Modelling transformative adaptation: Case of post-earthquake Lyttelton, New Zealand. Issue 125 (November 2021)

• Record Type: Journal Article
• Authors: Ajulo, Oluwadunsin; Asgary, Ali; Tang, Patrick; Von-Meding, Jason
• Is Part Of: Environmental science & policy. Issue 125(2021)
• Page Start: 247
• Page End: 262
• Publication Date: 2021-11
• Subjects: Transformative adaptation -- Agent-based modelling -- System dynamics modelling -- Disaster -- Adaptation; Environmental policy -- Periodicals; Environmental sciences -- Periodicals; Environnement -- Politique gouvernementale -- Périodiques; Sciences de l'environnement -- Périodiques; Environmental policy; Environmental sciences; Periodicals; Electronic journals; 363.70561
• DOI: 10.1016/j.envsci.2021.09.006
• Languages: English
• ISSNs: 1462-9011

Abstract

Abstract: There has been an increasing awareness of the unsustainable trends in human interaction with the environment and its hazards. Research over the years has been able to establish a relationship between human activities and the increasing rate of climate change based on empirical evidence. The effects of human activities on the environment are not only limited to the rapid change in climate but has resulted in a range of other complex problems which includes the rising occurrence of disasters and other associated losses. This calls for a holistic approach for understanding these complex interactions and identifying ways for integrated planning towards sustainability. Hence, this study aims at developing a hybrid model that explains the bottom-up and top-down processes of transformative adaptation in Lyttelton New Zealand after the 2010/2011 earthquakes. To achieve this, we explored existing literature on the appropriate modelling tools for adaptation studies, used agent-based modelling and system dynamics tools to simulate transformative adaptation in Lyttelton, performed some sensitivity tests and discussed the results. The research concludes that transformation is a process, not an event. The model also provides a valuable tool for fail-safe experimentation and for informing policy decisions.