A Multi-Criteria Decision Analysis and Risk Assessment Model for Carbon Capture and Storage

Abstract

Currently several disparate and incomplete approaches are being used to analyse and make decisions on the complex methodology of carbon capture and storage (CCS). A literature review revealed that, as CCS is a new and complex technology, there is no agreed-upon thorough assessment method for high-level CCS decisions. Therefore, a risk model addressing these weaknesses was created for assessing complex CCS decisions using a multi-criteria decision analysis approach (MCDA). The model is aimed at transparently and comprehensively assessing a wide variety of heterogeneous CCS criteria to provide insights into and to aid decision makers in making CCS-specific decisions.

The risk model includes a variety of tools to assess heterogeneous CCS criteria from the environmental, social, economic and engineering fields. The model uses decision trees, sensitivity analysis and Monte Carlo simulation in combination with utility curves and decision makers’ weights to assess decisions based on data and situational uncertainties. Elements in the model have been used elsewhere but are combined here in a novel way to address CCS decisions.

Three case studies were developed to run the model in scenarios using expert opinion, project-specific data, literature reviews, and engineering reports from Alberta, Saskatchewan and Europe. In collaboration with Alberta Innovates Technology Futures, a pilot study was conducted with CCS experts in Alberta to assess how they would rank the importance of CCS criteria to a project selection decision. The MCDA model was run using experts’ criteria weights to determine how CCS projects were ranked by different experts.

The model was well received by the CCS experts who believed that it could be adapted and commercialized to meet many CCS decision problems. The survey revealed a wide range in experts’ understanding of CCS criteria. Experts also placed more emphasis on criteria from within their field of expertise, although economic criteria dominated weights overall. The results highlight the benefit of a model that clearly demonstrates the trade-offs between projects under uncertain conditions. The survey results also revealed how simple decision analyses can be improved by including more transparent methods, interdisciplinary criteria and sensitivity analysis to produce more comprehensive assessments.