ACTIVIDADES DE EXTENSIÓN

What we can observe from the figures 3.3-3.5 in general is that the incentive to invest into wind energy even without the possibility to acquire sCER permits in either scenario remains low until until around year 10 increasing rapidly afterwards.. When sCER permits can be used, gas power investments become more lucrative. Even though the investment probability into gas decreases equally fast as in the EUA only scenario, the investment probability over the whole time horizon increases. As a sensitivity check for our results we consider a range of interest rates, volatilities, wind energy premiums, and trend values. We use the values from scenario 1 as a standard and report the changes in values we made below the figures. Scenario 1 is always in the middle of the set of figures that follow now, in order to make the comparison easier. The left and right side represent a decrease/increase in value we check for sensitivity respectively.

Testing for changing interest rates, we find that lower risk-adjusted interest rates lead to a higher investment probability for wind power. As an example we show how changing the risk adjusted rate to 3-5% impacts investment in Figures 3.6-3.8. We do not report the results for changing the risk-free interest rate, as these changes led to no significant change of the results.

0 10 20 30 40TimeHyearsL 0.2 0.4 0.6 0.8 1.0 Probability gas investment

with sCER without sCER

Figure 3.6: Risk free rate 2%; Risk adjusted rate 3%

10 20 30 40TimeHyearsL 0.2 0.4 0.6 0.8 1.0 Probability gas investment

with sCER without sCER

Figure 3.7: Risk free rate 2%; Risk adjusted rate 4%

10 20 30 40TimeHyearsL 0.2 0.4 0.6 0.8 1.0 Probability gas investment

with sCER without sCER

Figure 3.8: Risk free rate 2%; Risk adjusted rate 5%

When using the same specification as in scenario 1 and changing the interest rates to 3% (risk adjusted) and 2% (risk free), we find that gas power is not an investment choice anymore. This significant change stems from the fact that wind power becomes more profitable relative to gas power since emission prices increase over time. The less this future profit is discounted, the more profitable wind power becomes today. Thus, in an infinite time horizon model wind power would always be the more profitable than gas power despite the large investment cost difference11.

Consequently, when increasing the interest rate to 5%, the investment likelihood of gas increases. This also shows that the two power plants are already very similar in terms of cost, when considering a longer time period and increasing permit prices.

Secondly, we test for three different volatility values. Figures 3.9-3.11 show the effect of changing volatility from 10% to 20%, and then to 30%. Increasing the volatility leads to a higher probability that at a certain point in time an investor decides to invest into a gas power plant. This effect is more pronounced as we move along the time axis. A high level of volatility generally increases the value of options, since the value of the new information that arrives increases and it pays to wait for this information to materialize. In the context of our model a high volatility implies a higher chance in later periods that the emission price can be very low, rendering gas power investment more likely.

0 10 20 30 40TimeHyearsL 0.2 0.4 0.6 0.8 1.0 Probability gas investment

with sCER without sCER Figure 3.9: Volatility 10% 10 20 30 40TimeHyearsL 0.2 0.4 0.6 0.8 1.0 Probability gas investment

with sCER without sCER Figure 3.10: Volatility 20% 0 10 20 30 40TimeHyearsL 0.2 0.4 0.6 0.8 1.0 Probability gas investment

with sCER without sCER

Figure 3.11: Volatility 30%

Thirdly, we consider different levels of the wind premium. Figures 3.12-3.14 show the impact the subsidy has in investment behavior for the levels 10 e, 20 e, and 30 e. 10 20 30 40TimeHyearsL 0.2 0.4 0.6 0.8 1.0 Probability gas investment

with sCER without sCER Figure 3.12: Premium 10e 10 20 30 40TimeHyearsL 0.2 0.4 0.6 0.8 1.0 Probability gas investment

with sCER without sCER Figure 3.13: Premium 20e 10 20 30 40TimeHyearsL -1.0 -0.5 0.5 1.0 Probability gas investment

with sCER without sCER

Figure 3.14: Premium 30e

When the premium is only 10e, investing into wind power becomes very unlikely even towards the end of the time horizon. In contrast, at a level of 30 ewind power is so profitable that the investor start to build wind power plants from the very

beginning. This is not surprising as a 30 e subsidy implies a 50% mark-up over the market price. More interesting is that the 10 e premium is not sufficient to induce significant wind power investment over the whole time horizon. If policy makers have the goal to promote renewable investments in the medium term, a 20 e premium seems to be the necessary. However, such a result should be treated with care as we do not consider a range of other effects that impact investment behavior, such as the interrelation between emission, gas and electricity prices. What can be stated is that a premium is still necessary for wind energy up until the medium term, if it is a policy goal to promote investment into wind energy.

Finally, we test different trend values for the sCER price process (Figures 3.15- 3.17). Increasing the trend value for sCER lowers the positive effect of sCER on gas investments. This is intuitively appealing, since a higher trend value will lead to a lower gap in between sCER and EUA permits in later years rendering gas investment less profitable. However, even if both price processes have a trend value of 5%, the possibility of using sCER still has a positive effect on the investment probability in gas power. 10 20 30 40TimeHyearsL 0.2 0.4 0.6 0.8 1.0 Probability gas investment

with sCER without sCER

Figure 3.15: sCER

Trend=3%, EUA Trend = 5% 10 20 30 40TimeHyearsL 0.2 0.4 0.6 0.8 1.0 Probability gas investment

with sCER without sCER

Figure 3.16: sCER

Trend=4%, EUA Trend = 5% 10 20 30 40TimeHyearsL 0.2 0.4 0.6 0.8 1.0 Probability gas investment

with sCER without sCER

Figure 3.17: sCER

Trend=5%, EUA Trend = 5%

The results presented here should be treated with care, since we assume two stochastic price processes which represent a best guess of the future based on previ- ous modeling approaches in the literature and the current political situation. Also, since we simplified the model as much as possible in order to focus on the effect of the sCER permit class Many interacting features of energy markets, such as the dependence of the gas and electricity price on emission permit prices, have been left out. Including them would have resulted in an identification problem of the sCER effect. Leaving them out means that changes to the functioning of the permit market might not have the same effect as in our model, since other effects such as the interdependence of natural resource markets can overcompensate any effect stemming from permit markets. Therefore, the general direction of the effect should be taken as the main result of our simulations, not specific results for each year.

By conducting a range of sensitivity checks we could show which factors are crucial to the investment decision, namely the interest rate, the wind premium and volatility. The interest rate is the most crucial factor, as small changes can already trigger large differences in investment decision. This is not surprising considering the length of the investment decision we are considering here and the profit profile of gas power plants. Gas power plants are very profitable at the beginning with low permit prices. As prices rise, they become increasingly less profitable and the more this effect is discounted, the higher the incentive of the investor to choose wind power plants.