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 (CO₂) 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 CO₂. The CCS process can be costly, however, and large-scale implementation by industry will require government regulations and economic incentives. The incentives can price CO₂ 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 CO₂ to capture and optimize the various components of a CCS infrastructure network, given the price per tonne to emit CO₂ 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 CO₂. To illustrate how the new model based on CO₂ prices works, it is applied to a case study of CO₂ sources, reservoirs, and candidate pipeline links and diameters in California.