Repetitive floods intensify outmigration and climate gentrification in coastal cities

Abstract

Recent floods in America, Europe, Asia and Africa reminded societies across the world of the need to revisit their climate adaptation strategies. Rapid urbanization coinciding with a growing frequency and intensity of floods requires transformative actions in cities worldwide. While abandoning flood prone areas is sometimes discussed as a public climate adaptation option, little attention is paid to studying cumulative impacts of outmigration as an individual choice. To explore the aggregated consequences of households' outmigration decisions in response to increasing flood hazards, we employ a computational agent-based model grounded in empirical heuristics of buyers' and sellers' behaviour in a flood-prone housing market. Our results suggest that pure market-driven processes can cause shifts in demographics in climate-sensitive hotspots placing low-income households further at risk. They get trapped in hazard zones, even when individual risk perceptions and behavioural location preferences are independent of income, suggesting increasing climate gentrification as an outcome of market sorting.

Tracing resilience, social dynamics and behavioral change: a review of agent-based flood risk models

“…The former, although we have not experienced it yet, it is an additional factor of risk included in flood-ABMs through various forms. In the most simplistic, it enters as repetitive floods to proxy increasing future frequency (de Koning & Filatova, 2020). A more advanced way to include climate change into flood-ABMs is to use sea-level rise as an input of the aforementioned hazard sub-models (Han & Peng, 2019;Hassani-Mahmooei & Parris, 2012;Haer et al, 2017;McNamara & Keeler, 2013).…”

“…Within the MR group, 11:12 of articles focus on a city with flood-prone areas, with some models comparing two cities (Chandra-Putra & Andrews, 2019; De Koning & Filatova, 2020). Flood-ABMs from the SH community work at a variety of scales: from urban neighborhoods (Dubbelboer et al, 2017), counties (Han & Peng, 2019), and islands (Abebe et al, 2019a(Abebe et al, , 2019b, to provincial regions (Mustafa et al 2018) up to the entire European Union (Haer et al, 2019(Haer et al, , 2020.…”

“…The length of each time step is typically one year in 17 and 4 papers from the SH and MR groups respectively (Table 1). Alternatively, time steps can be semi-annual (de Koning & Filatova, 2020;de Koning et al, 2017), monthly (Filatova, 2015;Haer et al, 2017;Hassani-Mahmooei & Parris, 2012), or daily (Chandra-Putra & Andrews, 2019). Thus, each group employs a scale that fits the phenomena of interest.…”

“…Real estate is a common actor in MR flood-ABMs (Chandra-Putra & Andrews, 2019; de Koning et al, 2017Koning et al, , 2019 de Koning & Filatova, 2020;Filatova, 2015). The Real estate agent observes successful trades and identifies changes in market conditions, learning from hundreds of recent sales in the simulated decentralized housing market.…”

“…Yet, due to the reduced spatial scale under consideration, some SH ABMs consider all agents as neighbors (Tonn et al, 2019;Tonn & Guikema, 2018), with only one article that implements a social network structure at the regional level (Haer et al, 2016). The MR group tends to focus on economic interactions, with spatial externalities directly affecting Households agents' decisions to locate (Filatova, 2015; or what bid price to offer (de Koning & Filatova, 2020). In particular, such interactions are included in all flood-ABMs with a housing market where Households compete for the same resource: housing/land.…”