Technological deployment

6.1.1 Subsidies - a never-ending story?

The assumption on the subsidies for RES is that they gradually decrease, so that in the end the systems are no longer subsidized. This subsidy scheme reflects a situation where the policy maker is willing to provide a subsidy for market uptake, but is decreasingly willing to support systems that are not entering the market by itself. The zero support in 2050 reflects the fact that over a period of 50 years, the technologies supported should have reduced their costs suffi-ciently to make it on the market of their own - or fail.

Thus, the set-up of the long-term policy analysis is such that it is assumed that RES in the long term will gain competitiveness due to initial subsidizing. When considering the outcomes of the individual models, though, this appears to be a futile hope. The various analyses show a decreasing penetration of RES with decreasing subsidy level. By 2050, the effect in all three bottom-up models by and large show a ‘convergence’ of the outcome of the policy case to the baseline results - and hence the subsidy seems to have no lasting impact whatsoever.

The reason for lack of lasting effect of subsidies may be two-fold. First, in a world where the level of deployment of a technology does not influence its price, a non-lasting effect is under-standable. Introducing the subsidy will only benefit the cost level of a technology in a direct manner, and as soon as the subsidy is decreased, the effect for the deployment of the technology decreases proportionally.

Although this may seem a trivial conclusion, it is not, as it clearly indicates that support schemes will only be of lasting impact for those technologies that have a potential for cost re-duction under further deployment, i.e. for technology that can exhibit Learning by Doing (LBD)²⁷.

It may not suffice for a technology to exhibit LBD, though. This is most strikingly illustrated by the GMM model, which includes both wind power and solar PV as LBD-technologies. In spite of their learning potential, the policy case shows the convergence towards the baseline. As it happens, these two systems illustrate two possible frustrations for learning. The wind power technology is already quite successful in the in the baseline, and consequently physical limita-tions thwart further application of the technology. At the same time, the costs for solar PV sys-tems is too high to benefit from the support scheme, although the highest subsidy level studied with the model indicates that at some point this may no longer be the case.

6.1.2 Learning can increase effects of subsidies

Based on the seemingly robust, but unwanted conclusion that non-lasting subsidies will have non-lasting effects, all three models have extended their study to include (extended) learning effects in some way. Two out of three models show lasting effects for some technologies, with generally are modest due to physical constraints in combination with the modest subsidy level.

The third model, analyzing the competition between various sectors for biomass, shows that in-creases in one sector may be achieved at the expense of another sector. In all, the conclusion is supported that subsidizing endogenous learning renewable energy systems will likely lead to increased deployment of such technologies, even beyond the period where the technology is subsidized.

6.1.3 Subsidies as internalization of benefits

In the previous sections, it was argued that the potential to realize cost reductions through learn-ing-by-doing is essential for the success of a subsidy scheme for renewable energy systems.

There is an alternative view possible, based on the observation that the modest and decreasing subsidy levels in the case study give rise to an enhanced contribution from RES in the interme-diate periods. This is illustrated in the next figure, where the increased use of RES is shown for the case study, for the three models.

27 Endogenous Technological Learning, or learning-by-doing, is the effect that costs for a technology generally depend on the cumulative installed capacity, i.e. the more a technology is deployed, the lower the costs of the technology will be.

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Figure 6.1 Share of renewable energy in primary energy consumption (Eurostat convention) The figure shows that in all models the additional use of RES as percentage of primary energy source peaks around 2030 to 2040, at a value of 1.3 ± 0.4% in the latter period. At the same time, the subsidy is definitely not peaking in this period. Thus, it seems likely that in the long run a relatively low subsidy suffices to stimulate the contribution from renewable energy sys-tems. It is important to realize that such a small stimulus is already included in the baseline, where a moderate CO2 tax is included. Based on the observations done here, it could be interest-ing to see whether the combination of an initial subsidy scheme with a stronger CO2 tax from 2050 onwards would lead to similar result. Such a scheme could be justifiable from the point of view of internalizing costs for non-renewable energy resources, or alternatively internalizing the benefits from renewable resources.

6.1.4 Technology mix

There are only few robust conclusions to be drawn from the analysis on the level of technolo-gies in the three models. First, a major stimulus is provided by the subsidy scheme to the appli-cation of wind energy in all three models, as is illustrated in upper left panel in Figure 6.2. In GMM, the increase is relatively modest due to the high contribution of wind energy in the base-line, there due to learning effects. Note furthermore that the increase in use of wind is still sig-nificant by 2050 in all models, although in percentages it is small due to the rising contributions from wind in the baseline.

Contrary to the case of wind energy, the initial increase in application of biomass is annulled by the year 2050 in all three models, as is also illustrated in Figure 6.2, in the upper right panel.

This indicates that the low and decreasing subsidy level is insufficient to induce a lasting effect on the additional deployment of biomass. In one of the three models (GMM), the use of biomass is hardly increasing at all, when the potential for endogenous technological learning for the as-sociated technologies is disregarded. In the other two models biomass plays a significant role in the enhancement of renewables in any case. Using the MESSAGE model, it was shown that bio-mass can play a role in various sectors, and that a stimulus in a particular sector may cause

‘carbon leakage’ to other sectors, due to a shift in application of biomass, leaving the total use of biomass relatively unchanged.

The use of both solar and hydro energy is relatively insensitive to the subsidy scheme described here, albeit for different reasons. Again, this is illustrated in Figure 6.2, in the lower left and right panels, respectively. Solar remains roughly at its baseline level because the subsidy is too small to compensate for the higher costs. Note however that there is a large uncertainty in the development of solar energy. The use of hydro is limited by the availability of resources, and furthermore by the exclusion of large-scale facilities from the subsidy.

Figure 6.2 Electricity generation from renewable energy sources in the three world models, in the baseline scenario and in the RES policy scenario. Note the difference in scales A fifth renewable resource, geo-thermal energy, is not depicted in Figure 6.2. This resource is generally strongly limited by physical constraints, making extensions beyond the baseline utili-zations particularly difficult. Only in the GMM model roughly a doubling of the contribution from this resource is observed. In the other two models, the subsidy either does not influence the deployment (MESSAGE), or the technology is not considered at all (DNE21+). This latter ap-proach seems justified by the other two models, as the contribution in absolute terms remains small, even in the GMM analysis.

One striking feature from the figures above is that the uncertainty in the development of utiliza-tion of resources over the various models seems to be much higher than the effects of the sub-sidy investigated here. Thus, it would seem that it is more beneficial to first resolve this uncer-tainty, before trying to draw definitive conclusions on the application of subsidies to enhance the contribution from renewable energy sources.

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