Authors: John Michael Humphries Choptiany; Ronald Pelot; James Brydie; William Gunter
Addresses: Department of Industrial Engineering, Dalhousie University, 407 Prince of Wales, Saint Andrews, New Brunswick, E5B 1R1, Canada; Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, 00153 Rome, Italy ' Department of Industrial Engineering, Dalhousie University, Halifax, Nova Scotia, Canada ' Reservoir and Geoscience Group, Alberta Innovates – Technology Futures (AITF), Alberta, Canada ' G BACH Enterprises Incorporated, 11239 63 St., Edmonton, Alberta, T5W 4E5, Canada
Abstract: Carbon capture and storage (CCS) is a technology used to mitigate climate change by removing CO2 emissions from fossil-fuelled power plants. CCS is a new, large-scale technology with potentially large geographical and temporal impacts. There are considerable uncertainties surrounding CCS risks. CCS decision makers may benefit from a holistic framework to compare project options based upon many diverse criteria. The authors developed a decision analysis framework to assess CCS risks and to facilitate project selection. The framework incorporates utility curves, criterion weights, thresholds, decision trees, Monte Carlo simulation, critical events and sensitivity analysis. Criteria were chosen from environmental, social, economic and engineering fields. CCS experts provided inputs, which formed the basis for simulation model runs to compare preferences between three hypothetical CCS projects. The study demonstrated the value of a flexible model that can be tailored to individual decision makers and adapted to many complex decisions.
Keywords: carbon capture and storage; CCS; multicriteria decision analysis; MCDA; risk assessment; Monte Carlo simulation; preference modelling; utility curves; sensitivity analysis; critical events; decision trees; thresholds; decision analysis framework; carbon storage; climate change; CO2; carbon dioxide; carbon emissions; fossil fuels; power plants; project selection; flexible models; modelling.
International Journal of Decision Support Systems, 2015 Vol.1 No.4, pp.349 - 390
Received: 11 Nov 2014
Accepted: 19 Mar 2015
Published online: 04 Feb 2016 *