This study addresses the robust optimal source-sink matching in carbon capture and storage (CCS) supply chains under uncertainty. A continuous-time uncertain mixed-integer linear programming (MILP) model with physical and temporal constraints is developed, where uncertainties are described as interval and uniform distributed stochastic parameters. A worst-case MILP formulation and a robust stochastic two-stage MILP formation are proposed to handle interval and stochastic uncertainties, respectively. Then, two illustrative case studies are solved to demonstrate the effectiveness of the proposed models for planning CCS deployment under uncertainty.