In an era where urban areas are increasingly vulnerable to natural disasters, a new study published in Engineering offers a comprehensive framework for quantifying urban disaster resilience. The research, conducted by Ying Zhou, Yi Xiao, and Haoran Xu from Tongji University, Shanghai, aims to provide policymakers with actionable insights to develop informed mitigation strategies.

According to the United Nations, 55% of the global population now resides in urban areas, making cities critical centers of human activity, resources, and wealth. However, this concentration also makes them highly susceptible to the impacts of natural disasters. The World Economic Forum’s “Global Risks Report 2023” highlights natural disasters as a significant threat to sustainable development, particularly in densely populated urban regions. In response, resilience has emerged as a key focus in disaster risk reduction efforts, but the concept remains abstract and challenging to quantify due to the complexity of urban systems.

The study proposes a novel approach to measure urban resilience by focusing on the overall functionality of a city through sufficient socioeconomic data exploration. This method addresses three key issues: measuring city functionality, simulating post-disaster recovery, and calculating urban resilience indexes. The framework integrates buildings and infrastructure network functionalities, recognizing that buildings serve as central nodes across multiple networks. “Building functionality can act as a proxy for the functionality of associated infrastructure systems,” the authors note.

The research introduces two primary measures of city functionality: economic productivity (EP) and service capacity (SC). The EP index defines a city’s functionality as its capacity to generate wealth, measured by the total net wealth produced per day. This is calculated by aggregating the EP of all buildings, which is determined by multiplying the effective area of each building by its economic productivity per unit area. The SC index, on the other hand, quantifies a city’s ability to meet the living and production needs of its population, measured by the number of people it can support. This is calculated by considering both the baseline occupancy capacity and the supplementary service capacity of each building.

To simulate post-disaster recovery, the study adopts a semiflexible, dynamic optimization recovery strategy. This approach balances rigidity and flexibility, incorporating constraints on recovery resources and commitments while allowing for dynamic resource allocation to maximize short-term gains in city functionality. The recovery simulation outputs include evolving damage states of buildings and infrastructure networks, which are translated into reductions in A_effective and Q_baseline for buildings, and losses in service satisfaction for infrastructure.

The urban resilience indexes are derived by integrating the city’s functionality loss over the recovery period. From an economic perspective, the EP recovery curve allows for the calculation of the “total economic loss” caused by a disaster, which includes cumulative EP loss and direct repair costs. From a societal perspective, the SC recovery curve enables the quantification of “cumulative service capacity loss,” representing the cumulative unmet needs of people over a specified period.

The article points out that by quantifying resilience using physically significant measures, policymakers can make informed disaster mitigation decisions. Resilience is an abstract concept that becomes meaningful only when it is quantified. The proposed framework offers a practical approach to assess and enhance urban disaster resilience, providing critical insights for designing effective disaster preparedness and mitigation strategies.

The paper “Quantifying Urban Disaster Resilience for Informed Mitigation Strategies,” is authored by Ying Zhou, Yi Xiao, and Haoran Xu. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.05.007. For more information about Engineering, visit the website at https://www.sciencedirect.com/journal/engineering.