Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching

By Nikolaos Kouvaritakis, Antonio Soria, S. Isoard. 2000

248 citations recorded by the source corpus.

This paper presents a module endogenising technical change which is capable of being attached to large scale energy models that follow an adaptive-expectations. The formulation includes, apart from the more classical learning by doing effects, quantitative relationships between technology performance and R&D expenditure. It even attempts to go further by partially endogenising the latter by incorporating an optimisation module describing private equipment manufacturers' R&D budget allocation in a context of risk and expectation. Having presented this module in abstract, the paper proceeds to describe how an operational version of it has been constructed and implemented inside a large-scale partial equilibrium world energy model (the POLES model). Concerning learning functions problems associated with the data are alluded to, the hybrid econometric methods used to estimate them are presented as well as the adjustments which had to be effected to ensure a smooth incorporation into the large model. In the final sections is explained the use of the model itself to generate partial foresight parameters for the determination of return expectations particularly in view of CO2 constraints and associated carbon values.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/515e918d8ecb856d3b576291583fd6e37b029651

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Citations in the corpus

Experiments with a Methodology to Model the Role of R&D Expenditures in Energy Technology Learning Process (2004, unknown): “In an attempt to synthesize the two, Kouvaritakis et al. (2000) specified a learning curve with two factors (the 2FLC) describing learning-by-doing and learning-by-searching effects.”
Global experience curves for wind farms (2005, background): “Recently, attempts have been made to divide up the basic learning curve into a two-factor learning curve (2FLC), in which cost reductions depend on both cumulative production and RD&D investments (Kouvaritakis et al., 2000; Klaassen et al., 2002).”
A review of learning rates for electricity supply technologies (2015, background): “…formulation find that R&D contributes significantly to cost reductions in all stages of technological development, often more so than learning by doing (Watanabe, 1995; Kouvaritakis et al., 2000; Klaassen et al., 2005; Jamasb, 2007; Söderholm and Klaassen, 2007; Söderholm and Sundqvist, 2007).”
Relating R&D and Investment Policies to CCS Market Diffusion Through Two-Factor Learning (2013, background): “…’’technological learning’’, the phenomenon that the cost of a specific technology decreases along with its cumulative deployment (initially Wright, 1936, and Arrow, 1962), but is extended by the additional consideration of cumulative R&D efforts (Kouvaritakis et al. (2000a, 2000b); Jamasb, 2007).”
Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies (2007, background): “The learning effect of R&D is accounted for in “two-factor learning curves” that incorporate cumulative R&D spending or number of patents as proxies for stock of knowledge (Kouvariatakis et al., 2000).”
Historical construction costs of global nuclear power reactors (2016, unknown): “Kouvaritakis et al. (2000); Jamasb (2007), and Kahouli (2011) also derived learning rates for nuclear construction costs for the OECD and EU.”