sectoral economic costs and benefits of ghg mitigation - IPCC

Renewable Energy
promising technologies, according to their degree of risk aversion. With the new modules, the
model captures both the supply and demand sides of the energy technology market. Due to the
model specification chosen, energy capital equipment price is therefore influenced by two
factors, one controlled by demand (cumulative capacity) and the other by supply (cumulative
R&D effort).
The extension of the learning-curve formulation to take into account cumulative R&D has proven
its ability to mitigate the intrinsic instability associated to the traditional learning approach.
Instability is induced by cumulative capacity learning acting as a snowball effect: the more a
technology is demanded (due not only to its specific capital costs, but possibly also to the price
of its associated fuel and other variable costs) the cheaper it becomes from the supply side due to
its increasing cumulative installations. Cumulative R&D stabilises the capital costs path by
directing the largest investment shares to those technologies with relative little installed capacity
but exhibiting good prospects within future technology markets. The overall scheme is therefore
designed to capture the technology substitution process, taking into account the time-lags
inherent in cumulative variables and current demand for technologies (based, in contrast, on
expected demand for energy services).
Energy R&D budgets in the Reference Case
As private expenditure on generating technology R&D is assumed to be a constant proportion of
equipment manufacturers’ sales throughout the world, it therefore follows directly their
fluctuations. In the late 90s equipment sales have been somewhat subdued due to a degree of
over-investment in capacity during the early half of the decade followed by slackening markets
as world GDP and with it electricity generation growth slowed down as a result of the economic
downturn. This situation is expected to be dramatically reversed in the future with an
unambiguous change of trend setting in particularly after the middle of the next decade: total
power generating R&D is simulated as rising from current 3 G$90 pa to a peak of about 10 G$90
in 2020; following that date it is likely to recede somewhat and stabilise reflecting the new
equilibrium of a considerably expanded electricity market.
Figure 15
Industry R&D budget allocation in the Reference case
Total Private Energy Technology R&D Investments
0.25
0.2
0.15
Share
0.1
0.05
0
1995 2000 2005 2010 2015 2020 2025 2030
Year
ATC BGT CHP DPV GGC ICG MFC NND
OGC PFC SFC SHY SPP WND
Source: POLES model
Figure 15 summarises the overall picture of R&D distribution along the simulation period. It
shows:
128