling our clients to study their claims experience and increase their confidence in understand the hail risk.