Conclusions; Ways to Avoid Regulatory Failure

In practice, policymakers are never perfectly informed about renewable technologies. This is partly due to the rapid development and the changes these technologies experience, and also to the fact that it is the investors and project developers who know the most about the shape of their respective marginal cost curves, and they might be interested in the policymaker not being perfectly clear about those, in high hopes of earning some additional profit thanks to a potential regulatory failure. It might even happen that investors are not perfectly up-to-date on the characteristics – the ones that affect their returns – of their technology, either (e.g. solar modules’ lifespan and the expected decrease in efficiency during that period). Consequently, they use higher risk premia in their calculations, and thus expect higher feed-in tariffs than would be necessary. Therefore, in order for the tariff reduction potential to be correctly reflected in the actual prices, it is not only policymakers, but also investors, manufacturers and experts that should engage in active communication / the exchange of information (Szabó et al. 2010).

With the help of some graphic illustrations, I have shown that the slope of the marginal production cost curve of the renewable power generation technology to be promoted has an influence on the extent of the error that the imperfect information of the policymaker might lead to. Concerning steep marginal production cost curves, it is quantity-based regulations – acting „along‖ a vertical line – that may result in surprisingly high prices, while as regards flat marginal cost curves, it is the price- based, feed-in tariff type of system – represented by a horizontal line – that cause the bigger concern. In either case, the outcome may be that the policymaker has to face (because either the prices or the quantities exceed what has been expected) a green energy related financing need far above the expected level. If the difference is large enough to lead to resistance among end consumers, or if the intake of that increased amount of green power causes disturbances in the country’s electricity grid, then we clearly have a case of regulatory failure. Based on our analyses so far, the following conclusion can be drawn:

136

H1: For renewable technologies with a steep marginal cost curve, it is the quantity-based, while for renewable technologies with a flat marginal cost curve, it is the price-based regulation system that carries a greater risk of regulatory failure.

The policymaker needs to be aware of this fact, and they need to design their incentive system accordingly. In practice, the accurate estimation of the marginal cost curve may be facilitated by the monitoring of technological developments, active communication with the investors and, of course, an international perspective, that is, studying other countries’ promotion systems and, possibly, learning from their mistakes. For both types of incentive, there is a possibility and there are established techniques for avoiding the scenarios discussed above.

Under a quantity-based scheme, the risk lies in the required amount of green energy production possibly only being realizable at a price way higher than planned, and that the price of certificates may significantly exceed the level expected by the authorities. The solution might be, as already seen in a number of countries, a cap price/buy-out price, at which one can „escape‖ one unit of green certificate obligation. This price ensures that obligors have an opportunity to „buy out‖ their green energy obligation, if the price of green energy production happens to rise too high – because of the policymaker having been insufficiently informed or for any other reason. That is, instead of buying the overly expensive certificates, they may opt for paying the buy-out price/penalty. Accordingly, the policymaker can rely on this buy-out price to set an upper limit to the price of green energy, and hence to the extent of the (potentially) resulting regulatory failure, in addition to keeping the burden that the financing of the subsidies puts on end consumers in check.

Under a feed-in tariff scheme, the problem may become particularly severe if the marginal cost curve is flat or if the tariff set by the policymaker happens to be seriously far-fetched. In the Czech PV example we saw that the tariff they introduced was several times higher than in other countries, which was a clear predictor of soon- to-come regulatory failure. One of the reasons why photovoltaics is a very special field is that it is a technology with a raw material cost of zero; after all, solar energy is free, and therefore its marginal cost curve is flatter than that of the technologies that do have to incur raw material costs (e.g. biomass, biogas power plants).

137

Furthermore, recent years have witnessed a technological development of unseen proportions in the field, which acted to very significantly reduce the manufacturing costs of solar modules. The technology is becoming more and more efficient, that is, the per unit area energy output of solar panels is continuously on the rise. What is more, solar power stations are – in comparison to wind turbines, which have no raw material costs, either – rather quick to install, and thus the policymaker does not have very long to recognize the problem in its early stages.

As a consequence of all the above, the slope of PV technology’s marginal cost curve is continuously flattening, and doing so at a very quick rate. Therefore, as far as solar power stations are concerned, a policymaker who is not sufficiently up-to-date may very badly miss the mark – as illustrated by Figure 29:

Figure 29: Effect of a flawed tariff with extremely flat marginal cost curves

Source: author‟s own illustration

The figure will help me explain the effect of flawed feed-in tariffs. I plotted three marginal cost curves of differing slopes (MC1, MC2, MC3) and three feed-in tariffs

(p1, p2, p3) to be applied to the technology in question. Each quantity defined by the

intersection of a marginal cost curve with a feed-in tariff is denoted by a capital Q, with the number of the marginal cost curve in the subscript and that of the tariff in

the superscript. Thus Q23, for example, is the quantity defined by the tariff p3 on the

second marginal cost curve (MC2). I also used colors, in addition, to distinguish

between the different cases: blue is for the quantities defined by the tariff p1 (Q11,

Q21, Q31), green for p2, and red for p3.

If the policymaker employs a tariff of p1, for they believe the marginal cost curve to

be MC1, then what they expect in response is a quantity Q11. If, however, the real

marginal cost curve corresponds to MC2, then the quantity generated by the price p1

price p3 MC1 MC2 MC3 p2 p1 Q11 Q21 Q12 Q22 Q13 Q31 Q23 Q32 Q33quantity

138

will not be Q11, but Q21; if MC3 is the correct one, then it will be Q31. As clearly

evinced by the figure, the difference between the expected and the actually resulting

quantity is larger (the distance between Q11 and Q31 is larger than that between Q11

and Q21) in this latter case, simply because MC3 is flatter. Thus the more inaccurate

the policymaker’s estimate for the slope of the marginal cost curve, the bigger the „surprise‖ that may await them in terms of quantity.

My graphic is also suitable for illustrating the extent of the shift in quantity (along a given marginal cost curve) induced by a flawed tariff. If the policymaker happens to

introduce an excessive tariff of p2 or p3 – instead of p1, which would actually yield

the desired quantity –, then, given MC1, the amount of green power produced by the

technology in question will not be Q11, but Q21 or Q31 instead, respectively. We can

also see that if the marginal cost curve of the technology the policymaker wishes to

promote is not MC1, but MC2 or MC3, then the same mistake in the price level will

already lead to much larger errors, for the flatter the marginal cost curve of the technology, the larger the deviation in quantity can get. The distance between Q21

and Q23 – associated with a price level modification of p1p3 along MC2 – is

significantly larger than that between Q11 and Q13. But the difference between the

planned and the actual quantity caused by the flawed price is the greatest (equivalent

to the distance Q31-Q33) if it is MC3 that happens to be the correct curve. Thus it has

become apparent that:

Under a FiT scheme, the larger the mistake made by the policymaker, be it related to the slope of the marginal cost curve or the tariff they introduced, the more significant the difference between the actual volume of renewable production and what was intended.

And the excessive expansion in (e.g. PV) capacities a flawed tariff generates is quite certain to cause network and end consumer price issues, which are extremely difficult to correct – thus one should better strive to avoid the entire situation.

Of course, there is a solution. First of all, the monitoring of technological development is essential. Second, it should be clear to policymakers that this technology carries the risk of severe regulatory failure, the extent of which they should therefore try to keep within limits. A possible means might be a bit of

139

quantity-based regulation, as it is the case with the wind energy quotas in Hungary: even though there is a set KÁT tariff, only a certain total amount of quotas is allocated to the actors. Another solution might be the German model, where the promotion of solar power plants is kept under control by decreasing the PV feed-in tariff as the amount of installed PV capacity increases. The tariff is reduced gradually, in a stepwise manner, with the next step always being „triggered‖ by a certain level of total installed PV capacity. The Spanish solution, on the other hand, was to introduce annual/semi-annual limits on the solar PV capacities to be installed and eligible for guaranteed feed-in tariffs.

An appropriate way of avoiding regulatory failure might be, therefore, to make the individual schemes see and extend a bit beyond their own mechanisms of operation, and integrate into themselves an element or two from the other (price/quantity-based) incentive system. This way, they may be able to complement and assist each other in avoiding regulatory failure.

My analysis explored the causes of regulatory failure and offered possible solutions, as well – on a theoretical level. In practice, the situation is far more complicated, of course. Each renewable technology has its own unique marginal cost curve. The marginal cost curves of wind power, solar power, biomass, geothermal energy, etc. all have different shapes and slopes. We might add them all up to arrive at an aggregate marginal cost curve, which encompasses all the renewable technologies. What is more, what we have seen above can only count as a simplified representation of even one single technology, as real marginal cost curves rather tend to have a step- like shape, which reflects the endowments of the given country with respect to the technology in question. Having tapped the most favorable locations (which are the ones that have the lowest marginal costs), we have to take a step forward, towards less favorable locations (for example towards lower wind speeds, in the case of wind turbines), which is where the value of the marginal cost curve „jumps‖ and continues to remain very flat afterwards (Haas et al., 2011).

Accordingly, my analysis mostly pertains to those FiT and TGC systems that are differentiated by technology, because those are the ones that take into account the differing marginal cost curves of the individual technologies. Considering a non- differentiated system, different sections of the curves arrived at by aggregating the

140

marginal cost curves of the individual technologies may be characterized by differing slopes, thus it might even happen that the recommended incentive system is different for different sections of the curve.

Thus the task of the policymaker is not an easy one, knowing that real life is far more complex than the analysis I have just presented. I am convinced, however, that the basic conclusions are actually useful: it is important that the policymaker be clear about how the slopes of technologies’ marginal cost curves are related to the extent of the mistake they can make with the incentive system they have in place, and that they decide on the measures to be taken with that knowledge in mind. Generally valid is the conclusion, as well, that the other type of incentive is a valuable source of „assistance‖ in such cases. Under a price-based (feed-in tariff) scheme, for example, it might be worth to limit the annual amount of capacity expansion for each technology, or to make the tariff itself degressive with total installed capacity; for example, by reducing the initial tariff by 5/10/15% after each 10 MW of installed solar PV capacity.

Another good solution is the one employed by the Hungarian policymaker with respect to wind power, that projects did not automatically become eligible for the guaranteed feed-in tariff; instead, projects in a certain stage of preparation were required to submit a tender, and it was only a part of the project development opportunities submitted within the given period that was actually granted eligibility for the guaranteed feed-in tariff. The 330 MW quota was determined on the basis of grid control and network load management considerations, back in 2006. Until mid- March of the same year, license applications were filed for a total wind power capacity of 1138 MW (Tóth, 2009). In such cases, the policymaker still has an opportunity to make their choice from among the applications based on the country’s priorities.

The Hungarian regulation does not really differentiate between technologies in terms of feed-in tariffs, and thus the relative expensiveness of the PV technology has prevented investors from deploying significant solar power capacities in Hungary. However, should the METÁR prescribe a PV feed-in tariff above the current level, hypothesis H1 will at once become relevant to the Hungarian regulation, as well. Recognizing, developing an awareness of and avoiding the possibility of regulatory

141

failure will be of key importance during the phase when the PV capacities laid down in the NMCST are being deployed – that is: in the years directly ahead.

Another related consideration worth calling attention to is that a correct tariff can only be correct for a certain period of time. Regular updates to the tariffs – that is, decreasing the tariff in line with technological developments – are particularly important with respect to rapidly developing solar PV technologies. Because a „sticky‖ tariff that remains unchanged for several years, even though changes in the technology would already allow for a 10-20% price reduction, is also a potential case of regulatory failure.

Accordingly, in order to avoid regulatory failure, the policymaker does not only need to be sufficiently up-to-date once – that needs to convert into a „constant state of up- to-dateness‖.

142