Energy Policy 51 (2012) 465–473

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Energy Policy

0301-42

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n Corr

E-m

journal homepage: www.elsevier.com/locate/enpol

A public policy aid for bioenergy investment: Case study of failed plants

Asa O. Gonzalez, Berna Karali n, Michael E. Wetzstein

Department of Agricultural and Applied Economics, The University of Georgia, Athens, GA 30602, USA

H I G H L I G H T S

c The role net present value (NPV) analysis is investigated in failed ethanol plants.c NPV optimal entry and exit margins are compared to real options approach (ROA).c The entry–exit margin gap is smaller under the NPV than it is under the ROA.c Government policymakers employing NPV tend to react aggressively to margin stimuli.

a r t i c l e i n f o

Article history:

Received 25 June 2012

Accepted 21 August 2012Available online 14 September 2012

Keywords:

Ethanol

Real options

Net present value

15/$ - see front matter & 2012 Elsevier Ltd. A

x.doi.org/10.1016/j.enpol.2012.08.048

esponding author. Tel.: þ1 706 542 0750; fax

ail address: bkarali@uga.edu (B. Karali).

a b s t r a c t

Recent failures of renewable energy plants have raised concerns regarding government’s role in

providing credit subsidies and have harmed the long-run development of renewable energy. The major

reason for these failures lies in government loan appraisers not having a model that addresses these

root causes and instead relying on traditional net present value (NPV) analysis. What is required is a

model representing entrepreneurs’ investment decision processes when faced with uncertainty,

irreversibility, and flexibility that characterize renewable energy investments. The aim is to develop

such a model with a real options analysis (ROA) criterion as the foundation. A case study comparing

NPV with ROA decisions for 50 and 100 million gallon ethanol plants is used as a basis for future

development of a template government loan appraisers can use for evaluating the feasibility of

renewable energy investments.

& 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Development of a sustainable US renewable energy industryhas had successes but has also been plagued with failures.Underlying these failures are highly public closures of plants thatreceived significant state and federal government support. Asdemonstrated by the public and political backlash associated withthe recent bankruptcy of Solyndra, the current margin of error foralternative energy state and federal subsidies in this austerityenvironment is very small.

The Solyndra and other renewable energy loan guarantees arecredit subsidies, based on net present value (NPV) criteria forweighting government’s exposure in covering default risk andagainst likely recoveries. Such NPV criteria are standard invest-ment criteria employed by government loan appraisers in select-ing investments to subsidize. A justification for such governmentcredit subsidies is the possible existence of adverse selection incredit contracting with a credit lender rejecting projects with

ll rights reserved.

: þ1 706 542 0739.

positive NPV (Allcott and Greenstone, 2012). Thus, as developedin Karel (2011) government credit guarantees and interest-ratesubsidies may have a positive effect on social efficiency with thefunding of socially efficient projects that would not be financedotherwise.

Although static NPV can be an appropriate tool for evaluatinginvestment decisions in mature technologies with relatively smalluncertainty parameters, economic theory suggests that it is notan appropriate tool for a newly emergent industry such asrenewable energy, which is characterized by input-price andpolicy uncertainties. Under such conditions, parameterization ofdeterminants including investment uncertainty, irreversibility(i.e., inability to recover sunk costs), flexibility (i.e., ability todelay investment), and their impacts on future options are criticalfor development of techno-economic models that can effectivelyand accurately inform actual outcomes. With none of thesedeterminants effectively captured by traditional NPV approaches,it is not surprising that NPV-based government investmentdecisions in alternative energy firms have to date proven unsa-tisfactory (Allcott and Greenstone, 2012).

For government-based credit subsidies, what is required is amodel representing entrepreneurs’ investment decision processes

A.O. Gonzalez et al. / Energy Policy 51 (2012) 465–473466

when faced with uncertainty, irreversibility, and flexibility thatcharacterize renewable energy investments. For practical applica-tion, such a model would have to abstract the very complexinvestment decision processes entrepreneurs employ. The aim isto develop such a model with a real options analysis (ROA)criterion as the foundation. The basic premise underlying ROA isthe assumption that producers weigh the NPV of expected cashflow against both the full investment cost for entry into a marketand the value of the range of options they have to postpone aninvestment. As compared to traditional NPV approaches, ROAprovides an integration of irreversibility, uncertain parameters(including policy interventions and performance of unproventechnologies), and timing flexibility into models of investmentdecisions. Recently ROA has appeared in a number of areasfrom management to engineering decisions (Krychowski, 2008;Nembhard and Aktan, 2010).

With ROA as a possible alternative to NPV analysis, the hypo-thesis to be addressed is: The failure in recent governmentpolicy and subsidy interventions for promoting alternative energyis hinged on the inappropriate use of static NPV criterion forinvestment decisions. Working from this hypothesis, the aim is tooutline a ROA that could be employed to evaluate the feasibilityof renewable energy investments. In support of this hypothesis,a case study is developed investigating the role NPV analyseshave played in failed attempts to establish an ethanol plant in aregion where past plants have failed with government subsidies.Specifically, entry and exit thresholds for ethanol–corn pricedifference are calculated under both NPV and ROA.

Results indicate that employing NPV analysis causes aggres-sive reaction to margin stimuli: the gap between entry and exitmargins is smaller than it is under the ROA. By considering thestochastic nature of the ethanol–corn price margin, the irreversi-bility of investment in the ethanol plant, and the possibility todelay the (dis)investment decisions, the ROA yields thresholdscloser to market conditions. This empirical evidence indicates theadvantage of using ROA over NPV in a stochastic environment.

1 Technically the plant was not cellulosic in the true sense of breaking down

sugars in the stems of plants to make fuel. Instead, the facility was designed to

burn wood chips with limited oxygen to create gas that can be converted into

alcohols.

2. Bioenergy investments

In practice, tools used in evaluating investment projectsinclude payback period, internal rate of return (IRR), and NPV.Payback period measures the time required to recover the initialcost of the project. It is very simple to calculate, however ignorestime value of money. IRR is the expected rate of return, oreffective interest rate from a project. An important drawback ofIRR is that it assumes reinvestment of interim cash flows inprojects at IRR itself, overstating the annual equivalent rate ofreturn for a project whose interim cash flows are reinvested at alower rate. NPV, on the other hand, is the present value of theinterim cash flows less the initial cash outflow. It is consideredsuperior to IRR because it assumes that the interim cash flowsare reinvested at the discount rate, not at IRR. However, usingNPV for investment projects that have stochastic elements isquestionable.

Examples of poor investment decisions spurred by a combina-tion of recent biofuel policy incentives and entry strategies basedon NPV analysis are the corn–ethanol production facilities locatedwell outside of core raw supply lines in the Midwestern Corn Belt.Many such facilities have entered into bankruptcy as costs forcorn and transportation far exceeded NPV assumptions whileethanol prices did not rise accordingly. A prime example ofsuch failure is a corn ethanol facility in Camilla, Georgia thatbecame operational in November 2008 and declared bankruptcyin February 2011. The Camilla facility was constructed withsignificant support from the local government ($8.6 million in

revenue bonds). This failure is associated with the United States’first cellulosic ethanol plant located in Soperton, Georgia becom-ing insolvent in January 2011.1 The federal government guaran-teed $40 million in credit subsidy loans along with a $44 millionUS DOE grant. At bankruptcy, the federal government’s $84million investment returned $2 million in revenue. The state ofGeorgia granted $6.25 million in site improvements and the localgovernments provided 138 acres of an industrial park at $10 peryear with exemption from taxes (Duncan, 2011). It is estimatedover $162 million in local, state, and federal grants, loans, andother subsidies were lost (Chapman, 2011). Additionally, thehigh-profile failure of government-funded companies such asthe Soperton plant and Solyndra has soured the public on renew-able energy. Unfortunately, this government failure in renewableenergy is not an isolated occurrence. A similar scenario occurredin the 1970s with failed government investments in solar tech-nology, which was also not ready for commercial development.

This has prompted some economists and political scientists tosuggest governments should not be involved with credit sub-sidies. In contrast, Cotti and Skidmore (2010) in their survey ofbiofuel states’ subsidies/tax credits, found subsidies can have asignificant effect on a state’s production capacity. If adverseselection does exist particularly with new emerging industrytechnologies such as renewable energy, then a government rolemay be warranted. However, the tools used by governments inassessing which investments to subsidize warrant investigatingand sharpening.

ROA has not been utilized to its full potential by governmentloan appraisers while evaluating renewable energy investments.If changes in policy affect expected cash flows, investment costs,or option value, then producers would alter their investmentchoices in ways that can be empirically modeled. The objectiveof policymakers should be then to understand the real optionsavailable to producers and based on this understanding design anappropriate set of policies that address the barriers to adoption.

3. Literature review

Badcock and Lenzen (2010) develop a comprehensive globalcollation of energy subsidies. They determine, relative to otherrenewables, bioenergy subsidies are large per unit of electricitygenerated. Sadeghi and Ameli (2012) develop an analytical hier-archy process for determining the optimal allocation of energysubsidies among subsectors within Iran, and Batlle (2011) con-siders a mechanism for funding the subsidies within the EU. Interms of reallocating energy subsidies, Zelenika-Zovko and Pearce(2011) determine that a transfer of nuclear insurance subsidies tophotovoltaic credit subsidies (loan guarantees) will result inincreased energy generation over the life cycle of the techno-logies. Lin and Jiang (2011) and Liu and Li (2011) employcomputable general equilibrium (CGE) models to investigate theeconomic effects of China’s energy subsidies. Lin and Jiang’s(2011) results indicate China’s energy subsidies are equivalentto 1.43% of GDP and removing the subsidies will have significantnegative impacts on the economy unless they are offset by otherpolicies such as supporting sustainable development measures.As an alternative Liu and Li (2011) suggest a gradual reduction inthe subsidies by first cutting coal subsidies and then oil. Similarly,Manzoor et al. (2010), also based on a CGE model, determine that

A.O. Gonzalez et al. / Energy Policy 51 (2012) 465–473 467

Iranian economic activity and consumer welfare would bereduced with a reduction in energy subsidies.

However, the literature is limited in providing improved toolsfor determining the feasibility of renewable energy projects.Cheng et al. (2011) employ a modified sequential compoundROA in considering the characteristics of uncertain future elec-tricity demand and lead time for implementing Taiwan’s cleanenergy policy. Examining how a staged commercialization of anunconventional energy technology could proceed under uncer-tainty, Siddiqui and Fleten (2010) take a ROA approach.Bednyagin and Gnansounou (2011) develop a fuzzy compoundROA for a fusion energy research and development program.Schmit et al. (2011) and Lin and Huang (2011) both extend theROA by considering two stochastic variables, with Lin and Huang(2011) also incorporating a Poisson jump process based on Linand Huang (2010). ROA is combined with portfolio optimizationby Fortin et al. (2008) to investments in the electricity sector.They investigate the dependence of optimal portfolios on risk-return constraints and present a comparison with the traditionalmean-variance approach. In terms of government policy analysis,Lee and Shih (2010) develop a policy benefit evaluation modelthat integrates the two-factor cost efficiency curve for renewableenergy technologies into a ROA.

This recent literature is pushing the bounds of our theoreticalunderstandings of ROA and yielding new insights into how ROAcan be mated with other techniques. However, it is important tokeep the applied policy use of ROA in its proper perspective.In terms of private firm investment decisions, there are a wholehost of other variables a firm considers besides the determinantswithin ROA. Examples are input supply, employees, and environ-mental and community impacts. For many firms the minimumacceptable rate of return or hurdle rate is where the present valueof benefits is two to three times cost before it will purchase anasset. A rule-of-thumb that some firms follow is to undertake aninvestment only if it results in $1 back each year for every$2 spent initially. Just as firms do not explicitly maximize profitsby calculating the first-order conditions for profit maximization,they also do not explicitly determine their investment options byempirically calculating their real options. However, as revealed bySchmit et al. (2009) and Schmit et al. (2011), ROA by abstractingfrom firms’ decision processes can be consistent with theirinvestment decisions. As such, ROA may serve well as a tool fora third party to determine the market feasibility of an investment.When lending institutions are hesitant to loan due to theuncertainty inherited from possible adverse selection, govern-ment agencies in considering credit subsidies and grants requiretools that augment NPV analysis for mitigating selecting invest-ment failures. ROA in its ability to model firm’s investmentdecisions is one possible tool. Such a tool is particularly importantin providing an objective criterion to be used for selecting oneinvestment application over another. With such an objectivecriterion, charges of governmental political favoritism can beavoided. This is the major advantage over NPV analysis; ROAoffers a clear-cut method to incorporate uncertainty, irreversi-bility, and flexibility that is missing in NPV.

2 Brownian motion is a continuous-time process that is a fundamental

building block for real option models. It can be derived as the continuous limit

of a discrete-time random walk. For details, see Dixit and Pindyck (1994).3 There are other options besides permanently exiting the industry. Instead, a

plant could be temporarily suspended, allowing it to be reactivated in the future at a

sunk cost lower than the cost of building the plant from scratch. This adds another

option value for the firm and needs to be incorporated into the investment problem.

Real option models including such suspension (i.e., mothballing) options are devel-

oped in detail in Dixit and Pindyck (1994, pp. 229–242).

4. Methodology

Investment valuation methods that take into consideration theimplications of uncertainty and timing of investment have beencreated to overcome the disadvantages of the NPV approach.In terms of ethanol plant investments, corn and ethanol exhibithistorical price volatility. Commodity prices especially rose dra-matically during the ‘‘commodity boom’’ of 2006–2008 period.Although the prices declined by the end of 2008, they rose again

in late 2009. Thus, the results from an analysis that does notincorporate price uncertainty into investment decisions will belimited when prices change significantly.

Options pricing theory that does consider price volatility wasdeveloped by Black and Scholes (1973), Merton (1973), and Coxand Ross (1976) as a tool to price financial securities based on thevolatility of returns. Beginning with McDonald and Siegel (1985),options pricing theory was applied to tangible assets and ‘‘real’’options theory was born. The real options approach encompassesa growing body of research emphasizing the fact that managershave opportunities to invest and thus their task is to decide onhow to better seize those opportunities. Opportunities areoptions—rights but not obligations to take some action in thefuture (Dixit and Pindyck, 1994). Thus, opportunity to invest isconsidered similar to holding a call option. When a firm makes anirreversible investment, it exercises (kills) the option to invest atsome future date. The NPV approach therefore should be modifiedto include the value of holding an option. So, the firm shouldinvest when the difference between the value of a unit of capitaland the initial investment cost is greater than or equal to thevalue of keeping the investment option alive (Dixit and Pindyck,1994, p. 6). Under this framework, the value of waiting is weighedagainst the opportunity cost of current profit over the waitingperiod.

4.1. The investment problem

Consider the investment decision of a firm with the followinginvestment opportunity: at any time t, the firm can pay I to buildthe project (startup costs). The cost of capital is d. Expected futurenet cash flows conditional on embarking on the project have apresent value P, which denotes the ethanol gross margin com-puted as the difference between the price of ethanol per gallonand the price of corn necessary to produce one gallon of ethanol.If entry is made, the firm acquires a project that produces a fixedamount of product (ethanol) each year, which is normalized tounity. Variable operating costs, C, are known and constant. Oncein operation, the firm has the option to abandon the project at acost of E. Additionally, the firm takes the stochastic process P asgiven and this ‘‘price’’ is assumed to follow a geometric Brownianmotion2:

dP¼ aPdtþsPdz, ð1Þ

where a is the drift and s is the volatility of P, dt is the timeincrement, and dz is the increment of a Wiener process.

From the perspective of ROA, the live project can be viewed asa composite asset, part of which is an option to abandon. If thefirm exercises this option, the project goes back to the inactivestate. In this case, the firm acquires another asset: the option toinvest. When the firm subsequently exercises this option, theyhave a live project once more. As a result, the values of a live firmand an idle firm are interlinked, so they are determined simulta-neously (Dixit and Pindyck, 1994).

A rational investor will enter the industry when marketconditions become sufficiently favorable, and an active firm willexit when conditions become sufficiently poor.3 The optimalstrategy for entry and exit is determined in the form of two

A.O. Gonzalez et al. / Energy Policy 51 (2012) 465–473468

threshold prices, PH and PL, respectively, with PH4PL. The optimalstrategy for an idle firm facing P below PH is to remain idle, and toinvest as soon as P reaches the threshold level PH. Conversely, theoptimal strategy for an active firm is to remain active as long as P

is greater than PL but exit as soon as P falls to PL. With the aboveassumptions, the investment problem is to determine when anidle project should be initiated and when an active project shouldbe terminated as a response to the stochastic margin P, given theconstant parameters I, E, a, s, and d.

The value of the firm is now a function of the exogenous statevariable P, and of the discrete variable that indicates whether thefirm is idle (subscript 0) or active (subscript 1). Let V0 representthe value of an idle firm, or the option to invest, and V1 representthe value of an active firm. An idle project does not generaterevenue and does not incur any costs. However, if and whenprices become favorable enough, it might become an activeproject and start generating revenue. Once active, the plantgenerates a stochastic net return of p¼P�C in each period, whilestill having the option of shutting down. Thus, V1 comprises twoparts, the rights to the profit from the venture, and the option toexit if P falls below PL. The Bellman equations are then:

dV0dt¼ E½dV0�, ð2aÞ

dV1dt ¼ E½dV1�þðP�CÞdt ð2bÞ

Eq. (2a) states a normal return from an idle project should beequal to expected capital gain from undertaking the project, while(2b) states a normal return from an active project should be equalto expected capital gain plus net revenue from the project.Applying Ito’s lemma to dV0 and substituting (2a) yields:

dV0 ¼ V 0OaPdtþV 00sPdzþ1

2V 000a

2P2ðdtÞ2

þ1

2V 000s

2P2ðdzÞ2þV 000asP2dtdz: ð3Þ

After taking the expectation of (3) and substituting into (2a) theresult is

1

2s2P2V 000þaPV 00�dV0 ¼ 0: ð4Þ

Applying the same procedure to (2b) yields:

1

2s2P2V 001þaPV 01�dV1þP�C ¼ 0: ð5Þ

The general solution to differential Eq. (4) can be denoted as

V0 ¼ A1Pb1þA2Pb2 , ð6Þ

where A1 and A2 are constants yet to be determined and b1 and b2

are roots of the quadratic equation (1/2)s2b(b�1)þab�d¼0given by:

b1 ¼1

2�

as2

� �þ

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

2�

as2

� �2

þ2ds2

s41; ð7aÞ

b2 ¼1

2�

as2

� ��

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

2�

as2

� �2

þ2ds2

so0, ð7bÞ

with d4a. As discussed in Dixit and Pindyck (1994) this optionbecomes nearly worthless as P approaches zero, resulting in thecoefficient A2 becoming zero. Therefore, the solution for the idlefirm over the interval (0,PH) becomes

V0 ¼ A1Pb1 : ð8Þ

The general solution to differential Eq. (5) is given by:

V1 ¼ B1Pb1þB2Pb2þP

d�a�C

d: ð9Þ

The last two terms in (9) can be interpreted as the value of theactive project when the firm must keep it operational foreverregardless of any losses. The first two terms can be interpreted asthe value of the option to abandon. As P approaches 1, thelikelihood of abandonment becomes very small, thus the value ofthe exit option should go to zero as P becomes extremely large,resulting in the coefficient B1 becoming zero. Thus, the solutionfor the active firm over the interval (PL,N) becomes

V1 ¼ B2Pb2þP

d�a�C

d: ð10Þ

if the entry threshold PH is reached, the investing firm pays thelump-sum cost I to exercise its investment option, relinquishingthe rights to the asset of value V09P ¼ PH

to get the active projectwith value V19P ¼ PH

. In this case, value matching and smoothpasting conditions are

V09P ¼ PH¼ V19P ¼ PH

�I, V 009P ¼ PH¼ V 019P ¼ PH

: ð11Þ

Similarly, at the exit threshold PL, the value matching and smoothpasting conditions are:

V19P ¼ PL¼ V09P ¼ PL

�E, V 019P ¼ PL¼ V 009P ¼ PL

: ð12Þ

Substituting (8) and (10) into the value matching and smoothpasting conditions results in the following four equations:

�A1Pb1

H þB2Pb2

H þPH

d�a�

C

d�I¼ 0, ð13aÞ

�b1A1Pb1�1H þb2B2Pb2�1

H þ1

d�a¼ 0, ð13bÞ

�A1Pb1

L þB2Pb2

L þPL

d�a�

C

dþE¼ 0, ð13cÞ

�b1A1Pb1�1L þb2B2Pb2�1

L þ1

d�a¼ 0: ð13dÞ

Using numerical methods, this four unknown, four-equationsystem can be solved for the entry PH and exit PL thresholds andthe coefficients A1 and B2.

4.2. Net present value thresholds

The entry and exit thresholds under the NPV approach arederived by first defining the net return for year i aspi¼P(1þa)i

�C. The present value of this net return over aninfinite time horizon is then given by:

X1i ¼ 1

P1þa1þd

� �i

�C

ð1þdÞi

!¼

Pð1þaÞd�a

�C

d: ð14Þ

For an investment to be carried out, the present value of the netreturn should exceed the initial investment costs, I. Therefore, theinvestment decision under the NPV approach is given by

Pð1þaÞd�a �

C

d�I40: ð15Þ

Furthermore, an investment must be abandoned if the presentvalue of the net return of the investment is less than the plant’sscrap value, E. Thus, the exit decision under the NPV approach is

Pð1þaÞd�a

�C

do�E: ð16Þ

Solving (15) and (16) for P yields NPV entry and exit thresholds:

PEntryNPV ¼

C

dþ I

� �d�a1þa

� �, ð17aÞ

Fig. 1. Ethanol and corn prices for the period 1989–2011.

Table 1Investment cost: 50 and 100 million gallon ethanol plants.

50 milliongallon (dollars)

100 milliongallon (dollars)

Ethanol plant 70,000,000 140,000,000

Railroad system 2,800,000 2,800,000

Site prep/underground/fire 7,000,000 7,000,000

Construction contingency (5%) 4,137,400 7,694,900

Engineering and permitting 328,000 328,000

Phase I and II environmental 10,000 10,000

Electrical and utilities 2,000,000 3,000,000

Administration building 300,000 300,000

Land 300,000 450,000

Site survey 10,000 10,000

Feedstock/working/start-up 5,500,000 9,000,000

Total investment cost 92,385,400 170,592,900Total investment cost per gallon 1.85 1.71

A.O. Gonzalez et al. / Energy Policy 51 (2012) 465–473 469

PExitNPV ¼

C

d�E

� �d�a1þa

� �: ð17bÞ

5. Case study

5.1. State of Georgia location

With the past failures of government-backed bioenergy firmsin the state of Georgia as a backdrop, a case study estimating thedifferent entry and exit conditions under NPV versus ROA for aGeorgia biofuel (ethanol) plant are developed. In contrast to theUS Midwest, it is questionable if Georgia has a comparativeadvantage in the production of biofuels. The Midwest has acomparative advantage in biofuel production with its readilyavailable biomass (mainly corn and soybeans) for use as feedstockand the existence of well-developed rail lines making it possibleto distribute biofuels across the continental United States.

Investors’ interest in establishing an ethanol production facil-ity in Georgia are motivated in part by the large and growingmetropolitan areas within the US Southeast, relatively cheap land,a business-favorable state government, and a well-trained work-force. Atlanta alone consumes approximately three billion gallonsof gasoline annually, which results in a potential ethanol marketof 300 million gallons (Shumaker et al., 2007). This market size inconjunction with government subsidies was enough to enticeinvestment in Georgia ethanol production.

There are a number of drawbacks to consider, with the majorbeing Georgia is a corn deficit state—it consumes six times morethan it produces (Smith, 2012). Georgia’s 2011 corn crop was 43million bushels, far short of its consumption (NASS, 2012). Evenassuming a highly efficient ethanol plant that can convert onebushel of corn into three gallons of ethanol, a plant producing 100million gallons annually would require an additional 33 millionbushels of corn (77% of Georgia’s 2011 corn crop). As a result ofthe corn deficit, an ethanol plant could not rely solely on localcorn production and would then face transportation issuesincluding cost and logistic bottlenecks. On the other side, savingscould be realized with a biorefinery located in Georgia, whichremoves the cost of transporting the finished product (ethanol)from the producing states into Georgia. The proponents of theCamilla plant based their positive NPV analysis on this supposi-tion: although a corn–ethanol plant would have to import itsmajor input (corn), it would not have far to transport its finishedproduct (ethanol).

5.2. Data and parameter estimates

5.2.1. Price data

A manager is required to make an investment decision whenboth the revenue (ethanol price) and cost (corn price) arestochastic. The data on prices of ethanol (PE) and corn (PC) areobtained from the HART’s Ethanol and Biofuels News database.The data consist of weekly spot prices from March 1989 toSeptember 2011. The United States Department of Energy geo-graphically aggregates the 50 states and the District of Columbiainto five Petroleum Administration for Defense Districts (PADDs).The weekly series of ethanol prices is computed as the US averageof the price of ethanol across all five PADDs over the sampleperiod. The weekly series of corn prices is computed as theaverage of cash corn prices in Kansas City and Chicago net ofDDGS (Dried Distillers Grains with Solubles, a coproduct ofethanol production process used for animal feed) revenue. There-fore, they represent the dry mill net corn cost in dollars per gallonof ethanol on a weekly basis.

Fig. 1 illustrates annual ethanol and corn prices computed byaveraging weekly data annually from March 1989 to September2011. Note that corn prices are in dollars per gallon, representingthe price of corn required to produce one gallon of ethanol. Alsonote that both series generally move in the same direction andboth exhibit a general upward trend. Additionally, volatility hasincreased over time; both time series indicate more dramaticpeaks and troughs in the latter years.

In order to keep the entry–exit model simple, stochastic pricesof ethanol and corn are used to create a single stochastic variable,called gross margin P. This key variable is obtained by subtractingthe net corn cost from the gross revenue obtained from ethanolsales, its price. Specifically, the gross margin is P¼PE�PC.

5.2.2. Cost data

The cost data are based on Shumaker et al. (2007) and whereappropriate updated. Table 1 lists a summary of the estimatedinvestment costs from building and starting up a 50 and 100 milliongallon ethanol plant. Investment costs presented in Table 1 include allcosts associated with the construction of the ethanol plant includingthe actual plant, the railroad system, site preparation, engineering,and permitting costs. Investment costs also include costs that are notconstruction related such as the land, start-up inventories, andworking capital. This results in total capital costs of $1.85 and $1.71per gallon for the 50 and 100 million gallon plants, respectively.

In addition to investment costs, annual operating costs,denoted by C, are also obtained from Shumaker et al. (2007) for50 and 100 million gallon ethanol plants (Table 2). Processingcosts, which include chemical and utility costs, total $22.3 millionand $44.60 million for the 50 and 100 million gallon plants,

Table 2Annual operating costs: 50 and 100 million gallon ethanol plants.

Production costs 50 million gallon(dollars)

100 million gallon(dollars)

Processing costs 22,300,000 44,600,000

Labor 1,500,000 1,800,000

Repairs and maintenance 1,000,000 2,000,000

Insurance 400,000 800,000

Marketing and freight 3,500,000 7,000,000

Other; selling, general and

administrative expenses

4,000,000 8,200,000

Depreciation 5,800,000 10,700,000

Total operating cost 38,500,000 75,100,000Total operating cost per gallon 0.77 0.75

A.O. Gonzalez et al. / Energy Policy 51 (2012) 465–473470

respectively. Chemical costs include chemicals, raw materials andnon-corn feedstock cost such as denaturants, enzymes, andyeasts. Utility costs include fresh water source, natural gas,electricity, wastewater, storm water, and sanitary sewage.

Production costs also include depreciation of the plants andthe machinery that are used in the ethanol production process.Normal business operating expenses such as repairs and main-tenance, insurance, marketing, freight, and other expenses arealso included. Total operating costs per gallon, C, are $0.77 and$0.75 for the 50 and 100 million gallon plants, respectively.

Exit costs depend on the liquidation value of the assets. Nodata specific to the residual value of an ethanol plant are availablein the literature. Therefore, the sunk exit costs are defined as 10%of establishment costs (E¼�0.10� I) in the model. The cost ofcapital, d, is another major factor influencing a firm’s capitalinvestment decisions. It represents the minimum required rate ofreturn on investment funds. It can also be thought of as the rate ofreturn that an investor would earn from a different investmentwith similar risk. For this analysis, an annual 15% cost of capital isassumed.4

5.2.3. Geometric Brownian motion parameters

If the stochastic variable P¼PE�PC follows a geometric Brownianmotion as in (1) then ln P follows a Brownian motion with driftparameter a�(1/2)s2 and volatility s. Specifically,

d ln P¼ a�1

2s2

� �dtþsdz,

� mdtþsdz: ð18Þ

The weekly gross margin series P is averaged over each year toconstruct a yearly series. The resulting series were nonstationary.To remedy this problem the drift and volatility parameters arecomputed as the sample mean and standard deviation of the firstdifference of the natural log of the annual gross margin series,DlnPt¼ lnPt� lnPt�1. For the sample period 1989–2011, the driftparameter of the variable lnP, a, is found as 0.002 and volatilityparameter as 0.297. Thus, the drift and volatility parameters ofthe variable P are a¼�0.02þ(1/2)(0.297)2

¼0.042 and s¼0.297.In order to investigate the impact of the increasing price volatilitystarting late 2006 (see Fig. 1), the sample is split into two periods,1989–2005 and 2006–2011, and the drift and volatility para-meters for these sub-sample periods are calculated. Table 3 listsall the cost variables and stochastic parameters used in theempirical analysis. As listed in the table, the drift becomesnegative in the later sample period while volatility increases.

4 For simplicity, we assume a constant cost of capital and ignore the

implications of taxes in this study. However, alternative approaches to estimating

cost of capital can be found in Ruback (1986), Taggart (1991), Myers and Ruback

(1992).

5.2.4. Results

Entry and exit threshold margins are computed under bothROA and NPV employing the parameter values listed in Table 3.Table 4 summarizes these results for both plant sizes anddifferent sample periods. For the entire sample period under theROA, the trigger entry prices, PH, are $1.55/gallon and $1.49/gallonfor the 50 and 100 million gallon plants, respectively. Trigger exitprices, PL, are $0.44/gallon and $0.43/gallon for the 50 and 100million gallon plants, respectively.

Not surprisingly, the NPV approach estimates differ from thoseof the ROA. The trigger gross margin estimates for entry duringthe 1989–2011 sample period are much smaller under the NPVapproach for both plant sizes. The NPV entry trigger grossmargins are found as $0.72/gallon and $0.69/gallon for the 50and 100 million gallon plants, respectively. Conversely, the triggergross margin for exit are very close across the two approaches andplant sizes, with the NPV approach yielding slightly higherestimates than the ROA. In practice, these estimates suggest thatunder the ROA, a firm will wait to make an ethanol plantinvestment until market conditions make it possible to obtain ahigher gross margin. The results also suggest that once inoperation, a firm’s exit trigger gross margin is slightly lowerunder the ROA. The inaction gap between entry and exit pricethresholds (PH�PL) under the ROA is $1.11/gallon while that ofunder the NPV approach is only $0.22/gallon. These results areconsistent with Dixit and Pindyck’s (1994) argument: ‘‘the opti-mal thresholds with rational expectations (ROA) are spreadfarther apart than the Marshallian ones with static expectations’’(NPV). As the uncertainty over the gross margin is taken intoaccount by investors, they become more hesitant to invest; and ifthe ethanol plant is already active, the investors are more hesitantto exit the market. Put differently, uncertainties over futuremarket conditions enlarge the firm’s ‘‘status quo’’ zone. The ratioROA/NPV also indicates ROA entry thresholds are twice as large asNPV entry thresholds and ROA inaction gap is five times largerthan the NPV inaction gap.

Further insights are revealed by investigating threshold beha-vior when the drift (a) and volatility (s) parameters change indifferent sample periods (Table 3). Table 4 lists entry and exittrigger margins computed under both ROA and NPV approachesfor the earlier sample period of 1989–2005 and the later period of2006–2011. The earlier sample period has a positive drift, andwhen compared to the entire sample the drift is higher and thevolatility is slightly lower. The latter sample period, on the otherhand, has a negative drift and higher volatility. When comparedto the entire sample period, the inaction gap becomes smaller forthe 1989–2005 period under both approaches due to the largerpositive drift parameter (0.079 versus 0.042). A positive drift ratemeans that, ceteris parabus, the price margin is expected to behigher in the future. For the 50 million gallon plant, the ROAtrigger entry price is $1.46/gallon during 1989–2005. Since theprice margin is expected to be higher in the future, the firm’sminimum margin to invest now, PH, is lower. Once again theinaction gap is much larger under ROA than under the NPVapproach. A firm using the NPV approach is much more reactiveto changes in the positive drift rate, investing sooner when themargin is high and also exiting more readily when margin is low.

The drift parameter in the later sample period of 2006–2011 isnegative and volatility is higher. The ROA entry and exit thresholdmargins are found to be $1.83/gallon and $0.52/gallon for a50 million gallon plant and $1.75/gallon and $0.51/gallon for a100 million gallon plant. A declining drift indicates a long-runpotential decline in the ethanol gross margin. The value of theoption to invest increases and therefore idle firms find it moreprofitable to wait longer to invest. In contrast, active firms find itmore profitable not to wait too long before exiting the industry

Table 3Parameter estimates.

Cost parameters Value Description

50 million gallons 100 million gallons

I 185 171 Entry cost in b/gallon

E 0.10� I 0.10� I Exit cost in b/gallon

C 77 75 Operating cost in b/gallon

d 0.15 0.15 Cost of capital

Brownian motion parameters 1989–2011 1989–2005 2006–2011

a 0.042 0.079 �0.056 Annual drift rate

s 0.297 0.293 0.309 Annual rate of volatility

Table 4Threshold gross margins (b/gallon).

50 million gallon 100 million gallon

ROA NPV ROA/NPV ROA NPV ROA/NPV

1989–2011Entry (PH) 155.27 72.22 2.15 148.79 69.40 2.14

Exit (PL) 43.91 50.22 0.87 43.17 49.06 0.88

Inaction gap

(PH�PL)

111.36 22.00 5.06 105.62 20.34 5.19

1989–2005Entry (PH) 146.40 45.85 3.19 140.44 44.05 3.19

Exit (PL) 40.44 31.88 1.27 39.85 31.14 1.28

Inaction gap

(PH�PL)

105.96 13.97 7.59 100.59 12.91 7.79

2006–2011Entry (PH) 183.17 152.03 1.20 174.95 146.07 1.20

Exit (PL) 51.87 105.71 0.49 50.77 103.26 0.49

Inaction gap

(PH�PL)

131.30 46.31 2.83 124.18 42.81 2.90

A.O. Gonzalez et al. / Energy Policy 51 (2012) 465–473 471

given an expected negative margin trend. Thus, entry conditionsare higher (increased entry threshold) as well as exit conditions.Given the decline in drift, threshold margin has to be higher forthe firms not to be willing to exit. Further, as the volatility of grossmargin increases, there is increased value in waiting to investwhen the project is inactive. By the same token, there is anincreased value in ‘‘waiting it out’’ if margins are falling andmanagement is considering shutting down. The lesser the uncer-tainty over future prices, the smaller the investor’s inaction gap.Thus, waiting to make an entry or exit decision is less valuable forthe investor who has strong insights concerning the futuredirection of the market.

Both entry and exit thresholds increase dramatically in thislater sample period under the NPV approach. However, thereis an order of magnitude difference in the percentage change inthe ROA entry and exit thresholds compared with the NPVthresholds as a result of declining drift and increased volatility.The ROA entry threshold margin for the 50 million gallon plantincreases from $1.46/gallon in 1989–2005 period to $1.83/gallonin 2006–2011 period (a 25% increase) and the exit thresholdincreases from $0.40/gallon to $0.52/gallon (a 28% increase). TheNPV entry margin, on the other hand, increases from $0.46/gallonto $1.52/gallon (a 232% increase) and the exit margin increasesfrom $0.32/gallon to $1.06/gallon (a 232% increase). With NPVthresholds only considering the future long-run decline in theprice margin, this results in over a 200% rise in the entry and exitthresholds, leading to suppressed firm entry and rising exits.As the ROA thresholds also consider volatility, the substantialpercentage decrease in drift (170%) between the two time periods

results in a much smaller percentage change in the entry and exitthresholds.

The NPV thresholds only consider the trend (drift) in the grossmargin and they do not depend on volatility. In contrast, ROAthresholds also consider volatility. Even in the low-volatilityperiod of 1989–2005, incorporating the volatility into the com-putations of threshold margins yields a marked increase relativeto the NPV in both the entry ($1.46/gallon versus $0.46/gallon)and exit ($0.40/gallon versus $0.32/gallon) margins for the 50million gallon plant.

While declining drift in the 2006–2011 period has the sameeffect on entry and exit threshold under both ROA and NPV, theNPV thresholds are more sensitive to changes in the drift. Asindicated by (17) for calculating NPV thresholds, volatility doesnot have any impact on the entry and exit margins. The drasticpercentage change in the NPV thresholds are thus attributed todecreases in the drift. However, while higher volatility increasesthe ROA entry margin it decreases the exit margin. Thus, theimpact of increased volatility dampens the impact of decliningdrift on the ROA exit margin. This dampening effect results in theROA exit threshold ($52/gallon) being lower than the NPV thresh-old ($1.06/gallon) for the 50 million gallon plant. In summary,under both ROA and NPV approaches the entry and exit thresh-olds rise with the advent of the more volatile 2006–2011 period.This result is consistent with the observed market effects. Duringthe 2006–2011 period there was a marked decrease in the entryof firms associated with firms exiting.

6. Conclusions

As renewable energy continues to expand, the relatively low-cost technologies leading to highly feasible investments will beexhausted first. The NPV analysis in these cases yielding signifi-cant benefits over costs would generally not require augmenta-tion. Such was the case during the ethanol boom period in theMidwest, which ended with the Great Recession. However, asthese sites are exhausted less favorable options will come intoplay. An example, addressed in this analysis, is locating ethanolplants away from the Midwest corn sources. In such cases, anaugmentation of NPV for government feasibility analysis may bewarranted if some form of credit subsidy or grant is to beawarded. The ROA is suggested as a possible augmentation. Asillustrated in the case study it offers a clear-cut method ofconsidering uncertainty, irreversibility, and flexibility parameters.Government loan appraisers require such an analysis to modelthe market consideration of these parameters.

The case study demonstrates how ROA can be implemented toprovide policymakers the necessary tools to account for theseparameters. Results suggest loan appraisers employing NPV will

A.O. Gonzalez et al. / Energy Policy 51 (2012) 465–473472

tend to react aggressively to margin stimuli: the gap betweenentry and exit margins is smaller than it is under the ROA. Byconsidering the stochastic nature of the ethanol margin, theirreversibility of investment in the ethanol plant and the possi-bility to delay the (dis)investment decisions, the ROA providesquantifiable estimates of these parameters. This provides empiri-cal support for greater caution by yielding thresholds closer tomarket conditions: the inaction gap is consistently larger underthe ROA. If ROA was employed for the Solyndra, Soperton, andCamilla projects, it is unlikely they would have been undertaken.As indicated in the case study, the entry thresholds for cornethanol were over twice as high under ROA as under NPV. TheCamilla facility and others located outside of core supply linesprobably never would have been constructed if a ROA approachhad been employed in the up-front feasibility analysis. The imageof biofuel energy having a bright future as a feasible renewableenergy source would not have been tarnished.

Government loan appraisers operate in an environment wherethey make investment decisions in uncertain economic climates.Improvements in risk management and capital valuation methodsare necessary to minimize risk in possible future costly invest-ments similar to Solyndra, Soperton, and Camilla. The ROA mayprovide a tool that both limits downside risk and takes betteradvantage of upside potential. The results obtained in this casestudy suggest employing a ROA as a strategic decision-makingtool would allow government loan appraisers to make improvedand better timed investment and abandonment decisions. How-ever, caution is warranted in using the results of this case study asa blanket policy for all future bioenergy investments. As a casestudy, the results are not at all applicable to other bioenergyinvestments, although the methodology can be applied to otherinvestment options, particularly cellulosic ethanol plants. Withthe likelihood of US federal and state governments in the futureonly supporting second- and third-generation biofuel plants, theunique structure of these new technologies would have to beincorporated into any ROA of their feasibility.

For developing an effective set of policies that promote ratherthan hinder increased biomass production, existing and newcreative policies should be assessed in terms of how they affectthe underlying barriers of renewable energy investments. Incor-porating feasible alternative sets of policies into a ROA willestimate the change in entry and exit conditions for firmsinvesting in renewable energy. In contrast to NPV models, sucha model will reveal how policies affect the uncertainty, irrever-sibility, and flexibility of renewable energy investments. Based onthis investigation and the objectives of policymakers, an efficientset of feasible policies will emerge that will yield an enhancedsustainable renewable energy system.

Future efforts will hopefully build on this initial attempt atdeveloping a template for government loan appraisers to numeri-cally estimate firms’ entry and exit thresholds for determiningwhen to (dis)invest in a renewable energy plant. Not surprisingly,these entry and exit thresholds would also depend on the capitalmarket situation. Such a template will account for the barriersassociated with producer decisions: uncertainty, irreversibility,and flexibility. The template will consider the effect alternativefeasible policy sets have on the investment decision. These policysets could include accounting for any social and environmentalexternalities including enhanced energy security and mitigatinggreenhouse gas emissions. By incorporating these policy sets in aROA framework that mimics private investment decisions, gov-ernment loan appraisers will have a clearer delineation of thepolicy costs. Specifically, they can estimate the level of govern-mental incentives required to trigger an investment. NPV analysiswould instead underestimate these policy costs by not consider-ing the hurdle rate. However, such analysis does not come

without costs. NPV analysis is relatively easier to implementcompared to the ROA and loan appraisers may not have the timeor resources to employ ROA. Also, ROA is based on a number ofassumptions regarding the stochastic nature of the variablesfollowing a Brownian motion, irreversibility of the investmentoption, and on the capital markets. As research into ROA con-tinues, these restrictive assumptions may be relaxed. However,ROA is not just about calculating some entry and exit thresholdnumbers. ROA focuses on the dynamic complexity of the invest-ment problem by considering the evolution of complex factorsover time that determine the value of an investment. The rigor ofthinking about the strategic decisions as real option will aid indecision making.

As our ability of economic analysis expands, it is important forapplied economists to illustrate how the expanded analysis canbe employed in addressing current problems and issues. With thedevelopment of NPV, government loan appraisers and privateinvestors had a tool for the first time to quantify the benefits andcosts of an investment. This tool provided significant insights intothe investment problem, but it had limitations including itsinability to address stochastic investments. Recent advancementsin economic analysis have addressed this limitation with thedevelopment of ROA. As the results indicate, employing ROA hasthe potential to significantly improve government loan apprai-sers’ investment decisions by offering an improved tool formimicking actual private investment decisions. For economicanalysis to be relevant and have an active role in policy decisions,it is important for applied economists to make policymakersaware of the development of new tools like ROA. The analysisindicates if loan appraisers incorporated such tools into theiranalysis false positives in investments may be avoided. Loanappraisers are currently and will continue to seek improved toolsfor their analysis of uncertain events. Similar to NPV analysis, ROAor its variant will provide one such improved tool.

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