Characterizing the Effects of Policy Instruments on Cost and Deployment Trajectories of Direct Air Capture in the U.S. Energy System

Abstract

Capturing and sequestering carbon dioxide (CO₂) from the atmosphere via large-scale direct air capture (DAC) deployment is critical for achieving net-zero emissions. Large-scale DAC deployment, though, will require significant cost reductions in part through policy and investment support. This study evaluates the impact of policy interventions on DAC cost reduction by integrating energy system optimization and learning curve models. We examine how three policy instruments—incremental deployment, accelerated deployment, and R&D-driven innovation—impact DAC learning investment, which is the total investment required until the technology achieves cost parity with conventional alternatives or target cost. Our findings show that while incremental deployment demands significant learning investment, R&D-driven innovation is considerably cheaper at cost reduction. Under a baseline 8% learning rate, incremental deployment may require up to $998 billion to reduce costs from $1,154 to $400/tCO₂, while accelerated deployment support could save approximately $7 billion on that investment. In contrast, R&D support achieves equivalent cost reductions at less than half the investment of incremental deployment. However, the effectiveness of R&D intervention varies with learning rates and R&D breakthroughs. R&D yields net benefits in all cases except at extremely low breakthroughs (5%) and very high learning rates (20%), where they are slightly more expensive. For learning rates below 20%, R&D provides net benefits even at minimal breakthroughs. These findings underscore the need for comprehensive public policy strategies that balance near-term deployment incentives with long-term innovation investments if we are to ensure DACS becomes a viable technology for mitigating climate change.