sectoral economic costs and benefits of ghg mitigation - IPCC

Patrick Criqui, Nikos Kouvaritakis and Leo Schrattenholzer
The effects of the introduction of the carbon value on the direction of private R&D, given the
partial foresight context described above, are fairly dramatic. Clean coal technologies see the
share of total funds reduced to one fifth of their reference case value (from 26.3 to 5.3 percent) as
soon as the budget adjustment lag has elapsed. This drastic movement clearly reflects the
deterioration in the future market prospects for these technologies implicit in the introduction of
a tough constraint on world CO 2 emissions. From this low level, funding of R&D activities
related to clean coal starts increasing almost immediately initially maintained by the caution of
risk averse agents but subsequently fuelled also by risk takers’ choices revisiting some of the less
explored technologies (such as the advanced thermodynamic cycle) in view of the saturation of
opportunities experienced with regard to the obvious stars of the simulation i.e. the non-fossil
technologies. By the year 2030 the share of private R&D directed to clean coal technologies has
been restored to around 11 percent (instead of 20.2 in the reference case). Given the expansion of
total R&D budgets this translates to a level of funding equivalent to two thirds of the reference
case value.
An analogous situation in reverse pertains to the funding of renewable technology R&D. The
shares in R&D budgets increase substantially until the middle of the next decade and thereafter
are maintained at a higher level for a varying duration before starting to decline, in some cases
finding their reference case values around the end of the forecast horizon (small hydro and
biomass gasification with gas turbines) while in others approaching it much more slowly
(particularly slowly for wind and solar thermal). These differences indicate that over the whole
period 2000-2030 the funding for renewables from private sources is substantially higher and
occurs earlier in a way that cost reductions in these technologies are pronounced and accumulate
through the learning by doing effects.
Gas turbines in combined cycles are negatively affected at first in order to allow for the diversion
of funds towards non-fossil technologies but their budget share soon stabilises at just under eight
percent over a period of 15 years (instead of the continuous decline, admittedly from a higher
level, which characterised the reference case). Over the whole period private funding for the
above technology is reduced by about one third while funding for the somewhat related
combined heat and power plants remains almost unaffected albeit on the markedly declining path
which characterised the reference case. Fuel cells are initially affected very little (slightly
positively) but their take-off is delayed somewhat to allow for a more thorough exploration of the
non-fossil alternatives.
A striking feature of the target simulation has been the appearance of private R&D funding for
the new nuclear design option, which in the reference case (and indeed a number of other cases
informally examined) failed to attract the attention of private agents while at the same time
absorbed the lion’s share of public R&D expenditure. The share of nuclear R&D in the total
private budget rises quickly to 5.5 percent and subsequently increases gently to over 8 percent by
2025. This can be solely attributed to the choices of the most risk averse agent devoting close to
30 percent of total R&D budget on a technology the prospects of which are substantially and
assuredly enhanced by the imposition of the target, being as it is both non-fossil and susceptible
to large scale development but suffering from a very high R&D cost.
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