Indicators on Economic Risk fromGlobal Climate ChangeW 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 , * , §

A N D L O R E N Z M A G A A R D ‡

Wegener Center for Global and Climate Change andDepartment 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 MakingCenter, Carnegie Mellon University, 5000 Forbes Ave,Pittsburgh Pennsylvania 15213, phone: 412 268 5489

Received December 17, 2008. Revised manuscript receivedJune 24, 2009. Accepted June 25, 2009.

Climate change mitigation requires a rapid decrease ofglobal emissions of greenhouse gases (GHGs) from theirpresent value of 8.4 GtC/year to, as of current knowledge,approximately 1 GtC/year by the end of the century. The necessarydecrease of GHG emissions will have large impacts onexisting and new investments with long lifetimes, such as coal-fired power plants or buildings. Strategic decision making formajor investments can be facilitated by indicators that expressthe likelihood of costly retrofitting or shut-down of carbonintensive equipment over time. We provide a set of simpleindicators that support assessment and decision making in thisfield. Given a certain emissions target, carbon allowanceprices in a cap-and-trade plan will depend on the developmentof the global economy and the degree to which the target isapproached on the global and national levels. The indicatorsmeasure the degree to which a given emissions target isapproached nationally and assess risks for long-lived investmentssubject to a range of emissions targets. A comparative casestudy on existing coal-fired power plants with planned plants andutility-scale photovoltaic power-plants confirms that high riskfor coal-fired power plants is emerging. New legislation furtherconfirms this result.

IntroductionManagement responsible for emissions-intensive invest-ments has to make major decisions in an environment ofconsiderable scientific and socio-economic uncertainties.Rigid limitations of CO2 emissions are now introduced inpolitical bodies in many countries (e.g., the Waxman-Bill inthe U.S.) or have been implemented in some states. Emis-sions-intensive investments with long lifetimes such as coal-fired power plants or buildings will face the risk of prematureshutdown or demolition. Recently, some companies havecanceled all plans for new carbon-power plants whereasothers go ahead and build (1).

Different schemes to enforce a decrease of carbon dioxideemissions include the establishment of a cap on emissions.Allowances are sold or given out for free up to this cap; further

allowances can be bought. Technological fixes, e.g., carboncapture and storage (CCS) or biological sequestration ofemissions are other methods. Costs and the success of manyof these schemes are highly uncertain (2).

We propose a framework of indicators that project thedynamics of possible emissions regulations. These indicatorsassess investment risks through a systematic investigationof two central groups of uncertainties, uncertain emissionspolicies, and socio-economic-technological factors on whichemissions targets and the costs of emissions reductionsdepend.

Various methods, including real options approaches (3),have been applied to identify price levels of carbon allowancesin a cap and trade system at which different investmentchoices become profitable (3-5). The indicators can assessthe changing exposure of investments to retrofitting or shut-down, and can contribute to scenarios of future carbon pricesthat are used by methods such as real options approaches.The indicators are relatively simple, while addressing a varietyof issues:

(a) Given a certain emissions target, allowance prices willdepend on global economic development and the degree towhich an accepted CO2-cap is approached globally ornationally. As the global economy grows, specific emissionsper unit of GDP have to decrease. Similarly, specific emissionsof a country have to decrease to compensate nationaleconomic growth. Specific emissions of a country might haveto decrease also if the global economy is growing, even if theeconomy of that country is stagnant.

(b) Further decrease of anthropogenic emissions isbecoming more complicated and expensive to the degreethat emissions approximate zero.

(c) Given current uncertainties on emissions policies andclimate sensitivity, robust investment decisions need toconsider an appropriately large range of emissions targets.Indicators must be adaptable to possible downward orupward revisions of targets.

We begin with a discussion of current emissions targets,projections of global economic and population growth, andassociated uncertainties. The approach is to dynamicallyproject these elements by simple equations. The first indicatormeasures the degree to which a given climate target isapproached nationally. This provides a framework for judgingthe feasibility and security of an investment with respect torequests it may meet in the near future.

The next set of indicators assesses risks for long-livedinvestments such as coal-fired power plants and buildings.Projecting and comparing uncertainties and risks for specificdecisions of interest can be a useful tool in an environmentof high uncertainty (6) since it allows dynamic linking ofscientific and socio-economic uncertainties without the needfor probability distributions. We will discuss an applicationto risks of shutdown given timetables of required emissionsreduction and illustrate this with a case study which includescoal-fired plants of the Tennessee Valley Authority and anew utility-scale power plant with photovoltaics. The casestudy applies the indicators and confirms their relevance.

Framework for the IndicatorsUnpredictable Changes of Emissions Targets. The EU hasmandated that average global temperature increases shouldnot exceed 2 °C above preindustrial levels by year 2100 (7).According to the IPCC fourth Assessment Report, limitingtemperature increases to 2-2.4 °C implies a concentrationlevel of at most 350-400 ppm (ppm) CO2 (8). This is 10-35%below the concentration level of 450-550 ppm considered

* Corresponding author e-mail: irisg@andrew.cmu.edu.† University of Graz.‡ University of Hawaii at Manoa.§ Carnegie Mellon University.

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Published on Web 07/17/2009

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 simula-tions (run through climateprediction.net) Stainforth et al.(11) find climate sensitivities, that is, global surface tem-perature responses to doubled CO2 conditions, ranging from2-11°K (Kelvin). The uncertainty on the sensitivity of theclimate system to GHG and other forcings (factors influencingtemperatures) has various scientific roots, foremost amongthem the complexity of the climate system (12, 13). Schwartzet al. (13) find that the 5-95% confidence range in globalmean temperature change as projected by the IPCC is muchsmaller than that associated with the forcings considered,yielding, e.g., a factor of 2, while forcing uncertainties yielda factor of 4. Besides the aerosol contribution to climate(14, 15), factors that may be insufficiently understood includethe contribution of land use change to warming (16, 17) andthe climatic effects of large-scale atmosphere-ocean vari-ability (18-21).

If unanticipated and not well understood effects maskthe projected warming from GHG emissions temporarily,emissions targets may be relaxed or even abandoned(compare ref 18). Targets may be adjusted upward ordownward as understanding progresses. Robust planningmust evaluate the proposed indicators for a range of possibleemissions targets.

We next examine the socio-economic context withinwhich emissions reductions will proceed.

Projections of Global Economic Growth. Historically,during the last century the global economy has grown at anaverage of 3.2% y-1 and emissions of GHGs have increasedsublinearly with economic growth, due to improvements inresource 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 thescenarios of the IPCC we assume an economic growthbetween 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 developedcountries, for instance in the form of ‘grandfathering’ (22)would not be politically feasible. A possible global equityapproach to specify maximum national allowable emissionswould derive each country’s share from its fraction of theglobal population (23). Maximum allowable emissions willthen change according to how the country’s populationchanges relative to the global population. A global equityscheme would be most unfavorable for the US with thehighest per capita emission. With the present global popula-tion of 6.7 billion, the US with a population of 300 millionwould be entitled to 4.5% of global emissions or 71 ktC y-1.

Taking into account globally declining birth rates (albeitfrom different levels in different countries) and increasinglife expectancy, the current assumption is that the globalpopulation may peak at 8-10 billion (24). The population ofthe U.S. is increasing, whereas Western Europe’s populationis growing at a slow rate and expected to decline soon (24).If global population trends continue, maximum allowableemissions for Western Europe would have to decline byalmost 1/3 respective to the current value.

The Gross World Product (GWP) was $U.S. 54 trillion inyear 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 at8.4 GtC in 2006 (26). Stabilization at 350-450 ppm impliesa reduction of global emissions to 2.8 GtCy-1 or 55% belowthe level of 1990 in 2050, and to about 1 GtC/year or 84%below the 1990 level in year 2100 (27). Without economicgrowth, this would imply maximum specific emission of 0.051tCy1- per $U.S. 1000 in 2050 and 0.0246 tCy1- in 2100; witheconomic growth of 3.2% /year, specific emissions per $U.S.

of GDP should decrease to 0.013 tC per $U.S. 1000 in 2050and to about 0.00094 tC per $U.S. 1000 in year 2100 (seeSupporting Information (SI) section 1.1).

An indicator Showing Success or Failure Given Emis-sions Targets. The indicator Gyear(t) measures the degree towhich a given emissions target is approached globally inyear t. Using a percent scale, Gyear(t) shows how much of thereduction from the present level of emissions down to thelevel desired in the long term has been achieved. We hereillustrate Gyear(t) for emissions reductions up to 1 GtC y-1,corresponding to a threshold of 400 ppm.

We have chosen a very simple, straightforward form for Gwith the following required characteristics. The present valueof Gyear(t) is 11.6%. If emissions continue to increase, G willcontinue to fall. A decrease by 1 GtCy-1 at current levels willincrease the value of G only slightly; for further decreases,G will increase more rapidly. The technological and economicchallenge of achieving a decrease from 2 to 1 GtCy-1 isreflected in indicator values growing from 50 to 100.

The rapid increase of G at higher levels of achievedemissions reduction illustrates the need for near-term policiesto be oriented toward long-term targets (28). In parallel withthe substitution of current technologies and processes withalready existing more efficient technologies, technologyspecific policies are needed to enable large-scale deploymentof technologies that are currently being developed (28). Thebehavior of G signals whether and when new technologiesin combination with other approaches such as CCS arerequired.

An Indicator for National Climate Sustainability. Anindicator for national climate sustainability should allow theassessment of investment risks in that country and couldsupport the media in reporting the degree to which a countryhas realized a given emissions target. We adopt the notionof fair distribution of feasible carbon emissions by assumingan equal per capita right to emissions. Based on eq 1 andusing population numbers Popj(t) of country j in year t andglobal population Popglob(t), we define indicator Gj(t) forcountry j at time t:

This indicator takes percentage values between >0 and 100.Gj(t) will decrease (increase) if a country’s share of the worldpopulation decreases (increases).

We will illustrate these indicators for the U.S. and China.With its current population of 1.3 billion, China would beentitled to about 20% of 1 GtCy-1, i.e., 200 ktCy-1. It is nowemitting 1.7 GtC y-1, to which recent economic growth of10.9% y-1 added about 187 ktCy-1. The U.S. would be entitledto about 71 ktC y-1. At the average yearly economic growthrate of the last 20 years, 3.5%, and the recent increase inspecific emissions, the U.S. will add >56 ktCy-1 to its previousemission level each year. This means that the U.S. is presentlyincreasing its emissions per year by its total yearly allowable.

An Expression for Feasible Specific Emissions Subjectto Economic Growth. The product of specific emissionsc(t)and GWP K(t) at time t must be less than feasible totalemissionsa(t) in a given year t:

or

Gyear(t) ) 100 × (1GtCy-1/ ∑ global_emissionsyear(t))(1)

Gj(t) )

100(Popj(t)/Popglob(t))(1 · GtCy-1/ ∑ countryj -

emissions(t)) (2)

c(t)K(t) e a(t), for t g t1 (3)

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Inequalities 3 and 4 must hold for all years beginning witha year t1. By necessity, a(t) starts at the present value (whichis a kind of global grandfathering) and should approximate1 GtCy-1 in year 2100. Equation 5 describes the increaseof the GWP K(t) as in the last century, i.e., exponentialgrowth as assumed in several IPCC-scenarios, with growthrate v:

If economic growth follows that pattern, specific emissionsc(t) must follow a negative exponential to meet inequality 3.Equation 6 specifies feasible total emissions for the S400scenario. S400 is an emissions pathway to achieve 400 ppm(equivalent) in year 2100 [12]. It implies emissions at 41% ofthe year 2000 value in year 2050 and at 15% in year 2100.Normalizing emissions of 6.75 GtCy-1 in year 2000 to 1 andapplying nonlinear interpolation allows the following ex-pression of S400:

We have chosen this simple form ofS(t)as it has the samedeclining logistic shape as S400 and almost the same values(0.4 in year 2050 and 0.15 in year 2100). With economic growthas in 5 and S400 as in 6, an explicit form of feasible specificemissions c(t) can be derived

Specific emissions will have to decline dramatically to 0.008in year 2100, i.e., to 0.8% of the value of year 2000 (see Figure1). Here, K0 is normalized to 1 so that K(t) gives the factorof global economic growth.

Alternative scenarios for economic growth and decreaseof emissions can be specified in eqs 5 and 6, respectively, bychanging parameters. We select the general form of 6 (seeSI, section 1.2)

Equation 8 approximates its upper limit q + u for tf ∞. Fort ) b its value is q + u/2, for t ) 0 its value is q.

Risk of Premature Shutdown of an InvestmentAt present there is little reason to assume that the size ofthe global economy will eventually stagnate. Thus, theexisting economy will have to decrease emissions to meettargets and to make room for the additional emissions ofnew economic activity. In addition, even investments withcomparatively low emissions in a category with highspecific emissions may be at risk from costly retrofittingor premature shutdown due to unexpected breakthroughinnovations.

An Indicator for Risk of Shutdown Subject to Policiesof Emissions Decrease. Investments with longer lifetimesare particularly at risk from increasing emissions standards.Examples are power stations with lifetimes of at least 40 or50 years and buildings. In many developed countries theelectricity sector is contributing 25% to >35% to GHGemissions. 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 assumedeconomic growth rate of 3% y-1, and a constant share of thatcountry’s population to global population. An investmentwould be at risk of premature shutdown if its specificemissions cannot be decreased at acceptable costs to about

15%. The risk rt for shutdown in year t increases with theratio of specific emissions s to allowable specific emissionsst, i.e., as a monotonically increasing function:

Allowable specific emissions will depend on the sector of theindustry to which this investment belongs. However, speci-fications of sector-specific specific emissions are also subjectto change. For instance, considerable improvements arecurrently emerging in electricity production and in thebuilding sector. Sectors with high specific emissions mayalso be addressed by global agreements that seek to preventthem from moving to countries with less stringent emissionsregulations (“leakage” (29)).

Consequently, an indicator for the risk of prematureshutdown needs to show a nonlinear increase of risk independence on time and the ratio given in ref 9. Westandardize this function to values between 0% risk and 100%risk. The risk of an investment with specific emissions s andallowable 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 intermediatevalues we assume rapid growth of the risk in a shape whichcorresponds to the solution of eq 10. The coefficient n offunction (8) determines the slope of the gradient at inter-mediate 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 fors ) st, i.e., xt ) 0, and 100 for xt ) 6. With these definitions,eq 10 is similar to the solution of the logistic equation (seeSI, section 1.2) but more adaptable. It shows the risk forshutdown due to approximation of the capacity limit givenby feasible specific emissions.

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 decreasein specific emissions to meet given mitigation goals subject toeconomic growth. Equation 9 describes the growing misfit ofan emission-intensive investment the longer it exists, implyingincreasing risk for premature shutdown. This is shown usingc(t) from eq 7 as the argument in eq 10 (Figure 2).

For the selected parameters, risk becomes very high within20 years. As this is less than half the life expectancy of apower plant, the loss from shutdown could be considerable.This is illustrated in the case study below. This risk is evenhigher for buildings for which depreciation is slower than for

c(t) e a(t)/K(t) for t ∈ [2009, 2100] (4)

K(t) ) K(t0)ev(t-to) (5)

S(t) ) 1 - 0.9(t - 2000)3/(403 + (t - 2000)3) (6)

c(t) e (1 - 0.9(t - t0)3/(403 + (t - t0)3))/(K0exp(ln(v)(t - t0))) (7)

p(t) ) q + utn/(bn + tn) (8)

FIGURE 1. Function c(t) subject to economic growth anddeclining allowances for emissions.

rt ) rt(s/st) (9)

rt )100xt

4

2.54 + xt4

(10)

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power plants. As zero-energy buildings become state-of-theart in new construction, the pressure on existing buildingswill increase. While power plants can potentially be retrofittedwith CCS, it is very difficult for many types of buildings todrastically decrease their energy consumption.

With the symbols from eq 8 applied to eq 10 the rangesfor the sensitivity analysis of eq 10 and the parametersunderlying it are exponent n ∈{1,2,..5}, i.e. the shape changesfrom a declining ascent to slow sigmoid to rapid sigmoid;coefficient b ∈[25,75], i.e. the risk is at 50% within 25-75years, and economic growth rate v ∈[0.02,0.04] between 2 to4% per year so that the global economy grows by a factorbetween 6 and 34 in year 2100 (see SI Figure S1). Figure 3shows the risks of premature shutdown given variations inrequired emissions reduction (see also SI Figure S2) witheconomic growth fixed at 3%, Figure 4 overall sensitivity withadditional variations in economic growth between 2 and 4%.

Case StudyThe indicators will be illustrated with a case study of coal-fired plants of the Tennessee-Valley Authority (TVA), whichproduces 2/3 of its electricity from coal. We will examine (1)the profitability of existing coal-fired power plants subjectto different prices for carbon allowances, (2) the profitabilityof more efficient coal-fired plants, and (3) the prospects ofutility-scale power plants using PV. For both TVA’s currentcoal-fired power plants and planned more efficient plantswe consider revenues, emissions, age of the plants, depre-ciation and the financial effect of carbon allowances (see SIsection 2). We then compare these figures with the 550 MWPV power plant commissioned by the U.S. utility Pacific Gasand Electricity (PG&E).

Impact of Carbon Allowance Costs on TVA ElectricityPrices. For an investor in the electricity market, the indicatorof U.S. national climate sustainability (eq 2) is an important

indicator. This indicator currently has a low value of 2.8%and is decreasing. The necessary value is 100%. For com-parison, China, which has the same emissions per year asthe U.S., has an indicator value of 11.4%. Equation 7 signalsto the investor that in the long run emissions decreases toabout 4% of the present will be necessary if a cap-and-tradescheme similar to scenario S400 is pursued. Compared tothese 4%, the Waxman climate change bill is only a first step.It mandates emission reductions by 17% from 2005 levels by2020. By 2012, President Obama’s budget presumes a priceof about $13.70 for U.S. carbon allowances (30). This is at thelower end of the current European Union Allowances pricefluctuations between U.S. $12 and 25.

The total cost of 1 kWh produced from coal by the TVAin 2008 was Cent 5.35 (See SI Table S1). With a price of Cent6.51/kWh for Kentucky, as indicated by the Energy Informa-tion Administration (31), the profit per kWh for the TVA isCent 1.21/kWh. An average fuel consumption of 0.374 kgcoal 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 thiswould incur carbon costs of Cent 1.88/kWh (see SI section2, implying losses of Cent 0.76/kWh for the TVA.

In a cap-and-trade system, caps on emissions are set andpermits consistent with those limits are initially issued andcan subsequently be bought. The cap may be lowered overtime, for example according to Scenario S400. Lowering thecap should increase the costs of emissions. Based on a reviewof eleven models, Fischer and Morgenstern (32) indicatemarginal abatement costs of between $40 and $250 per toncarbon in 1990 dollars for a 20% emissions reduction relativeto 1990, the reduction required under S400 in approximately2025. This translates to $15-97 in 2006 dollars (33). Thehighest price would incur additional costs of Cent 13.3 perkWh for TVA. This would translate into an increase ofelectricity prices in Kentucky by 348% from Cent 5.35 to 18.65,a scenario where it appears very likely that TVA will not beable to pass on a significant proportion of the additionalcosts to consumers (34).

For a more efficient plant, costs per kWh of electricitydecrease about 5% compared to the TVA’s current plants(see SI Table S1). We further compare total costs of electricityfor a range of allowance costs (table 1). Overall, costs perkWh are markedly lower for a new power plant, with abouta 14% difference for low carbon prices and up to 32% forhigh carbon prices.

While this illustrates that a more efficient plant will bemuch more profitable at high carbon prices, recent eventsdemonstrate a high risk that investment costs for new powerplants cannot be recovered. Several dozen coal-fired powerplant projects have been canceled, delayed, or rejected inthe past few years (1, 34, 35). To investigate this risk, we

FIGURE 2. Risk of shutdown of an investment subject to itsspecific emissions in comparison with allowable specificemissions.

FIGURE 3. Changes in the risk of shutdown. The 50% riskthreshold varies between year 2025 and 2040.

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 allowingslower actions for emission control.

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compare the costs per kWh of both current and plannedmore efficient coal plants with the costs per kWh of electricityproduced by utility-scale PV plants.

Comparison of Coal Power Plants with a Utility-ScalePhotovoltaic Power Plant. PG&E has signed agreements topurchase electricity from two new PV plants with, respec-tively, 250 and 550 MW. We estimate the price per kWh ofelectricity generated by these plants in dependence of theinvestment costs per kW installed (SI section 2.5). If existinglandlines are not sufficient, construction of new lines couldincrease this price.

First Solar has announced panel production costs in year2012 of $0.63 Whp, This means costs for the completeinstallation utility scale of $1.31 and costs of 5.76 c/kWh infavorable locations. Stiff competition in the PV sector willdrive down First Solar’s present high profit rate (SI section2.5). The respective profit rate of 25 or 50% of First Solarincreases 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’scurrent plants at Cent 5.35 and Cent 5.1 for a new plant. Atcarbon allowance costs of $25 per ton, the price per kWh ofelectricity from a new plant will be at Cent 7.2 (Table 1), andat Cent 11.52 for allowance costs of $65/ton. Thus, PV maybecome competitive much sooner than expected for allscenarios considered, and it is additionally not affected bythe risks of increasing carbon costs or increasing coal prices.

The case study shows that the threat for coal power plantsis multifaceted and massive. Our sensitivity analysis showsa rapidly rising risk for all coal power plants. These resultsare fairly robust even at large variations of input parameters.The robustness of the risks to coal plants is due to the tightrelationship between revenues and profits. Additional costscannot easily be offset by increases of the electricity price,and price increases may not be permitted by utilitiescommissions (34). Even low costs per ton of CO2 threatenthe profitability of current and newer more efficient powerplants, in particular given the rapid emergence of crediblealternatives such as second generation PV. This means thatit is uncertain whether utilities will be able to recover newinvestments.

DiscussionWe have developed indicators to support decision-makingfor large investments, taking into account uncertain climatemitigation policies and uncertain socio-economic develop-ments. Climate policies are uncertain because of the com-plexity of the climate system and our current difficulties toproject and understand how different forcings impactregional and global climate. A further reduction of targetsappears likely if certain undesirable environmental changesresult. We have also shown how rapid technological changesand surprises can put carbon-intensive investments at risk.The rather sudden availability of low-cost PV is a majorexample.

Our indicators on climate sustainability and on the riskof premature shutdown of investments allow investors andmanagers to assess the consequences of unexpectedly fasteconomic 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, givenclimate policy and economic uncertainties; and understandthe impact of low-cost PV which, additionally, sets the markfor state-of-the art of specific emissions per kWh of electricity.

Indicators that express the uncertainties and sophistica-tion of climate change and mitigation through simple graphsand equations may effectively support understanding anddecision making. The ability to project risk from climatelegislation for planned investments is advantageous formaking decisions and avoiding exposure to risks that mayotherwise be underestimated.

AcknowledgmentsThis research was supported by the Austrian National BankResearch Fund (project 12449), which is thankfully acknowl-edged. The third author was supported by the ClimateDecision Making Center created through a cooperativeagreement between the National Science Foundation (SES-0345798) and Carnegie Mellon University.

Supporting Information AvailableAdditional text and data, figures and tables.This material isavailable free of charge via the Internet at http://pubs.acs.org.

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TABLE 1. Comparison of Total Costs of Electricity for a Rangeof Carbon Allowance Costs

CO2-costs perqa ton ($) 15 30 60 200

carbon costs TVA old$ per kWh

0.021 0.041 0.082 0.27

electricity costs TVA $/kWhincluding carbon costs

0.071 0.091 0.132 0.32

carbon costs powerplant new $ per kWh

0.015 0.030 0.061 0.2

electricity costs new plant $/kWhincluding carbon costs

0.062 0.077 0.108 0.247

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