Modeling the economic impact of utility damage

Abstract

Infrastructure systems (such as power and water utilities) are vulnerable to natural hazards and typically account for a significant portion of the economic losses in a region following disruptive events. Also, disaster risk management for infrastructure requires large investments. So, it is essential to carefully quantify the economic losses due to a disruptive event, including the post-disaster increase in service prices. However, probabilistic models for the economic impacts of utility damage and/or loss of service are not well developed, and there are no available models for predicting price increases after disasters. This paper first develops probabilistic models to predict the economic impact in terms of damage and recovery costs. The paper then develops a utility price model that takes the damage and recovery costs as input to predict the change in the utility price over time. We use structural fragility analysis (Gardoni et al. 2002; Der Kiureghian 2008) to model the damaged infrastructure and construction cost databases (Means 2022) to develop the economic loss models. We then use US Energy Information Administration open data (EIA 2022) to develop the utility price equations and validate the models for predicting post-disaster utility price change. The research outcomes can provide communities, utility companies, and governing, regulatory, and policy bodies with a clear understanding of the economic risk from damage to infrastructure assets, which can then inform regulations for insurance requirements and price control policies in the utility sector.

References

Der Kiureghian, A. (2008). Analysis of structural reliability under parameter uncertainties. Probabilistic Engineering Mechanics, 23(4), 351-358.

EIA (2022). Energy Information Administration open data. Retrieved from https://www.eia.gov/opendata/

Gardoni, P., Der Kiureghian, A., & Mosalam, K. M. (2002). Probabilistic capacity models and fragility estimates for reinforced concrete columns based on experimental observations. Journal of Engineering Mechanics, 128(10), 1024-1038.

Means (2022). Building construction costs book. 80th annual ed., Robert S. Means Company, Kingston, Massachusetts.