The disaster trap: cyclones, tourism, colonial legacies, and the systemic feedbacks exacerbating disaster risk

Abstract

The long, open-ended period of recovery from a disaster event is the phase of a disaster that the interdisciplinary field of disaster studies struggles to understand. In the process of rebuilding, places do not simply reset -they transform, often in ways that confound any reduction of disaster risk, instead making people and settings more vulnerable to future hazard events.Reducing disaster risk is regarded as a global priority, but policies intended to reduce disaster risk have been largely ineffective. This obduracy represents a grand challenge in disaster studies.Here, I propose that the correlated trends of runaway economic costs of disaster events, growing social inequity, environmental degradation, and resistance to policy intervention in disaster settings are hallmark indicators of a system trap -a dynamic in which self-reinforcing feedbacks drive a system toward an undesirable and seemingly inescapable state, with negative consequences that tend to amplify each other over time. I offer that these trends in disaster settings are the collective expression of an especially powerful and distinct kind of system trap, which here I term the "disaster trap" -a new theoretical concept to help explain and address runaway disaster risk. I suggest that disaster traps are likely strongest in tourism-dominated coastal settings with high exposure to tropical cyclones and colonial histories of racial capitalism, which I explore with an empirical illustration from Antigua & Barbuda. Formalising a linkage between gilded and safe-development traps matters because their effects likely compound each other nonlinearly, such that disaster risk only increases and disaster risk reduction becomes increasingly difficult to achieve. Addressing traps requires understanding them as dynamic systems, described as fundamentally and completely as possible -their components, mechanisms, drivers, and structure -in order to reveal when and where interventions might be most effective at reducing disaster risk.