Cellulosic biomass is an attractive source of renewable fuel because of its high greenhouse gas (GHG) mitigation potential. However, the optimal fuel conversion technology, amount of carbon capture and storage (CCS), and supply chain (SC) design depend on spatial features of the system which are essential to the systemwide GHG mitigation potential. We analyze the cost and GHG mitigation of a cellulosic biofuel SC with CCS using mixed-integer linear programming. While previous studies examine small high-resolution regions or large coarsely-represented regions, we consider a high-resolution SC for an 8-state region in the USA Midwest using, importantly, realistic biomass data. We show how the amount of biofuel produced and the level of carbon incentive contribute to substantial changes in the optimal SC configuration, biofuel conversion, and CCS technologies installed. While significant GHG mitigation is possible, sequestration credits may neglect to incentivize the further mitigation attainable by considering all sources of emissions.