𝑦 = 𝑎𝑥 -𝑏 3. Email: jpy1@hw.ac.uk RCCS, Heriot-Watt University Given that all DAC technologies are yet to fully commercialise and progress along the learning curve, capital cost learning rates must be selected as no observed values are available. This is inherent to ex-ante technology assessment and relies to an extent on judgement, but guidance is available to structure the selection. IEAGHG guidelines propose breaking technologies down into subsections and use learning rates reported for technologies that are identical or similar to each of the subsections. Given no identical technologies exist, they propose using learning rates from analogous technologies as a proxy. If this is not possible, they suggest using expert elicitation (recently applied to DACS by Sievert et al. ) or general heuristics on observed learning rates. For example, "the highest learning rates (e.g., 20-30%) are typically associated with smaller-scale technologies that are modular in nature and amenable to mass production" and "learning rates are significantly lower [e.g., 10-15%] for large-scale process systems and technologies that are typically field-erected and designed for a unique site or size"