What drives human behavior regarding global coastal migration and adaptation in response to sea level rise and extreme flood events?

Project Information

COASTMOVE

Grant agreement ID: 884442

DOI

10.3030/884442

EC signature date 29 April 2020

Start date 1 January 2021

End date 30 June 2026

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 2 499 608,75

EU contribution

€ 2 499 608,75

Coordinated by

STICHTING VU
Netherlands

Periodic Reporting for period 3 - COASTMOVE (What drives human behavior regarding global coastal migration and adaptation in response to sea level rise and extreme flood events?)

Reporting period: 2024-01-01 to 2025-06-30

Summary of the context and overall objectives of the project

Future sea level rise (SLR) will increase coastal flood risk and threaten to displace up to 187 million people globally. Currently, SLR has already exacerbated migration from small islands and low-lying coastal areas that flood regularly. This issue is particularly urgent in areas affected by extreme coastal flood hazards, particularly from tropical cyclones. In 2017 alone, for instance, approximately 7 million people were forced to move due to these cyclones. Moreover, in most low-lying areas, the number of people and assets exposed to coastal flooding continues to increase due to urbanization and socio-economic growth. Both trends indicate that coastal adaptation (dikes, elevating houses, etc.) and migration policies are urgently required to decrease the flood risk and vulnerability of coastal regions.

Goal: The ERC COASTMOVE project aims to address the challenges these trends pose to adaptation and migration policies in global coastal zones. To this end, the project will focus on the adaptive and migration behavior of residents and other agents (e.g. government and insurers) by developing a global coastal model (DYNAMO) on climate risk, adaptation, and migration for coastal areas. These adaptation and migration decisions and policies will be studied under flood, erosion, and salinization risks over the period 2020-2080.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

There are three key research activities:

1. Model: Core of the project is the DYNAMO model which integrates a coastal flood risk model (GLOFRIS) with an agent-based model (ABM). DYNAMO is recently implemented for France and will be upscaled to other global coastal regions. The decision rules in the ABM are based on subjective discounted utility theory, which describes how people make decisions under flood risk. Based on this theory, the agent-based model simulates the adaptive behavior of agents under flood risk (simulated by the GLOFRIS module). Agents include: households in the coastal zone, governments and the private sector (e.g. insurance). A gravity module then simulates the aggregated migration flows between inland regions and the coast. The DYNAMO model is calibrated with empirical data from surveys in France. Results show that from the in total 260,000 people in flood zones in France, SLR increase net out-migration exceeding 10,000 coastal inhabitants. This number is low because it is assumed that still people move to the coast because of socio-economic pull factors. These projections of coastal migration under SLR are most sensitive to migration costs and whether the government has installed coastal flood protection.

2. Surveys: With the survey research we aim to understand the determinants of why people migrate, adapt or do not adapt or migrate, both at present and under future scenarios of sea-level rise. The survey data will serve as input for the agent-based model in DYNAMO. We have surveyed people in France, the United States (Florida/NYC), Buenos Aires, Vietnam, Mozambique. First results indicate that not only flooding is associated with increased migration flows, and place attachment, risk perception and financial drivers play a large role in migration decisions. Salinization and erosion do generally lead to more out-migration.

3. Up-scaling drivers: We apply novel global databases on flood protection, demography and socio-economy and machine learning to extrapolate behavioral rules on migration and adaptation decision from the regional coastal cases to other global coastal areas. We have started this activity by analyzing the impacts of natural hazards (floods, forest fire, earth quakes) on internal migration in the United States. In an econometric analysis we search for a correlation between observed migration flows, damage from natural hazards, and socio economic data on households (income, unemployment, etc.). Next, the aim is to create a database with global agent populations and their characteristics, which can serve as a basis for the DYNAMO ABM

We have communicated our results at conferences such as EGU and the migration conference at Columbia University in NYC in 2023 (https://adaptation.ei.columbia.edu/retreat/home)

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The Coastmove project is the first research showing trade offs between investing in climate adaptation (e.g. flood protection) or to move away from low lying coastal areas. Adaptation and flood risk management to reduce future environmental risks is inevitable, but it is unclear which coastal areas will be protected against flooding and SLR and in which regions residents will be forced to migrate? The Caostmove project will answer this challenging question which regional case studies and a study at the global scale.

At the end of the project we expect to:

• simulate projections in environmental risk (flood, erosion, salinization) due to both SLR and socio-economic trends over the period 2020-2080
• simulate and evaluate adaptation and migration activities and policies under different risk scenarios (2020-2080).
• produce maps of future migration hotspots; to show which coastal areas will -most likely- be protected against sea level rise, and which areas are likely to be abandoned through migration.
• have improved understanding of which drivers (environmental, socio-economic, demographic, etc.) influence adaptation and migration decisions