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Catastrophe-related insurance (e.g. business interruption insurance) is an effective financing tool for global corporations to reduce economic losses caused by high impact events. Flexible operational planning is an often-used tool enabling rapid adjustment of operational plans for reducing catastrophe-related damage costs. The interaction between catastrophe insurance and flexible operations planning has rarely been studied. In this paper, we develop a stochastic programming model for a multi-echelon global supply chain network that we solve to investigate the impact of purchasing catastrophe insurance on supply chain operational planning in a catastrophe-prone environment. Computational simulations are developed for evaluating solution quality and measuring catastrophe-related damage costs. We find that it may be optimal for supply chains to scrap redundant products in catastrophes when customer demand falls below the expected level. Purchasing catastrophe insurance may encourage supply chains to scrap more products, which results in more catastrophe-related damages. From analysing supply chain costs, catastrophe-related damage costs, and operational plans, we find that a higher compensation rate of catastrophe insurance triggers more production activities being planned at the vulnerable node just before the vulnerable time period, especially for low residual value products. Finally, we give managerial insights to help reduce unnecessary damages in catastrophes. [ABSTRACT FROM AUTHOR]

Climate change is forever changing the risk environment for insurers and their customers. A recent example of this is the intense storm witnessed by the people of Bahrain and around the region on the eve of 16th April, 2024. There were minimal preparations made by either insurers or their customers, resulting in severe disruptions to daily life and property damage. To mitigate this, insurers need risk management programmes that move beyond traditional solutions to deal with climate change. These risk management solutions must be varied to cover all aspects of an insurance operation and provide customer value. Solutions can be focused on changing policy cover terms, new product development, altered investment strategies, use of technology and innovation, and engagement with the public. There are challenges, however, such as the availability and credibility of data, affordability and social equity, uncertain regulations and rising costs. Yet, the consequences of not acting on climate change will be higher for insurers eventually. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive review of the literature on climate risk insurance modeling to identify lessons learned and knowledge gaps to be addressed by future research. These models are increasingly relevant due to the rising losses attributable to climate change. Insurance models estimate risk for different perils and simulate risk‐related parameters for insurance schemes, such as premiums and deductibles. Most forward‐looking models indicate that climate change and socioeconomic developments highly exacerbate future risk and increase insurance premiums. Various studies recommend charging risk‐based premiums to incentivize adaptation efforts that limit this increase in climate risks. Other findings point toward introducing public–private insurance to cope with climate change and enhance risk spreading by introducing insurance purchase requirements or insurance products that cover multiple climate risks. Gaps that we identify in this literature include an underrepresentation of insurance assessments for developing countries and for hazards other than flooding. Additionally, we note a lack of research into insurance for non‐agricultural commercial sectors. Furthermore, less than half of the studies take a forward‐looking approach by incorporating climate change scenarios, and an even smaller percentage consider socioeconomic development scenarios. This limitation shows that current methods require additional development for assessing the effects of future climate risk on insurance. We recommend that future research develops such forward‐looking models, considers using a more refined spatial scale, broadens geographical and hazard coverage, and includes the commercial sector. [ABSTRACT FROM AUTHOR]

This article advocates smart mixes of mechanisms of post-disaster recovery in terms of boosting resilience and sustainability through liability rules, government intervention, insurance, and their combinations. Liability rules can provide compensation to victims and, in theory, also reduce disaster risks, while they have their limits in practice. The government widely intervenes in disaster compensation in many countries, but it faces challenges in how it can intervene fairly and efficiently, whether through informal channels, such as ad hoc charity, or structured approaches, such as compensation funds. As governments may struggle to provide efficient and effective compensation ex post, insurance may offer proactive solutions with models of first-party catastrophe insurance and third-party liability insurance ex ante. Where market failure, liability failure, and government failure may all arise, a smart mix of mechanisms is often preferable from a law and economic perspective, rather than relying solely on one framework. However, upon examining empirical evidence regarding the effectiveness of these instruments in China, the UK, France, Germany, and Turkey, it becomes apparent that a mix of mechanisms is not always applied in an effective manner. Models of mandatory comprehensive disaster insurance, and Public–Private Partnership (PPP), which both receive government intervention as a last resort to the market and liability failures, can reach the goal of more effective compensation for disaster victims, risk prevention and resilience when faced with disaster recovery, and should therefore be substantially implemented beyond the current levels. To be clear, the proposed mix of solutions mainly focuses on legal and economic dimensions which are rather limited, ignoring, for example, building codes for structures and physical interventions. [ABSTRACT FROM AUTHOR]

In China’s agricultural catastrophe insurance market, issues of non-equilibrium are prominent. To understand the causes of non-equilibrium in agricultural catastrophe insurance and to develop prevention strategies, this study employs an Evolutionary Game Model, incorporating disaster and insurance data for three types of cereal crops in Henan Province to analyse the Evolutionarily Stable Strategies in the agricultural catastrophe insurance market. The research also considers government policies and disaster reinsurance as implicit participants in the model. The findings reveal significant differences in the impact of non-equilibrium in the agricultural catastrophe insurance market and the choice of game strategies, dependent on the scale of farm operations, the type of crops cultivated, and regional variations. Significantly, decision-making evolutionary paths vary between small and medium-scale farmers, with rice growers emphasising income insurance more. In regions prone to frequent catastrophes, the insurance rates for agricultural catastrophe insurance exhibit greater flexibility. By scientifically delineating agricultural catastrophe risk zones, appropriately expanding the scale of cultivation, reducing insurance rates, and adjusting agricultural catastrophe insurance products, a balanced development in the agricultural catastrophe insurance market can be promoted. [ABSTRACT FROM AUTHOR]

This systematic literature review (SLR) explores the role of community-based catastrophe insurance (CBCI) as a tool for sustainable economic loss risk mitigation. Utilizing bibliometric analysis and a literature review, this study aims to reveal the methods employed in CBCI schemes from a novel perspective, highlighting their effectiveness in mitigating catastrophe risks. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was employed to systematically collect and analyze articles sourced from the Scopus, ScienceDirect, and Dimensions databases. The findings provide a comprehensive summary of the CBCI implementation, including various considerations such as risk-sharing mechanisms, premium determination, and policy frameworks. This research offers a fresh perspective on CBCI as a sustainable approach to catastrophe risk mitigation, contributing valuable insights to policymakers, practitioners, and researchers interested in community resilience and disaster risk management. [ABSTRACT FROM AUTHOR]

This paper uses the survey data of "Earthquake disaster insurance literacy of Chinese residents" to construct the earthquake disaster insurance literacy index system, and analyzes the mechanism of the influence of earthquake disaster insurance literacy level on residents' earthquake insurance purchasing behavior from the perspective of risk preference. Empirical study shows that the earthquake disaster insurance literacy level of residents can significantly promote their earth-quake insurance purchasing behavior. Further analysis reveals that within the impact of earth-quake disaster insurance literacy on earthquake insurance purchasing behavior, the risk preference of financial investment products plays a positive intermediary role, but the mediating effect is not as large as the direct effect. Therefore, as a critical influencing factor of earthquake insurance purchasing behavior, it becomes crucial and urgent to improve earthquake disaster insurance literacy. We will improve residents' earthquake disaster insurance literacy through multiple channels, focusing on the mediating effect of risk preference. This also provides certain policy references for government authorities and insurance institutions to formulate relevant policies and effectively promote earthquake insurance products. [ABSTRACT FROM AUTHOR]

Improving Disaster Preparedness Through Mutual Catastrophe Insurance In "A Mutual Catastrophe Insurance Framework for Horizontal Collaboration in Prepositioning Strategic Reserves," H. Zbib, B. Balcik, M.-È. Rancourt, and G. Laporte present an innovative approach to collaborative disaster preparedness. The novel framework considers a risk-averse mutual insurer offering multiyear insurance contracts with coverage deductibles and limits to a portfolio of risk-averse policyholders. It is designed to foster horizontal collaboration among policyholders for joint disaster preparedness by effectively integrating operational and financial functions. The problem is modeled as a large-scale nonlinear multistage stochastic program and solved by using an effective Benders decomposition algorithm. The framework is validated with real data from 18 Caribbean countries focusing on hurricane preparedness. Given the predicted impacts of climate change, the proposed multiyear mutual catastrophe insurance framework promises to reshape global disaster preparedness and make a profound societal impact by providing a transparent disaster financing plan to protect vulnerable regions. The study's findings stress the importance of long-term cooperation, prenegotiation of indemnification policies, and strategic setting of deductibles and limits by taking into account the correlation between policyholders. We develop a mutual catastrophe insurance framework for the prepositioning of strategic reserves to foster horizontal collaboration in preparedness against low-probability high-impact natural disasters. The framework consists of a risk-averse insurer pooling the risks of a portfolio of risk-averse policyholders. It encompasses the operational functions of planning the prepositioning network in preparedness for incoming insurance claims, in the form of units of strategic reserves, setting coverage deductibles and limits of policyholders, and providing insurance coverage to the claims in the emergency response phase. It also encompasses the financial functions of ensuring the insurer's solvency by efficiently managing its capital and allocating yearly premiums among policyholders. We model the framework as a very large-scale nonlinear multistage stochastic program, and solve it through a Benders decomposition algorithm. We study the case of Caribbean countries establishing a horizontal collaboration for hurricane preparedness. Our results show that the collaboration is more effective when established over a longer planning horizon, and is more beneficial when outsourcing becomes expensive. Moreover, the correlation of policyholders affected simultaneously under the extreme realizations and the position of their claims in their global claims distribution directly affects which policyholders get deductibles and limits. This underlines the importance of prenegotiating policyholders' indemnification policies at the onset of collaboration. Funding: G. Laporte and M.-È. Rancourt were funded by the Canadian Natural Sciences and Engineering Research Council (NSERC) [Grants 2015-06189 and 2022-04846]. Funding was also provided by the Institute for Data Valorisation (IVADO) and the Canada Research Chair in Humanitarian Supply Chain Analytics. B. Balcik was partially supported by a grant from the Scientific and Technological Research Council of Turkey (TUBITAK) 2219 program. This support is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2021.0141. [ABSTRACT FROM AUTHOR]

This paper derives finite sample results to assess the consistency of Generalized Pareto regression trees introduced by Farkas et al. (Insur. Math. Econ. 98:92–105, 2021) as tools to perform extreme value regression for heavy-tailed distributions. This procedure allows the constitution of classes of observations with similar tail behaviors depending on the value of the covariates, based on a recursive partition of the sample and simple model selection rules. The results we provide are obtained from concentration inequalities, and are valid for a finite sample size. A misspecification bias that arises from the use of a "Peaks over Threshold" approach is also taken into account. Moreover, the derived properties legitimate the pruning strategies, that is the model selection rules, used to select a proper tree that achieves a compromise between simplicity and goodness-of-fit. The methodology is illustrated through a simulation study, and a real data application in insurance for natural disasters. [ABSTRACT FROM AUTHOR]

The primary aim of this study was to develop a model of a socially inclusive climate risk insurance (CRI) mechanism based on the differential risk transfer approach. This study focused on the department of La Guajira, Colombia, as a case study. La Guajira is the department in Colombia that, due to its critical disaster risk conditions, presents the adequate configuration for implementing a climate risk transfer mechanism. The article starts by analyzing risk conditions by using secondary data. Based on fieldwork, this research explored the perspectives of the most vulnerable sectors in La Guajira Department on the socioeconomic impacts and needs they experience regarding climate-related hazards, their adaptive measures for risk reduction, and their willingness to adopt CRI. This represents the fundamental input for the formulation of the CRI model. Consequently, this research proposed an operational structure as input for future implementations of the model. The results indicate that national and local disaster risk management public policies align with the sectors' needs and priorities. Strengthening sectoral associations can enhance representation in CRI projects. In-kind indemnization is preferred for women entrepreneurs and the indigenous community. The CRI model includes a risk pool through the family compensation fund of La Guajira as a sectoral agglomerator, with contingent credit and traditional/parametric insurance. The methodology developed in this study can be applied in different contexts worldwide as a guidance for informing national and international climate risk finance initiatives. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Hazards & Environment (JNHE) / Doğal Afetler ve Çevre Dergisi (DACD) is the property of Artvin Coruh University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The frequency and economic damage of natural disasters have increased globally over the last two decades due to climate change. This increase has an impact on the disaster insurance field, particularly in the calculation of premiums. Many regions have a shortcoming in employing insurance because the premium is too high compared with their budget allocation. As one of the solutions, the premium calculation can be developed by applying the cross-subsidies mechanism based on economic growth. Therefore, this research aims to develop premium models of natural disaster insurance that uniquely involve two new variables of an insured region: cross-subsidies and the economic growth rate. Another novelty is the development of the Black–Scholes model, considering the two new variables, and it is used to formulate the premium model. Following the modeling process, this study uses the model to estimate the premiums for natural disaster insurance in each province of Indonesia. The estimation results show that all new variables involved in the model novelties significantly affect the premiums. This research can be used by insurance companies to determine the premium of natural disaster insurance, which involves cross-subsidies and economic growth. [ABSTRACT FROM AUTHOR]

Yogyakarta, an Indonesian province prone to earthquakes, frequently suffers extensive damage to buildings, necessitating insurance coverage to mitigate potential losses. This study aims to forecast earthquake insurance premiums by predicting building damage levels resulting from earthquakes. Utilizing data from buildings affected by the June 30, 2023, earthquake in Yogyakarta, we employ K-Medoids Clustering and Support Vector Machine (SVM) to predict two categories of building damage: minor (labelled as 1) and heavy (labelled as 2). The total premiums for minor damage range from approximately USD 86.55 to USD 288.50, while for heavy damage, they range from USD 120.05 to USD 400.18 using the K-Medoids algorithm. Meanwhile, premiums for minor damage range from USD 83.14 to USD 277.13, and for heavy damage, they range from USD 223.67 to USD 745.55 using the SVM algorithm. [ABSTRACT FROM AUTHOR]

We study the role of insurance in improving household and community disaster recovery. Our analysis harnesses both a unique survey of residents impacted by four land‐falling hurricanes in the United States and foot traffic data on visits to local businesses. Households sort into two groups when it comes to financing recovery: those primarily using property insurance and those largely uninsured, instead relying more on friends and family. Insurance improves households' ability to participate in the local economy. Postflood visitation rates to many local businesses increase with greater flood insurance take‐up rates. Expanded participation in the local economy is consistent with insurance reducing financial constraints; indeed, insured households are less likely to report experiencing high financial burdens in both the short and longer‐run postdisaster and are less likely to have unmet funding needs. Despite this, motivating the purchase of disaster insurance remains challenging, particularly among low‐income households who perceive insurance as less useful. [ABSTRACT FROM AUTHOR]

This paper examines the causal impact of natural disasters on property insurance take-up of firms. Using the data of industrial firms in China, we find that a one-standard-deviation increase in typhoon damage leads to a 2.6% increase in the purchase of property insurance the following year. This increase gradually declines and returns to the previous level three years later. Our results demonstrate that this impact is driven not by changes in risk preferences or supply-side variation but by the updated risk beliefs learned from the typhoon experience. Unlike household insurance decisions, the learning effect of firms demonstrates an indirect but positive effect of typhoons on the firm's insurance decisions from its related firms in the upstream or downstream sectors, but not from competitors. [ABSTRACT FROM AUTHOR]

Insurance against disasters plays a critical role in community recovery by providing policyholders with reliable and timely payments for repairing or reconstructing damaged houses. By allowing homeowners to transfer risk, insurance enables homeowners to address house without experiencing significant financial burdens. Although historical events have highlighted the importance of insurance, its quantitative impact on community recovery, particularly in tornado-impacted communities, is understudied. This study focuses on advancing our understanding of whether sufficiently insured houses can have a positive impact on the recovery of tornado-impacted communities (i.e., the main research question). This paper proposes a two-stage simulation framework to quantitatively evaluate the effects of insurance on community recovery. In the first stage of the framework, we developed statistical models to estimate homeowners' insurance decisions prior to a tornado event. In the second stage, we examined the effects of insurance on various aspects of community recovery. To develop empirical and statistical models regarding insurance decisions and their impacts on housing recovery, we collected data through online surveys targeting residents whose properties were damaged by the tornadoes that occurred in May 2019 in the United States. Finally, the proposed simulation framework was applied to the City of Dayton, Ohio following those May 2019 tornado events to address the main research question. The results of the simulation concluded that sufficiently insured houses can have a positive impact on community recovery and highlighted the need for effective policies and economic incentives to encourage individuals to purchase insurance. [ABSTRACT FROM AUTHOR]

Recurrence statistics of large earthquakes has a long-term economic and societal importance. This study investigates the temporal distribution of large (M ≥ 6) earthquakes in the Nepal Himalaya. We compile earthquake data of more than 200 years (1800–2022) and calculate interevent times of successive main shocks. We then derive recurrence-time statistics of large earthquakes using a set of twelve reference statistical distributions. These distributions include the time-independent exponential and time-dependent gamma, lognormal, Weibull, Levy, Maxwell, Pareto, Rayleigh, inverse Gaussian, inverse Weibull, exponentiated exponential and exponentiated Rayleigh. Based on a sample of 38 interoccurrence times, we estimate model parameters via the maximum likelihood estimation and provide their respective confidence bounds through Fisher information and Cramer–Rao bound. Using three model selection approaches, namely the Akaike information criterion (AIC), Kolmogorov–Smirnov goodness-of-fit test and the Chi-square test, we rank the performance of the applied distributions. Our analysis reveals that (i) the best fit comes from the exponentiated Rayleigh (rank 1), exponentiated exponential (rank 2), Weibull (rank 3), exponential (rank 4) and the gamma distribution (rank 5), (ii) an intermediate fit comes from the lognormal (rank 6) and the inverse Weibull distribution (rank 7), whereas (iii) the distributions, namely Maxwell (rank 8), Rayleigh (rank 9), Pareto (rank 10), Levy (rank 11) and inverse Gaussian (rank 12), show poor fit to the observed interevent times. Using the best performed exponentiated Rayleigh model, we observe that the estimated cumulative and conditional occurrence of a M ≥ 6 event in the Nepal Himalaya reach 0.90–0.95 by 2028–2031 and 2034–2037, respectively. We finally present a number of conditional probability curves (hazard function curves) to examine future earthquake hazard in the study region. Overall, the findings provide an important basis for a variety of practical applications, including infrastructure planning, disaster insurance and probabilistic seismic hazard analysis in the Nepal Himalaya. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic created an environment of unprecedented shock among global economies. Prolonged lockdown across countries, national economic down-turns and unexpected disruptions of demand and supply cycles created a likeliness towards a phase of slowdown that could last for an uncertain period. The current study is an attempt to investigate the possible post-pandemic implications on selected financial dimensions of an economy. Secondary data have been relied on to highlight the social and economic impact of this pandemic by drawing insights from literature that have been able to foretell or assess the impacts of likely situations of crisis. Major findings of the study are indicative towards the need of fulfilling unmet investments in preparedness against pandemics, the obvious advantages of having pandemics like COVID-19 being covered by insurance, possible long-term shifts in global spending patterns and the importance of financial markets enduring through such uncertainty. Understanding the impacts of past events that might closely parallel the COVID-19 pandemic brings out avenues of future research in this direction and a possibility of supplementing the scant literature available in this area of study. [ABSTRACT FROM AUTHOR]

Starting from the context of the risks arising from individual losses that natural disasters may cause, the fear that these risks may cause, and the desire for compensation systems (natural disaster insurance) that this fear may cause, the aim of this research is to examine the risks that may be caused by a possible high-risk disaster through a natural disaster exercise and to investigate the relationship between possible fear and taking out natural disaster insurance. The main population of the research is 2162 tradesmen operating in Bayburt province. Convenience sampling method, one of the non-random sampling methods, was used in the research. A total of 764 people from this population were included in the study. The research was conducted in Bayburt center and two districts between 02/02/2023 and 02/06/2023 using the survey method. While preparing the research survey, the sources specified in the theoretical framework were generally used, and the survey included demographic information, questions about drills, insurance and individuals' feelings about disaster. As a result of the research, the subjects are highly afraid of disasters, do not experience fear during disaster drills, and only half of the subjects are willing to take out disaster insurance after the drill. In addition, the feeling of fear felt during the disaster is not a factor that determines the desire to have disaster insurance after the disaster. [ABSTRACT FROM AUTHOR]

Conventional earthquake risk modeling involves several notable simplifications, which neglect: (1) the effects on seismicity of interactions between adjacent faults and the long-term elastic rebound behavior of faults; (2) short-term hazard increases associated with aftershocks; and (3) the accumulation of damage in assets due to the occurrence of multiple earthquakes in a short time window, without repairs. Several recent earthquake events (e.g. 2010–2011 Canterbury earthquakes, New Zealand; 2019 Ridgecrest earthquakes, USA; and 2023 Turkey–Syria earthquakes) have emphasized the need for risk models to account for the aforementioned short- and long-term time-dependent characteristics of earthquake risk. This study specifically investigates the sensitivity of monetary loss (i.e. a possible earthquake-risk-model output) to these time dependencies, for a case-study portfolio in Central Italy. The investigation is intended to provide important insights for the catastrophe risk insurance and reinsurance industry. In addition to salient catastrophe risk insurance features, the end-to-end approach for time-dependent earthquake risk modeling used in this study incorporates recent updates in long-term time-dependent fault modeling, aftershock forecasting, and vulnerability modeling that accounts for damage accumulation. The sensitivity analysis approach presented may provide valuable guidance on the importance and appropriate treatment of time dependencies in regional (i.e. portfolio) earthquake risk models. We find that the long-term fault and aftershock occurrence models are the most crucial features of a time-dependent seismic risk model to constrain, at least for the monetary loss metrics examined in this study. Accounting for damage accumulation is also found to be important, if there is a high insurance deductible associated with portfolio assets. [ABSTRACT FROM AUTHOR]

The devastating consequences of tsunamis on coastal infrastructure have highlighted the urgent need for effective disaster risk reduction strategies. To mitigate tsunami disasters, the insurance industry plays a vital role in implementing risk transfer measures by providing financial protection against asset damage. However, the current research on catastrophe insurance policies for coastal infrastructure lacks consideration of climate change effects. It is essential to take into account the non-stationary effects of sea-level rise to develop long-term tsunami disaster mitigation measures and promote socioeconomic resilience in coastal communities. This paper aims to provide an insurance portfolio optimization framework for coastal residential buildings subjected to tsunamis considering non-stationary sea-level rise effects based on a stochastic simulation approach. A spatiotemporal probabilistic sea-level rise hazard assessment is carried out by utilizing available climate models and considering several emission scenarios. Tsunami propagation analyses under various sea-level rise cases are performed to evaluate the time-variant tsunami hazard curves based on Monte Carlo simulation. A life cycle-based stochastic insurance claim model associated with the cumulative loss of building assets is developed based on a non-stationary compound renewal process. Finally, a sample average approximation method is leveraged to estimate the optimum basic insurance premium rate by maximizing the insurer's profit under a cost-constrained insurance purchase decision of homeowners. As a case study, the proposed framework is applied to multiple municipalities situated in the tsunami-prone region of Kochi Prefecture, Japan. Sea-level rise substantially decreases the maximum profits of tsunami insurers and increases the premium rate and ruin probability. [ABSTRACT FROM AUTHOR]

In this paper, we draw insights from a 15-year qualitative research program exploring catastrophe insurance and gaps in insurance protection as risks escalate within a world impacted by a climate emergency. We suggest that the ebb-and-flow of our research team's composition and activities through time was inextricably linked with our ability to have a sustained impact on such a large-scale societal issue. The essay situates itself within the research impact and team literature, narrates the trajectory of our research program and team development, and develops a framework for effectively managing impact-oriented qualitative research teams over time. Our framework illustrates key aspects of this process including team (re)forming, building team and individual stickability, performing, and managing team flux. We also present 10 practical takeaways for how these aspects can be managed effectively to produce long-term impact work to address grand challenges. [ABSTRACT FROM AUTHOR]

This study explores how the System Dynamics modeling approach can help deal with the problem of conventional insurance mechanisms by studying the feedback loops governing complex systems connected to the disaster insurance mechanism. Instead of addressing the disaster's underlying risk, the traditional disaster insurance strategy largely focuses on providing financial security for asset recovery after a disaster. This constraint becomes especially concerning as the threat of climate-related disasters grows since it may result in rising long-term damage expenditures. A new insurance mechanism is suggested as a solution to this problem to lower damage costs while safeguarding insured assets and luring new assets to be protected. A local case study utilizing a System Dynamics stock and flow model is created and validated by examining the model's structure, sensitivity analysis, and extreme value test. The results of the case study performed on a city in Latvia highlight the significance of effective disaster risk reduction strategies applied within the innovative insurance mechanism in lowering overall disaster costs. The logical coherence seen throughout the analysis of simulated scenario results strengthens the established model's plausibility. The case study's findings support the innovative insurance mechanism's dynamic hypothesis and show the main influencing factors on the dynamics within the proposed innovative insurance mechanism. The information this study can help insurance firms, policy planners, and disaster risk managers make decisions that will benefit local communities and other stakeholders regarding climate-related disaster risk mitigation. [ABSTRACT FROM AUTHOR]

Deposit insurance involves catastrophe risk because bank failures spike during a banking crisis. Although mutual insurance is ill-suited for catastrophe risk, the US government structures deposit insurance like a mutual company owned by policyholders. Furthermore, an intertemporal mispricing resulting from a dividend-surcharge arrangement produces several problems. Most importantly, it worsens moral hazard. Through dividends and surcharges, deposit insurance transfers the premium burden from highrisk banks to low-risk banks and in effect erodes the charter value of low-risk banks. In addition, high-risk banks taking catastrophe risk can outperform low-risk banks for many years in a row, putting pressures on managers of low-risk banks. Owing to these factors, an underpricing of catastrophe risk before its realization can ratchet up risk-taking by banks. Other problems with the intertemporal mispricing include making the banking business more procyclical, distorting the prices of banking products, and exposing taxpayers to asymmetric downside risk. To ensure fair competition and improve economic efficiency, the government should set the insurance premium based not on the realized loss but on the expected loss. [ABSTRACT FROM AUTHOR]

Over the second half of the twentieth century, the moral economy of insurance has shifted from solidarity and mutual support to individual responsibility. In this context, the French regime for the protection against natural catastrophes that took shape in the 1980s exemplifies a strong and almost anachronical political will to foster solidarity at the national level, thus questioning the moral economy of responsibility. This paper offers a textual analysis of the official debates that led to the launch of the regime. It shows how the representatives chose to separate compensation, financed by equal individual participation, from state prevention. This contrasts with other schemes worldwide that rely on rational decision theory to situate the responsibility for prevention at the individual level. In this alternative, risk-based premiums play a theoretically crucial role in risk signals. In practice, however, they lead to affordability issues while failing to govern prevention as theoretically expected. With the climate crisis exacerbating this phenomenon, the examination of the French regime thus allows to fruitfully revive other moral economies of insurance. [ABSTRACT FROM AUTHOR]

Copyright of Revista CEA is the property of Revista CEA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While flooding is the costliest natural disaster risk, public‐sector investments provide incomplete protection. Moreover, individuals are in general reluctant to voluntarily invest in measures which limit damage costs from natural disasters. The moral hazard hypothesis argues that insured individuals take fewer other preparedness measures based on their assumption that their losses will be covered anyway. Conversely, the advantageous selection hypothesis argues that individuals view insurance and other risk reduction measures as complements. This study offers a comprehensive assessment of factors related to the separate uptake of natural disaster insurance and the flood‐proofing of homes as well as why people may take both of these measures together. We use data from a survey conducted in Paris, France, in 2018, after several flood events, for a representative sample of 2976 residents facing different levels of flood risk. We perform both main effects regressions and interaction analyses to reveal that home adaptation to flooding is positively associated with comprehensive insurance coverage, which includes financial protection against natural disasters. Furthermore, actual and perceived risks, as well as awareness of official information on flood risk, are found to explain some of the relationship between home adaptation and comprehensive insurance purchase. We suggest several recommendations to policymakers based on these insights which aim to address insurance coverage gaps and the failure to take disaster risk reduction measures. In particular, groups in socially vulnerable situations may benefit from subsidized insurance, low interest loans, and decision aids to implement costly adaptation measures. [ABSTRACT FROM AUTHOR]

Collaboration in the prevention and control of water resource disasters is an important part of the prevention and cooperation of international river conflicts. It is also an important means to promote the de-securitization of water resources. Taking the Ili River and Lancang-Mekong River, two typical transboundary rivers in China, as the objects of analysis, this paper identifies the deficiencies in disaster early warning and emergency management of transboundary rivers in China from the perspectives of breadth, depth, and closeness. The main existing problems are summarized as follows: (J) There is no mature collaborative management mechanism for water resource disasters within the basin with an integrated platform to share data, predict disasters, formulate strategies, and communicate information. © Due to the lack of an efficient two-way disclosure system, there is little information sharing among the basin countries. © Special funds for water resource cooperation in the basin are far from enough, and they are characterized as scattered, small in amount, and intermittent. Accordingly, this paper proposes four principles for collaborative management over transboundary river disasters: information sharing, equal consultation, no significant harm, and integrated planning. Furthermore, a mechanism for disaster early warning and emergency cooperative management over transboundary rivers is proposed, including a special fund for basin water resource cooperation, an information sharing system, a disaster risk assessment system, and an emergency management decision system. Safeguard measures to support the operation of the management mechanism are put forward, including joint construction of water conservancy projects, technical assistance, interest compensation, legal system construction, fund establishment, disaster insurance, and so on. The mechanism proposed in this paper can not only make up for the deficiencies of the existing management mechanism framework of transboundary rivers in China, but also provide a reference for the implementation of disaster early warning and emergency management of other transboundary rivers. [ABSTRACT FROM AUTHOR]

With the intensification of human activities, rapid economic and social development, the continuous accumulation and increase of social wealth, the phenomenon and impact of natural disasters have become more and more complex, the academic attention and research on the issue of disaster economy have been deepening. The theoretical research results and development dynamics of disaster economy in foreign countries are sorted out. It is started from the impact of disasters on the economy, and the overall evaluation, long-term and short-term impacts and regional differences are given. Then the disaster risk identification and pre-disaster risk defense are addressed from the perspectives of disaster risk assessment, the role of enterprises, families and communities, and disaster insurance. Lastly, the assessment of disaster losses and post-disaster economic recovery are discussed. It summarizes the cutting-edge achievements and characteristics of foreign research on disaster economy, which has essential inspiration and references for current development of disaster economic theory and practical application in China. [ABSTRACT FROM AUTHOR]

This paper critically assesses debt as a response to ecological, fiscal, and climate disasters that have emerged within the 'blue economy' agenda in the Caribbean. Caribbean countries routinely suffer major losses of life, internal social and economic displacement, increased debt burdens, and significant economic damages due to hurricanes and ecological disasters in the context of an ongoing fiscal crisis. In response, regional public and national agencies have proposed 'blue economy' initiatives to address the regional need for finance, to rejuvenate financial flows, and to compensate for extremely constrained fiscal resources and externally imposed austerity (or debt bondage). Major recent hurricanes and ecological shocks illustrate uneven and interconnected spatial histories of anti‐Black dispossession, disenfranchisement, and deprivation, offering important empirical terrain from which to appreciate how contemporary 'disasters' have become new means to extend hierarchical plantation formations to the seascape through debt‐driven finance and austerity. The paper demonstrates the ways in which coercive financial instruments like catastrophe insurance, debt swaps, 'blue bonds', and traditional public debt constitute tools to further integrate these societies differentially into racialised financial geographies and entrench a coloniality of being. As traditional plantation structures become exhausted and lack capacity to effectively ensure growth, these innovative finance mechanisms are required for 'blue' accumulation. We situate spiralling debt burdens and these new instruments spurred by socially produced and postcolonial disasters within postplantation ecologies that describe socio‐political relations and spatial dependencies linked to interwoven logics of disaster‐based financial capitalism that seek to extend the extractive capacity of the plantation anew. These arrangements tend to naturalise and render disaster, death, and debt as ordinary events and obligations arising from postcolonial statehood, and take for granted their origins in racialised plantation structures. Climate and fiscal crises currently characterise the Caribbean and place major pressure on the region's limited sovereignties. To address these problems, Caribbean actors have proposed a number financialised products, such as 'blue bonds', catastrophe insurance, and debt‐for‐nature swaps and associated governance arrangements, to develop the blue economy. These configurations and relations deepen what I call the post‐plantation ecology, by intensifying extraction commodification, and exploitation of racialized labour towards 'blue' accumulation within the context of conditional sovereignty. [ABSTRACT FROM AUTHOR]

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Earthquakes pose potentially substantial risks to residents in the Western Quebec seismic zone of eastern Canada, where Ottawa and Montreal are located. In eastern Canada, the majority of houses are not constructed to modern seismic standards and most homeowners do not purchase earthquake insurance for their homes. If a devastating earthquake strikes, homeowners would be left unprotected financially. To quantify financial risks to homeowners in the Western Quebec seismic zone, regional earthquake catastrophe models are developed by incorporating up-to-date public information on hazard, exposure and vulnerability. The developed catastrophe models can quantify the expected and upper-tail financial seismic risks by considering a comprehensive list of possible seismic events as well as critical earthquake scenarios based on the latest geological data in the region. The results indicate that regional seismic losses could reach several tens of billions of dollars if a moderate-to-large earthquake occurs near urban centres in the region, such as Montreal and Ottawa. The regional seismic loss estimates produced in this study are useful for informing earthquake risk management strategies, including earthquake insurance and disaster relief policies. [ABSTRACT FROM AUTHOR]

Insurance serves modern society and commerce by pooling risk to reduce the economic impact of disasters. Concurrently, Disaster Risk Management (DRM) scientists, responders and policymakers are co-developing proactive resilience and mitigation strategies with European citizens against accelerating climate-related natural catastrophes. The increasing frequency and severity of natural catastrophes exacerbates the insurance coverage gap by incurring even greater losses for (re)insurers, leading to higher premiums in exchange for cover or the withdrawal of services entirely. This paper presents a conceptual framework for cross-sectoral collaboration between the insurance and DRM communities towards open, transparent and optimised disaster risk management for all EU citizens and businesses. Furthermore, this research identifies key enabling technologies (satellite, drone, artificial intelligence, blockchain) and novel risk transfer mechanisms with the potential to accelerate societal resilience to climate disasters through effective risk management. The study emphasises the critical role of the insurance industry in effective DRM and highlights where insurers could take a more active role across the temporal plane of a natural disaster by engaging in ex-ante interventions to protect those vulnerable to climate change-related risk. [ABSTRACT FROM AUTHOR]

Planning for community resilience through public infrastructure projects often engenders problems associated with social dilemmas, but little work has been done to understand how individuals respond when presented with opportunities to invest in such developments. Using statistical learning techniques trained on the results of a web‐based common pool resource game, we analyze participants' decisions to invest in hypothetical public infrastructure projects that bolster their community's resilience to disasters. Given participants' dispositions and in‐game circumstances, Bayesian additive regression tree (BART) models are able to accurately predict deviations from players' decisions that would reasonably lead to Pareto‐efficient outcomes for their communities. Participants tend to overcontribute relative to these Pareto‐efficient strategies, indicating general risk aversion that is analogous to individuals purchasing disaster insurance even though it exceeds expected actuarial costs. However, higher trait Openness scores reflect an individual's tendency to follow a risk‐neutral strategy, and fewer available resources predict lower perceived utilities derived from the infrastructure developments. In addition, several input variables have nonlinear effects on decisions, suggesting that it may be warranted to use more sophisticated statistical learning methods to reexamine results from previous studies that assume linear relationships between individuals' dispositions and responses in applications of game theory or decision theory. [ABSTRACT FROM AUTHOR]

Economic activities in disaster-prone areas are significantly susceptible to mountain disasters, and enhancing the resilience of new rural collective economies (RRCEs) is a pressing challenge that needs to be overcome in the areas of disaster risk management and sustainable development. The target research area comprises 48 representative villages in Liangshan Yi Autonomous Prefecture (LP). An assessment framework based on the Resilience Index Measurement Analysis (RIMA) model is established to evaluate the RRCEs in the face of mountain disasters, and the influencing factors regarding the RRCEs are examined. The results show that (1) typical villages in the new rural collective economies (NRCE) have a low level of resilience. (2) Transformational capacity is the key to improving RRCEs. (3) Off-farm villages exhibit the highest level of collective economic resilience, followed by diversified villages, while the lowest resilience level is observed in purely agricultural villages. (4) Talent security and institutional security are important for achieving a high level of resilience. Both of these factors significantly influence RRCEs. (5) The combined influence of talent, financical, institutional, technological, and business security contributes to the diverse factors that shape RRCEs. In other words, the path to achieving resilience in the new rural collective economies is characterized by multiple routes that lead to a common goal. Building on this, we propose recommendations in five key areas, namely, encouraging scientific research and innovation, improving disaster insurance coverage, strengthening the emergency protection system, facilitating collective economic development, and selecting suitable strategies to enhance resilience based on local conditions. The aim is to offer valuable insights for disaster-prone areas to enhance RRCEs and realize sustainable development and rural revitalization. [ABSTRACT FROM AUTHOR]

This article presents a novel data set describing the frequency of materially inadequate homeowner insurance in the event of a total loss. For decades, after a natural disaster, large percentages of homeowners who have lost their homes report suffering a second devastating loss— that, entirely to their surprise, they are vastly underinsured. These reports provocatively suggest that a large majority of all insured homes in the United States—not just homes destroyed by a natural disaster—might be profoundly, unknowingly, and unintentionally underinsured. Insurance companies reject this possibility. Insurers posit that underinsurance is rare, that other than after natural disasters it may be almost unheard of, and that no matter when it occurs, homeowners are at best complicit. Until now, there has not been robust data that could resolve insurers’ and insureds’ competing narratives. The novel data set presented in this article may end the ambiguity of data on the frequency of and predominant cause of underinsurance. The new data describes that the point-of-sale algorithms insurers ubiquitously use to estimate how much it would cost to rebuild the insured home, and homeowners then almost inevitably rely upon to identify adequate policy coverage, persistently understate costs. By clarifying the cause of underinsurance, the novel data set also explains why underinsurance persists despite the collective desire of homeowners, insurers, and regulators that homes be fully insured. The data exposing the algorithm error rate heretofore only has been visible to insurers. This heretofore has left insurers with an untenable choice. An insurer who unilaterally corrects for the error also must unilaterally raise coverage and premiums, and so will be at a competitive disadvantage. But antitrust laws put insurers in legal peril if they act collectively. This article, after presenting the data and its implications, ends by proposing a new jurisprudential paradigm allowing insurers to profitably and successfully compete while resolving the ubiquity of homeowners being unwittingly underinsured. [ABSTRACT FROM AUTHOR]

Understanding the heterogeneous preferences of individuals for disaster insurance attributes is critical for product improvement and policy design. In an era of global environmental change, the Qinghai-Tibet Plateau is a hotspot of natural hazards. Improving the capability of rural housing disaster insurance to foster local residents' disaster resilience is of great significance but remains under addressed. We used a discrete choice experiment approach to provide the first estimates of rural residents' preferences for rural housing disaster insurance attributes in central and western Tibet. We estimated residents' preferences and willingness-to-pay for the sum insured, subsidy rate, insured object, and perils covered. The potential impacts of increasing the sum insured, expanding the insured object, and lowering subsidy rates were evaluated. Our results suggest that residents prefer products with a high sum insured, high subsidy rate, and a complete list of insured objects. Residents who have experienced specific hazards tend to prefer the corresponding perils covered. Females and residents who have a closer social network are more likely to purchase insurance. Product improvement and policy simulation results suggest that, while lowering the subsidy rate, increasing the sum insured and expanding the insured object could promote participation and improve residents' welfare. Our results could improve the understanding of the preferences of households in remote regions and support policy implementations. [ABSTRACT FROM AUTHOR]

West Java is one of Indonesia's Provinces with a high disaster risk index. The Province has experienced earthquakes, volcanic eruptions, tidal waves, droughts, landslides, floods and tsunamis. In the event of a disaster, the government's budget allocation is used for handling losses due to disasters. Seeing these conditions, it is necessary to carry out disaster risk management efforts to reduce losses by using natural disaster insurance by households. Publication of household insurance expenditures is essential to see the community's ability to pay insurance for handling losses due to disasters. This study aims to estimate the average household expenditure on insurance and the average total household expenditure by district/city in the West Java Province. The analytical method used is the Small Area Estimation using the Fay-Herriot model with logarithmic transformation. The data source is the National Socio-Economic Survey (SUSENAS) for March 2019. The results show the proportion of insurance expenditure to total household expenditure (ability to pay) was relatively low, ranging from 0.93%–2%. This result is still far from the ability to pay insurance in developing countries, which is 5%. This finding can be used to see the power of households to pay for insurance (ability to pay). The government can take policies related to this to minimize losses due to natural disasters in the West Java Province. [ABSTRACT FROM AUTHOR]

Chinese government-led disaster relief mode has been timely, comprehensive, and highly efficient. China usually adopts a government-led disaster relief model to burden a share of catastrophe losses. However, adopting such a model has also exposed some important problems, such as excessive pressure on the government relief fund and an emerging imbalance in the catastrophe insurance market. The government disaster relief fund is subdivided into four categories: pre-disaster subsidy provided to insurance companies, pre-disaster subsidy to residents, post-disaster relief subsidy to insurance companies, and post-disaster relief to residents. By constructing a three-party evolutionary game model involving the government, potential catastrophe insurance participants, and insurance companies, the equilibrium of the catastrophe insurance market and the influencing factors that promote the stability of the evolutionary game was explored under the cooperation mechanism of bounded rationality in this paper. The allocation of the government disaster relief fund to insurance companies of potential catastrophe insurance participants for both before and after a disaster was discussed. Furthermore, possible shortcomings of catastrophe insurance disaster relief mode in China were analyzed. Lastly, a solution to the problem of how to relieve pressure on the government relief fund and how to enhance the ability of catastrophe risk protection was put forward. It is of great significance for China to establish a catastrophe insurance market based on commercial insurance products. [ABSTRACT FROM AUTHOR]

The risk of climate disasters has spurred increased interest in public policies that help expand the number of households with disaster insurance, particularly for financially vulnerable populations. In recent years, local governments, concerned with their community's resilience to climate disasters, have started to consider insurance programs as part of their adaptation strategies. However, we do not yet have a robust body of research that shows if, and how, expanded insurance take-up among households affects community-wide recovery after a climate disaster. Filling this disaster insurance research gap will better equip public sector leaders to assess if investments in insurance programs can further community resilience goals and when disaster insurance is an appropriate climate adaptation tool. In this article, we assess the state of empirical evidence through existing frameworks of community recovery and put forward an agenda for future research that is attuned to local policy needs. Key policy insights: There is increasing interest by policymakers in expanding the number of households with disaster insurance to provide financial protection in the face of increasing climate risks, but little evidence exists on how household-level insurance affects community-level resilience outcomes. Expanded research on this topic could support local decision-makers in designing effective and efficient policies to support community recovery from climate extremes. Future research should focus on testing more holistic disaster recovery measures and downscaling global and national research to the local scale. Emerging pilot insurance programs and experimental policies offer novel opportunities to study impacts at the local level. [ABSTRACT FROM AUTHOR]

Researchers have long sought to understand what drives people to prepare for natural hazards. In this paper, we extend existing theories on protection motivation and decision-making to examine the mediating roles of perceived coping efficacy in disaster preparedness. Drawing on data from a national survey of US households, we analyze multiple preparedness measures including making emergency plans, stockpiling supplies, setting aside financial savings for emergencies, and purchasing disaster insurance in a multi-hazard context. Using path analysis, we find that experiencing a disaster in the past five years increases one's perceived coping efficacy, including self-efficacy in undertaking preparedness measures and perceived efficacy of preparedness. Moreover, heightened self-efficacy is found to increase individuals' propensity to stockpile supplies, make emergency plans, and increase financial savings for emergencies. Perceived response efficacy is positively associated with emergency planning and savings. We also find that recent disaster experience has a direct, positive impact on making emergency plans and purchasing flood insurance. Our findings shed light on the cognitive mechanisms driving disaster preparedness behaviors and enabling factors for different preparedness choices. [ABSTRACT FROM AUTHOR]

Malicious cyberattacks are increasing in frequency and severity with healthcare institutions spending an average of over 10 million dollars to resolve the consequences of healthcare data breaches. This cost does not include the effect of a downtime event should a healthcare system electronic medical record (EMR) lose functionality. An Academic Level 1 trauma center suffered a cyberattack resulting in a total EMR downtime of 25 days. Orthopedic operative time was used as a surrogate for OR functionality during the event and a framework with specific examples is presented to promote rapid adaptation during downtime events. Operative time losses were identified by calculating a running average of weekday total in room operative time during a total downtime event secondary to a cyberattack. This data was compared to week-of-the-year matched data from the year prior and the year after the attack. A framework for creating adaptations to a total downtime event was created by repeatedly interviewing different provider groups and identifying how they adjusted care to the challenges faced. Total weekday in room operative time during the attack decreased by 53.4% ± 12.2% and 53.2% ± 14.9% when comparing the matched period one year prior and one year after, respectively. Immediate challenges to patient care were identified by small groups of highly motivated individuals, with self-assigned agile teams formed. These teams sequenced system processes, identified failure points, and created real-time solutions. A frequently updated EMR backup mirror and hospital disaster insurance were crucial for mitigating the impact of the cyberattack. Cyberattacks are expensive and their downstream effects, including downtime events, can be crippling. Agile team formation, process sequencing, and understanding EMR backup times are tactics used to combat the challenges of a prolonged total downtime event. Level III retrospective cohort [ABSTRACT FROM AUTHOR]

(1) Background-The problems of unitary rate determination, lack of regional division of risk and low level of security restrict the high-quality development of forest insurance. Regional division of risk and differential rate determination of forest insurance in a smaller region has become the key problem to be solved in the development of forest insurance. (2) Methods-The evaluation indexes of regional division of forest fire risk mainly include forest fire disaster data index, forest fire risk data index and forest resource data index. Among them, the forest fire disaster data index includes the number of forest fires, the fire area, the affected area and the expenditure for fighting fire, the forest fire risk data index includes average temperature, average relative humidity and average precipitation, and the forest resource data index includes relative forest coverage rate and forest resource share rate. The research data came from " Forestry and Grassland Disasters Statistics in Inner Mongolia (2000-2022) ", the forest resources inventory data provided by Forestry and Grassland Administration of Inner Mongolia Autonomous Region, and " Inner Mongolia Statistical Yearbook (2000-2022) ". The classification of risk levels and estimation of differential rates of forest fire insurance in Inner Mongolia were discussed by using cluster analysis method and exponential smoothing method. (3) Results-According to the results of regional division of forest fire risk level, the forest fire risk level in Inner Mongolia can be divided into three levels, namely high risk regions, medium risk regions and low risk regions. The high risk regions include Hulunbuir City and Hinggan League, the medium risk regions include Hohhot City, Tongliao City, Baotou City, Chifeng City, Erdos City, Xilin Gol League and Ulanqab City, and the low risk regions include Wuhai City, Bayannur City and Alxa League. The forest fire risk level is affected by many factors such as the forest resources area, the occurrence of fires and the climatic conditions in the league or city where it is located. The more abundant forest resources and the more frequent fires occur in the region, the higher the fire risk level. Therefore, each league and city should take forest fire prevention measures according to its forest resources area, fires occurrence and climate conditions, and establish forest fire monitoring system to reduce the harm of forest fires to forest resources. The results of estimating the forest fire insurance rate of the leagues and cities in Inner Mongolia by using exponential smoothing method show that the region with the highest forest fire insurance rate is Hulunbuir City, which is 4. 19%c, and the lowest forest fire insurance rate is 2. 48%c in Wuhai City. Combined with the results of regional division of forest fire risk level, the forest fire insurance rate in high risk regions should range from 3. 81%c to 4. 19%c, the forest fire insurance rate in medium risk regions should range from 2. 51%c to 3. 81%c, and the forest fire insurance rate in low risk regions should range from 2. 48%c to 2. 51%o. The estimated result is significantly different from the current level of forest fire insurance rate in Inner Mongolia, and higher than the current level of forest fire insurance rate 2. 25%c. (4) Conclusions and Discussions-On one hand, establish a data sharing platform dominated by the government for collaboration between the forestry departments and insurance institutions based on the principle of sustainable development of forest insurance, and formulate the data usage principles to provide data support for the determination of forest insurance rates. On the other hand, establish the dynamic adjustment mechanism of forest insurance rate level. Define the forest risk levels of each league and city in Inner Mongolia scientifically on the basis of the sharing of forest disaster data and insurance payment data. Determine the forest insurance rate level differentially and adjust the forest insurance rates dynamically according to the change of the insurance participation rates. [ABSTRACT FROM AUTHOR]

In 2012, SCOR, one of the world's largest reinsurers, sued the French state, challenging a public–private natural disaster insurance scheme, called the "cat nat" regime. It campaigned in favour of a "real" natural disaster insurance market. The purpose of this paper is to grasp the logic of this litigation. It reveals growing tensions regarding the insurance of natural disasters, against the backdrop of climate change. To make sense of this, we will build on Christian Parenti's theory of the "environment making state". This approach aims at understanding the interaction between capitalism, states, and nature. The question we will pose is: what happens to "environment making" institutions in times of crisis? Crisis leads to the "politicisation" of "environment making": decisions concerning the management of natural hazards are increasingly taken by the executive branch. This "politicisation" may represent an important evolution in the relationship between capitalism, states, and nature in the future. [ABSTRACT FROM AUTHOR]

Local, national, and global catastrophes entail significant risk for landowners. The governmentsponsored National Flood Insurance Program illustrates how subsidizing insurance against catastrophe risk can result in overinvestment in risk-prone properties. Government intervention, however, has largely been a response to the historical failure of the private insurance industry to provide adequate protection against correlated risks, a failure with the potential to generate underinvestment in land and devastate existing owners. When data is available about the incidence and severity of potential disasters, improvements in technology have made it more feasible for insurers to calibrate premiums and discounts with greater accuracy, and sophisticated financial instruments not available until recent decades should be sufficient to provide insurers with the capital to overcome the correlated risk problems that might otherwise threaten their solvency. Government's primary role should be on the demand side, educating owners about the need to purchase insurance. When reliable data is unavailable, as it is not for pandemics and economic crises, private insurance may remain difficult to obtain. Even then, the efficiency case for government intervention is plausible in two limited circumstances: when failure to compensate would cause damage to the broader economy, or when government has played a significant role in creating losses. The distributive justice case for compensation to landowners as a class is also weak, although there may be a stronger case for compensating owners of modest means who have suffered catastrophic losses due to events for which insurance was not available. [ABSTRACT FROM AUTHOR]