Rapid urbanization and climate change exacerbate natural hazard risks worldwide. The (re)distribution of climate risks and the escalation of inequality due to climate change are hot on scientific and policy agendas. A complex interplay of factors, including geography and diversity in individual capacities to adapt, create feedback that reinforces poverty traps, and undermine societal resilience. Yet, decision-support tools that trace the dynamics of complex social systems are underdeveloped.
The project addresses this gap by employing state-of-the-art computational and data analytics methods to advance the science of climate change adaptation (CCA). To achieve this goal, we rely on our disciplinary knowledge, our small-scale computational spatial agent-based model (SABM), a unique micro-level data on CCA from four countries, and specific skills that the eScience offers. The project will innovate by (i)revealing hidden patterns in the high-dimensional survey, going beyond the standard regression analysis in social CCA studies, (ii)scaling up the SABM to systematically explore the factors reinforcing poverty traps as climate change intensifies, and (iii)building a web-based decision-support to examine the cumulative effect of individual CCA. The team will develop a reusable software that is accessible to a wide range of users and permits experts to trace socio-economic resilience to climate change.