A Markov Decision Process Approach for Cost-Benefit Analysis of Infrastructure Resilience Upgrades

Area of Science:

• Engineering
• Environmental Science
• Economics

Background:

• Climate change intensifies natural disasters, necessitating critical infrastructure resilience.
• Traditional cost-benefit analysis (CBA) struggles with the stochastic nature and uncertain lifespans of disaster resilience investments.

Purpose of the Study:

• To develop a novel Markov decision process (MDP) model for CBA of infrastructure resilience upgrades.
• To derive analytical expressions for willingness to pay (WTP) for resilience enhancements.
• To analyze how WTP varies with key problem parameters.

Main Methods:

• Developed a Markov decision process (MDP) model incorporating disaster probability, mitigation, and investment lifespan uncertainty.
• Derived analytical expressions for willingness to pay (WTP).
• Conducted comparative static analysis and applied the framework to electric utility infrastructure case studies.

Main Results:

• The MDP model provides a framework for valuing infrastructure resilience upgrades.
• Willingness to pay (WTP) for resilience is influenced by disaster probability, mitigation effectiveness, and investment lifespan.
• Case studies on electric utility infrastructure demonstrate the model's applicability and sensitivity to outage costs and customer types.

Conclusions:

• The developed MDP model offers a robust approach to CBA for infrastructure resilience.
• Decision-making for resilience investments is sensitive to assumptions about economic impacts of disruptions and customer characteristics.
• The framework aids in optimizing investments for critical infrastructure protection against climate change-induced disasters.