Path to Low-Cost Direct Air Capture

TL;DR

This paper analyzes the potential for low-cost Direct Air Capture (DAC) technology, highlighting that learning-by-doing can significantly reduce costs, making DAC a feasible climate solution at smaller scales than previously thought.

Contribution

It provides a simplified cost analysis of DAC, evaluates GT's technology against low-cost benchmarks, and emphasizes the importance of performance parameters for cost reduction.

Findings

GT's DAC technology can achieve costs as low as $25 per tonne.

DAC costs can be reduced through learning-by-doing at smaller scales.

Current DAC embodiments are less relevant than their potential cost limits.

Abstract

It is now accepted that gigatonnes of Carbon Dioxide Removal (CDR) from the atmosphere are needed to avoid the threat of catastrophic climate change. Direct Air Capture (DAC) is a promising scalable CDR with a relatively small environmental footprint. But questions about DAC cost and energy use remain that are delaying the needed DAC policy decisions to create a mobilization effort like was done in the Manhattan Project and to address the Covid crisis. Global Thermostat (GT) has publicly claimed costs of under 50 dollars per tonne for mature GT technology deployed at a climate relevant scale. Why this low DAC cost is achievable will be addressed by a simplified analysis of generic DAC costs and using that analysis combined with experimental data to evaluate GT's DAC technology. A detailed cost analysis of different approaches to DAC by the National Academy of Sciences (NAS) found an approach to DAC that had a learning cost limit as low as 25 dollars per tonne GT's DAC technology will be shown in Appendix 1 to have the same performance characteristics of the lowest-cost DAC identified in the NAS study. Thus, like solar costs, DAC costs can be reduced by learning by doing, but in the case of DAC, only one order of magnitude in cost reduction is needed. Therefore DAC technology can reach its low learning by doing cost limit at a scale much smaller than necessary to address climate change. From a climate perspective, current DAC embodiments costs and scale have less relevance than their learning curve cost limit. While GT's technology has demonstrated the crucial performance parameters to achieve a low-cost DAC, no inference should be drawn that other approaches cannot achieve low or lower cost, if they can demonstrate the crucial performance parameters. Continued R&D on those performance parameters is needed.