A meta-analysis of carbon capture and storage technology assessments: Understanding the driving factors of variability in cost estimates. (1st December 2015) - Akbilgic, Oguz; Doluweera, Ganesh; Mahmoudkhani, Maryam; Bergerson, Joule
Graphical abstract: Highlights: Meta-analysis to explain discrepancies in previous studies assessing CCS costs. Regression models have strong predictive power ( R 2  > 0.90). Capital cost & relative thermal efficiency penalty have largest impact on cost. First analysis to quantify the contribution of parameters to variability of CCS cost. Analysis can be used to make future cost studies more transparent and comparable. Abstract: The estimated cost of reducing carbon emissions through the deployment of carbon capture and storage (CCS) in power systems vary by a factor of five or more across studies published over the past 8 years. The objective of this paper is to understand the contribution of techno-economic variables and modeling assumptions to explain the large variability in the published international literature on cost of avoided CO2 (CACO2) using statistical methods. We carry out a meta-analysis of the variations in reported CACO2 for coal and natural gas power plants with CCS. We use regression and correlation analysis to explain the variation in reported CACO2. The regression models built in our analysis have strong predictive power ( R 2  > 0.90) for all power plant types. We find that the parameters that have high variability and large influence on the value of CACO2 estimated are levelized cost of electricity (LCOE) penalty, capital cost of CCS, and efficiency penalty. In addition, the selection of baseline technologies and more attention and transparency around the calculation of capital costs will reduce the variability across studies to better reflect technology uncertainty and improve comparability across studies.