Cost Dynamics of Clean Energy Technologies

41 Pages Posted: 6 May 2021 Last revised: 23 Jun 2021

See all articles by Gunther Glenk

Gunther Glenk

University of Mannheim - Mannheim Institute for Sustainable Energy Studies

Rebecca Meier

University of Mannheim

Stefan Reichelstein

Stanford University - Graduate School of Business

Multiple version iconThere are 3 versions of this paper

Date Written: April 30, 2021

Abstract

The rapid transition to a decarbonized energy economy is widely believed to hinge on the rate of cost improvements for certain clean energy technologies, in particular renewable power and energy storage. This paper adopts the classical learning-by-doing framework of Wright (1936), which predicts cost (price) to fall as a function of the cumulative volume of past deployments. We examine the learning rates for key clean energy system components (e.g., solar photovoltaic modules) and the life-cycle cost of generating clean energy (e.g., wind energy and hydrogen obtained through electrolysis). Our calculations point to significant and sustained learning rates, which, in some contexts, are much faster than the traditional 20% learning rate observed in other industries. Finally, we argue that the observed learning rates for individual technologies reinforce each other in advancing the transition to a decarbonized energy economy.

Keywords: learning-by-doing, renewable energy, energy storage, electrolysis, levelized cost of energy

JEL Classification: M2, M4, O33, Q4, Q5

Suggested Citation

Glenk, Gunther and Meier, Rebecca and Reichelstein, Stefan, Cost Dynamics of Clean Energy Technologies (April 30, 2021). Available at SSRN: https://ssrn.com/abstract=3839968 or http://dx.doi.org/10.2139/ssrn.3839968

Gunther Glenk (Contact Author)

University of Mannheim - Mannheim Institute for Sustainable Energy Studies ( email )

Mannheim, 68131
Germany

Rebecca Meier

University of Mannheim ( email )

L 7, 3-5
Mannheim, 68161
Germany

Stefan Reichelstein

Stanford University - Graduate School of Business ( email )

Stanford, CA 94305
United States

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