Interdependent ranking of sources and sinks in CCS systems using the analytic network process

Abstract

CO2 capture and storage (CCS) is widely regarded as an important low-carbon technology for reducing greenhouse gas emissions from large industrial point sources. It entails the capture of a relatively pure CO2 from exhaust gases using different techniques, and then storing this captured gas in various geological sinks. Large-scale deployment of CCS requires the comprehensive evaluation of candidate sources and sinks present in a given geographical region. In this study, we propose an analytic network process (ANP) approach to rank simultaneously the potential CO2 sources and sinks in a CCS system. Such ranking can be used to identify sites for CCS demonstration projects. This ANP decision model allows us to incorporate the feedback dependency that exist in the preference ranking of sources and sinks due to the importance of geographic proximity as a decision criterion. A case study is then solved to demonstrate the proposed model.