Environmental Engineering Reference
In-Depth Information
For instance, if we consider a technological breakthrough in the wind power
sector, it results in an equiproportional cost reduction of 65 % in 2030 compared
to 2011 levels for all EU countries. By taking into account the effect of learning
by doing inertia that does not consider such a technological breakthrough, costs'
reductions in 2030 amount to 11 % compared to 2011 levels. The same can be said
for solar thermal sector: a technological breakthrough might bring a projected costs'
reduction of electricity generation from solar thermodynamic in 2030 by 68 % com-
pared to 2011 levels, while the inertial learning by doing should lower costs by 23 %
in 2030 3 .
We have designed two alternative scenarios up to 2030 for the introduction of
several technological breakthroughs, differentiated by country, leading to a quanti-
tative measure of projected costs' reduction of electricity generation from the main
three RES: solar and thermal (CSP) photovoltaic, wind energy, and biomass. Op-
erationally, in the two scenarios, we have labeled ENESH and RESSH, we assume
that the cost reduction in each EU country is proportional to the relative share of
that country in the total EU energy consumption and RES deployment in 2011,
respectively. So the maximum reduction cost takes place only in the leader country
while all other countries will have costs reduction lower than the one that occurs in
the leader country. The rationale is as follows. In the ENESH scenario, we assume
that the success of deployment of new technologies is a function of the size of the
energy sector in 2011 in each country. This is based on the concept of economies of
scales which impacts on the technology innovation; therefore, the larger is the en-
ergy sector, the higher is the probability (or the lower the associated uncertainty) of
success of the country's effort in research and development of new RES technolo-
gies. We assume a metric that yields a given cost reduction for each country, within
the range of minimum and maximum values, as reported in the literature, for each
RES, i.e., photovoltaic, wind, and biomass, according to the ranking of the energy
consumption share in the EU in 2011. In this way, it follows that larger countries
have a comparative cost advantage in 2030, because they would experience a larger
cost reduction than smaller countries.
In the RESSH scenario, we assume that successful cost reduction is a function
of the size of the RES deployment in 2011 in each country. The idea is that histori-
cal vocation somehow impacts on future success of RES innovation. Therefore, the
larger the RES deployment in the initial period, the higher the success in reaching
further technological developments in the future. Like the previous case, we assume
that cost reduction in each country is proportional to the ranking of the RES deploy-
ment share in the EU in 2011. In this way, it follows that countries with larger RES
sectors would have a larger cost reduction in 2030 than those lagging behind in RES
deployment today.
Based on the estimation of convex cost functions for each technology in the EU
regions, we have computed the additional cost that all other countries would have
to incur, if some country behaves as a potential free rider, because she avoids com-
plying with the targets of climate and energy package. This additional cost arises
3 See Chap. 4, Sect. 4.3 of this topic.
Search WWH ::




Custom Search