Optimal Spatial Deployment of CO2 Capture and Storage Given a Price on Carbon

Carbon dioxide capture and storage (CCS) links together technologies that separate carbon dioxide (CO 2 ) from fixed point source emissions and transport it by pipeline to geologic reservoirs into which it is injected underground for long-term containment. Previously, models have been developed to minimize the cost of a CCS infrastructure network that captures a given amount of CO 2 . The CCS process can be costly, however, and large-scale implementation by industry will require government regulations and economic incentives. The incentives can price CO 2 emissions through a tax or a cap-and-trade system. This paper extends the earlier mixed-integer linear programming model to endogenously determine the optimal quantity of CO 2 to capture and optimize the various components of a CCS infrastructure network, given the price per tonne to emit CO 2 into the atmosphere. The spatial decision support system first generates a candidate pipeline network and then minimizes the total cost of capturing, transporting, storing, or emitting CO 2 . To illustrate how the new model based on CO 2 prices works, it is applied to a case study of CO 2 sources, reservoirs, and candidate pipeline links and diameters in California.