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The process of innovation production exhibits characteristics
that may determine their subsequent market diffusion
dynamics. Among these, there are several (positive
and negative) feedback mechanisms: returns to production
scale, to product adoption and to scope (i.e. fostering
the joint production of innovative techniques) may
prove to be important issues to look at to understand
the process of technology substitution. The long-term
learning effects, related to cumulative experience
in capital production and R&D activities, appear
as an important self-reinforcing mechanism as well.
The diffusion of new technologies analysis therefore
requires that several types of natural monopoly and
economic regulation be distinguished according to
the prevailing returns patterns. In particular, the
occurrence of flexible returns brings into evidence
new dynamics of technology diffusion and new dimensions
for technological policy actions. An active and balanced
technology policy appears advisable to achieve a reasonable
degree of technological diversity, that is, to keep
options open with respect to the success of a wide
range of technologies.
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A module endogenising technical change is presented
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.
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Learning curve for power generation
technologies,
historic data and POLES WETO reference projection
up to 2030.
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The endogenous technical change module that has been
incorporated in POLES is presented and the main quantitative
results of the new version of the model and corresponding
exercises. Section 2 presents the methodology that
has been used in order to assess the returns to R&D
for the main power generation technologies identified
in the model. R&D budget allocation is then analysed
for the base case in Section 3, which also illustrates
the differences in the behaviour, respectively of
the least and most risk-averse agents. Section 4,
analyses in detail the changes in budget allocation
that are induced by the introduction of CO2 emission
constraints to 2030, as well as their impacts on marginal
and total abatement costs for the main world regions.
As a last step, the consequences of changes in public
R&D are examined in Section 5. This exercise shows
that the performance and diffusion of the technologies
benefiting from the shift in public R&D are largely
improved, in spite of noticeable ‘crowding out’
effects - of private research by public research -
for these technologies.
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This paper explores the respective contributions
of the effects of learning and returns to scale in
the capital costs reduction pattern experienced by
renewables. Causality analysis and econometric estimation
of learning curves are performed on two emerging renewable
energy technologies, namely PV and wind. Learning
effects appear as an essential driving force, a result
which concurs with the widely acknowledged importance
of experience in technical progress inherent to economic
growth. The existence of non-constant and flexible
returns to scale are further highlighted. Their effect
on diffusion dynamics is shown to be potentially considerable,
particularly at the outset of innovation deployment.
However, returns to scale are suggested to be constant
in the long term. Institutional commitment in supporting
innovation therefore seems justified, both on the
basis of the occurrence of learning effects and diseconomies
of scale. These findings appear to be essential to
the dynamics of innovation diffusion and market structure.
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