High-impact low-likelihood extreme weather events and their impacts are of considerable interest to a variety of stakeholders across both the public and private sectors. Within the financial sector, there has been a focus on understanding how these kinds of extremes may change in the future, and quantifying the impact of such changes. However, we suggest that significant effort is still needed to fully assess the present day risk from such extremes, especially given the recent increase in apparently “unprecedented” extremes.
Within academic research, the “UNSEEN” framework has recently gained traction as one approach to understanding the limits of extreme weather. However, this framework has typically focussed on using seasonal forecast simulations as they explore a wider range of longer-scale modes of climate variability than near-term forecasts. Using seasonal forecast simulations, however, places limits on the direct applicability to local extremes and introduces challenges resulting from model drift. Here, we present a corresponding approach using state-of-the-art medium-range reforecasts to explore the extreme upper tail of the weather distribution, inspired by the ensemble boosting methodology, which has thus far been implemented within relatively coarse resolution climate models. A key feature of basing our analysis on weather forecast simulations, as opposed to high resolution climate model simulations, is that the events produced are explicitly linked to the weather that actually occurred. We can analyse dynamically what would have had to happen differently for the UNSEEN extreme to become reality — and therefore assess how plausible it is and find the key synoptic precursors.
These “boosted realities” are of wide utility - they provide physically consistent event storylines which can be used for emergency management and infrastructure design, or for the validation of the upper tail of event sets produced by the natural catastrophe models used in insurance. These plausible extremes could be ideal candidates for generating so called “Tales of Future Weather”, through the application of recently developed approaches in extreme weather attribution.
How to cite: Leach, N., Ermis, S., Fischer, E., Vashti Ayim, O., Brocklehurst, A., Ng, K., and Leckebusch, G.: “Boosted” realities: exploring the plausible limits of extreme weather through ensemble forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19673, https://doi.org/10.5194/egusphere-egu25-19673, 2025.
Natural disasters often result from compound events, where multiple interacting drivers converge across spatial and temporal scales, significantly amplifying their severity. The concept of compound events has gained increasing attention in recent literature, offering opportunities to enhance disaster understanding, while also presenting challenges and open issues for modern risk assessment frameworks. Traditional classification systems, which primarily focus on single hazards, often fail to capture the complex interconnections and cascading effects that define compound events. This study investigates the potential for reclassifying disasters from a compound perspective, leveraging insights derived from existing databases. By analyzing patterns of hazard interactions and co-occurrence, the research underscores the critical need for a paradigm shift in disaster classification. It highlights the limitations of conventional approaches in representing the multidimensional nature of risks and the cascading impacts that emerge from compound hazards. Reclassifying disasters from a compound perspective not only enriches our knowledge of hazard dynamics but also provides actionable pathways for improving risk assessment, informing adaptive policies, and enhancing resilience to the growing complexity of environmental challenges. In an era of rapid climatic and socio-environmental changes, such an approach is crucial for effective disaster preparedness and mitigation strategies.
How to cite: De Michele, C. and Banfi, F.: Reclassifying disasters in a compound perspective: Insights from existing databases, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18319, https://doi.org/10.5194/egusphere-egu25-18319, 2025.
There is mounting evidence that we are living in a time of turbulence, with many disruptive events becoming increasingly common and intense. In these times, understanding the dynamics of past shocks can help us better prepare and potentially prevent severe impacts in the future. The Shock Tracker is a living database of cases, encompassing everything from wildfires, to floods, disease outbreaks and conflict. The case studies are formulated as storylines, describing the event and its multiple drivers and impacts, through a standardized reporting protocol. The database currently has over 100 documented cases and, thanks to its living nature, it is growing every day. The cases are submitted by people from diverse backgrounds who become part of our growing Shock Tracker Network. All cases then undergo rigorous review before being added to the final archive. There is a particular focus in the protocol on how the shock was shaped by the interactions between people and nature. Through the case studies, the Shock Tracker highlights how anthropogenic climate change contributed to shock events, where mild but multiple drivers led to extreme impacts, and what the role of human action and agency were in both driving and mitigating these events and their impacts. The Shock Tracker is therefore a collection of cases that show that climate-induced events are already happening and are not only a future problem, that they are not only caused by extreme conditions, and that they are not only natural but often triggered by social decisions. We hope that the Shock Tracker can be a source of both direct learning from past events to better prepare us for the future and a useful resource for academic research into the patterns of drivers and impacts of shocks.
How to cite: Wassénius, E. and Rubin, G.: Shock Tracker: A living database of shocks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6470, https://doi.org/10.5194/egusphere-egu25-6470, 2025.
We introduce a novel multisectoral and point-level economic model, demonstrated through a case study assessing the Norwegian economic exposure to coastal flooding.
An important prerequisite for accurately characterizing economic impacts from climate change is a spatial inventory of economic activity and value creation. Current options for creating spatial inventories of economic activity are limited. The main product that has been used for assessing economic exposure to hazards are gridded GDP models, which rely on proxies such as nighttime lights. However, they suffer from coarse spatial resolution and lack sectoral detail. They cannot capture building-level exposure and are constrained by biases, such as saturation in urban cores and unrealistic homogeneity in densely populated areas. While asset-level datasets offer high spatial precision, they are typically restricted to specific sectors (location of infrastructure, schools, residential buildings…), making them unsuitable for comprehensive, multi-sectoral analyses that are necessary for a full national-scale evaluation. These limitations highlight the need for a more integrated approach that combines fine spatial resolution with economic comprehensiveness and sectoral differentiation.
To bridge this gap, we present a novel, high-resolution mapping of national GDP that achieves fine spatial granularity while maintaining comprehensive sectoral differentiation. Our approach disaggregates national gross value added (GVA) to the point level using a public business register. We integrate this model with meter-scale flood hazard maps to quantify direct GDP and employment exposure to flooding. Additionally, we leverage an input-output analysis framework, specifically the hypothetical extraction method (HEM), to estimate indirect economic exposure, revealing how disruptions could propagate through intersectoral linkages.
To demonstrate the utility of this approach, we evaluate economic exposure to coastal flooding in Norway under a range of scenarios, from present-day extreme events to future projections under SSP3-7.0 for the year 2100. Results reveal the scale of both direct exposure at fine spatial scales and the broader systemic risks posed by intersectoral economic linkages. Our findings underscore the critical need for high-resolution, sectorally differentiated economic data to support the development of robust mitigation and adaptation strategies.
How to cite: Barre, F., Bouman, E., Hertwich, E., and Moran, D.: High resolution economic modelling for climate risk assessments: An application to coastal storm surges in Norway, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-269, https://doi.org/10.5194/egusphere-egu25-269, 2025.
Southeast Asia is becoming more and more vulnerable to extreme weather events and especially flooding due to its susceptibility to tropical cyclones, storm surges and heavy rainfall, as demonstrated by the frequency and magnitude of catastrophic events like those in 2020. This vulnerability is driven by many factors, including growing population, rapid urbanization and extensive land use changes driven by agricultural expansion, especially in Cambodia, which is situated in one of the most flood-vulnerable zones in mainland Southeast Asia. In this region, flood hazards have caused severe damages on households and on infrastructures, such as roads and bridges, causing extensive impacts on the national economy. These challenges are expected to intensify in the future due to climate change, particularly through compound events such as the interactions between riverine flooding and tropical cyclones. Despite these growing risks to critical infrastructures, two crucial gaps persist in the current practice: the integration of both direct damages and indirect impact assessments, and the understanding of the economic impacts of compound events, particularly on how these events could potentially amplify economic disruptions. In order to address these gaps, this study presents a framework that is able to bridge direct and indirect impact modeling through the combination of the open-source CLIMADA platform with the agent-based model Disrupt-SC, thanks to their spatially explicit nature. CLIMADA is adopted to quantify direct infrastructure damages from flooding events, while Disrupt-SC, simulates the cascading effects through transport and supply chain networks, including rerouting, price adjustments, and product shortages. In particular, this framework is particularly suitable to analyze the economic impacts of spatially and temporally compounding hazards. To test its applicability, the framework is applied to Cambodia. Using high-resolution data on households, firms, and trade flows we mapped the cascading effects of critical infrastructure network disruptions during compound events, enabling a comprehensive evaluation of both immediate damages and the propagation of economic impacts through supply chains. In particular, the analysis reveals crucial infrastructure components whose disruption during compound events could trigger country-wide economic impacts. This methodology offers a comprehensive framework for understanding flood impacts and their propagation through interconnected systems, contributing to more effective adaptation strategies in vulnerable and developing countries and providing decision-makers with actionable insights for prioritizing infrastructure resilience investments in Cambodia's most vulnerable regions.
How to cite: Nobile, E. G. L. and Colon, C.: Assessing the cascading economic impacts of critical infrastructure failures on supply-chains: a case study of Cambodia floods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19090, https://doi.org/10.5194/egusphere-egu25-19090, 2025.
Climate-related disasters often disproportionately impact the welfare of poor or marginalized households. However, the distributional impact of disasters on household welfare i.e., how these impacts vary across socioeconomic groups, remains underexplored in climate risk assessments. Further, existing frameworks often fail to capture the multidimensional nature of these impacts, such as disruptions to education, health services, food, livelihoods, work and well-being. These frameworks also fail to represent the temporal dynamics of impacts, particularly how they evolve during and after a disaster event. These limitations pose a challenge to develop quantitative models that adequately inform equitable policy responses.
To address this gap, our research examines how multiple impact channels of disasters influence household welfare over time. Using high-frequency, longitudinal survey from Malawi (from the World Bank's Living Standard Measurement Study (LSMS)), this study analyses over 1,600 households across all districts of Malawi over 21 survey rounds (2021-2024). This timeframe includes major events like Cyclone Freddy in February 2023 and widespread floods in February 2024. The survey covers diverse indicators serving as proxies for household welfare, such as access to essential services, employment, food insecurity, price fluctuations (food, fuel, transport), and subjective welfare. Using descriptive statistics, regression models and time series analysis, we aim to highlight the diverse pathways through which disasters exacerbate socio-economic vulnerabilities, examining how these impacts vary across different regions and over time.
Preliminary results draw attention to the complex relationship between climate-related hazards and differential household-level impacts, both spatially and across households. For example, food price responses show a sharp surge in the cost of domestically produced staples, such as maize, in flood-impacted areas due to Cyclone Freddy. Additionally, subjective welfare responses reveal that households in rural regions were disproportionately affected. Unlike their urban counterparts, rural families struggled to acquire sufficient food, fuel and other essential goods for their households, as higher prices reduced their purchasing power and further undermined their well-being.
By capturing these spatiotemporal dynamics, our study increases our understanding of disaster impacts on household welfare. Our study paves the way for integrating these impact pathways into quantitative climate risk assessment models, ultimately aiming to make more informed and equitable decisions in disaster risk management.
Keywords: Climate-related disasters, household welfare, high-frequency data, distributional impact, multidimensional impact pathways, temporal dynamics
How to cite: Bansod, S., Verschuur, J., and Comes, T.: High-frequency survey data reveal complex impact pathways of climate-related disasters on household welfare in Malawi , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5930, https://doi.org/10.5194/egusphere-egu25-5930, 2025.
Climate-linked bonds are an innovative financial tool designed to address the growing challenges of climate change. These bonds, ideally issued by governments and supranational organizations, adjust their payouts based on measurable climate variables, such as average temperatures or greenhouse gas (GHG) concentrations. By directly linking financial returns to climate outcomes, climate-linked bonds provide a strong incentive for issuers to align their actions with climate change mitigation goals. The instrument not only signals a government’s commitment to addressing climate risks but also offers investors a mechanism to hedge against the long-term economic consequences of climate change.
This paper introduces an asset pricing model for climate-linked bonds, demonstrating the growing demand for these instruments amid anticipated long-term climate risks. We evaluate the factors that facilitate risk-sharing and highlight how these bonds provide favorable terms to counterparties willing to assume climate risks, while offering long-term hedging opportunities to those seeking protection against such risks.
For governments, climate-linked bonds offer an opportunity to integrate climate accountability into their fiscal frameworks. Because the financial cost of servicing these bonds goes down with better climate outcomes, their issuance incentivizes governments to adopt robust climate policies to reduce emissions and mitigate long-term risks. Additionally, climate-linked bonds formalize the implicit role of governments as insurers of last resort, providing a structured mechanism for managing climate-related damages while enhancing fiscal predictability.
At the same time, climate-linked bonds provide investors with long-term financial protection against climate risks. Unlike alternative dynamic hedging strategies, which can be complex and costly, climate-linked bonds offer a streamlined and efficient way to mitigate exposure to climate uncertainties. As their yields are less correlated with traditional market cycles, this also makes them a valuable addition to long-term investment strategies.
Furthermore, climate-linked bonds contribute to the resilience of the financial system by addressing the ``insurance gap,'' the large portion of climate-related damages that remain uninsured. By providing a pre-emptive financial mechanism to manage these risks, climate-linked bonds reduce reliance on ad-hoc government interventions and ensure a more systematic approach to addressing the economic costs of climate change. In addition, the market-driven pricing mechanism of these bonds embeds climate risks into financial valuations, facilitating price discovery and helping to establish a term structure for long-term climate risks. This feature thereby provides valuable insights into how the market perceives climate challenges and the potential effectiveness of mitigation strategies.
Despite their benefits, implementing climate-linked bonds comes with challenges. Designing bonds tied to clear and actionable climate metrics, such as GHG concentrations or temperature anomalies, is critical to ensure their effectiveness and credibility. Standardizing these metrics across countries and markets is equally important to foster a robust and liquid global market for climate-linked bonds. Additionally, international coordination is necessary to address the inherently global nature of climate change and ensure that the bonds incentivize collective action rather than enabling free-riding. Market liquidity is another key consideration, as a liquid market attracts diverse investors and allows the bonds to meet varying maturity needs, from short-term hedges for insurers to long-term instruments for pension funds.
How to cite: Dimitrov, D., Broeders, D., and Verhoeven, N.: Climate-linked bonds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19540, https://doi.org/10.5194/egusphere-egu25-19540, 2025.
Strong evidence on mounting disaster and climate-related risks across the globe and first evidence on adaptation limits indicate that conventional approaches are challenged in terms of effectively preventing disasters and deliver on the Paris climate ambitions - even in an only 1.5oC warming world, which is, however, being exceeded. At the same time countries and communities across the world are already today stressed by current risk associated with hydrometeorological and geophysical hazards. Prior work on country extreme event risk has identified fiscal thresholds for multiple single hazards, where government’s ability to provide relief to the affected population and rebuild post-event has been exhausted leading to long-term declines in socio-economic development indicators. While in a changing climate, hazards are increasingly compounding (floods, windstorms, landslides etc.), it has been less clear what this may mean for risk and risk management overall.
Building on state-of-the-art disaster and climate risk modelling, we develop global insight on fiscal stress arising compound hazard risk. We probabilistically identify fiscal risk thresholds (“financing gaps”) for single and compound hazard risk from flood, cyclone, earthquake, tsunami and landslide hazards. The analysis shows that compound hazard risk leads to lower, i.e. more frequently occurring, gap return period year events, which may incur fiscal crises. For many (61) vulnerable countries this means such events may occur more often than every 10 years (equivalent to annual probability larger than 10%). As well, according to our analysis 54 low income, emerging and advanced economies would face such thresholds more often than once in 50 years (annual probability of larger than 2%).
In this context, policy responses ought to be ramped up including consideration for risk prevention and risk finance. In terms of risk finance, as part of the Bridgetown initiative and Loss&Damage discourses the enhanced use of IMF's Special Drawing Right (SDR) entitlements has been discussed as a means to increase resilience of the most vulnerable countries. We show that the use of SDR can soften the impact from disasters. If low income and emerging economies are allowed to access 10% of their SDR entitlements post disaster, the chance of fiscal crises can be pushed out by 19 years for low income and by 12 years for emerging economies (change in annual probability of 5 and 8 percentage points, respectively. With international debate on climate finance gaining momentum, we suggest the international community ought to further consider the innovative use of climate finance mechanism to help build climate and disaster resilience.
How to cite: Mechler, R., Hochrainer-Stigler, S., Yokomatsu, M., and Zhu, Q.: Increasing fiscal stress from compound hazard risk across the globe: how can innovative finance options build resilience?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15169, https://doi.org/10.5194/egusphere-egu25-15169, 2025.
With a recent green climbdown in global finance including the world’s largest money manager BlackRock leaving the high-profile Net Zero Asset Managers group, the debate on the financial risks from climate change is reignited. While there is an agreement on the catastrophic potential of unmitigated climate change itself, many important players in the global financial system have reevaluated to what degree climate risk equals investment risk. A key point in this debate is who owns the financial risks from climate change and who is subsequently responsible for managing them. This study looks into the question of climate change risk ownership by examining two megatrends unfolding in the global financial system over the last three decades: the financialization of climate change and the parallel evolution of the asset management industry. The analysis shows how the interconnection between these two trends has resulted in a financial system where climate risk disclosure demanded by newly introduced regulations such as TCFD and intended to enhance risk management, actually enables private entities to shield profits from climate-related losses, while leaving systemic risks unaddressed. Drawing on the literature from the financialization of nature, risk ownership, and climate risk assessment, the study highlights how technological advancements in climate risk models and government incentives for low-carbon investments create adverse selection and moral hazards for both physical and transition risks from climate change. Introducing the concept of ‘climate risk ownership’ through case studies on renewable energy investments and disaster insurance, the study highlights the gaps in the management of the financial risks of climate change between public and private entities.
How to cite: Rözer, V.: Climate risk ownership in the age of asset management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7026, https://doi.org/10.5194/egusphere-egu25-7026, 2025.
The economic impacts of climate change are becoming increasingly important in thecontext of chronic physical risks, but they are often assessed in isolation from the costsof mitigation, potentially skewing perceptions of mitigation efforts. Such impacts areunevenly distributed across regions, resulting not only in immediate economic lossesbut also in reduced capacity for long-term adaptation and mitigation.This work aims to advance integrated assessments of climate impacts and mitigationcosts and explore the underlying uncertainty through climate scenarios and by linkingdifferent econometric damage functions (Burke et al., 2018, Waidelich et al. , 2024, Kotzet al. , 2024) with an integrated assessment model (IAM).Using the Rapid Impact Model Emulator to link macroeconomic impacts to temperaturelevels, the MESSAGEix-GLOBIOM IAM to assess energy-land-climate responses, and theMAGICC climate model, we assess regional and global economic risks and mitigationcosts, highlighting the feedback loops between economic damages, energy, emissions,and climate outcomes.We show how climate-related losses could constrain socio-economic development,particularly in low- and middle-income regions that are most vulnerable to climatechange impacts. We further extend the analysis by incorporating principles of equity toallocate regional mitigation costs and illustrative contributions to a Loss and Damage(L&S) Fund based on historical and projected emissions, recognising the historicalresponsibility of high-income countries. This nuanced approach provides insights intoglobal and regional financial needs for both mitigation and addressing global loss anddamage, which are critical for equitable international climate agreements.This work aims to refine the quantitative assessment of climate risk by exploringuncertainty at different levels using scenarios, multiple macroeconomic models andprobabilistic output from the MAGICC climate model, thus providing confidenceintervals for both the costs of climate change and the actions needed to mitigate it.
How to cite: Vinca, A., Kikstra, J., Andrijevic, M., Byers, E., Pelz, S., Gidden, M., Krey, V., and Riahi, K.: Climate finance needs under macroeconomic and climate uncertainty:damage functions integration in IAM, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20093, https://doi.org/10.5194/egusphere-egu25-20093, 2025.
Floods cause large disruptions to society by causing both direct and indirect damages. These impacts will be further exacerbated by climate change and socioeconomic development. In addition to direct impacts, businesses may face indirect losses resulting from disruptions to their operations, adding extra complexity to business risk assessments. Additionally, business closures can have far-stretching economic repercussions. Flood insurance is an instrument to reduce the impact of floods for businesses by spreading the risk over space and time. While the (future-) increase in flood damages puts pressure on businesses, insurance systems tailored to businesses remain underexplored.
This research applies and extends the ‘Dynamic Integrated Flood Insurance’ (DIFI) model to analyse flood insurance for businesses in the Netherlands, taking into account both insurance against direct damages and insurance against business interruption damages. We analyse the responses of various insurance systems to changes in flood risk. These systems include voluntary insurance, solidarity-based insurance, and public-private partnership insurance. In addition, we assess the effect of adaptation on the viability of flood insurance by allowing businesses to take building-level measures to reduce their flood risk.
To facilitate the insurance analysis, flood damages are estimated using an object-based approach that takes high resolution (25m x 25m) inundation maps as input. To simulate the insurance uptake, company-level financial data obtained from the Dutch Chamber of Commerce is used in a subjective expected utility framework. This module is calibrated on actual insurance uptake numbers and takes risk misperception into account. DIFI simulations until 2080 show how premiums, insurance uptake, and policyholder adaptation efforts develop over time for various insurance market structures. These projections provide valuable insights into the viability and effectiveness of different insurance market structures in the face of climate change and shifting socioeconomic conditions.
The novelty of this research lies not only in incorporating businesses into the insurance analysis, but also in introducing a focus on business interruption damages, offering a more comprehensive perspective on flood impacts for businesses. Initial results reveal that, in certain sectors, flood-related business interruption damages are nearly as high as, or even exceed, direct damages. These findings offer new insights into the impact of flooding on businesses and the challenges of insuring such damages.
Consequently, the findings are relevant for policymakers and insurers by identifying which insurance market structures are more resilient to the increasing flood risk, providing guidance on designing financially sustainable insurance framework. Moreover, the study highlights the need for targeted insurance incentives to encourage business-level adaptation, and it informs decisions regarding potential government involvement in the insurance system to ensure equitable access to flood insurance.
How to cite: Ingels, M., Botzen, W., Aerts, J., Brusselaers, J., and Tesselaar, M.: Business-level flood insurance coverage and adaptation under climate change in the Netherlands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11087, https://doi.org/10.5194/egusphere-egu25-11087, 2025.
Understanding the spatiotemporal variations and driving forces of groundwater-dependent ecosystems (GDEs) resilience can provide scientific evidence for GDEs protection under natural and anthropogenic perturbations. However, the differences in the spatiotemporal variations of GDEs and non-GDEs resilience and their responses to climatic and anthropogenic disturbances are still unclear. Here, we applied lag-1 month temporal autocorrelation (AR(1)) based on kernel Normalized Difference Vegetation Index (kNDVI) to explore the spatiotemporal pattern of GDEs and non-GDEs resilience, and used propensity score matching (PSM) to identify the difference. XGBoost and Shapley model are applied to spatially quantify the marginal contributions from each single drivers. We found that over a third of both GDEs and non-GDEs experienced a shift from an increasing to a declining resilience trend from 1982 to 2022, with the resilience decline in GDEs being 7.5% slower than in non-GDEs. GDEs resilience are mostly responsive to precipitation and VPD, while non-GDEs resilience are mostly responsive to temperature and PET variations. Plant biodiversity significantly boosts GDEs resilience, which has a different impacting threshold compared with non-GDEs resilience. The impact of stocking density on resilience is much higher in GDEs than in non-GDEs. These findings highlight the urgent need for policy interventions to protect and manage groundwater and plant biodiversity in GDEs to maintain its resilience.
How to cite: Wu, T. and Liu, Y.: The resilience of global groundwater dependent ecosystems (GDEs) declined less than non-GDEs in the last forty years under differentiated spatial driving forces, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1845, https://doi.org/10.5194/egusphere-egu25-1845, 2025.
Nature degradation directly impacts company portfolio performance by disrupting ecosystem services critical to operations (such as water availability, pollination, etc.), while on the other hand company activities (such as deforestation, pollution, and resource extraction) significantly contribute to nature's degradation. This reciprocal relationship has intensified the need for robust methodologies to assess the underling nature-related financial risks as a pathway to allow companies to engage in mitigation and adaptation activities. While various approaches are currently in use, both business-focused (e.g nature value at risk) and nature-centered (e.g earth system index), significant gaps remain in harmonised methodologies that are comprehensive, user-friendly, and replicable, especially within biodiversity and ecosystems services at company level. An examination of existing approaches and contextual applications to company or industry archetype reveals both advantages and limitations in representing the double-materiality of risk associated with businesses. Here, we explore a potential framework for companies at the sector archetype that can assist in assessing current and potential nature-related financial risks. This is done by integrating NRFR metrics multidimensionally from the perspective of nature-related dependencies, exposures, and pressures across high climate impact industry archetypes, including agriculture, energy, and the built environment. However, the inherent challenges in representing complex and adaptive systems like nature through a metric approach should be held with caution. Regardless, this approach offers optimal direction on which companies can adopt for their individual NRFR assessments.
Keywords: Risk Assessment, Nature degradation, nature-related financial risks, business
How to cite: Nyasulu, M. K., Sheikh, H., Christiaen, C., Lockwood, P., Jean-Pierre, J.-P., Wassenius, E., and Quek, C.: Nature-Related Financial Risks Assessments framework for Company and Industry Archetype: Metrics Review, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4495, https://doi.org/10.5194/egusphere-egu25-4495, 2025.
Between 1998 and 2017, tropical cyclones (TCs) caused 233,000 deaths, affected approximately 726 million people globally, and led to an average of 9.3 million human displacements annually between 2017 and 2020 (Kam et al., 2024). Within countries impacted by TCs, economically disadvantaged populations are disproportionately affected (Jing et al., 2024). Adaptation to TCs is impregnated with uncertainty within a global context where coastal adaptation efforts are unbalance distributed (Magnan et al., 2023).
While adaptive capacity varies widely at subnational levels (Magnan et al., 2023), adaptive information is provided at the national level or, at best, at the second administration level (e.g., states). There is a lack of local adaptation information specifically related to TCs. As part of the TRANSCLIMA project (https://transclima.ihcantabria.com/), we developed global, local risk indicators at the fourth administration level, based on changes in TC characteristics, exposed population, and TC-related adaptive capacity.
This study identifies TC regions where changes in intensity and frequency are observed. Based on these changes, regions where minor or major TCs shift and assesses whether TCs may become an unprecedented hazard, leading to emergent risks. These hazard indicators resulted from analysing TC characteristics under two climatological periods: a baseline climate (1980-2017) and a future high-emission climate scenario, Shared Socioeconomic Pathway SSP8.5 (2015-2050). We used synthetic tracks datasets of four Global Climate Models (CMCC, CNRM, EC-Earth, and HadGEM3) (Bloemendaal, et al., 2022). Population data were obtained from the fourth version of the gridded population of the world with a 1 km resolution of the Socioeconomic Data and Application Center for the base year 2000 and for the years 2040 and 2050 under the SSP5 scenario (Center For International Earth Science Information Network-CIESIN-Columbia University, 2017), calculated for each coastal locality (Odériz et al., 2024). We assessed the adaptive capacity of each TC region using an index that combines local adaptive capacity, such as indicator local experience based on IBTrACS data (Knapp, 2018; K. R. Knapp et al., 2010). Additionally, we proposed a national-level insurance coverage indicator and a national-level adaptation readiness indicator.
Using this global, local-resolution risk assessment, we provided a detailed overview of the adaptation status of countries, considering subnational levels, that can be used to identify hotspots for financial adaptation plans.
How to cite: Odériz, I. and Losada, I.: Local-resolution risk assessment for tropical cyclones: toward global adaptation , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6917, https://doi.org/10.5194/egusphere-egu25-6917, 2025.
Despite substantial progress in understanding infrastructure interdependencies and their economic implications, quantifying the criticality of infrastructure and its vulnerabilities to climate impacts remains challenging. Existing models often oversimplify the consequences of infrastructure failure, assuming total cessation of activities, which can lead to unrealistic risk assessments and inefficient resource allocation. This study addresses these gaps by focusing on the criticality of four infrastructure groups, energy, water, information and communication technologies (ICT), and transport, regarding economic activities and material transactions (input-output relationship). Leveraging data from the OECD Input-Output tables, the research identifies key infrastructure-related sectors based on the International Standard Industrial Classification (ISIC Rev.4) and analyzes their roles within the economy. The research begins by examining the intermediate inputs provided by infrastructure-related sectors (sectors of which activities are related to the critical infrastructure services, e.g., land transport, water supply) to all sectors across the 11 largest economies, determining which infrastructure sectors or groups generate the highest monetary flows. Subsequently, network analysis is used to evaluate the structural importance of these sectors by measuring their centrality within the economic network. To further explore their criticality, the study simulates disruptions to individual and combined infrastructure groups, assessing their impacts on network topology and economic connectivity. The findings highlight the pivotal role of the transportation and energy sectors, which together account for 70% of infrastructure-related expenditures in the economy. Among these, the energy sector emerges as the most central and influential, underscoring its critical function across all industries. A disruption in energy infrastructure could result in a 12% reduction in in-strength centrality across the network, emphasizing its widespread economic impact. Transportation infrastructure, while essential for manufacturing industries, demonstrates its criticality in enabling production and logistics. Similarly, ICT infrastructure is shown to be indispensable for service-oriented sectors, reflecting its growing importance in the modern economy. The water sector, while less centralized in its role, exhibits a dispersed yet significant influence across various industries, underscoring its essential but less direct contribution. Overall, the study advances our understanding of the economic significance and interdependence of critical infrastructure groups, providing a robust framework to evaluate their roles, vulnerabilities, and potential impacts on economic activities.
How to cite: Phan, T. and Arosio, M.: Assessing infrastructure criticality using input-output table and network analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15331, https://doi.org/10.5194/egusphere-egu25-15331, 2025.
The insurance sector faces escalating costs from both ordinary and catastrophic weather events. According to the IPCC, insurance acts as a crucial “risk-spreading mechanism”, redistributing the financial impacts of natural hazards across policyholders and society. The insurance sector also provides empirical data and expert assessments of hazard-related damages, fostering advancements in scientific research.
Generali France, a local subsidiary of Assicurazioni Generali, has operated in mainland France and overseas since 1832. Through its Climate Lab and Reinsurance Department, we are collecting and analyzing natural hazard claims not only for regulatory purposes but also as part of its internal research and development initiatives. Our claims database comprises approximately 400,000 records collected over the last decade (2014–2024), linked to an annual exposure dataset of 1.5 million policies.
The catalog was constructed though meticulous steps of data collection, standardization and enhancement. A date, geolocation, economic variables such as reported damages or insured values is associated to each claim which is then categorized by lines of business, natural hazards, weather-related events and triggered coverages. Such modular structure enables an analysis at multiple levels, from individual claims to aggregated data by reinsurance event or geographical area. The financial impact of each peril can thus be studied precisely: loss ratios, destruction rates, event costs both observed and net of (re)insurance protection.
The database was designed under the assumption of relatively stable climatic variability and was cross-referenced with external data sources which ensure accuracy and reliability. As an illustration, hurricane Irma 2017 in the Caribbean, hailstorms of 2022, French South-East floods in 2015 and extra-tropical storm Ciaran in 2023 are clearly visible.
Such approach should foster collaboration between the insurance sector and geoscience to address climate risks. By leveraging Generali France’s claims data, researchers can validate regional climate models, quantify the financial impacts of natural disasters, and improve socioeconomic projections at local and national scales.
How to cite: Hénaff, Q. and Poletti, A.: Leveraging Empirical Insurance Data for Climate Risk Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17348, https://doi.org/10.5194/egusphere-egu25-17348, 2025.
Climate variability and weather extremes can have large impacts on local crop production. Droughts and heat extremes have been identified as main drivers on crop yield variability and therefore might pose a threat to global food security under future emission scenarios. In addition, instability may arise from associated financial losses in countries in which the economy is heavily reliant on income from agricultural production.
Using latest ISIMIP3a/b data, we assess the relative importance of drought, soil moisture, mean temperature and extreme heat for regional crop variability and establish a simple statistical model for future crop yield projections under different climate futures and associated impacts on national economies.
How to cite: Kornhuber, K., Hwong, Y.-L., and Lesk, C.: Assessing the financial impacts from drought and heat induced crop yield losses, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17797, https://doi.org/10.5194/egusphere-egu25-17797, 2025.
There have been rapid developments in mandatory Environment, Social, and Governance (ESG) information disclosure in recent years. Under the requirements that have been developed, companies, including financial institutions, are required to analyse not only the climate risk they face but also the risk from biodiversity loss and ecosystem degradation, and their interactions with climate risk. For EU nations and other European nations including Norway, companies are subject to the Corporate Sustainability Reporting Directive (CSRD) requirements that came into effect last year and require such disclosures. Many financial institutions lack the expertise to sufficiently manage these requirements and need support not only to satisfy the requirements, but also to proactively manage their assets against the risks they face.
Under such regulatory developments, we have been working with financial institutions in Norway to support their ESG information disclosure activities, focusing on physical climate risk, nature risk, and their potential connections. In this talk, we highlight our approach, focusing on the opportunities we see for both financial institutions and the scientific community. These will be presented through a case study we conducted, focusing on data flow and availability, methodologies developed on bridging climate and nature research, and on the limits we faced as academic researchers. The collaboration has led to the development of methods that could position the financial institutions as leaders in the sector regarding risk management and building resilience towards climate and nature risk. Given the disclosure requirements, transparent methods and coherent data generation on physical and transition risks will be an opportunity for enhancing awareness of climate and nature risk, and for getting a comprehensive picture of the economic impacts of climate change (including the impacts of extreme events) and the benefits of avoided impacts via mitigation and adaptation actions.
How to cite: Kunimitsu, T., Daloz, A. S., Kusch, E., and Sillmann, J.: Interactions of physical climate risk and nature risk for ESG information disclosure in the financial sector, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10005, https://doi.org/10.5194/egusphere-egu25-10005, 2025.
Systemic risks, emerging from dynamic interactions among natural, technological, and societal systems, pose multifaceted challenges to modern, interconnected societies. These risks emerge from the complex, interdependent relationships between various system elements and can lead to cascading effects across multiple domains. The complexity, non-linearity, and transboundary nature of these risks require a systems thinking approach for effective governance. This presentation introduces a taxonomy of systemic risks, categorizing elements, clarifying relationships, and fostering interdisciplinary dialogue to improve risk understanding and response strategies.
By systematically categorizing SR based on core elements, relationships, and characteristics, the taxonomy facilitates structured data collection and enables comparative analysis across diverse risk scenarios. It facilitates the identification of shared features and distinct differences among systemic risks, supporting more effective research and informed policymaking. Moreover, the taxonomy’s adaptive design ensures its continued relevance, allowing it to evolve as systemic risks change due to shifting societal, technological, and environmental dynamics. Thus, grounded in Forrester’s iterative system dynamics approach, the taxonomy evolves alongside systemic risk assessment, capturing new patterns and dynamics while remaining applicable across diverse contexts. This flexibility enables both granular analysis of specific risks and comparative studies across multiple domains.
An exemplary application of the taxonomy demonstrates its utility, while ongoing research critically evaluates its strengths and limitations. This work also explores the ethical implications of the taxonomy, critically assessing the normative assumptions underlying risk classification. This approach ensures that the taxonomy supports inclusive and equitable risk governance, recognizing diverse values and interests across stakeholders.
By identifying leverage points and key indicators, the taxonomy helps detect and mitigate systemic risks by efficiently pinpointing areas where interventions are most effective. It offers practical insights for developing resilience and improving decision-making by facilitating more targeted and efficient data collection. Hence, the taxonomy’s full potential will unfold as it is populated with data, enabling more effective interventions through a deeper understanding of systemic risks.
The proposed taxonomy is a significant contribution to SR research and governance, offering a structured framework and a first step towards a holistic assessment framework targeted at systemic risks. It holds the potential to improve responses to climate extremes and compound events, driving data-informed decision-making, and contributing to sustainable development, climate change resilience, and disaster risk reduction.
How to cite: Einhäupl, P., Hofbauer, B., and Schweizer, P.-J.: Towards a Taxonomy of Systemic Risks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13433, https://doi.org/10.5194/egusphere-egu25-13433, 2025.
In many risk domains, such as occupational health and safety, transportation, and food safety, modern risk governance is a success story. Despite these advancements, risk governance still struggles with systemic risk in the context of extreme climate and weather events, associated disasters and emergent risks. Systemic risk affects entire systems on which society depends, such as the health care system or the energy system. Systemic risk can be defined as “the risk or probability of breakdowns in an entire system, as opposed to breakdowns in individual parts or components” (Kaufman & Scott, 2003, p. 371). Connectivity between systems is the key enabler for systemic risk to manifest through cascading effects. Systemic risks originate and evolve in the nexus of tightly-coupled dynamic systems. The convergence of systemic risks with conventional risks as well as one systemic risk with another systemic risk challenges the established modes of risk analysis and governance that still rest to a large extent on differentiation and compartmentalisation.
Governance of systemic risk is concerned with the analysis of tightly coupled systems, their various interdependencies, and the resulting dynamics. Risk analysis here investigates feedback mechanisms between components of a system at the intra-system level and at the interaction with other systems at the inter-system level which result in transboundary cascading effects. In addition, governance of systemic risk is also concerned with procedural considerations of governance. Tentative, experimentalist and adaptive governance concepts, together with inclusive risk governance approaches, provide stepping stones for governance of systemic risks.
The presentation will analyse the governance challenges around systemic risk relating to issues of complexity, uncertainty, and ambiguity. Based on an extensive literature review and drawing on the case studies of the COVID-19 pandemic and climate change, a risk governance framework for systemic risks will be proposed that aims to address these challenges.
How to cite: Schweizer, P.-J. and Juhola, S.: Navigating systemic risk, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15930, https://doi.org/10.5194/egusphere-egu25-15930, 2025.
Nature-based Solutions (NBS) for climate resilience offer great opportunities to address the crises of climate change, biodiversity loss, and land degradation in an integrated way. Innovative climate risk transfer and financing instruments have emerged to scale up financing for NBS. Nature-positive finance models play a key role in closing the adaptation and nature finance gaps. This systematic review structures the evidence on mechanisms emerging from academic literature and practice that combine ‘NBS for climate resilience’ and ‘risk transfer and financing mechanisms’ and their effectiveness and economic viability. The review follows the reporting guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. We identified 104 academic publications (based on a screening of over 2000 studies listed on Web of Science and Scopus) and 132 non-academic publications that mentioned a risk transfer or financing mechanism, a Nature-based Solution, and hazard regulation benefits of the intervention. One key contribution of the review is an inventory with over 70 examples of risk transfer and financing mechanisms that incentivize investments in NBS for climate resilience. In addition, the systematic review highlights knowledge gaps and needs for further research in this field, including the quantification of co-benefits, disaggregation of benefits by socio-economic characteristics, and consideration of equity / inequity in the distribution of risks and benefits.
How to cite: Bill-Weilandt, A., Lallemant, D., Chan Khim Sun, V., Patel, M., and Hamel, P.: Nature-positive Climate Risk Transfer & Financing: A Systematic Review, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14413, https://doi.org/10.5194/egusphere-egu25-14413, 2025.
Co-occurring weather extremes can cause significant damage across various sectors. For instance, low spring precipitation combined with a summer heatwave may lead to crop failures, wildfires, drinking water shortages, increased mortality rates, and reduced energy production. Conversely, prolonged high precipitation on already saturated soils can trigger widespread flooding, which, when combined with extreme wind, may result in additional impacts such as fallen trees obstructing critical roads and railways.
Traditionally, these extremes have often been modeled independently in risk analyses. However, neglecting the interactions between extremes can lead to a significant underestimation of risk.
To better understand the likelihood of co-occurring weather extremes, stochastic weather data offers the ability to generate a wide range of weather scenarios beyond the historical record. Using a newly developed copula-based stochastic weather model, this research estimates the likelihood of high-impact co-occurring extreme weather events. By analysing European case studies of extreme weather conditions, such as hot and dry periods or wet and windy events, we identify the prevailing factors during these events that resulted in financial damage. The stochastic weather data allows us to assess the frequency and likelihood of these extreme conditions, providing critical insights into their potential recurrence and allows for a better management of the associated financial risk.
How to cite: Claassen, J., de Ruiter, M., Jäger, W., Koks, E., Champion, A., Daniell, J., and Ward, P.: Assessing the Likelihood of High-Impact Co-Occurring Weather Extremes in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16825, https://doi.org/10.5194/egusphere-egu25-16825, 2025.
Today's decisions on climate change mitigation affect the damage that future generations will bear. Discounting future benefits and costs of climate change mitigation is one of the most critical components of assessing efficient climate mitigation pathways. We extend the DICE model with stochastic discount rates to reflect the uncertain nature of discount rates. Stochastic rates give rise to a stochastic mitigation strategy, resulting in all model quantities becoming stochastic.
We show that the optimization procedure of the DICE model induces intergenerational inequality: lacking a mechanism to regulate burden, future generations have to bear higher costs from abatement and damage relative to GDP.
Further, we show that considering uncertainty of discount rates and their feedback to abatement policies, which can be interpreted as successive re-calculation, increases intergenerational inequality (and adds additional risks).
Motivated by this, we consider additional financing risks by investigating two modifications of DICE. We find that allowing financing of abatement costs and considering non-linear financing effects for large damages improves intergenerational effort sharing. To conclude our discussion of options to improve intergenerational equity in an IAM, we propose a modified optimization to keep costs below 3 % of GDP, resulting in more equal distribution of efforts between generations.
How to cite: Fries, C. and Quante, L.: Intergenerational Equitable Climate Change Mitigation: Negative Effects of Stochastic Interest Rates; Positive Effects of Financing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16844, https://doi.org/10.5194/egusphere-egu25-16844, 2025.
Over the past 30+ years, Moody’s/RMS has been at the forefront of catastrophe modelling, developing and supporting models for the global (re)insurance market. Those granular, bottom-up models bring together carefully calibrated stochastic simulations of extreme events with detailed assessments of the vulnerability of a wide range of building types. For any given portfolio of assets, loss distributions that incorporate a variety of local market considerations can be generated. Those models have been validated against extensive geophysical observations and against hundreds of billions of dollars of granular damage and building-specific claims data.
In this context, Moody’s/RMS has developed a novel bottom-up approach to assess the financial impacts of climate change for the broader financial sector, which leverages the respective strengths of catastrophe models and climate change model output. The ‘Climate on Demand Pro’ platform provides physical and financial risk metrics at both location- and portfolio-levels, which includes the impacts of portfolio concentration or diversification. Those metrics are provided globally, across the 21st century, for various climate scenarios and for six acute and chronic climate perils (tropical cyclones, wildfires, inland floods, coastal floods, heat stress and water stress), as well as earthquake risk. For acute perils and in core insurance markets, model development and validation benefits from the availability of the full-fledged RMS stochastic catastrophe models. However, for chronic perils (heat stress and water stress), a different approach has been used to generate the hazard and vulnerability components of the model.
This presentation will provide an overview of the methodology underpinning the heat stress model in ‘Climate on Demand Pro’, with a specific focus on the hazard and vulnerability components. Detailed results for key regions across various climate scenarios will be discussed, with a specific focus on the impact of urban heat islands on financial losses. It will be shown that heat stress could play a sizable role in future climate risk profiles. Finally, a brief overview of other features currently in development will be provided.
How to cite: Roy, K. and Khare, S.: Bottom-Up Assessment of the Financial Impacts of Climate Change: Heat Stress Modelling in the ‘Climate on Demand Pro’ Platform, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19680, https://doi.org/10.5194/egusphere-egu25-19680, 2025.
Hazard impacts in the Anthropocene increasingly spill over different spatio-temporal scales, societal sectors, and risk types (e.g., from natural drivers to technological failures). Recent research efforts point towards broadening the risk systems outlines to rise to this challenge. They also indicate a need for further depth, capturing the emergent aspects and managing the (information) complexity of the risk systems at hand. These two efforts have so far been achieved separately, and holistic approaches remain costly and rare. We thus present a review of systemic risks, multiple stressors, and coupled hazards, and a four-stage framework that responds to the identified challenges. The four stages include an initial co-design stage, followed by a quantitative spatio-temporal risk assessment. A bottom-up thematic analysis follows and an agent-based model wraps up the framework, connecting scales, social sectors, and mixing evidence. We implemented the framework to analyse COVID-19 in Brazil and our mixed top-down and bottom-up evidence markedly differentiates exposure and vulnerability across social classes. Since the framework’s publication, our work has adapted the framework to the climate domain, drawing from the lessons learned to overcome disciplinary siloing, taking cross-sectoral losses into account, and tracking feedback between environmental and social factors. We believe these innovations are key for promoting evidence-based and context-sensitive policies essential for fairer and more effective adaptation.
How to cite: Pereira Santos, A., Rodriguez Lopez, M., Peng, Y., and Scheffran, J.: Making Sense of Multiple Stressors, Coupled Hazards, and Systemic Risks: How can we advance inter- and transdisciplinary approaches to vulnerability with a translator model?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19269, https://doi.org/10.5194/egusphere-egu25-19269, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot 2
EGU25-14530 | Posters virtual | VPS29
Potential Index Insurance Changes under Climate Change in St. Kitts and Nevis: A Case Study Using Monte Carlo methods, observational and GCM dataThu, 01 May, 14:00–15:45 (CEST) | vP2.13
Climate change poses significant challenges to Small Island Developing States (SIDS) through heat extremes, hydrometeorological extremes and sea level rise. The societal impacts of these climate hazards are closely connected to both quantifiable and non-monetary loss and damage across multiple sectors and to presently or potentially insurable risks. One type of insurance that has been explored in many developing country contexts but is particularly sensitive to the recurrence frequency of extreme events is climate index insurance (analogous to parametric insurance), in which the contract is based on a geophysical index, rather than verified material losses.
This study explores the historical risk of heat and precipitation extreme events in the small Caribbean Island nation of St. Kitts and Nevis over the period of available record (1981-2024) and the projected frequency and severity of such events over the next 50 years (2025-2075), using historical analysis, model data and Monte Carlo statistical simulation methods. Observational data will include merged station/satellite data from the products of the Climate Hazards Group at University of Santa Barbara (CHIRPS, CHIRP and CHIRTS) and may include local station data. Climate model data will include output from CMIP6 runs of the NMME and Copernicus model suites. The Monte Carlo methods used for estimating extreme event frequencies are based on earlier research (Siebert and Ward 2011, Siebert 2016). As climate risks increase, theoretical index/parametric insurance premiums are expected to increase.
Since the frequency of threshold crossing extreme events is the primary basis for pricing index (parametric) insurance contracts, this study will explore the evolving price of relevant parametric insurance contracts for specified return liabilities (defined through recurrence interval). This project is being conducted by the company Climate Analytics and is funded by the UN Office for Project Services (UNOPS). This methodology may inform the quantification of a national loss and damage policy and plan, in coordination with multiple stakeholders in St. Kitts and Nevis and the Caribbean Climate Risk Insurance Facility (CCRIF).
How to cite: Siebert, A.: Potential Index Insurance Changes under Climate Change in St. Kitts and Nevis: A Case Study Using Monte Carlo methods, observational and GCM data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14530, https://doi.org/10.5194/egusphere-egu25-14530, 2025.
