sectoral economic costs and benefits of ghg mitigation - IPCC

Patrick Criqui, Nikos Kouvaritakis and Leo Schrattenholzer
3.4. Endogenous variables 2: The two-factor learning curve
This part of the technology endogenisation module is more classical in the type of description
and specification it proposes. It is largely inspired from the experience curves described in Subsection
1.3. and many times applied to energy technologies (Ayres and Martinas, 1992;
Christiansson, 1995; Neij, 1997). The basic scheme of the learning curve, in which technology
cost reductions are a function of cumulative capacities (with a negative elasticity) has been
extended in the POLES endogenous technology module, in order to represent the impacts of
R&D on technology improvements.
This endeavour had to face many data and methodological difficulties:
- while reasonable data exist for public R&D spending by main category of technologies, data
on private energy R&D are aggregated, scarce and very incomplete;
- very few empirical studies have been dedicated to the analysis of the impacts of R&D on
energy technology costs and performance improvements;
- a rapid examination of past statistics for cumulative R&D for technologies as the breeder
reactor amply demonstrate that large R&D spendings are in no way a sufficient condition to
the development of a new technology.
In spite of these difficulties and also of the absence of systematic empirical evidence, it has been
considered that the hypothesis of a positive impact of cumulative R&D on technology
performance still was a reasonable one and as such could not be ignored in a modelling scheme
designed for technology endogenisation. Efforts have thus been dedicated to:
• the definition of a two-factors learning curve with a “Cobb-Douglas” type function and
cumulative installed capacities and cumulative total (public and private) R&D as the
explanatory variables for technology improvement in time; this function thus exhibits
both a “learning by doing” and “learning by searching” elasticity;
• the development of a set of data for both government and industry energy R&D by main
category;
• the econometric estimate of the functions for the key POLES technologies in order to
provide, in spite of the difficulties due to colinearity in the explanatory variables, sets of
elasticities consistent with existing data on capacities and cumulative R&D and consistent
across technologies.
In conformity with the notation used above for the conventional learning by doing equation
described above in Sub-section 1.3. the two-factor learning curve can be described as follows:
Cost(Ccap,CRD) = A* CCap -b * CRD -c (1)
where: Cost (.) …………..….Specific capital costs
A ………….………..Specific capital costs at a total cumulative (initial) capacity of 1
CCap ……………….Total cumulative installed capacity
CRD………………...Cumulative R&D
-b …..……………….Learning elasticity
-c ………………...…R&D elasticity
The specifications used in the full two-factors learning curves of the model incorporate two
characteristics that allow for decreasing returns of R&D and of experience effects: the first one
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