Risk-averse community resilience planning considering retrofit and insurance incentives

By taking proactive mitigation actions, a community can improve its resilience against natural disasters, which entails reducing vulnerability, minimising social impacts, and facilitating faster recovery. Mitigation planning for the built environment involves making appropriate decisions regarding strengthening interdependent utility network systems, adding redundancies, and retrofitting buildings. Due to the presence of numerous stakeholders with diverse socio-economic characteristics, different risk attitudes, and propensity to take voluntary actions, the planning problem is challenging, especially when the hazards, magnitudes, occurrence times, and impacts on the community’s built environment and population are uncertain. An optimisation model, framed as a two-stage mean-risk stochastic programming model, was developed to address these challenges and support mitigation decisions. The optimisation model aims to minimise the costs and risks associated with hazards by integrating their impacts on the built environment and social functions. Under various incentive policies and risk preferences, the model helps generate decision alternatives and evaluate their effectiveness in achieving community resilience goals. A case study is presented using a community in Shelby County, Tennessee, subjected to earthquake hazards to demonstrate the model’s capability to develop alternative mitigation strategies under varied risk preferences and incentive policies.