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We develop a theoretical model to study the combined effect of lobbying and time preferences on emission tax policies. With a two-period model, we show that the influence of lobbying, by dirty industries and by environmental organizations, on the equilibrium tax decreases with the time horizon of the policymakers. An extension of the model to four periods shows that social welfare maximising policymakers may implement a tax higher than the marginal cost in the first period to speed up the transition to green technology. A policymaker influenced by lobby groups may, however, do the opposite, because future lobbying income will decrease if more firms invest in green technology. The results of this study indicate that countries with powerful lobby groups and a short-sighted policymaker are not likely to implement the optimal carbon tax. The influence of lobbying in combination with time preferences may explain some of the diversity in carbon taxes that we observe in practice. The results lead to the policy recommendation to combine carbon taxes with trade policies, which create an incentive for short-sighted governments to participate in carbon pricing policies.

Coastal flood risk is expected to increase as a result of climate change effects, such as sea level rise, and socioeconomic growth. To support policymakers in making adaptation decisions, accurate flood risk assessments that account for the influence of complex adaptation processes on the developments of risks are essential. In this study, we integrate the dynamic adaptive behavior of homeowners within a flood risk modeling framework. Focusing on building-level adaptation and flood insurance, the agent-based model (DYNAMO) is benchmarked with empirical data for New York City, USA. The model simulates the National Flood Insurance Program (NFIP) and frequently proposed reforms to evaluate their effectiveness. The model is applied to a case study of Jamaica Bay, NY. Our results indicate that risk-based premiums can improve insurance penetration rates and the affordability of insurance compared to the baseline NFIP market structure. While a premium discount for disaster risk reduction incentivizes more homeowners to invest in dry-floodproofing measures, it does not significantly improve affordability. A low interest rate loan for financing risk-mitigation investments improves the uptake and affordability of dry-floodproofing measures. The benchmark and sensitivity analyses demonstrate how the behavioral component of our model matches empirical data and provides insights into the underlying theories and choices that autonomous agents make.

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In this study we aim to improve our understanding of household adaptation and migration responses to coastal hazards induced by sea level rise, such as coastal flooding. We apply a global ABM (agent-based model; DYNAMO-M), which simulates all ~200 million individual people and households in coastal flood zones around the world. The model simulates in yearly timesteps flood events and changing flood risk and whether residents migrate or adapt (e.g. flood proof their house) to lower their risk. Agents’ migration and adaptation decisions are based on the Subjective Expected Utility Theory (SEUT). Here, agents maximize their utility based on subjective risk assessments, such as their subjective perception of flood risk. However, the current risk perception parameter in the SEUT equation in DYNAMO-M is based on a single empirical study in France. Additional data is needed to address the heterogeneity of risk perceptions across different global coastal households. In order to assess the differences in risk perceptions in different areas around the world, we combine different data sets: (1) we conducted unique surveys on perceptions of flood risk and their determinants as well as people’s intention to adapt or migrate under future SLR scenarios in 6 countries with varying socio-economic backgrounds (Argentina, France, Mozambique, the Netherlands, the US, and Vietnam). Using these survey data, we identify the generic decision rule for the determinants of risk perception parameters such as the perceived probability and damage of flooding through regression analysis. (2) Next, we use additional global datasets on individual characteristics such as the World Value Survey (demographic and residential information data) and Cloud2Street (flood experience data) and use these data as explanatory variables for transferring risk perception parameters to countries where no primary survey data is available. This analysis may aid the understanding of global patterns in risk perceptions of people/agents. We believe our study serves as a basis for research on individual behavior under risk, the role of risk perception, and the use of the data in global ABMs.

Natural disasters, such as flood events, are expected to increase in their frequency and severity, which results in higher flood risk without additional adaptation measures. The information gained from flood risk models is essential in flood risk management. However, vulnerability model components in the form of depth-damage curves are often a large driver of uncertainty and empirical vulnerability curves are rarely estimated due to a lack of damage data from flood events. This study uses a unique dataset with experienced damages and the implementation of flood damage mitigation (FDM) measures on the household level, collected after the flood event in the Netherlands in 2021. Two main findings emerge from an analysis of this dataset. First, depth-damage curves that control for several hazard, exposure and vulnerability indicators are estimated. These curves serve as an update to current input in flood risk models, where previous vulnerability estimates from the Netherlands are based from a flood event in the 1950s. Second, it is found that previous studies on the effectiveness of FDM measures are prone to a selection bias, as households that do and do not take FDM measures systematically differ in their risk profiles. By using an Instrumental Variable (IV)-estimation, this study overcomes this selection bias and finds significant reductions in flood damage to both buildings and household contents due to FDM measures.

The Horn of Africa Drylands are increasingly experiencing severe droughts, which imposes a thread on traditional livelihood strategies of pastoralist communities. Understanding adaptation behaviour in rural communities is essential to help reducing the impact of these droughts. In this study, we identify drivers and barriers of drought risk adaptation decisions in pastoralist communities, by analysing household survey data from 502 Kenyan households. To provide theoretically sound insights into adaptive behaviour, we have grounded our empirical research in four established economic and psychological theories on decision-making under risk: Expected Utility Theory (EUT), Rank Dependent Utility Theory (RDU), Protection Motivation Theory (PMT) and Theory of Planned Behaviour (PMT). The variables of all theories are measured by multiple survey questions and we have included an economic experiment in the survey to measure the risk aversion parameters of Expected Utility Theory and Rank Dependent Utility theory. With regression models, we analyse the relation between the theory variables and adaptation behaviour. To measure adaptation behaviour, we have selected 15 different adaptation measures for which we asked about current uptake and the intention to adopt these measures in the future. Regression analyses show that important factors in adaptation decisions are risk attitudes, financial constraints, perceives self-efficacy and adaptation by family and friends. An analysis of adaptation intention for each adaptation measure separately shows that drivers and barriers of adaptation are different for different types of adaptation measures. Risk-averse pastoralists are more likely to implement adaptation measures that are adjustments to current pastoral practices, and less likely to implement adaptation measures that require a (partial) shift to other livelihood activities. A person’s belief in their own ability to implement an adaptation measure (perceived self-efficacy) is an important factor in explaining which measure people are going to adopt. Furthermore, we find that some measures are more likely to be taken by women and others more likely to be taken by men and we find significant effects for differences in education levels. Our analysis can help to gain more knowledge on the drivers of individual adaptation decisions of pastoralists, which can enhance policies promoting adaptation of dryland communities. Our results indicate that drivers and barriers of adaptation can be quite different for different groups, which suggests that policies should be carefully targeted at specific groups.

There is an increasing rise in the number of publications addressing social vulnerability, resilience, and adaptation (SVRA) aspects of natural hazards and climate change. Despite the abundance of research in this field, a systematic understanding of how these studies are theoretically grounded is lacking.In this study, we conducted a systematic review of 4432 articles that address SVRA across a range of disciplinary fields (e.g. psychology, sociology, geography, mathematics) and natural hazards (e.g. floods, droughts, landslides, storm surges, wildfires, tsunamis, earthquakes, and volcano eruptions). We investigate the extent to which these studies explicate the frameworks, theoretical constructs or theories they rely on.Our findings indicate that about 90% of the studies under consideration do not explicitly refer to a theoretical underpinning. Overall, theories focusing on individuals' SVRA were more frequent than those focusing on systems, society, groups, and networks. Furthermore, the uptake of theories varied according to the hazard investigated and field of knowledge, being more frequent in wildfire and flood studies and articles published in social science journals.We argue that the abundance of empirical material in SVRA research that lacks explicit theoretical grounding is objectionable. As a result, SVRA research seems to spin in circles: researchers repeatedly conduct similar analyses in different geographical settings with inconsistent or incommensurable findings. Thus, we recommend making theoretical considerations salient to foster more transparent, comparable, and robust empirical research on SVRA.

Coastal adaptation dynamics under future shoreline changesCurrently 24% of the worlds sandy beaches are eroding with rates exceeding 0.5m/ year, and future climate conditions of increased storm wave action and sea level rise (SLR) are likely to increase the rate of shoreline recession in a large part of the world. The loss of sandy beaches affects coastal communities by degrading natural flood protection and coastal amenities. People experiencing these changes may choose to adapt by implementing flood protection measures, or by migrating towards safer areas. To maintain beach width, a growing number of coastal managers is investing in beach renourishment projects. Beach renourishment restores coastal amenity value and flood protection, allowing further coastal development. Current assessments of coastal adaptation in face of SLR often do not account for the interactions of household adaptation and coastal management decisions on coastal flood risk. In this study we aim provide a better representation of coastal adaptation dynamics by simulating the interactions between coastal management decisions and household adaptation behavior under sea level rise. Therefor we develop an agent-based model grounded in expected utility theory, that simulates household and government agents adapting to shoreline change and increasing coastal flood risk. The model is calibrated using empirical survey data on household adaptation and household characteristics are derived from local census data. We then apply the model in France to simulate coastal adaptation dynamics for 2020-2080 under different Shared Socioeconomic Pathways (SSP) and climate change scenarios. By explicitly simulating coastal adaptation decisions and we provide a more realistic model of coastal adaptation dynamics under future development.

The collection of publicly available databases about climate change and its impacts on natural and human systems is unprecedented and ever-growing. However, the requirements of information can vary widely among users depending on their region, socioenvironmental context, and interests. Moreover, in the current era of active mitigation and adaptation policies, information needs are frequently not satisfied even by these massive and variated collections of databases. The development and use of emulators can help closing this information gap by allowing users to approximate the output from complex models and create user-defined experiments, without being technically or computational demanding on the user. Here, a simple emulator of the EPIC biophysical crop model is presented which is able to adequately reproduce the changes in rainfed maize and to create projections for user-defined scenarios. Moreover, it allows to produce risk measures that are not available with the original model. The proposed methodology is illustrated with a case study of rainfed maize production in Mexico for a reference emissions scenario (SSP370) and two user-defined international mitigation policy scenarios. These scenarios represent 1) current international mitigation commitments and 2) a scenario in which China withdraws from international mitigation efforts. Results showed that, under the reference scenario, climate change could have widespread consequences on rainfed production all over the country with decreases in yields reaching up to 80% in the southeast and northeast of the country. These impacts can be partially modulated by the moderately ambitious mitigation commitments assumed in recent international agreements if all countries comply. However, a potential withdraw of China from these efforts would significantly reduce any benefits from international mitigation. Under all scenarios, changes in productivity impose increasing risks for already vulnerable populations and considerable economic costs at the state and national levels. These results suggest the urgent need for critical planning for adaptation in the agricultural sector of the country.

A substantial literature about future flood risk assessment focuses on changing hazard conditions as a result of climate change, where flood exposure is assumed to remain static or develop according to exogenous scenarios. This study presents an innovative method to project future flood risk by simulating population growth in floodplains, where settlement location decisions of households endogenously depend on environmental and institutional factors, including amenities associated with river proximity, flood risk, and insurance against this risk. Our results show that population growth in floodplains, and consequently rising flood risk, are considerably higher when the disamenity caused by flood risk is offset by insurance. This outcome is particularly evident in countries where flood risk is covered collectively, and notably less where premiums reflect the risk of individual households.

Sea-level rise (SLR) threatens millions of people living in coastal areas through permanent inundation and other SLR-related hazards. Migration is one way for people to adapt to these coastal changes, but presents an enormous policy challenge given the number of people affected. Knowledge about the relationship between SLR-related hazards and migration is therefore important to allow for anticipatory policymaking. In recent years, an increasing number of empirical studies have investigated, using survey or census data, how SLR-related hazards including flooding, salinization, and erosion together with non-environmental factors influence migration behavior. In this article, we provide a systematic literature review of this empirical work. Our review findings indicate that flooding is not necessarily associated with increased migration. Severe flood events even tend to decrease long-term migration in developing countries, although more research is needed to better understand the underpinnings of this finding. Salinization and erosion do generally lead to migration, but the number of studies is sparse. Several non-environmental factors including wealth and place attachment influence migration alongside SLR-related hazards. Based on the review, we propose a research agenda by outlining knowledge gaps and promising avenues for future research on this topic. Promising research avenues include using behavioral experiments to investigate migration behavior under future SLR scenarios, studying migration among other adaptation strategies, and complementing empirical research with dynamic migration modeling. We conclude that more empirical research on the SLR-migration nexus is needed to properly understand and anticipate the complex dynamics of migration under SLR, and to design adequate policy responses. This article is categorized under:. Climate Economics < Aggregation Techniques for Impacts and Mitigation Costs Vulnerability and Adaptation to Climate Change < Learning from Cases and Analogies Assessing Impacts of Climate Change < Evaluating Future Impacts of Climate Change.

Many individuals experience problems understanding and preparing for low-probability/high-impact risk, like natural disasters and pandemics – unless they experience these events, yet then it is often too late to avoid damages. Individuals with recent disaster risk experience are, on average, better prepared. This seems to be mediated through emotions and a better understanding of the consequences. In this study, we use immersive virtual reality (VR) technology to examine whether a simulated disaster can stimulate people to invest in risk reducing measures in the context of flooding, which is one of the deadliest and most damaging natural disasters in the world. We investigate the possibility to boost risk perception, coping appraisal, negative emotions and damage-reducing behavior through a simulated flooding experience. We find that participants who experienced the virtual flood invest significantly more in the flood risk investment game than those in the control group. The investments in the VR treatment seem to decrease after four weeks but not significantly so.

Elevated flood risk due to sea level rise is expected to increase migration from coastal areas. This presents an enormous policy challenge given the hundreds of millions of people living in low-lying coastal areas globally. Despite its relevance, little empirical research has been done on what drives coastal residents to migrate or stay under increased flood risk. This study aims to improve the knowledge base on this topic by collecting and analyzing unique survey data of flood-prone coastal residents in Central Vietnam. To explain permanent migration intentions under increased flood risk, we present respondents with realistic scenarios of more frequent severe flooding and utilize a theoretical framework that incorporates flood risk indicators as well as key indicators of sustainable livelihoods. Results indicate that flood risk could play a major role in future migration behavior; permanent migration intentions strongly increase under the scenarios of increased flood risk. Several individual characteristics also play an important role in the migration decision. Regression analyses reveal that respondents who implemented in situ flood adaptation, and thereby reduced their flood risk, are less inclined to migrate. Past experiences during flooding such as evacuation or the reception of help from community members or the government are also strong predictors of migration intentions. Of the sustainable livelihood indicators, social capital plays the most important role, where a larger social network inside (outside) the place of residence is negatively (positively) related to migration intentions. We draw lessons from these insights for policymakers aiming to anticipate the challenge of sea level rise-induced migration.

On 1 January 2018, the municipality of Amsterdam changed the system for the reception of unaddressed mail from presumed consent to explicit consent to reduce paper waste. This policy can be defined as a default nudge. The no-choice population received unaddressed mail in the presumed consent system but not in the new explicit consent system. Residents receive unaddressed mail only when they actively decide to put an opt-in sticker on their mailbox. This study assesses the effectiveness and social benefits of this nudge. The effect on paper waste is estimated using a difference-in-differences approach in which several other Dutch municipalities function as the control group. Our main finding is that the default nudge results in a reduction of paper waste between 5.3% and 11%. Social benefits of this reduction include, for example, lower carbon emissions for collection and transport for paper waste, which are equivalent to yearly benefits between approximately €135,000 and €285,000 in Amsterdam. If all Dutch municipalities implement the system of explicit consent for unaddressed mail, the yearly benefits are approximately between €14 million and €30 million. The default nudge is a low-cost policy to implement and, therefore, offers municipal policymakers a cost-effective way to reduce waste.

Natural disasters have large social and economic consequences. However, adequate economic and social data to study subnational economic effects of these negative shocks are typically difficult to obtain especially in low-income countries. For this reason, the use of night light data is becoming increasingly popular in studies which aim to estimate the impacts of natural disasters on local economic activity. However, it is often unclear what observed changes in night lights represent exactly. In this paper, we examine how changes in night light emissions following a severe hurricane relate with local population, employment, and income statistics. We do so for the case of Hurricane Katrina, which struck the coastline of Louisiana and Mississippi in August 2005. Hurricane Katrina is an excellent case for this purpose as it is one of the biggest hurricanes in recent history in terms of human and economic impacts, it made landfall in a country with high-quality sub-national socioeconomic data collection, and it is covered extensively in the academic literature. We find that overall night light changes reflect the general pattern of direct impacts of Katrina as well as indirect impacts and subsequent population and economic recovery. Our results suggest that change in light intensity is mostly reflective of changes in resident population and the total number of employed people within the affected area and less so but positively related to aggregate income and real GDP.

The Nationally Determined Contributions (NDCs) represent the world's first effort toward the Paris Agreement goal of keeping global temperature increase well below 2 °C and pursuing 1.5 °C. Little is known about how much the proposed mitigation efforts can reduce the risks and economic damages from unabated climate change and about the consequences if key emitters drop the Paris Agreement. Here, we use CLIMRISK, an integrated assessment model designed to support climate policy at the global, national, and subnational scales where mitigation and adaptation policy decisions are made. We characterize the consequences of unabated climate change and the benefits of current climate policy proposals by means of probabilistic estimates of the economic damages of climate change and uni- and multivariate dynamic climate risk indices at a detailed spatial resolution. The results presented reveal that the economic costs and risks are highly unequally distributed between and within countries and larger than previously estimated when warming in urban areas and temporal persistence of impacts are accounted for. Costs and risks can be significantly limited by strict implementation of NDCs, but increase noticeably under noncompliance by large emitters, like the United States.

The mixed results in Pielke (2020) for natural disaster loss normalisation studies are due to methodological differences. Flaws exist in commonly used normalisation approaches that assume unitary elasticities between exposure indicators and losses. We refute Pielke’s arguments that statistical studies estimating these relationships are biased. We conclude with an agenda for future research.

Global warming may cause abrupt and non-linear climate tipping points, with large impacts to established socio-economic systems [1]. The socio-economic system itself also exhibits many non-linear change processes, and therefore may experience manifold unintentional climate-change induced socio-economic tipping points (SETPs) that could already follow from relatively small changes in climatic conditions. Examples are the gentrification of vulnerable groups or abrupt unplanned retreat from areas of increasing climate risk, abrupt transitions in financial markets, large-scale systematic malfunction of critical infrastructure networks during weather extremes, sudden reconfigurations of insurance markets and house price collapses. Such SETPs are defined as ‘a climate change induced, abrupt change of a socio-economic system, into a new, fundamentally different state’ [2]. It is important for spatial-economic planners and capital investors to know if and under what conditions SETPs may happen, and what can be done to anticipate and manage their causes and effects.With three model-based case studies we demonstrate a stepwise approach to identify SETPs and to support adaptation and mitigation policy. The first is a house price collapse and radical transformation of long-term flood risk policy in a coastal city like Rotterdam, following rapid sea level rise due to Antarctic ice-sheet instability. Using a model that simulates flood risk, house prices and adaption integrally, we identify abrupt house price collapses in hundred-thousands possible futures spanning the uncertainty in sea level rise, storm surge and house market scenarios. We explicitly explore the long-term impacts of four dynamic adaptive strategies to anticipate flood risk and their successfulness in avoiding a SETP [3]. The second case is the financial collapse of the winter sports industry in the European Alps following a gradually retreating snowline [4]. The third is a large-scale systematic malfunction of national road networks of European countries due to increasing river flood hazards. The focus of our contribution is on showing how decision making can be supported despite the large uncertainties around SETPs. Finally, we discuss how the SETP-concept aligns with socio-ecological regime shifts [5] and deliberate positive social tipping points to achieve large mitigation and adaptation challenges [6,7].Types of tipping points along the cause-effect chain from increasing GHG, to biophysical changes, to socioeconomic impacts and transformative adaptation and mitigation response. Source [2], CC-BY3.0 license.Refs (doi): [1] 10.1073/pnas.2103081118; [2] 10.1088/1748-9326/ab6395; [3] 10.2139/ssrn.3935775; [4] 10.1016/j.envsci.2021.09.005; [5] 10.1088/1748-9326/aaaa75; [6] 10.1073/pnas.1900577117 [7] v10.1016/j.ecolecon.2021.107242

Sea-level rise (SLR) and socioeconomic trends are increasing the population and assets exposed to extreme coastal flood events in the coming decades. People residing in communities experiencing this increase in coastal flood risk may choose to stay, to stay and adapt, or to migrate towards safer areas. However, these migration decisions are influenced by many socio- economic and environmental factors. For example, current assessments of SLR adaptation and migration do often not address risk perceptions of residents related to different environmental risks, such as flooding and erosion. These factors influence adaptation decisions, and thus exposure and vulnerability. In this study, we aim to improve the representation of the dynamics of adaptive behavior of coastal communities in flood risk assessment by including human behavior and its effect on adaptation decisions, in face of SLR. Therefore, we develop an agent-based model grounded in subjective expected utility theory and simulate adaptation- and migration decisions of households facing coastal flood risk in France between 19xx and 2020. The model is empirically calibrated using survey data on flood risk perception and people’s willingness to implement adaptation measures. Then, we use socio-demographic projections to estimate future changes (2020-2080) in demographic composition, and apply the model to simulate coastal adaptation. The agent-based model presented in this study functions as a platform for further development of 1) more realistic decision models and 2) global modelling approaches of both coastal adaptation and migration under projections of future development.

The U.S. 2020 hurricane season was extraordinary because of a record number of named storms coinciding with the COVID-19 pandemic. This study draws lessons on how individual hurricane preparedness is influenced by the additional risk stemming from a pandemic, which turns out to be a combination of perceptions of flood and pandemic risks that have opposite effects on preparedness behavior. We conducted a survey in early June 2020 of 600 respondents in flood-prone areas in Florida to obtain insights into households’ risk perceptions and preparedness for the upcoming hurricane season under COVID-19. The results show that concerns over COVID-19 dominated flood risk perceptions and negatively impacted people’s evacuation intentions. Whereas hotel costs were the main obstacle to evacuating during Hurricane Dorian in 2019 in the same geographic study area, the main evacuation obstacle identified in the 2020 hurricane season is COVID-19. Our statistical analyses investigating the factors influencing evacuation intentions show that older individuals are less likely to evacuate under a voluntary order, because they are more concerned about the consequences of becoming infected by COVID-19. We observe similar findings based on a real-time survey we conducted in Florida with another group of respondents under the threat of Hurricane Eta at the end of the hurricane season in November 2020. We discuss the implications of our findings for risk communication and emergency management policies that aim to improve hurricane preparedness when dealing with additional health risks such as a pandemic, a situation that may be exacerbated under the future climate. Supplementary Information The online version contains supplementary material available at 10.1007/s11069-021-05064-2.

This study aims to improve the estimates of the economic impacts of climate change by developing a river flood risk model CLIMRISK-RIVER and introducing it into an existing climate-economy integrated assessment model (IAM). It operates on a local scale and can project climate change-related river flood damage for various socioeconomic, climate and flood adaptation scenarios. Whereas other IAMs have relied on temperature as a climate change proxy, we show that precipitation is a key variable in projecting river flood damage. The way adaptation is accounted for in our flood damage functions has a large influence on the results, highlighting the relevance of modelling local level adaptation in IAMs. Results presented at different spatial scales demonstrate the relevance of river flood damage functions for estimating the economic impacts of climate change and allows for exploration of the spatial distribution of impacts through local estimates.

Flooding is one of the most significant natural disasters worldwide. Nevertheless, voluntary take‐up of individual damage reduction measures is low. A potential explanation is that flood risk perceptions of individual homeowners are below objective estimates of flood risk, which may imply that they underestimate the flood risk and the damage that can be avoided by damage reduction measures. The aim of this article is to assess possible flood risk misperceptions of floodplain residents in the Netherlands, and to offer insights into factors that are related with under‐ or overestimation of perceived flood risk. We analyzed survey data of 1,848 homeowners in the Dutch river delta and examine how perceptions of flood probability and damage relate to objective risk assessments, such as safety standards of dikes, as well as heuristics, including the availability heuristic and the affect heuristic. Results show that many Dutch floodplain inhabitants significantly overestimate the probability, but underestimate the maximum expected water level of a flood. We further observe that many respondents apply the availability heuristic.

One of the key challenges for risk, vulnerability, and resilience research is how to address the role of risk perceptions and how perceptions influence behaviour. It remains unclear why people fail to act adaptively to reduce future losses, even when there is ever richer information available on natural and human-made hazards (flood, drought, etc.). The current fragmentation of the field makes it an uphill battle to cross-validate the results of existing independent case studies. This, in turn, hinders comparability and transferability across scales and contexts and hampers recommendations for policy and risk management. To improve the ability of researchers in the field to work together and build cumulative knowledge, we question whether we could agree on (1) a common list of minimal requirements to compare studies, (2) shared criteria to address context-specific aspects of countries and regions, and (3) a selection of questions allowing for comparability and long-term monitoring. To map current research practices and move in this direction, we conducted an international survey – the Risk Perception and Behaviour Survey of Surveyors (Risk-SoS). We find that most studies are exploratory in nature and often overlook theoretical efforts that would enable the comparison of results and an accumulation of evidence. While the diversity of approaches is an asset, the robustness of methods is an investment. Surveyors report a tendency to reproduce past research design choices but express frustration with this trend, hinting at a turning point. To bridge the persisting gaps, we offer several recommendations for future studies, particularly grounding research design in theory, improving the formalisation of methods, and formally comparing theories and constructs, methods and explanations while collecting the most-in-use themes and variables and controlling for the most-in-use explanations.

Many individuals experience problems understanding and preparing for low-probability/high-impact risk, like natural disasters and pandemics – unless they experience these events, yet then it is often too late to avoid damages. Individuals with recent disaster risk experience are, on average, better prepared. This seems to be mediated through emotions and a better understanding of the consequences. In this study, we use immersive virtual reality (VR) technology to examine whether a simulated disaster can stimulate people to invest in risk reducing measures in the context of flooding, which is one of the deadliest and most damaging natural disasters in the world. We investigate the possibility to boost risk perception, coping appraisal, negative emotions and damage-reducing behavior through a simulated flooding experience. We find that participants who experienced the virtual flood invest significantly more in the flood risk investment game than those in the control group. The investments in the VR treatment seem to decrease after four weeks but not significantly so.

Natural disaster losses are projected to increase worldwide, in part due to climate change. As well as offering financial protection against the impacts of climate change, insurers can promote behavioural change by stimulating individuals’ preparedness choices. Norm nudges aim to improve preparedness for risks through information provision that appeals to social norms. Based on data collected in an online experiment among homeowners, we tested for the influence on flood insurance demand of providing individuals with a norm nudge about the flood insurance purchase decisions of their neighbours. Potential moderators of the effectiveness of this norm nudge were examined, such as whether the norm is consistent with homeowners’ prior beliefs as well as whether homeowners trust insurance agents, who are used as the messenger of the norm. Based on this investigation, we suggest recommendations for policies that aim to improve preparedness against increasing losses related to climate change.

This paper provides a systematic review of the literature on 80 experimental, hypothetical survey and market data studies of insurance demand against low-probability/high-impact risks. The objective of the review is to extract lessons from these studies and to outline an agenda for future research. We contrast the results of experimental and survey studies to findings from market data. We focus on experimental design methods, insurance characteristics, as well as results about theories, heuristics, behavioural biases and explanatory variables. Lessons for policymakers are drawn which can facilitate disaster preparedness.

In the original article, there was an error. In equation (3) in section Behavioral Theories, Expected Utility Theory, the discount factor in the budget constraint was presented as (Formula presented.). The standard formula for the discount factor is however (Formula presented.). Theminus in the original article should be a plus. A correction has been made to Behavioral Theories, Expected Utility Theory, paragraph 3, Equation 3: (Formula presented.). The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.