In this study, we couple an integrated flood damage and agent-based model (ABM) with a gravity model of internal migration and a flood risk module (DYNAMO-M) to project household adaptation and migration decisions under increasing coastal flood risk in France. We ground the agent decision rules in a framework of subjective expected utility theory. This method addresses agent’s bounded rationality related to risk perception and risk aversion and simulates the impact of push, pull, and mooring factors on migration and adaptation decisions. The agents are parameterized using subnational statistics, and the model is calibrated using a household survey on adaptation uptake. Subsequently, the model simulates household adaptation and migration based on increasing coastal flood damage from 2015 until 2080. A medium population growth scenario is used to simulate future population development, and sea level rise (SLR) is assessed for different climate scenarios. The results indicate that SLR can drive migration exceeding 8000 and 10,000 coastal inhabitants for 2080 under the Representative Concentration Pathways 4.5 and 8.5, respectively. Although household adaptation to flood risk strongly impacts projected annual flood damage, its impact on migration decisions is small and falls within the 90% confidence interval of model runs. Projections of coastal migration under SLR are most sensitive to migration costs and coastal flood protection standards, highlighting the need for better characterization of both in modeling exercises. The modeling framework demonstrated in this study can be upscaled to the global scale and function as a platform for a more integrated assessment of SLR-induced migration.
<p><strong>Coastal adaptation dynamics under future shoreline changes</strong></p> <p>Currently 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.</p>
<p>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&#8217;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.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.