Technological learning: Lessons learned on energy technologies

Abstract

The concept of technological learning is a method to anticipate the future development of the costs of technologies. It has been discussed since the 1930s as a tool for determining manufacturing cost reductions, starting in an airplane manufacturing plant, by means of learning curves and has been widely used since the 2000s in energy models to endogenize technological change. In this paper, the theoretical concept of technological learning based on energy technologies is analyzed based on examples from the literature. The main low‐carbon power generation technologies, photovoltaics, concentrated solar power, wind and nuclear energy were analyzed, showing different cost trends. Additionally, the impact of policy support on technological learning was discussed in concrete examples of bioethanol and heat pumps. We find that the homogeneity and the modularity of a technology are essential for high learning rates. A good proof is the manufacturing cost development of photovoltaics in recent decades, where a rather stable learning rate of 20% has been identified. On the contrary, nuclear power did not evolve into a homogeneous technology due to required environmental adaptations caused by accidents and the lack of standardization and application of new engineering approaches. In that case, the overall price further increased. Finally, another important condition is stable legal and regulatory conditions regarding the implementation. This article is categorized under: Policy and Economics > Green Economics and Financing

Leveraging patent analysis to measure relatedness between technology domains: an application on offshore wind energy

“…First, for modelers, our method can help improve models of technological change through time. For example, technological forecasting models, such as learning curve models, usually ignore the relatedness of offshore wind energy to these more mature industries and consider it as a purely novel technology (van der Zwaan et al 2012, Haas et al 2022). Our method produces a quantitative value for relatedness that can be used in learning curves as in the framework introduced in (Hernandez-Negron et al 2023).…”

“…However, this relatedness is often ignored when modeling; and discussions tend to rely on practitioner know-how and informal expert opinion. For example, offshore wind clearly has similarities to onshore wind, yet it is often considered a novel technology (van der Zwaan et al 2012, Haas et al 2022).…”