As mentioned earlier, our scenarios assume an expected value for the carbon price of about 4.6 US$/tC in 2010 which increases with the discount rate to about 370 US$/tC in 2100. This corresponds to the mean over all scenarios in the IPCC scenario database [Hanaoka et al., 2006] with CO2-equivalent concentration stabi-lization targets of 650 ppmv and above. The deterministic implementation of this carbon price trajectory results in our modeling framework in cumulative CO2 emis-sions of ∼ 880 GtC over the course of the century, corresponding to CO2 concen-trations around 530 ppm towards 2100. Considering also non-CO2 emissions, based on the scenario classification from Chapter 3 of the IPCC Fourth Assessment Re-port [Fisher et al., 2007], this would correspond to about 650 ppmv CO2-equivalent concentrations. This result, although not surprising, illustrates that our modeling approach leads to very similar results than other deterministic models assessed by the IPCC.
Considering the uncertainty of the carbon price, however, we observe signifi-cant changes of the emissions pathway towards more stringent mitigation. This is particularly due to the lognormal distribution of the carbon price, including low probability events in the tail with much higher carbon prices of several thousand US$/tC. Therefore, in the stochastic cases hedging against the tail of high carbon prices becomes a major motivation to reduce carbon emissions, even if additional technology uncertainty is considered. This response to uncertainty has also been observed in previous studies, such as [Manne and Richels, 1992; Pizer, 1999; Yohe et al., 2004]. While we derive the same conclusion, the reason for the response is different. Both Manne and Richels [1992] and Yohe et al. [2004] conclude that rel-atively lower emissions would be rectified due to the uncertainty of climate change damages (i.e. uncertainties in the response of the physical climate system to an increase in GHG emissions), whereas our analysis suggests lower emissions because of the economic risk of uncertain carbon prices.
We find further that the stringency of mitigation is critically dependent on the risk premium. The relationship between the risk premium and annual carbon emis-sions and resulting atmospheric CO2 concentrations are summarized in Figure 9.14
14The annual carbon emissions in Figure 9(a) are a direct model output whereas the CO2
con-Figure 9: (a) Annual energy-related carbon emissions in GtC and (b) atmospheric CO2 concentrations in ppm as a function of the risk premium f
We observe that already very small additional hedging investments of only 0.1%
result in a reduction of cumulative emissions by 50 GtC or 5.5% in comparison to the deterministic case. This corresponds to a CO2 stabilization level of about 515 ppm. In our standard case with a risk premium of 1%, cumulative emissions are approximately reduced by an additional 22% (∼ 690 GtC) for the linear risk mea-sure (upper mean absolute deviation), corresponding to a further reduction of the CO2 stabilization level to ∼480 ppm. The impact of the distribution’s tail is more pronounced by the quadratic and CVaR risk measures. Thus, in the quadratic case emissions are reduced by another 2%-points, to about 670 GtC in comparison with the linear risk measure. The 95%-CVaR risk measure even results in cumulative emissions of ∼ 620 GtC reaching a CO2 concentration level of close to 460 ppm CO2 by the end of the century. For higher risk premiums even more effort is put into carbon abatement to limit the impacts of eventually high carbon prices, e.g. at f = 3% we find a reduction in excess of one third (580 GtC, 450 ppm) in comparison with the deterministic optimization and atf = 5% cumulative emission reductions constitute even more than 40% (510 GtC, 430 ppm).
The peaking year of energy-related CO2 emissions is only marginally affected by the risk premium and varies just between 2030 and 2040. However, the magnitude of the emission peak changes considerably from 12 GtC in the deterministic case (f = 0) to 8.8 GtC at a risk premium of f = 5%. The impact on near-term emissions is relatively smaller in our standard case with a risk premium of 1%, where emissions are about 10 GtC around 2030, and stay relatively unaffected until 2020, because of the energy system’s inertia (see Figure 9).
5.4 Diversification
As discussed in the context of the 3-technology model in Section 3, but also in the previous paragraphs, diversification may serve as a possible hedging strategy to increase resilience of a system. While we are focusing in this section particularly
centrations in the atmosphere in Figure 9(b) are calculated with the climate model MagiCC 4.1 [Wigley, 2003]. As our model calculates only energy-related emissions, we added for this purpose non-energy-related CO2 emissions (e.g. land-use change, cement production, gas flaring) from a 670 ppmv stabilization scenario developed at IIASA [Riahi et al., 2007].
on the diversification within the electricity sector, it needs to be stressed that the energy system is more complex, and diversification may also occur as a result of shifting investments between different sectors. These aspects are discussed later in Section 5.5, which is focusing on the investment patterns under uncertainty.
In order to measure diversity, we employ an integrated multi-criteria diversity index developed by Stirling [1998], which is based on distance metrics and will be referred to in the following as the Stirling index15. We are in particular interested in the relationship between the Stirling index and the risk premium, and to which extent increasing risk aversion is triggering diversity as a response to uncertainty.
For this purpose, Figures 10(a) and (b) display the electricity generation portfo-lio’s dependence on the risk premiumf in 2030 and 2050 respectively. In addition to the technology shares in electricity generation, the relationship between the Stirling index and the risk premium is shown on the right axes of the graphs. As can be seen from Figures 10(a) and (b) the Stirling index is generally increasing at higher risk premiums, but the behavior is quite different in 2030 and 2050.
Fossil power generation from natural gas dominates electricity generation in the short- to medium-term as is evident from its high share in 2030 (Figure 10(a)). In the deterministic case hydro and wind are the only other two technologies contributing to electricity generation. At risk premiums below 2% this situation only marginally changes towards a larger share of hydro power plants. Above 2% nuclear power comes in as a fourth option, resulting in a significant increase of the Stirling index.
This situation indicates that the gap in levelized electricity generation costs between natural gas and nuclear is substantial (0.96 ct/kWh which corresponds to ∼ 26%
higher costs for nuclear) which requires a relatively high risk premium of 2% to bring in this alternative. Coal power generation is phased out until 2030 as a result of a moderate carbon price and the uncertainties that come along with it.16
Figure 10: Technology shares in electricity generation and corresponding Stirling index in (a) 2030 and (b) 2050 as a function of the risk premiumf.
15The index is defined as M =∑
ijdijpipj, where pi is technology i’s share of electricity gen-eration and dij the distance in Euclidean disparity space between technology i and j [Stirling, 1998][chp. 3.2]. For the graphs a distance of 0.5 between fossil energy technologies (coal and gas) is used whereas for all other technologies we assume a distance of 1.
16We assume on average a lifetime of 30 years for fossil power plants. Therefore by 2030 all power plants that were built in the base year 2000 reach the end of their lifetime.
In 2050 the deterministic electricity generation portfolio features four technolo-gies, namely gas, nuclear, hydro and wind that contribute to electricity generation.
Already at relatively low risk premiums of less than 1% the share of hydro more than triples at the cost of gas and nuclear power generation, resulting in a noticeably higher Stirling index. The reason for this early diversification is that levelized elec-tricity generation costs are very close for these three technologies with hydro only being some 8% and 6% more expensive than gas and nuclear respectively. With further increasing risk premium the technology portfolio grows to seven technolo-gies with biomass CCS power plant, coal CCS power plant and solar PV joining in. In addition, the shares are much more evenly distributed, such that at f = 5%
no technology supplies more than 31% of total electricity in comparison to almost 42% in the deterministic model run. This is an illustration of the previously cited Don’t put all your eggs in one basket rule. The observed diversification is though significantly stronger by 2050 compared to 2030, due to the short-term inertia of the system against rapid structural changes.
It has to be emphasized that in contrast to modeling frameworks that explicitly aim at diversification as an objective (e.g. [Stirling, 1994]) in our modeling frame-work diversification is an endogenous result driven by the aim to reduce risk. The extent of diversification is, however, critically dependent on the nature of the system and the dependence structure of joint input distributions which we have assumed (see Appendix A.2). Our sensitivity analysis of the same scenarios indicate that in absence of any correlation between the costs of power generation technologies diversification would be significantly more pronounced.
5.5 Energy-related Investments
We finally review the implications of the risk-hedging strategies for energy-related investments. Our systems engineering perspective permits us to explore shifts of investment between various technology clusters in fuel extraction, electric, non-electric (liquid fuels), and the energy end-use sectors.
The cumulative energy system investments between 2010 and 2050 are summa-rized in Figure 11. Although we are dealing with a moderate stabilization scenario (∼ 690 ppmv CO2-equivalent concentration in the deterministic case), investments into fossil fuel technologies still dominate the first half of the century. In particular upstream investments, but also electricity generation and liquid fuel production are characterized by high shares of fossil fuels, particularly in absence of uncertainty.
This situation changes in the stochastic cases with increasing shares of investments into low-carbon options such as biomass, nuclear and renewable electricity genera-tion and synthetic fuels. In addigenera-tion, increased efforts to improve energy efficiency in the end-use sectors become a more important factor in the stochastic cases, where the strongest increase occurs at risk premiums higher than 1%. Most of these ef-ficiency improvements take place in the non-electric and transport end-use sectors, because decarbonization is typically more costly in these sectors than e.g. for elec-tricity.
Total energy-related investments in the deterministic case are estimated to be around 49 trillion US$2000 between 2010 and 2050. Additional investments into risk-hedging range between 7% and 30%, corresponding to a total of 52 and 64
Fossil Biomass Coal Gas Nuclear Renewable T&D Refinery Synfuel Efficiency
Trillion US$2000
0 5 10 15 20 25
Extraction Electric Non−Electric End−Use risk premium = 0%
risk premium = 1%
risk premium = 5%
Figure 11: Cumulative energy system investments for the period 2010 – 2050 in different sectors as a function of the risk premium
trillion US$ in the cases with 1% and 5% risk premium respectively. Despite the comparatively modest increase of total costs, which is determined by the risk pre-miums, a significant increase in investments is required. Along with the increase of the total energy investments, we observe a considerable reallocation of investments among the different sectors of the energy system, most notably from the supply-side sectors to the end-use sectors, but also from fossil to renewable technology clusters.
For example, the reallocation of investments between the four major sectors indi-cated in Figure 11 (i.e. resource extraction, electricity generation, non-electric sector and end-use) comprises 4.5% and 15% of total energy-related investments between 2010 and 2050 under the 1% and the 5% risk premium respectively. These numbers increase further if reallocation of investment within the four major sectors, e.g. from fossil electricity generation towards renewables, are taken into account. It is inter-esting to note that the reallocation of investments becomes increasingly important for the lowest risk premiums of 1% and below, simply because the total increase of energy system expenditures, and therefore also investments, is tightly constrained by the risk premium. For these cases, the reallocation effect is comparable to the absolute increase of energy investments.
In terms of energy expenditures, i.e. in addition to investments also including operation and maintenance costs, the reallocation effect is much more drastic. We find that the reallocation of energy-related expenditures is up to a factor of 10 higher than the total increase in expenditures in the case of ver low risk premiums. In Sec-tion 5.1 it was shown that more robust soluSec-tions can be obtained even at very low hedging costs. However, the dominance of redistribution of sectoral investments and
expenditures over the actual increase in costs, in particular at low risk premiums, illustrates that hedging results in significantly different investment patterns in com-parison with the deterministic least expected cost solution. Therefore, it is not so much the total costs of hedging against technology- and carbon price uncertainties that need to be in the focus of attention, but rather how investments are allocated within the energy system17, with major implications also for the appropriate port-folio of up-front R&D expenditures.
6 Summary and Conclusions
Traditional deterministic energy models without an endogenous representation of uncertainty favor cost-optimal investments into a limited set of technologies that are expected to perform best in the future. Exploring the uncertainty of future energy systems costs, however, we find that such strategies can be very costly. This is in particular due to the nature of imputed energy systems uncertainties, characterized by long tails and the possibility of very high costs in case future uncertainties are resolved in an unfavorable direction.
In this paper we thus presented a new modeling framework of the global energy system, which combines traditional elements of systems engineering modeling ap-proaches with salient features of a risk management perspective. Employing stochas-tic optimization techniques with fully endogenous representation of uncertain costs and associated risks along the energy chain, including extraction and conversion technologies as well as demand-side management costs, permitted us to identify fu-ture development pathways that are cost effective not only from todays perspective and expectations, but factor in also the imputed risk of uncertainty.
Through a series of sensitivity analysis we identify characteristics of risk hedging strategies that are adapted to considerably reduce future risks and are hence robust against a wide range of future uncertainties. We observe significant changes in response to energy system and carbon price uncertainties with major implications for the expected energy system costs, timing of investments, the choice of technology as well as resulting emission levels.
Firstly, we find that hedging strategies under uncertainty are characterized by higher short- to medium-term investments into advanced technologies, including earlier deployment of renewables, but also exploration of unconventional natural gas resources. Our results illustrate that while in absence of uncertainty it seems to be cost-effective to postpone investments into new alternatives in order to maximize profits from available low-cost options early on; a more comprehensive view of the future including the uncertainty that new options might not become available at the expected costs imposes long-term deployment risks, and thus triggers early up-front investments into niche markets and demonstration plants.
Secondly, we find that CO2 emission reductions to be much more pronounced under uncertainty. This response to uncertainty has been observed in previous studies, such as Manne and Richels [1992]; Yohe et al. [2004]. While we derive the
17From a more technical perspective this illustrates that quasi-degeneration is an important prob-lem in modeling, i.e. quite different solutions can be accommodated within very small variations of the objective function value of optimization models.
same conclusion, the reason for the response is different. Yohe et al. [2004] conclude that relatively lower emissions would be rectified due to the uncertainty of climate change damages (i.e. uncertainties in the response of the physical climate system to an increase in GHG emissions), our analysis suggests lower emissions because of the economic risk of uncertain carbon prices and technology costs.
Thirdly, our analysis suggests a considerable diversification of the technology portfolio under uncertainty. Diversification helps not only to reduce the “average risk”, but results in significant reduction of the risk of high impact tail events. In our analysis, for example, a modest risk premium of about one percent of total energy expenditures reduces the value of the 99th percentile by up to a factor of two relative to the expected value expenditures, thus reducing the risk of large losses significantly. This conclusion has important implications for energy and climate policy, emphasizing the risk of unbalanced R&D portfolios or picking winners at a premature stage, and thus focusing on a too narrow policy portfolio.
With respect to costs, we find that modest risk premiums (or hedging invest-ments) can significantly reduce the vulnerability of the energy system against the associated uncertainties. The extent of early investments, diversification and emis-sions reductions, however, depends on the risk premium that decision makers are willing to pay to respond to prevailing uncertainties. In other words, our modeling framework helps to understand how much risk can be avoided through which mech-anisms and at what costs. How much risk needs to be avoided is though dependent on the risk aversion of the society by large or decision makers in the respective sectors - and remains thus one of the key policy variables.
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