Unpacking the bottlenecks of deploying Direct Air Capture at scale

Pathways limiting global warming to well below two degrees presume the transition to low-carbon energy sources and deployment of carbon dioxide removal technologies. Among these technologies, the modeling literature consistently shows the value of direct air capture (DAC) for achieving climate stabilization in the long run. DAC offers unique advantages from a policy perspective: it is modular, less land-intensive than many comparable technologies, and enables straightforward accounting of removed emissions. However, as a novel technology, significant uncertainties remain about the barriers to scaling DAC, especially for what concerns the financial and economic viability of supporting policies and their capacity to develop DAC at scale. In this study, we explore the sensitivity of DAC deployment in an ambitious but realistic mitigation pathway (the long-term strategies committed by all major economies, or LTS) using a detailed-process Integrated Assessment Model, WITCH, across four dimensions of uncertainty: technological characteristics, financing, market requirements, and policy environments. We use recently developed probabilistic estimates to endonegize technological learning in DAC. We focus on the global level and on two time periods, namely 2025-2050, the critical moment for DAC deployment at scale, and 2050-2075, the moment where most of the net-zero goals are set. Using formal methods in statistics and sensitivity analysis, we analyze the amount of removed emissions, the energy and storage consumption, as well as the cost of the policy.