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Energy production techniques have undergone extensive technological change. To understand future dynamics, long-term studies adapt a learning-by-doing model from manufacturing to understand productivity gains. We examine the suitability of this approach.

Energy and climate policy studies with a long-term outlook need to anticipate potential developments in technology and the temporal nature of today’s resource-reserve definitions for oil, gas and coal. Accordingly, economic concepts of learning formulated from research on manufacturing industries inspire a common approach to modeling technological change in hydrocarbon energy resource production. This theory expects future costs of fossil energy supply to benefit from a cumulative learning effect which results from ongoing extraction. With three decades of data since the initial formulation of this theory by Rogner (1997), some key regions of conventional oil and gas production have matured. Fresh data on industry cost trends are now available, allowing for a closer examination and validation of whether this learning model hypothesis is relevant for long-run cost projections. Empirical cost and productivity data challenge the broad application of a learning model to the total geologic occurrences of fossil energy resources. We find that oil and gas industry operating costs indicate a learning effect, but capital expenditures do not. Coal resource-reserve dynamics have not developed as anticipated. Nordhaus (2009) suggests technological change models of energy supply calculated with a learning curve will consistently overestimate productivity gains, producing biased cost estimates of future technologies. This paper considers the Rogner (1997) learning-by-extracting model for fossil energy supply as a specific case of Nordhaus’ argument.