资源环境科技发展态势分析平台

Learning rates enable the generation of first-order estimates of the cost of a technology as cumulative production grows, and play an important role in energy-economic modelling. This paper extends the component-based approach to estimating future learning rates to the case of a technology with multiple variants. It sets out a bottom-up method for estimating the composite learning rate including the situation where the proportional contribution of the different variants changes as cumulative production increases. The method is demonstrated for carbon storage using representative cost and distribution data from recent studies of storage in the European region.

Carbon storage comprises four technology variants defined by the nature of the storage reservoir - (onshore or offshore) depleted oil & gas reservoirs and (onshore or offshore) saline aquifers - and each variant has a different learning rate reflecting its different cost structure. Moreover, the proportional contribution of each variant to total storage is likely to change with the growth of global storage capacity. The composite learning rate for carbon storage is estimated for scenarios in which the relative contributions change: a negative learning rate is determined in one scenario.