Risk aware planning of CO2 transport and geological storage networks for net zero buildings under capacity and injectivity uncertainty

Buildings require deep cuts in both operational and embodied emissions from carbon-intensive materials. Carbon capture, utilization, and storage (CCUS) can support these pathways, but only if CO 2 transport and storage infrastructure remains reliable under uncertainty in pipeline deliverability and reservoir injectivity. This paper develops a network mixed-integer linear programming model for CCUS source-sink design that minimizes discounted net cost while selecting integer pipelines and injection wells. We extend the formulation with a Gamma-robust variant that hedges against per-pipeline capacity shrinkage and per-well injectivity reductions, and a Conditional Value-at-Risk penalty that limits exposure to severe storage shortfalls. The model is evaluated on a fully synthetic dataset enabling controlled comparisons among deterministic, robust, and risk-aware designs. Under representative conditions of moderate transport distances and typical cost structures, hedging a 40% pipeline capacity derate alone increases system cost by 18%, whereas hedging an equivalent injectivity derate increases cost by 52%, and hedging both simultaneously raises cost by 72%. The CVaR analysis further shows that halving the discounted tail shortfall from 200 MtCO 2 to 100 MtCO 2 requires a 52% increase in expected system cost. These results quantify how uncertainty triggers step changes in well deployment and pipeline sizing, and how risk-aware designs trade higher upfront investment for reduced exposure to extreme shortfalls. Though illustrative, the framework can be instantiated with public data for real regions, providing decision support for CCUS planning linked to net-zero building strategies.