. Malhotra and Schmidt systematised such general heuristics into a matrix that compares the degree of design complexity of a technology with its need for customisation to its use environment. The high learning rates achieved by the modular technologies are analogous to the high learning rates achieved by wind and solar power, fuel cells, and electrolysers, which are enabled by mass production, along with the ease and speed of implementing research and development breakthroughs into the system. Another reason behind the higher learning rate is their potential to gain learning from other industries, such as CO2 supply to niche markets in the case of DAC, i.e., via diversification. However, large-scale plants may be better suited to supply CO2 to large-scale utilisation processes, such as a sustainable aviation fuel plant. We applied both analogies and Malhotra & Schmidt's typology to the four DAC technologies investigated here, and this is presented in Table . Note that the Malhotra & Schmidt typology leads to identical learning rate ranges (10-15%) for each technology. We captured the lowest and highest capital cost learning rates predicted by the analogous technology approach and the Malhotra and Schmidt approach to use as our final range of learning rates.