Strategic Design and Multiperiod Optimization under Uncertainty of Solid Sorbent Direct Air Capture Supply Chains in Europe

Abstract

This study develops a multiperiod mixed-integer linear programming model for strategic planning of direct air capture (DAC) supply chains across Europe aiming at minimizing overall costs under uncertainty. DAC is pivotal for achieving net-zero targets and removing CO₂ from the atmosphere to enable negative emissions. The optimization considers uncertainty in key parameters to ensure resilient decision-making. The model incorporates the influence of ambient air conditions on DAC performance, with temperature and humidity impacting productivity and energy consumption. Country-specific energy costs and greenhouse gas emission factors are accounted for, impacting the net cost of CO₂ removal. Results indicate that with ambitious targets, technology learning curves, and renewable electricity transition, costs can fall to approximately 121 €/t CO₂ by 2050, with 108 €/t attributed to capture costs. The findings highlight the importance of technological advancements and provide a systematic framework for policymakers to design resilient and cost-effective supply chains for large-scale deployment, positioning DAC as a potential decarbonization alternative for hard-to-abate emissions.