Decision Support for Climate Mitigation Decision Making

Researchers: Kunsinee (Kelly) Srivichaiin, Yu (Yura) Gao, and Kaumudi (Anu) Dande

Our project focuses on the development of a decision support system that can be applied to any facility where current or future concern exists associated with severe weather events such as floods, high winds, heavy snow, and extreme heat. These weather events are expected to increase in frequency and intensity, underscoring the need for facility preservation. Our system helps a facility manager choose the best alternative among 3-5 retrofitting options. The challenge of quantifying climate risk impact is overcome by applying indifference analysis, which accounts for disruptions to operational activities and associated ripple effects. 

The R-Shiny web application uses mathematical methods with advanced visualization tools. Color-coded matrices display uncertainty for indifference analyses by presenting sets of pairwise comparisons in an iterative manner using a binary search. The pairwise differences in the expected values of risk mitigation options, calculated from the indifference probabilities, are converted into option rankings utilizing the AHP methodology. The application is illustrated using a robotics laboratory facility located in a high wind risk area of the BU campus. Four risk response options were compared, including the installation of windows with different design pressure ratings and accepting the risk of window failure. The project team hopes that a pilot implementation of the DSS can be arranged to evaluate its efficacy in the future.