Environ. Sci. Technol. 2009, 43, 6421–6426
Indicators on Economic Risk from Global Climate Change W O L F D . G R O S S M A N N , †,‡ K A R L S T E I N I N G E R , † I R I S G R O S S M A N N , * ,§ AND LORENZ MAGAARD‡ Wegener Center for Global and Climate Change and Department of Economics, University of Graz, Leechgasse 25, A-8010 Austria, International Center of Climate and Society, University of Hawaii at Manoa, 1680 East-West Road, Honolulu, Hawaii 96822, and Climate Decision Making Center, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh Pennsylvania 15213, phone: 412 268 5489
Received December 17, 2008. Revised manuscript received June 24, 2009. Accepted June 25, 2009.
Climate change mitigation requires a rapid decrease of global emissions of greenhouse gases (GHGs) from their present value of 8.4 GtC/year to, as of current knowledge, approximately 1 GtC/year by the end of the century. The necessary decrease of GHG emissions will have large impacts on existing and new investments with long lifetimes, such as coalfired power plants or buildings. Strategic decision making for major investments can be facilitated by indicators that express the likelihood of costly retrofitting or shut-down of carbon intensive equipment over time. We provide a set of simple indicators that support assessment and decision making in this field. Given a certain emissions target, carbon allowance prices in a cap-and-trade plan will depend on the development of the global economy and the degree to which the target is approached on the global and national levels. The indicators measure the degree to which a given emissions target is approached nationally and assess risks for long-lived investments subject to a range of emissions targets. A comparative case study on existing coal-fired power plants with planned plants and utility-scale photovoltaic power-plants confirms that high risk for coal-fired power plants is emerging. New legislation further confirms this result.
Introduction Management responsible for emissions-intensive investments has to make major decisions in an environment of considerable scientific and socio-economic uncertainties. Rigid limitations of CO2 emissions are now introduced in political bodies in many countries (e.g., the Waxman-Bill in the U.S.) or have been implemented in some states. Emissions-intensive investments with long lifetimes such as coalfired power plants or buildings will face the risk of premature shutdown or demolition. Recently, some companies have canceled all plans for new carbon-power plants whereas others go ahead and build (1). Different schemes to enforce a decrease of carbon dioxide emissions include the establishment of a cap on emissions. Allowances are sold or given out for free up to this cap; further * Corresponding author e-mail:
[email protected]. † University of Graz. ‡ University of Hawaii at Manoa. § Carnegie Mellon University. 10.1021/es8035797 CCC: $40.75
Published on Web 07/17/2009
2009 American Chemical Society
allowances can be bought. Technological fixes, e.g., carbon capture and storage (CCS) or biological sequestration of emissions are other methods. Costs and the success of many of these schemes are highly uncertain (2). We propose a framework of indicators that project the dynamics of possible emissions regulations. These indicators assess investment risks through a systematic investigation of two central groups of uncertainties, uncertain emissions policies, and socio-economic-technological factors on which emissions targets and the costs of emissions reductions depend. Various methods, including real options approaches (3), have been applied to identify price levels of carbon allowances in a cap and trade system at which different investment choices become profitable (3-5). The indicators can assess the changing exposure of investments to retrofitting or shutdown, and can contribute to scenarios of future carbon prices that are used by methods such as real options approaches. The indicators are relatively simple, while addressing a variety of issues: (a) Given a certain emissions target, allowance prices will depend on global economic development and the degree to which an accepted CO2-cap is approached globally or nationally. As the global economy grows, specific emissions per unit of GDP have to decrease. Similarly, specific emissions of a country have to decrease to compensate national economic growth. Specific emissions of a country might have to decrease also if the global economy is growing, even if the economy of that country is stagnant. (b) Further decrease of anthropogenic emissions is becoming more complicated and expensive to the degree that emissions approximate zero. (c) Given current uncertainties on emissions policies and climate sensitivity, robust investment decisions need to consider an appropriately large range of emissions targets. Indicators must be adaptable to possible downward or upward revisions of targets. We begin with a discussion of current emissions targets, projections of global economic and population growth, and associated uncertainties. The approach is to dynamically project these elements by simple equations. The first indicator measures the degree to which a given climate target is approached nationally. This provides a framework for judging the feasibility and security of an investment with respect to requests it may meet in the near future. The next set of indicators assesses risks for long-lived investments such as coal-fired power plants and buildings. Projecting and comparing uncertainties and risks for specific decisions of interest can be a useful tool in an environment of high uncertainty (6) since it allows dynamic linking of scientific and socio-economic uncertainties without the need for probability distributions. We will discuss an application to risks of shutdown given timetables of required emissions reduction and illustrate this with a case study which includes coal-fired plants of the Tennessee Valley Authority and a new utility-scale power plant with photovoltaics. The case study applies the indicators and confirms their relevance.
Framework for the Indicators Unpredictable Changes of Emissions Targets. The EU has mandated that average global temperature increases should not exceed 2 °C above preindustrial levels by year 2100 (7). According to the IPCC fourth Assessment Report, limiting temperature increases to 2-2.4 °C implies a concentration level of at most 350-400 ppm (ppm) CO2 (8). This is 10-35% below the concentration level of 450-550 ppm considered VOL. 43, NO. 16, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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by the third Assessment Report in 2001 (9). Hansen et al. (10) give 350 ppm CO2 as the long-time maximum. In the first multithousand member ensemble of simulations (run through climateprediction.net) Stainforth et al. (11) find climate sensitivities, that is, global surface temperature responses to doubled CO2 conditions, ranging from 2-11°K (Kelvin). The uncertainty on the sensitivity of the climate system to GHG and other forcings (factors influencing temperatures) has various scientific roots, foremost among them the complexity of the climate system (12, 13). Schwartz et al. (13) find that the 5-95% confidence range in global mean temperature change as projected by the IPCC is much smaller than that associated with the forcings considered, yielding, e.g., a factor of 2, while forcing uncertainties yield a factor of 4. Besides the aerosol contribution to climate (14, 15), factors that may be insufficiently understood include the contribution of land use change to warming (16, 17) and the climatic effects of large-scale atmosphere-ocean variability (18-21). If unanticipated and not well understood effects mask the projected warming from GHG emissions temporarily, emissions targets may be relaxed or even abandoned (compare ref 18). Targets may be adjusted upward or downward as understanding progresses. Robust planning must evaluate the proposed indicators for a range of possible emissions targets. We next examine the socio-economic context within which emissions reductions will proceed. Projections of Global Economic Growth. Historically, during the last century the global economy has grown at an average of 3.2% y-1 and emissions of GHGs have increased sublinearly with economic growth, due to improvements in resource productivity. For instance, between 1990 and 2004, economic growth has increased GHG emissions by 1.57% per year, thereafter by 2.7% (8). In agreement with the scenarios of the IPCC we assume an economic growth between 2 and 4% per year. Global Population Growth and National Emissions. Statements from the two largest countries, China and India, on GHGs strongly suggest that privileges for developed countries, for instance in the form of ‘grandfathering’ (22) would not be politically feasible. A possible global equity approach to specify maximum national allowable emissions would derive each country’s share from its fraction of the global population (23). Maximum allowable emissions will then change according to how the country’s population changes relative to the global population. A global equity scheme would be most unfavorable for the US with the highest per capita emission. With the present global population of 6.7 billion, the US with a population of 300 million would be entitled to 4.5% of global emissions or 71 ktC y-1. Taking into account globally declining birth rates (albeit from different levels in different countries) and increasing life expectancy, the current assumption is that the global population may peak at 8-10 billion (24). The population of the U.S. is increasing, whereas Western Europe’s population is growing at a slow rate and expected to decline soon (24). If global population trends continue, maximum allowable emissions for Western Europe would have to decline by almost 1/3 respective to the current value. The Gross World Product (GWP) was $U.S. 54 trillion in year 2006 (25) with specific emissions of 0.154 tCy-1 per $U.S. 1000. Global CO2 emissions were at 6.16 GtC in 1990 and at 8.4 GtC in 2006 (26). Stabilization at 350-450 ppm implies a reduction of global emissions to 2.8 GtCy-1 or 55% below the level of 1990 in 2050, and to about 1 GtC/year or 84% below the 1990 level in year 2100 (27). Without economic growth, this would imply maximum specific emission of 0.051 tCy1- per $U.S. 1000 in 2050 and 0.0246 tCy1- in 2100; with economic growth of 3.2% /year, specific emissions per $U.S. 6422
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of GDP should decrease to 0.013 tC per $U.S. 1000 in 2050 and to about 0.00094 tC per $U.S. 1000 in year 2100 (see Supporting Information (SI) section 1.1). An indicator Showing Success or Failure Given Emissions Targets. The indicator Gyear(t) measures the degree to which a given emissions target is approached globally in year t. Using a percent scale, Gyear(t) shows how much of the reduction from the present level of emissions down to the level desired in the long term has been achieved. We here illustrate Gyear(t) for emissions reductions up to 1 GtC y-1, corresponding to a threshold of 400 ppm.
(
Gyear(t) ) 100 × 1GtCy-1
/ ∑ global_emissions ) (1) year(t)
We have chosen a very simple, straightforward form for G with the following required characteristics. The present value of Gyear(t) is 11.6%. If emissions continue to increase, G will continue to fall. A decrease by 1 GtCy-1 at current levels will increase the value of G only slightly; for further decreases, G will increase more rapidly. The technological and economic challenge of achieving a decrease from 2 to 1 GtCy-1 is reflected in indicator values growing from 50 to 100. The rapid increase of G at higher levels of achieved emissions reduction illustrates the need for near-term policies to be oriented toward long-term targets (28). In parallel with the substitution of current technologies and processes with already existing more efficient technologies, technology specific policies are needed to enable large-scale deployment of technologies that are currently being developed (28). The behavior of G signals whether and when new technologies in combination with other approaches such as CCS are required. An Indicator for National Climate Sustainability. An indicator for national climate sustainability should allow the assessment of investment risks in that country and could support the media in reporting the degree to which a country has realized a given emissions target. We adopt the notion of fair distribution of feasible carbon emissions by assuming an equal per capita right to emissions. Based on eq 1 and using population numbers Popj(t) of country j in year t and global population Popglob(t), we define indicator Gj(t) for country j at time t: Gj(t) )
(
100(Popj(t) /Popglob(t)) 1 · GtCy-1
/ ∑ country j
emissions(t)
)
(2)
This indicator takes percentage values between >0 and 100. Gj(t) will decrease (increase) if a country’s share of the world population decreases (increases). We will illustrate these indicators for the U.S. and China. With its current population of 1.3 billion, China would be entitled to about 20% of 1 GtCy-1, i.e., 200 ktCy-1. It is now emitting 1.7 GtC y-1, to which recent economic growth of 10.9% y-1 added about 187 ktCy-1. The U.S. would be entitled to about 71 ktC y-1. At the average yearly economic growth rate of the last 20 years, 3.5%, and the recent increase in specific emissions, the U.S. will add >56 ktCy-1 to its previous emission level each year. This means that the U.S. is presently increasing its emissions per year by its total yearly allowable. An Expression for Feasible Specific Emissions Subject to Economic Growth. The product of specific emissions c(t)and GWP K(t) at time t must be less than feasible total emissionsa(t) in a given year t: c(t)K(t) e a(t), for t g t1 or
(3)
c(t) e a(t)/K(t) for t ∈ [2009, 2100]
(4)
Inequalities 3 and 4 must hold for all years beginning with a year t1. By necessity, a(t) starts at the present value (which is a kind of global grandfathering) and should approximate 1 GtCy-1 in year 2100. Equation 5 describes the increase of the GWP K(t) as in the last century, i.e., exponential growth as assumed in several IPCC-scenarios, with growth rate v: K(t) ) K(t0)ev(t-to)
(5)
If economic growth follows that pattern, specific emissions c(t) must follow a negative exponential to meet inequality 3. Equation 6 specifies feasible total emissions for the S400 scenario. S400 is an emissions pathway to achieve 400 ppm (equivalent) in year 2100 [12]. It implies emissions at 41% of the year 2000 value in year 2050 and at 15% in year 2100. Normalizing emissions of 6.75 GtCy-1 in year 2000 to 1 and applying nonlinear interpolation allows the following expression of S400: 3
3
3
S(t) ) 1 - 0.9(t - 2000) /(40 + (t - 2000) )
(6)
We have chosen this simple form ofS(t)as it has the same declining logistic shape as S400 and almost the same values (0.4 in year 2050 and 0.15 in year 2100). With economic growth as in 5 and S400 as in 6, an explicit form of feasible specific emissions c(t) can be derived c(t) e (1 - 0.9(t - t0)3 /(403 + (t - t0)3))/ (K0exp(ln(v)(t - t0)))
(7)
Specific emissions will have to decline dramatically to 0.008 in year 2100, i.e., to 0.8% of the value of year 2000 (see Figure 1). Here, K0 is normalized to 1 so that K(t) gives the factor of global economic growth. Alternative scenarios for economic growth and decrease of emissions can be specified in eqs 5 and 6, respectively, by changing parameters. We select the general form of 6 (see SI, section 1.2) p(t) ) q + utn /(bn + tn)
(8)
Equation 8 approximates its upper limit q + u for t f ∞. For t ) b its value is q + u/2, for t ) 0 its value is q.
Risk of Premature Shutdown of an Investment At present there is little reason to assume that the size of the global economy will eventually stagnate. Thus, the existing economy will have to decrease emissions to meet targets and to make room for the additional emissions of new economic activity. In addition, even investments with comparatively low emissions in a category with high specific emissions may be at risk from costly retrofitting or premature shutdown due to unexpected breakthrough innovations. An Indicator for Risk of Shutdown Subject to Policies of Emissions Decrease. Investments with longer lifetimes are particularly at risk from increasing emissions standards. Examples are power stations with lifetimes of at least 40 or 50 years and buildings. In many developed countries the electricity sector is contributing 25% to >35% to GHG emissions. In 2050, the emissions of a new power station (constructed in 2009) should have decreased to about 15% of their initial value under Scenario S400, an assumed economic growth rate of 3% y-1, and a constant share of that country’s population to global population. An investment would be at risk of premature shutdown if its specific emissions cannot be decreased at acceptable costs to about
FIGURE 1. Function c(t) subject to economic growth and declining allowances for emissions. 15%. The risk rt for shutdown in year t increases with the ratio of specific emissions s to allowable specific emissions st, i.e., as a monotonically increasing function: rt ) rt(s/st)
(9)
Allowable specific emissions will depend on the sector of the industry to which this investment belongs. However, specifications of sector-specific specific emissions are also subject to change. For instance, considerable improvements are currently emerging in electricity production and in the building sector. Sectors with high specific emissions may also be addressed by global agreements that seek to prevent them from moving to countries with less stringent emissions regulations (“leakage” (29)). Consequently, an indicator for the risk of premature shutdown needs to show a nonlinear increase of risk in dependence on time and the ratio given in ref 9. We standardize this function to values between 0% risk and 100% risk. The risk of an investment with specific emissions s and allowable specific emissions st will increase with ratio s/st. We could assume that at 5 times the allowable emissions, the risk of shutdown will approximate 100%. For intermediate values we assume rapid growth of the risk in a shape which corresponds to the solution of eq 10. The coefficient n of function (8) determines the slope of the gradient at intermediate values of the argument. With these assumptions, suitable parameters in function (8) are q ) 0, u ) 100, b ) 2.5, n ) 4. These values will be assessed with a sensitivity analysis. Using xt ) (s/st) - 1 as the argument in eq 9, the risk is 0 for s ) st, i.e., xt ) 0, and 100 for xt ) 6. With these definitions, eq 10 is similar to the solution of the logistic equation (see SI, section 1.2) but more adaptable. It shows the risk for shutdown due to approximation of the capacity limit given by feasible specific emissions. rt )
100x4t
(10)
2.54 + x4t
Risk of Shutdown Subject to Timetables of Mitigation. We now incorporate emissions reduction scenarios into eq 10. Feasible emissions a(t) as in eq 6 specify the necessary decrease in specific emissions to meet given mitigation goals subject to economic growth. Equation 9 describes the growing misfit of an emission-intensive investment the longer it exists, implying increasing risk for premature shutdown. This is shown using c(t) from eq 7 as the argument in eq 10 (Figure 2). For the selected parameters, risk becomes very high within 20 years. As this is less than half the life expectancy of a power plant, the loss from shutdown could be considerable. This is illustrated in the case study below. This risk is even higher for buildings for which depreciation is slower than for VOL. 43, NO. 16, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Risk of shutdown of an investment subject to its specific emissions in comparison with allowable specific emissions.
FIGURE 3. Changes in the risk of shutdown. The 50% risk threshold varies between year 2025 and 2040. power plants. As zero-energy buildings become state-of-the art in new construction, the pressure on existing buildings will increase. While power plants can potentially be retrofitted with CCS, it is very difficult for many types of buildings to drastically decrease their energy consumption. With the symbols from eq 8 applied to eq 10 the ranges for the sensitivity analysis of eq 10 and the parameters underlying it are exponent n ∈{1,2,..5}, i.e. the shape changes from a declining ascent to slow sigmoid to rapid sigmoid; coefficient b ∈[25,75], i.e. the risk is at 50% within 25-75 years, and economic growth rate v ∈[0.02,0.04] between 2 to 4% per year so that the global economy grows by a factor between 6 and 34 in year 2100 (see SI Figure S1). Figure 3 shows the risks of premature shutdown given variations in required emissions reduction (see also SI Figure S2) with economic growth fixed at 3%, Figure 4 overall sensitivity with additional variations in economic growth between 2 and 4%.
Case Study The indicators will be illustrated with a case study of coalfired plants of the Tennessee-Valley Authority (TVA), which produces 2/3 of its electricity from coal. We will examine (1) the profitability of existing coal-fired power plants subject to different prices for carbon allowances, (2) the profitability of more efficient coal-fired plants, and (3) the prospects of utility-scale power plants using PV. For both TVA’s current coal-fired power plants and planned more efficient plants we consider revenues, emissions, age of the plants, depreciation and the financial effect of carbon allowances (see SI section 2). We then compare these figures with the 550 MW PV power plant commissioned by the U.S. utility Pacific Gas and Electricity (PG&E). Impact of Carbon Allowance Costs on TVA Electricity Prices. For an investor in the electricity market, the indicator of U.S. national climate sustainability (eq 2) is an important 6424
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FIGURE 4. Fast economic growth causes higher emissions, which in turn affect a tightening of emissions regulations, whereas slow growth delays the build-up of GHGs allowing slower actions for emission control. indicator. This indicator currently has a low value of 2.8% and is decreasing. The necessary value is 100%. For comparison, China, which has the same emissions per year as the U.S., has an indicator value of 11.4%. Equation 7 signals to the investor that in the long run emissions decreases to about 4% of the present will be necessary if a cap-and-trade scheme similar to scenario S400 is pursued. Compared to these 4%, the Waxman climate change bill is only a first step. It mandates emission reductions by 17% from 2005 levels by 2020. By 2012, President Obama’s budget presumes a price of about $13.70 for U.S. carbon allowances (30). This is at the lower end of the current European Union Allowances price fluctuations between U.S. $12 and 25. The total cost of 1 kWh produced from coal by the TVA in 2008 was Cent 5.35 (See SI Table S1). With a price of Cent 6.51/kWh for Kentucky, as indicated by the Energy Information Administration (31), the profit per kWh for the TVA is Cent 1.21/kWh. An average fuel consumption of 0.374 kg coal per kWh in the TVA coal plants emits 1.37 kg CO2/kWh. At a carbon allowance cost of $13.70 per ton of CO2 this would incur carbon costs of Cent 1.88/kWh (see SI section 2, implying losses of Cent 0.76/kWh for the TVA. In a cap-and-trade system, caps on emissions are set and permits consistent with those limits are initially issued and can subsequently be bought. The cap may be lowered over time, for example according to Scenario S400. Lowering the cap should increase the costs of emissions. Based on a review of eleven models, Fischer and Morgenstern (32) indicate marginal abatement costs of between $40 and $250 per ton carbon in 1990 dollars for a 20% emissions reduction relative to 1990, the reduction required under S400 in approximately 2025. This translates to $15-97 in 2006 dollars (33). The highest price would incur additional costs of Cent 13.3 per kWh for TVA. This would translate into an increase of electricity prices in Kentucky by 348% from Cent 5.35 to 18.65, a scenario where it appears very likely that TVA will not be able to pass on a significant proportion of the additional costs to consumers (34). For a more efficient plant, costs per kWh of electricity decrease about 5% compared to the TVA’s current plants (see SI Table S1). We further compare total costs of electricity for a range of allowance costs (table 1). Overall, costs per kWh are markedly lower for a new power plant, with about a 14% difference for low carbon prices and up to 32% for high carbon prices. While this illustrates that a more efficient plant will be much more profitable at high carbon prices, recent events demonstrate a high risk that investment costs for new power plants cannot be recovered. Several dozen coal-fired power plant projects have been canceled, delayed, or rejected in the past few years (1, 34, 35). To investigate this risk, we
TABLE 1. Comparison of Total Costs of Electricity for a Range of Carbon Allowance Costs CO2-costs perqa ton ($) carbon costs TVA old $ per kWh electricity costs TVA $/kWh including carbon costs carbon costs power plant new $ per kWh electricity costs new plant $/kWh including carbon costs
15
30
60
200
0.021 0.041 0.082 0.27 0.071 0.091 0.132 0.32 0.015 0.030 0.061 0.2 0.062 0.077 0.108 0.247
compare the costs per kWh of both current and planned more efficient coal plants with the costs per kWh of electricity produced by utility-scale PV plants. Comparison of Coal Power Plants with a Utility-Scale Photovoltaic Power Plant. PG&E has signed agreements to purchase electricity from two new PV plants with, respectively, 250 and 550 MW. We estimate the price per kWh of electricity generated by these plants in dependence of the investment costs per kW installed (SI section 2.5). If existing landlines are not sufficient, construction of new lines could increase this price. First Solar has announced panel production costs in year 2012 of $0.63 Whp, This means costs for the complete installation utility scale of $1.31 and costs of 5.76 c/kWh in favorable locations. Stiff competition in the PV sector will drive down First Solar’s present high profit rate (SI section 2.5). The respective profit rate of 25 or 50% of First Solar increases the price to between Cent 7.2 or 8.64, respectively, or 11.52 at the current profit rate. This compares to cost per kWh of electricity for TVA’s current plants at Cent 5.35 and Cent 5.1 for a new plant. At carbon allowance costs of $25 per ton, the price per kWh of electricity from a new plant will be at Cent 7.2 (Table 1), and at Cent 11.52 for allowance costs of $65/ton. Thus, PV may become competitive much sooner than expected for all scenarios considered, and it is additionally not affected by the risks of increasing carbon costs or increasing coal prices. The case study shows that the threat for coal power plants is multifaceted and massive. Our sensitivity analysis shows a rapidly rising risk for all coal power plants. These results are fairly robust even at large variations of input parameters. The robustness of the risks to coal plants is due to the tight relationship between revenues and profits. Additional costs cannot easily be offset by increases of the electricity price, and price increases may not be permitted by utilities commissions (34). Even low costs per ton of CO2 threaten the profitability of current and newer more efficient power plants, in particular given the rapid emergence of credible alternatives such as second generation PV. This means that it is uncertain whether utilities will be able to recover new investments.
Discussion We have developed indicators to support decision-making for large investments, taking into account uncertain climate mitigation policies and uncertain socio-economic developments. Climate policies are uncertain because of the complexity of the climate system and our current difficulties to project and understand how different forcings impact regional and global climate. A further reduction of targets appears likely if certain undesirable environmental changes result. We have also shown how rapid technological changes and surprises can put carbon-intensive investments at risk. The rather sudden availability of low-cost PV is a major example.
Our indicators on climate sustainability and on the risk of premature shutdown of investments allow investors and managers to assess the consequences of unexpectedly fast economic growth in large parts of the world, including China, India, or the “Next Eleven” identified by Goldman Sachs (36); project different scenarios of future carbon prices, given climate policy and economic uncertainties; and understand the impact of low-cost PV which, additionally, sets the mark for state-of-the art of specific emissions per kWh of electricity. Indicators that express the uncertainties and sophistication of climate change and mitigation through simple graphs and equations may effectively support understanding and decision making. The ability to project risk from climate legislation for planned investments is advantageous for making decisions and avoiding exposure to risks that may otherwise be underestimated.
Acknowledgments This research was supported by the Austrian National Bank Research Fund (project 12449), which is thankfully acknowledged. The third author was supported by the Climate Decision Making Center created through a cooperative agreement between the National Science Foundation (SES0345798) and Carnegie Mellon University.
Supporting Information Available Additional text and data, figures and tables.This material is available free of charge via the Internet at http://pubs.acs.org.
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