MODELING IMPACTS OF BIOENERGY MARKETS … in docs/recent research/Modeling... · a potential solution to the problem (Cook and Beyea [2000]). Recent research also suggests that cellulosic

NATURAL RESOURCE M ODELINGVolum e 22, Number 3, August 2009

MODELING IMPACTS OF BIOENERGY MARKETS ONNONINDUSTRIAL PRIVATE FOREST MANAGEMENT

IN THE SOUTHEASTERN UNITED STATES

ANDRES SUSAETA∗

374 Newins Ziegler Hall, School of Forest Resources and Conservation,University of Florida, P.O. Box 110410, Gainesville, FL 32611-0410

E-mail: [email protected]

JANAKI R.R. ALAVALAPATI313 Cheatham Hall, Department of Forestry, College of NaturalResources, Virginia Polytechnic Institute and State University,

Blacksburg, VA 24061E-mail: [email protected]

DOUGLAS R. CARTER357 Newins Ziegler Hall, School of Forest Resources and Conservation,

University of Florida, P.O. Box 110410, Gainesville, FL 32611-0410E-mail: [email protected]

Abstract. The potential impacts of bioenergy marketson slash pine plantation management on nonindustrial pri-vate forestlands in the southeastern United States were ana-lyzed. We developed an integrated Black–Scholes and modifiedHartman model to achieve this task. The risk of damage fromcatastrophic natural disturbances such as wildfires and pestoutbreaks associated with the exclusion/incorporation of thin-nings and variation in timber salvage rates was also included.Three scenario sets were considered: status quo or no thin-ning scenario, thinning scenario for pulpwood, and thinningscenario for bioenergy at differing levels of risk and salvage.The results indicated that the incorporation of thinnings ei-ther for pulpwood or bioenergy increases the forestland valueregardless of the risk when the salvage value of the stand is0.8. When the two thinning scenarios were compared, the landexpectation value for the thinning scenario for bioenergy wasgreater at any level of risk compared with the thinning sce-nario for pulpwood , averaging a difference of 11.5% and 11.7%for salvageable portions of 0.8 and 0, respectively.

Key Words: Black-Scholes formula, land expectationvalue, risk, salvage, bioenergy.

∗Corresponding author: Andres Susaeta; 374 Newins Ziegler Hall, School ofForest Resources and Conservation, University of Florida, P.O. Box 110410,Gainesville, FL 32611-0410, [email protected].

Received by the editors on 15th November 2007. Accepted 3rd December 2008.

Copyright c©2009 W iley Period ica ls, Inc.

345

346 A. SUSAETA, J.R.R. ALAVALAPATI, AND D.R. CARTER

1. Introduction. The southern United States consists of 214 mil-lion acres of forestland. Out of this, 204 million acres (95.3%) are cat-egorized as timberland. About 145 million acres of timberland (71.8%)are owned by nonindustrial private forest (NIPF) landowners, alsocalled family forests. NIPF landowners contribute 67% (7.7 millioncubic feet) of annual growth and 68% of annual removals (Smith et al.[2004]). Thus, private forests and, particularly, NIPF landowners aresignificant contributors to the southern U.S. forest sector.

Currently, southeastern U.S. NIPF landowners managing slash pine(Pinus elliotti) plantations face several challenges, including catas-trophic risk events, increased offshore competition (Paun and Jackson[2000]), and low pulpwood prices. The combined effect of fire suppres-sion, the lack of prescribed burning, and high planting densities hasbeen that extensive areas are overstocked and susceptible to wildfiresand pest attacks (Graham et al. [1999], Le Van Green and Livingstone[2003], Polagye et al. [2007]). For example, the average annual num-ber of wildfires in the state of Florida is as high as 5,550 incidentsburning 220,000 acres each year (Florida Department of CommunityAffairs and Florida Department of Agriculture and Consumer ServicesDivision of Forestry [2004]). In order to prevent and manage wild-fires, a $58 million budget was allocated for the fiscal year 2007–2008.Pest outbreaks such as those of fusiform rust (FR; Cronartium quer-cuum [Berk.] Miyabe ex Shirai f. sp. fusiforme) and southern pinebeetle (SPB; Dendroctonus frontalis Zimmermann) have caused signif-icant damage to NIPF landowners. For example, FR caused an annualeconomic loss of $35 million in five southern states and $8 million inFlorida (Schmidt [1998]); SPB caused a southwide damage of $1.5 bil-lion between 1970 and 1996 (Price et al. [1998]).

In general, catastrophic events can yield several economic implica-tions that can affect all timber market participants. Prestemon andHolmes [2000, 2004] and Prestemon et al. [2006] illustrated the short-and long-run timber price dynamics after a natural catastrophe. In theshort run, inventory is reduced and salvaged timber gluts the market.Prices fall, thus decreasing (increasing) producer (consumer) welfare. Inthe long run, this situation might be reversed: prices increase becauseof losses of standing inventory and contracted supply (time of salvage-able exhaustion), thus improving (reducing) producers (consumers)benefits.

BIOENERGY MARKETS AND FOREST MANAGEMENT 347

Foreign competition is also of concern. Costs for producing fiber inthe southern hemisphere are lower compared with the United Statesbecause of lower labor and other input costs. Wear et al. [2007] founda delivered cost differential of 24% and 27% in Brazil and Chile ascompared with the southern United States, respectively. Furthermore,low pulpwood prices have resulted from a contraction of domestic pulpand paper demand, as evidenced by declining pulp mill capacity and ex-panded use of recycled material. Timber inventory reductions, however,have not kept pace with reduced demand, thus further exacerbating theproblem.

Silvicultural practices such as stand thinnings are commonly usedto extract small-diameter wood and reduce excessive amounts of forestbiomass, which enhances residual stand growth as well as lowers wildfireand pest risk. Research suggests that thinning from below is moreeffective in reducing crown fire compared with crown and selectionthinnings (Graham et al. [1999], Peterson et al. [2003]). Furthermore,it is well known that maintaining an appropriate stand density is aneffective way to reduce SPB damage. Overstocked stands reduce treevigor, thus making them more susceptible to SPB attack (Cameron andBillings [1988] and Belanger et al. [1993]). Unfortunately, landownersare less inclined to thin in light of low pulpwood prices and the lack ofmarkets for small-diameter trees (Mason et al. [2006]).

Despite these challenges, several opportunities are on the horizon thatmight help NIPF landowners meet management goals. Concerns overgreenhouse emissions from fossil fuel use, high energy prices, and depen-dency on foreign oil have prompted both policy makers and the indus-try to explore environmentally benign energy sources. Forest biomass,a renewable and more carbon-neutral energy resource, could providea potential solution to the problem (Cook and Beyea [2000]). Recentresearch also suggests that cellulosic ethanol can be competitive andpreferable to other food-based biofuels (Hill et al. [2006]). Although themain utilization of forest biomass for bioenergy purposes has been inthe generation of steam or electricity for the forest products industry(Guo et al. [2007]), with further advancements in cellulosic, enzymatic,and thermochemical technologies, forest biomass-based bioenergy couldopen up new opportunities for NIPF landowners.

Bioenergy development has been promoted through differentgovernment policies, laws, and programs since the 1970s. Woody


biomass-based bioenergy production has seen renewed support duringthe last 10 years (Guo et al. [2007]). For example, one of the purposesof the Healthy Forest Restoration Act of 2003 (United States Congress[2003]) was to support research to improve the commercial value offorest biomass for producing electric energy, transportation fuel, andpetroleum-based product substitutes. The Energy Policy Act of 2005(United States Congress [2005]) supported a research program coveringrenewable resources, providing grants to producers of cellulosic ethanol,and developing cellulose conversion technologies. The United StatesDepartment of Agriculture Forest Service [2004] established goals re-lated to wildfire risk reduction and national energy needs. The Farm,Nutrition, and Bioenergy Act of 2007 (2007 Farm Bill; United StatesCongress [2007]) targets cellulosic ethanol production and the use ofnew technologies to use woody biomass resources more efficiently. TheBiomass Research and Development Technical Advisory Committee, apanel established by the U.S. Congress, predicted a 30% replacementof current U.S. petroleum consumption with biofuels by 2030 based onagricultural and forestland biomass potential—around 1.3 billion drytons per year (72% and 18% coming from agricultural and forestlands,respectively) of biomass potential (Perlack et al. [2005]). Thus, small-diameter wood and wood residues are becoming an attractive optionfor bioenergy production markets. However, concerns have arisen frompulp and paper producers and panel manufacturers because of directcompetition with biorefineries for future wood supplies (Steierer andFischer-Arnken [2007]).

In this paper, we assess the potential impacts of forest biomass mar-kets for energy in slash pine plantations by applying a model thatcombines the Black and Scholes [1973] formula and a modified Hart-man [1976] model. In particular, we assess three scenarios: status quoor no thinning scenario, thinning scenario for pulpwood, and thinningscenario for bioenergy. The first scenario considers the production oftraditional products such as sawtimber, chip and saw, and pulpwood.In the second scenario, the thinned material is considered only forpulpwood production. In the third scenario, bioenergy is incorporatedas a new product. The thinned material and wood residues such asbranches and bark are considered for bioenergy. We also integrate therisk of natural disturbances such as wildfire and pest outbreaks. Weapply the model to conduct an economic assessment of a slash pine


(P. elliotti) plantation in the southeastern United States. Specifically,we answer the following questions:

• What is the impact of bioenergy markets on the profitability ofthe forest stand?

• What is the effect on profitability of reducing the risk of naturaldisturbances through thinnings and changes in price and pricevolatility?

• What is the break-even price for bioenergy that makes the foreststand profitable relative to the no thinning scenario?

2. Model specification. The stochastic condition of foreststumpage prices has been extensively explored in the literature. Ithas been shown that the expected value of the stand increases whenstochastic prices are incorporated (Lohmander [1987, 1994, 2000],Haight [1991], Haight and Smith [1991], Plantinga [1998], Lu and Gong[2003]). The Black and Scholes [1973] formula, which considers thestochastic nature of prices, was primarily developed to value options1

but has been widely used in forestry analyses as well. Shaffer [1984]proposed the option pricing methodology for valuing long-term tim-ber cutting contracts. Zinkhan [1991] applied the option pricing theoryto the problem of valuing the land-use conversion option. Thomson[1992] explored the binomial option pricing model to determine theoptimal forest rotation age. Yin and Newman [1996] studied the effectof catastrophic risk on forest investment decisions following the optionapproach under investment uncertainty. Plantinga [1998] analyzed therotation age problem, highlighting the role of the option value in deter-mining the optimal timing of harvest by assuming that stumpage pricesfollow a random walk or an autoregressive process. Hughes [2000] usedthe Black–Scholes (BS) option formula to value the forestry corpora-tion’s forest assets. Yap [2004] modeled the Philippine forest plantationlease as an option, considering market uncertainty and irreversible sunkestablishment costs.

Unlike previous studies, we extend the BS formula to include theprobability of risk of natural disturbances and integrate it with theHartman model. The BS formula assumes that prices follow a diffusionprocess (random walk with a drift or geometric Brownian motion) thatcan be represented as

dP = μP dt + σP dz,(1)


where P is the stock price, μ is the drift rate of the stock price, dtis the time increment, σ is the volatility of stock price, and dz is theincrement of a Weiner process defined as dz = εt

√dt, where εt is a

normally distributed random variable with E (εt) = 0, E (ε2t ) = 1, and

E (εtεs) = 0 for all t �= s (Dixit and Pindyck [1994]); dz is independentand normally distributed with mean 0 and variance dt . Equation (1)states that a change in P depends on a deterministic component μPdtand stochastic term σPdz . The other underlying assumptions of the BSformula are that the stock pays no dividends, the option is exercised atthe time of expiration, there are no transaction costs, and there are nopenalties to short selling (Black and Scholes [1973]). The BS formulais represented as

C = SN (d1) − Xe−rTN(d2)(2)

where:

C = value of an option (premium),

S = market or stock price,

N (d) = cumulative normal density function,

X = strike or exercise price, and

r = risk-free interest rate.

d1 and d2 show a relationship among the stock price, risk-free interestrate, strike price, volatility, and current and maturity dates. d1 and d2are represented as follows:

d1 =ln

(S

X

)+ (r + σ2/2)(T − t)

σ√

T − t,(3)

d2 = d1 − σ√

T − t,(4)

σ =

√√√√√√n∑

i=1

[ln

(Si

Si−1

)− ln(S̄)

]2

n − 1,(5)

where t and T are the current and maturity date, respectively.


S̄ represents the average stock price, n is the horizon time in whichvolatility is calculated, and N (d1) and N (d2) stand for the probabil-ities that a normal variable takes on values less than or equal to d1or d2, respectively. N (d1) is also known as the option delta, the de-gree to which an option value will change, given a small change in thestock price. N (d2) is the probability that the option will be exercisedor the change of the stock price at expiration time. N (d1) is alwayslarger than N (d2) because d1 is greater than d2 by σ

√T − t. Thus, the

difference between N (d1) and N (d2) will be greater for higher stockvolatilities and/or long-dated options. SN (d1) reflects the benefits ofacquiring the option, whereas XN (d2) represents the price of payingthe option at expiration time.

Following Hughes [1987, 2000], S is the stumpage price at time t , σis the stumpage price volatility (the standard deviation of the naturallogarithm of stumpage prices), and X is the cumulated exercise forestcosts per unit of merchantable volume at time T . The exercise costhas to be interpreted as the option of the forest landowner of holdingthe forest stock (stumpage) and incurring costs associated with certainactivities such as site preparation, planting, fertilization, weed control,management, and so forth, or selling the stumpage. The revenues fromthinnings are considered as a negative cost (Hughes [2000]). The de-cision to sell the stumpage will depend on whether the payoffs fromdoing so are greater than the value of waiting (Plantinga [1998]). If thevalue of the timber exceeds the cumulated cost incurred by the forestlandowner, the stumpage will be sold; otherwise, the sale will be putoff. The expected value and net expected value of the timber can berepresented, respectively, as

val(T ) = V (T ) × [S × N(d1)],(6)

Nval(T ) = V (T ) × [S × N(d1) − X × e−rT × N(d2)],(7)

where V (T ) is the total merchantable volume at time T . Contrary tofinancial options in which the time of expiration of the option is fixed,the harvest date T for forest options can be variable (Hughes [2000]).The expected net present value of the timber for the first rotation canbe expressed as


Npv(T )timber = Nval(T ) × e−δT ,(8)

where δ is the discount rate. If the land is assumed to be used fortimber production for perpetuity, the land expectation value can bemodeled as

LEV (T ) =Nval(T ) × e−δT

(1 − e−δT ),(9)

where LEV (T ) is the BS formula-based land expectation value (LEV).Starting from bare land2 (t = 0) and simulating harvest dates T 1 ,T 2 , T 3 , and so forth the time T that maximizes LEV (T ) is the ex-pected optimal rotation age3. Several studies have explored the ef-fect of stochastic prices on the optimal rotation age and compared itwith the traditional deterministic model (the Faustmann formula) andfound that it is usually longer when uncertainty is considered. Thom-son [1992] found that the optimal rotation age for Douglas fir whenstumpage prices follow a diffusion process was 65 years, whereas theFaustmann rotation age was 40 years. Brazee and Mendelson [1988],using the reservation price approach, found that the expected rotationage was 2 years and 1 year longer than the Faustmann age for Douglasfir and loblolly pine, respectively. Haight and Smith [1991] prescribeda rotation age of 35 years using stochastic prices versus 30 years usingdeterministic prices for loblolly using the same approach.

As mentioned earlier, forest can be affected by catastrophic eventssuch as wildfires and pest outbreaks. In general, catastrophic distur-bance rates in forests are around 1% annually, ranging from 0.5% to 2%(Runkle [1985]). Reed [1984]) extended the Hartman model by incor-porating the probability of a stand of being affected by catastrophicevents. A similar approach was recently followed by Stainback andAlavalapati [2004] to model the effect of catastrophic risk on carbonsequestration in slash pine forests. Furthermore, Englin et al. [2000] ex-plored the optimal rotation age in a multiple-use forest in the presenceof fire risk.

It is assumed that these events follow a Poisson process, which meansthat they are independent and occur at the same average probabilityλ per unit of time. Thus, the Poisson parameter represents the averagerate of a catastrophic event. The second assumption is that the waiting


time between successive catastrophic events is also a random variable.Following Reed [1984], the times between each successive destructionof the stand are denoted by x 1 , x 2 , . . . , xn . Further λ occurs everyyear and x follows the exponential distribution (1 − e − λx ). Theprobability density function of x before reaching the optimal rotationage (x < T ) is given by (λe−λx). At the optimal rotation age (x = T ),the probability density function is (e−λT ). Therefore, the probabilityof a stand being destroyed by a catastrophic event both before and atthe time of rotation age T is, respectively,

prob(x < T ) = 1 − e−λT , prob(x = T ) = 1 − prob(x < T ) = e−λT .

(10)

The net return will depend on both the timing of the catastrophicevents and the timing of the landowner’s decisions during the rotation(Amacher et al. [2005]). It is also considered that some portion of thestand is salvageable on a proportion k after a catastrophic event. If acatastrophic event occurs, the landowner will harvest any salvageabletimber and replant to start a new rotation. Thus, the value of onerotation can be represented for the following two states:

Y n =

[Nval(T ) if x = T

kNval(x) if x < T

].(11)

If a catastrophic event happens at time (x < T ), the landowner salvagesa proportion of the stand and incurs the exercise costs associated withthe development of a new forest stand. The net rent at time x is givenby kNval(x). However, if the stand reaches the optimal rotation agewithout being affected by a catastrophic event (x = T ), the landownerharvests all the timber and incurs in the exercise costs associated withthe development of a new forest stand. The net rent obtained at timeT is Nval(T ).

Reed [1984] showed that when risk is present, the LEV can be mod-eled as follows:

LEV (x) = E

[ ∞∑n=1

e−δ(x1 +x2 +···+xn )Yn

].(12)


Furthermore, because of the independence of the variables xn , equation(12) can be rewritten as

LEV (x) =∞∑

n=1

E[e−δ(x1 +x2 +···+xn−1 )]E[

e−δxn Yn

]

=∞∑

n=1

n−1∏i=1

E[e−δxi

]E

[e−δxn Yn

]

=E

[e−δxn Yn

]1 − E

[e−δxn

] .

(13)

In addition,

E[e−δxn

]=

∫̄ ∞

0e−δxn dFx(t)

=∫ T

0e−δxλe−λxdx + e−δT e−λT

=λ + δe−(λ+δ)T

(λ + δ).

(14)

Using equations (10), (11) and (14), we obtain an expression that in-corporates the outcomes represented in equation (12). The left-handside of equation (15) represents the expected value of a single rota-tion that can be expressed as the sum of the stand being affected by acatastrophic event with a salvageable portion harvested before reach-ing the optimal rotation (first term of the right hand side) and thestand being harvested at the optimal rotation age (second term of theleft hand side). Both expressions are multiplied by their probabilitiesof occurring and discounted to year 0. Thus,

E[e−δxn Yn

]=

∫ T

0e−δxkNval(x)λe−λxdx + e−δT [Nval(T )]e−λT .

(15)

Using equations (14) and (15) and substituting them into equation(13), the LEV can be redefined as


LEV (T ) =(λ + δ)

δ(1 − e−(λ+δ)T )[e−(λ+δ)T [Nval(T )]

+∫ T

0e−(λ+δ)xλkNval(x) dx.

(16)

Again, the time T that maximizes the LEV is the optimal rotationage. Recall that if λ is set to 0, equation (16) reverts to equation (9). Anumerical solution with slash pine will be presented next to facilitatemodel comprehension.

3. Model application to slash pine stands in the southeast-ern United States. Slash pine (P. elliottii) is one of the main com-mercial timber species in the southern United States, occupying around10 million acres. It is a fast-growing species that yields good-qualityfiber and lumber (Barnett and Sheffiled [2002]). The software GaPPS4.20 (Georgia Pine Plantation Simulator [University of Georgia, War-nell School of Forest Resources, Athens, GA]; Bailey and Zhou [1997])was used to obtain the growth and yield data. Three scenario sets wereconsidered: status quo or no thinning scenario, thinning scenario forpulpwood , and thinning scenario for bioenergy . The thinning age wasset at year 16, and the percentage of trees left was 70%. Slash pinestands are typically thinned between years 12 and 18 or when the totaltree height reaches 40 feet (Dickens and Will [2002]).

The site index and stand density at year 5 were assumed to be 70and 585 trees acre−1 , respectively. Four product classes were defined:sawtimber (st), chip and saw (cs), pulpwood (pw), and forest biomassfor bioenergy (fbb). It was assumed that the small-end diameter forst , cs, pw , and fbb was 10, 6, 3, and 0.1 inches, respectively, whereasthe minimum length for st , cs, pw , and fbb was 8, 8, 5, and 0.1 feet,respectively. St , cs, and pw were assumed to be obtained under the nothinning scenario and thinning scenario for pulpwood , whereas st , cs,pw , and fbb were assumed to be obtained under the thinning scenariofor bioenergy .

The nominal stumpage prices for st , cs, and pw were obtained fromTimber Mart South (TMS [2006]). TMS has been one of the mainsources of prices and trends for forest products in southern states.Several studies have used TMS’s reports for their economic analyses


such as Newman [1987], Royer [1987], Washburn and Binkley [1990],Prestemon and Holmes [2000], Munn et al. [2002], and Stainback andAlavalapati [2004] among others. The nominal prices were deflated us-ing the lumber Producer Price Index (PPI; base year = 2005) providedby the United States Department of Labor, Bureau of Labor Statistics[2007]. Thus, the real stumpage prices for st , cs, and pw were $42.2ton−1 , $25.75 ton−1 , and $7.46 ton−1 , respectively. Although there isno formal market for forest biomass to utilize for bioenergy, we assumedthe real price for fbb to be $3 ton−1 .

The historical real volatility for sawtimber (σst), chip and saw (σcs),and pulpwood (σpw ) was obtained from Yin and Caufield [2002], andthe values for σst , σcs , and σpw were 19%, 24%, and 24%, respec-tively. The volatility for wood residue for bioenergy (σfbb = 15%) wascalculated using the 1970–2004 deflated time series of biomass-basedelectricity industrial prices for five southeastern U.S. states: Florida,Georgia, Alabama, Arkansas, and Virginia (Energy Information Ad-ministration [2007]). The risk-free interest rate and the real discountrate were set to 3% and 5%, respectively.

The common costs associated with the silvicultural activities for thethree scenarios were based on Smidt et al. [2005]. Costs of $205 acre−1

and $58 acre−1 were assumed for mechanical site preparation (shear,pile, rake, and bed) and mechanical planting, respectively. Weed con-trol and fertilization costs were assumed to be $78 acre−1 and $55acre−1 , respectively. Fertilization was considered at years 1 and 7. Anextra fertilization for both thinning scenario for pulpwood and thinningscenario for bioenergy was considered in year 16 after thinning. In ad-dition, a timber marking cost of $14.6 acre−1 before thinning (year 16)was considered. Annual forest management costs such as taxes, gen-eral fire protection, and management plans were set to $6 acre−1 . Inboth scenarios in which thinning was undertaken, the thinning cost isreflected by the stumpage price that is paid to the landowner. For thiscase, fbb was multiplied by a factor of 0.9.

Slash pine plantations under no thinning scenario and both thinningscenarios are expected to have different rates of catastrophic risk. Theformer was modeled with a risk of 3%, whereas both thinning scenarioswere modeled with risk levels from 0 to 3%. Outcalt and Wade [2004]found that the highest tree mortality rate of southern pines after acatastrophic even such as fire occurred when prescribed burning had


not been used since plantation establishment, averaging a mortality of89%. Thus, two situations concerning the salvageable portion after acatastrophic event for the three scenarios were considered: the standbeing completely destroyed (k = 0), and 80% of the stand is salvageable(k = 0.8).

4. Results and discussion. The maximum LEV s for the threescenarios are shown in Table 1. With a positive salvageable portion (k =0.8), the LEV for the thinning scenario for pulpwood and the thinningscenario for bioenergy was greater than the no thinning scenario at allrisk levels—4.6% and 5.8% higher LEV s, respectively. When salvageis zero (k = 0), the land value for the no thinning scenario exceededboth of the thinning scenarios at the same risk level (λ = 0.03) by3.5% and 2.6%, respectively (for thinning for pulpwood and thinningfor bioenergy).

The LEV for the thinning scenario for bioenergy was greater than thethinning scenario for pulpwood at all comparable risk/salvage levels,exceeding the latter by 11.2%, 11.4%, 11.6%, and 11.7%, respectively,for a salvage level of k = 0.8. When k = 0, the difference betweenLEV s was steady at 11.7% for all levels of risk. A 1% reduction inrisk increased LEV s between 9% (k = 0.8) and 19% (k = 0). Thus,land values are impacted less by increased risk levels when salvage ispossible. The optimal rotation for the thinning scenarios was longerthan the no thinning scenario, 27 versus 21 years, respectively.

The incorporation of thinnings for bioenergy increased the profitabil-ity of slash pine forestry over the no thinning scenario when salvagewas possible, but not when salvage was not possible at a risk level of3%. In fact, if the stumpage price for fbb was set equal to $0 ton−1 (λ =0.03 and k = 0.8), the land value for the thinning scenario for bioenergywas still greater ($659.17 acre−1) than the no thinning scenario, thereason being the high revenues obtained by producing more proportionof sawtimber and less proportion of pulpwood. The break-even pointfor stumpage price for fbb was $2.1 ton−1 (λ = 0.03 and k = 0.8) whenthe thinning scenario for bioenergy was compared with the thinningscenario for pulpwood . At this price level, the land value was the samefor both thinning scenarios, $679.2 acre−1 . For k = 0, the break-evenstumpage price for fbb was slightly greater, $2.2 ton−1 .


TA

BL

E1.

LEV

($ac

re−

1)

for

the

thre

esc

enar

ios

atdi

ffer

ent

leve

lsof

risk

and

salv

age.

Thi

nnin

gT

hinn

ing

Rel

ativ

ein

crea

seR

elat

ive

incr

ease

No

thin

ning

scen

ario

for

scen

ario

for

inLEV

for

a1%

inLEV

for

a1%

scen

ario

pulp

woo

dbi

oene

rgy

redu

ctio

nin

risk

redu

ctio

nin

risk

Scen

ario

Ris

k\s

alva

gek

=0.

8k

=0

k=

0.8

k=

0k

=0.

8k

=0

k=

0.8

k=

0k

=0.

8k

=0

λ=

090

1.1

901.

191

1.7

911.

7−

−−

−λ

=0.

0182

2.3

762.

383

1.8

771.

31.

091.

181.

091.

18λ

=0.

0274

8.3

641.

575

6.8

649.

01.

091.

191.

091.

19λ

=0.

0364

9.3

556.

267

9.2

537.

168

6.8

543.

41.

101.

191.

101.

19


0100200300400500600700800900

1000

5 7 9 11 13 15 17 19 21 23 25 27 29 31 33

LEV US$ acre-1

Age

Land value thinning scenario for bioenergy λ= 0.03Land value thinning scenario for bieoenergy λ=0.02Land value thinning scenario for bioenergy λ=0.01Land value thinning scenario for bioenergy λ=0Land value no thinning scenario λ=0.03

FIGURE 1. LEV s for both the no thinning scenario and thinning scenariofor bioenergy when the salvageable portion is 0.

Regardless of the risk level, and consistent with findings of Stainbackand Alavalapati [2004], the LEV was greater when the salvageableportion increased for the three scenarios (Figures 1–4). Furthermore,as risk decreased for both thinning scenarios, the relative difference inLEV s when k = 0 as compared with when k = 0.8 also decreased.The increase in LEV for a stand partially salvaged as compared with astand completely destroyed under both thinning scenarios was consis-tent across thinning scenarios at 26.4%, 16.6%, 7.8%, and 0% for risklevels of 0.03, 0.02, 0.01, and 0, respectively. When the risk continu-ously drops, the probability of selling the stumpage and replanting dueto a catastrophic event declines, and as such, the difference decreases.

The decrease in merchantable volume due to thinnings caused theland values to drop in year 16 (Figures 1–4). Although the total volumebefore the thinning was the same for the three scenarios, the land valuefor the no thinning scenario was lower than the thinning scenario forbioenergy due to the inclusion of biomass for bioenergy that could beharvested at age 16. In the no thinning scenario, only sw , cs, andpw were included as merchantable volume. However, in the thinningscenario for bioenergy , fbb was included along with these other three


0100200300400500600700800900

1000

5 7 9 11 13 15 17 19 21 23 25 27 29 31 33

LEV US$ acre-1

Age

Land value thinning scenario for bieoenergy λ=0.03Land value thinning scenario for bioenergy λ=0.02Land value thinning scenario for bioenergy λ=0.01Land value thinning scenario for bioenergy λ=0Land value no thinning scenario λ=0.03

FIGURE 2. LEV s for both the no thinning scenario and thinning scenariofor bioenergy when the salvageable portion is 0.8.

0100200300400500600700800900

1000

5 7 9 11 13 15 17 19 21 23 25 27 29 31 33

LEV US$ acre-1

Age

Land value thinning scenario for pulpwood λ= 0.03Land value thinning scenario for pulpwood λ=0.02Land value thinning scenario for pulpwood λ=0.01Land value thinning scenario for pulpwood λ=0Land value no thinning scenario λ=0.03

FIGURE 3. LEV s for both the no thinning scenario and thinning scenariofor pulpwood when the salvageable portion is 0.


0100200300400500600700800900

1000

5 7 9 11 13 15 17 19 21 23 25 27 29 31 33

LEV US$ acre-1

Age

Land value thinning scenario for pulpwood λ=0.03Land value thinning scenario for pulpwood λ=0.02Land value thinning scenario for pulpwood λ=0.01Land value thinning scenario for pulpwood λ=0Land value no thinning scenario λ=0.03

FIGURE 4. LEV s for both the no thinning scenario and thinning scenariofor pulpwood when the salvageable portion is 0.8.

products. This difference became greater when risk (λ) was reducedregardless of the salvageable portion (Figures 1 and 2). The results(except for the impacts of risk) did not extend to the thinning scenariosfor pulpwood (Figures 3 and 4) because fbb was not a factor. Withregard to the thinning scenario for pulpwood , there was no differencein LEV compared with the no thinning scenario before the year 16 forthe same level of salvageable portion and catastrophic risk. For bothscenarios, the merchantable volume was the same without includingfbb. However, as λ was reduced, the LEV for the thinning scenariofor pulpwood became greater than the status quo (Figure 3 and 4). Ingeneral, the difference between LEV s for the no thinning scenario andthinning scenarios became greater when λ was reduced because therate at which the LEV was discounted became higher.

At year 16, the LEV s for the thinning scenarios fell strongly becauseof 30% of tree removal and the cost of thinning and marking. Afterthinning, the LEV started increasing because a larger proportion ofsw and a lesser proportion of pw were produced. In addition, λ wasreduced; thus, the growing LEV trend was accelerated by increasingthe discount rate. The timing when the LEV s for the thinning scenarios


started exceeding the no thinning scenario varied depending on k orλ. When k = 0 and regardless of λ, the break-even age occurred earlierthan when k = 0.8. For example, for a k = 0.8 and λ = 0.03, whencomparing with the thinning scenario for bioenergy , the break-evenage was 24 years compared with 22 years when k = 0 and λ = 0.03(Figures 1 and 2). On the other hand, regardless of k , the break-evenage occurred earlier as λ was decreased. For example, for a k = 0.8,it was 24 and 21 years when λ = 0.03 and λ = 0.02, respectively, forthe thinning scenario for bioenergy (Figure 2). Thus, when positiveenvironmental effects are considered once thinnings are carried out,landowners would financially benefit as the land value peaks faster,exceeding the value when thinnings are not undertaken.

Although the inclusion of thinnings increased LEV s, the differ-ence between the thinning scenarios versus the no thinning scenar-ios was relatively small for the same risk level. As forest biomass-based bioenergy markets continue to expand, it is plausible that pricespaid for woody biomass may increase as well as their volatility. Wesimulated independently two impacts: increased price ($5 ton−1 , $10ton−1 , and $15 ton−1) and increased price volatility (0.2, 0.25, and0.3) for fbb. Table 2 shows the LEV s for different levels of prices andvolatility.

From Table 2, and consistent with expectations, the LEV increasedas fbb price increased. This increase was slightly higher when salvagewas possible. For example, on average, the increase when fbb pricechanged from $5 ton−1 to 10 ton−1 and from $10 ton−1 to $15 ton−1

was 6.4% and 6.1% for a k = 0.8 and k = 0, respectively. Further-more, when comparing to the original scenario for bioenergy, the LEVincreased by 9.4% and 9% for k = 0.8 and k = 0, respectively. In ad-dition, the profitability of the forest stand was greater when comparedto the no thinning scenario and thinning scenario for pulpwood . Com-pared with the former, the LEV increased, on average, by 16% and6.4% for k = 0.8 and k = 0, respectively, when fbb price was increased.With regard to the latter, the LEV increased by 10.6% and 10.2% fork = 0.8 and k = 0, respectively.

Regarding volatility, the increase of the LEV was lower comparedwith the increase of the LEV when price was increased. The averageincrease in the LEV when volatility changed from 0.2 to 0.25 and


TA

BL

E2.

LEV

($ac

re−

1)

for

the

thin

nin

gsc

enari

ofo

rbi

oener

gyfo

rdi

ffer

ent

bioe

nerg

ypr

ices

and

leve

lsof

vola

tilit

y.

Fbb

pric

eT

hinn

ing

scen

ario

Fbb

Thi

nnin

gsc

enar

io$

ton−

1Sc

enar

iofo

rbi

oene

rgy

vola

tilit

ySc

enar

iofo

rbi

oene

rgy

5R

isk\s

alva

gek

=0.

8k

=0

0.2

Ris

k\s

alva

gek

=0.

8k

=0

λ=

093

4.9

934.

9λ

=0

917.

491

7.4

λ=

0.01

853.

579

0.9

λ=

0.01

837.

477

6.1

λ=

0.02

777.

166

5.5

λ=

0.02

762.

265

7.0

λ=

0.03

705.

855

7.2

λ=

0.03

692.

154

6.9

10R

isk\s

alva

gek

=0.

8k

=0

0.25

Ris

k\s

alva

gek

=0.

8k

=0

λ=

099

3.3

993.

3λ

=0

922.

992

2.9

λ=

0.01

908.

384

0.3

λ=

0.01

842.

778

0.7

λ=

0.02

828.

370

7.1

λ=

0.02

767.

470

7.1

λ=

0.03

753.

659

2.0

λ=

0.03

697.

155

0.0

15R

isk\s

alva

gek

=0.

8k

=0

0.3

Ris

k\s

alva

gek

=0.

8k

=0

λ=

010

52.2

1052

.2λ

=0

928.

592

8.5

λ=

0.01

963.

789

0.2

λ=

0.01

848.

278

5.5

λ=

0.02

880.

374

9.1

λ=

0.02

772.

766

1.0

λ=

0.03

802.

262

7.1

λ=

0.03

702.

255

3.4


0.25 to 0.3 was 0.6% steady for all levels of salvageable portions. Withregard to the no thinning scenario for k = 0.8, the land value was, onaverage, 1.07% higher, but for k = 0, it was almost the same, in anaverage ratio ≈1. The difference with regard to the previous bioenergyscenario and the thinning scenario for pulpwood accounted for 2.5%and 3.1% for k = 0.8 and k = 0, respectively. Thus, as bioenergy pricesare expected to rise and, consequently, their volatility, this combinedeffect will result in greater returns to forest landowners.

5. Summary and conclusions. The incorporation of thinningsincreased forestland values regardless of the risk level when the salvage-able value of the stand was positive. The results suggested that wheneither pulpwood or forest biomass for bioenergy is incorporated and thewhole stand became commercially marketable, the revenues obtaineddue to stochastic price variation could offset the cost of performingsilvicultural activities such as thinning. However, when the landownerwas not allowed to salvage any portion of the stand and the risk levelwas assumed to be 0.03, the land value for the no thinning scenariowas higher than for the thinning scenarios. Under these conditions, therevenues associated with the increase of the volumetric growth afterthinning for greater value-added product and the low price for forestbiomass-based bioenergy were not enough to cover the loss of volumeand, consequently, the profits at the time of thinning. Bioenergy priceof $5 ton−1 broke even the land value for the bioenergy scenario withregard to the status quo when stand was completely destroyed and riskwas 0.03.

Including thinnings for bioenergy increased the land value by around11.6% compared to the thinning for pulpwood-only scenario. As ex-pected, increased risk decreased land values for all salvage levels,dropping greater when salvage was zero. On average, the increaseof the land value when risk was decreased by 1% was 10% and19% for both thinning scenarios when the salvageable portion was0.8 and 0, respectively—the higher risk damage being proportionallymore compensated by revenues from salvage. On average, salvage in-creased land values by 17%. Thus, policies that help landowners mit-igate risk through silvicultural interventions to reduce the size of thedamage would have a positive impact on the profitability of foreststands.


Although the inclusion of thinnings increased the land value of aforest stand, the difference with the status quo scenario might beconsidered small. Furthermore, landowners would have to wait longerto harvest. However, it is expected that as the supply and demandof bioenergy increase, bioenergy prices will also increase. Thus, by in-creasing stumpage price and volatility for bioenergy, and consistentwith features of the BS formula, the land value increased. The impacton the land value was higher when price was increased: the increase inthe land value with regard to the original scenario for bioenergy was9.4% and 9% when the salvageable portion was 0.8 and 0, respectively,whereas it was 2.5% and 3.1% for the same salvageable portions whenvolatility was increased.

Increase in land values due to thinning and bioenergy markets willbenefit landowners. Current NIPF landowners will become more com-petitive, and future landowners can be influenced to undertake thin-ning and even switch from other land uses to forestry. Thinningswill concentrate growth on fewer large trees, which will bring higherstumpage prices. Furthermore, as bioenergy markets continue evolv-ing, small-diameter wood for bioenergy purposes will become a com-petitor for other uses for this type of product, for example pulpwood,thus raising its price. However, current fluctuating pulpwood marketsand the lack of a formal market for forest biomass-based bioenergycould be a threat for NPIF landowners to undertake thinnings. Inthis study, the thinning age was set at year 16. Further research isneeded to set an optimal thinning age, maximizing the amount of for-est biomass to be thinned and the benefit cost of this silviculturalpractice.

In addition, the incorporation of thinnings will also benefit soci-ety. Other commercial activities such as the possibility of silvopas-toral use will be allowed. Because of a decrease of risk and the inten-sity of the catastrophic event, positive externalities will arise. Foresthealth and wildlife habitat will be improved because of a reductionof pest outbreaks and wildfires, respectively. Dependency on externalmarkets for oil and concerns about greenhouse emissions can be al-leviated. In addition, other environmental services such as landscapeand recreation values will be enhanced. Thus, more factors can be re-quested and incorporated in order to assess the profitability of southernpines.


ENDNOTES

1. An option is a contract between a holder of an option and a seller in whichthe holder has the right to buy a stock at a predetermined price for a specific timeperiod.

2. By assuming that the land has not been planted, the current time t of theforest option is set to be equal to T 0 , which is the time at the beginning of therotation (T 0 = 0).

3. The optimal rotation age T is sometimes denoted as T∗.

REFERENCES

G.S. Amacher, A.S. Malik, and R.G. Haight [2005], Not Getting Burned: TheImportance of Fire Prevention in Forest Management , Land Econ. 81(2), 284–302.

R. Bailey and B. Zhou [1997], GaPPS 4.20 (Georgia Pine Plantation Simula-tor). Forest Biometrics Consulting, University of Georgia, Warnell School of ForestResources, Athens, GA.

J. Barnett and R. Sheffiled [2002], Slash Pine: Characteristics, History, Statusand Trends, in E.D. Dickens, J.P. Barnett, W.G. Hubbard and E.J. Jokela, eds.,Proceedings of the Slash Pine Symposium, Slash Pine: Still Growing and Growing! ,Jekyll Island, GA, April 23–25, 2002.

R. Belanger, Hedden, and P. Lorio [1993], Management Strategies to ReduceLosses from the Southern Pine Beetle, Southern J. Appl. Forestry 17(3), 150–154.

F. Black and M. Scholes [1973], The Pricing of Options and Corporate Liabilities,J. Polit. Econ. 81(3), 637–654.

R. Brazee and R. Mendelson [1988], Timber Harvesting with Fluctuating Prices,Forest Sci. 34(2), 359–372.

R. Cameron and R. Billings [1988], Southern Pine Beetle: Factors Associatedwith Spot Occurrence and Spread in Young Plantations, Southern J. Appl. Forestry12(3), 208–214.

J. Cook and J. Beyea [2000], Bioenergy in the United States: Progress and Pos-sibilities, Biomass Bioenergy 18, 441–455.

D. Dickens and R. Will [2002], Planting Density Impacts on Slash Pine StandGrowth, Yield, Product Class Distribution, and Economics, In Proceedings of theSlash Pine Symposium, Slash Pine: Still Growing and Growing!, E.D. Dickens,J.P. Barnett, W.G. Hubbard, and E.J. Jokela, eds., Jekyll Island, GA, April 23–25,2002.

A. Dixit and R. Pindyck [1994], Investment Under Uncertainty, Princeton Uni-versity Press, Princeton, NJ.

Energy Information Administration [2007], State Energy, State Energy Con-sumption, Price and Expenditures Estimates. Available at: http://www.eia.doe.gov/emeu/states/_seds.html. Accessed August 10, 2007.

J. Englin, P. Boxall, and G. Hauer [2000], An Empirical Examination of OptimalRotations in a Multiple-Use Forest in the Presence of Fire Risk , J. Agric. Resour.Econ. 25(1), 14–27.


Florida Department of Community Affairs and Florida Department of Agricul-ture and Consumer Services Division of Forestry [2004], Wildfire Mitigation inFlorida: Land Use Planning Strategies and Best Development Practices. Availableat: http://www.fl-dof.com/wildfire/wf_pdfs/Wildfire_Mitigation_in_FL.pdf.Accessed August 20, 2008.

R. Graham, A. Harvey, T. Jain, and J. Tonn [1999], The Effects of Thinning andSimilar Stand Treatments on Fire Behavior in Western Forests, General TechnicalReport PNW-GTR-463, United States Department of Agriculture Forest Service,Pacific Northwest Research Station, United States Department of the Interior Bu-reau of Land Management, Portland, OR.

Z. Guo, C. Sun, and D.L. Grebner [2007], Utilization of Forest derived Biomassfor Energy Production in the U.S.A.: Status, Challenges, and Public Policies, Int.Forestry Rev. 9(3), 748–758.

R. Haight [1991], Stochastic Log Price, Land Value, and Adaptive Stand Man-agement: Numerical Results for California White Fir , Forest Sci. 37(5), 1224–1238.

R. Haight and W. Smith [1991], Harvesting Loblolly Pine Plantations with Hard-wood Competition and Stochastic Prices, Forest Sci. 37(5), 1266–1282.

R. Hartman [1976], The Harvesting Decision When a Standing Forest Has Value,Econ. Inquiry 14(1), 52–58.

J. Hill, E. Nelson, D. Tilman S. Polasky, and D. Tiffany [2006], Environmental,Economic, and Energetic Costs and Benefits of Biodiesel and Ethanol Biofuels,Proc. Natl. Acad. Sci. U S A 103(30), 11206–11210.

W. Hughes [1987], Forestry Valuation using Option Pricing Theory. The Uni-versity of Waikato, Working Papers in Economics 87(2), 14.

W. Hughes [2000], Valuing Forest as a Call Option: The Sale of Forestry Cor-poration of New Zealand , Forest Sci. 46(1), 32–39.

S. Le VanGreen and J. Livingstone [2003], Uses for Small-Diameter and Low-Value Forest Thinnings, Ecolog. Restor. 21(1), 34–38.

P. Lohmander [1987], The Economics of Forest Management Under Risk , Report79, Doctoral dissertation, Department of Forest Economics, Swedish University ofAgricultural Sciences, Uppsala, Sweden. p. 311.

P. Lohmander [1994], Adaptive Decision Making in Forestry , In Proceedings ofthe International Symposium on Systems Analysis and Management Decisions inForestry, Forest Management and Planning in a Competitive and EnvironmentallyConscious World, G. Paredes, ed.,Valdivia, Chile, March 9–12, 1993, pp. 411–421.

P. Lohmander [2000], Optimal Sequential Forestry Decisions Under Risk , Ann.Oper. Res. 95(1–4), 217–228.

F. Lu and P. Gong [2003], Optimal Stocking Level and Final Harvest Age withStochastic Prices, J. Forest Econ. 9(2), 119–136.

L. Mason, B. Lippke, Z. Zobrist, T. Bloxton, K. Ceder, J. Comnick, J. McCarter,and H. Rogers [2006], Investment in Fuel Removals to Avoid Forest Fires Resultin Substantial Benefits, J. Forestry 104(1), 27–31.

I. Munn, S. Barlow, D. Evans, and D. Cleaves [2002], Urbanization’s Impact onTimber Harvesting in the South Central United States, J. Environ. Manage. 64(1),65–76.


D. Newman [1987], An Econometric Analysis of the Southern Softwood StumpageMarket: 1950–1980 , Forest Sci. 33(4), 932–945.

K. Outcalt and D. Wade [2004], Fuels Management Reduces Tree Mortality fromWildfires In Southeastern United States, Southern J. Appl. Forestry 28(1), 28–34.

D. Paun and G. Jackson [2000], Potential for Expanding Small-Diameter TimberMarket: Assessing Use of Wood Posts in Highway Applications, General Techni-cal Report 120, U.S. Department of Agriculture, Forest Service, Forest ProductsLaboratory, Madison, WI. p. 28.

R.D. Perlack, L.L Wright, A. F., Turhollow, R.L Graham, B.J. Strokes, and D.CErbach [2005], Biomass as Feedstock for a Bioenergy and Bioproducts Industry:The Technical Feasibility of a Billion-Ton Annual Supply . Oak Ridge NationalLaboratory, Oak Ridge, TN. p. 59.

D. Peterson, M. Johnson, J. Agee, T. Jain, D. Mckenzie, and E. Reinhardt [2003],Fuels Planning: Managing Forest Structure to Reduce Fire Hazard , Proceedingsof the 2nd International Wildland Fire Ecology and Fire Management Congress,Orlando, FL, November 16–20, 2003.

A. Plantinga [1998], The Optimal Timber Rotation: An Option Value Approach.Forest Sci. 44(2), 192–202.

B. Polagye K. Hodgson, and P. Malte [2007], An Economic Analysis of Bio-Energy Options Using Thinnings from Overstocked Forests, Biomass Bioenergy31(2–3), 105–125.

J.P. Prestemon and T.P. Holmes [2000], Timber Price Dynamics Following aNatural Catastrophe, Am. J. Agric. Econ. 82(1), 145–160.

J. Prestemon and T.P. Holmes [2004], Market Dynamics and Optimal TimberSalvage After a Natural Catastrophe, Forest Sci. 50(4), 495–511.

J.P. Prestemon, D.N. Wear, F.J. Stewart, and T.P. Holmes [2006], Wildfire, Tim-ber Salvage, and the Economics of Expediency , Forest Policy Econ. 8(3), 312–322.

T. Price, C. Doggett, J. Pye, and B. Smith [1998], A History of Southern PineBeetle Outbreaks in the Southeastern United States. The Georgia Forestry Com-mission, Macon, GA.

W. Reed [1984], The Effects of the Risk on the Optimal Rotation of a Forest , J.Environ. Econ. Manage. 11, 180–190.

J. Royer [1987], Determinants of Deforestation Behavior among SouthernLandowners, Forest Sci. 33(3), 654–667.

J.R. Runkle [1985], Disturbance Regimes in Temperate Forests. in (S.T.A. Pickettand P.S. White, eds.), The Ecology of Natural Disturbances and Patch Dynamics,Academic Press, Orlando, FL. pp. 17–33.

R. Schmidt [1998], Fusiform Rust Disease of Southern Pines: Biology, Ecologyand Management , Technical Bulletin 903, Agricultural Experiment Station, Insti-tute of Food and Agricultural Sciences, University of Florida, Gainesville, FL.

M. Shaffer [1984], Valuation of Certain Long Term Cutting Contract , Forest Sci.30(3), 774–787.

M. Smidt, B.S. Folegatti, and M. Dubois [2005], Costs and Cost Trends forForestry Practices in the South, For. Landow. 64(2), 25–31.


B. Smith, P. Miles, J. Vissage, and S. Pugh [2004], Forest Resources of the UnitedStates, 2002 , General Technical Report NC-241, U.S. Department of Agriculture,Forest Service, North Central Research Station, St. Paul, MN.

A. Stainback and J. Alavalapati [2004], Modeling Catastrophic Risk in EconomicAnalysis of Forest Carbon Sequestration, Nat. Resour. Model. 17(3), 299–317.

F. Steierer and A. Fischer-Ankern [2007], Wood Energy in Europe and NorthAmerica: A New Estimate of Volumes and Flows, Joint Wood Energy EnquiryReport. Geneva, Switzerland. p. 35.

T. Thomson [1992], Option Pricing and Timberland’s Land Use Conversion Op-tion: A Comment , Land Econ. 68(4), 462–466.

Timber Mart South [2006], Timber Mart South Market Newsletter: QuarterlyStumpage Prices 1976–2005, Athens, GA.

United States Congress [2003], Healthy Forest Restoration Act of 2003.United States Congress [2005], The Energy Policy Act.United States Congress [2007], Farm, Nutrition, and Bioenergy Act of 2007 (2007

Farm Bill).United States Department of Labor, Bureau of Labor Statistics [2007], Bureau

of Labor Statistics Data. Available at: http://data.bls.gov/cgi-bin/surveymost.Accessed August 7, 2007.

United States Department of Agriculture Forest Service [2004], USDA ForestService Strategic Plan for Fiscal Years 2004–08. FS-810 , U.S. Department of Agri-culture, Forest Service, Washington, DC. p. 32.

C. Washburn and C. Binkley [1990], Informational Efficiency of Markets forStumpage, Am. J. Agric. Econ. 72(2), 394–405.

D. Wear, D. Carter, and J. Prestemon [2007], The U.S. South’s Timber Sectorin 2005: A Prospective Analysis of Recent Change, General Technical Report SRS-99, U.S. Department of Agriculture, Forest Service, Southern Research Station,Asheville, NC. p. 29.

R.C. Yap [2004], Option Valuation of Philippine Forest Plantation Leases, Env-iron. Dev. Econ. 9(2), 315–333.

R. Yin and J. Caufield [2002], A Profile of Timber Markets in the U.S. Southeast ,Forest Product J. 52(3), 25–34.

R. Yin and D.H. Newman [1996], The Effect of Catastrophic Risk on ForestInvestment Decisions, J. Environ. Econ. Manage. 31, 186–197.

F. Zinkhan [1991], Option Pricing and Timberland’s Land Use Conversion Op-tion, Land Econ. 67(3), 317–325.

Documents

MODELING IMPACTS OF BIOENERGY MARKETS … in docs/recent research/Modeling... · a potential solution to the problem (Cook and Beyea [2000]). Recent research also suggests that cellulosic