ww.sciencedirect.com

b i om a s s a n d b i o e n e r g y 4 6 ( 2 0 1 2 ) 4 1 9e4 2 8

Available online at w

http: / /www.elsevier .com/locate/biombioe

Modeling nonindustrial private forest landowner behaviorin face of woody bioenergy markets

Andres Susaeta a,*, Pankaj Lal b, Douglas R. Carter c, Janaki Alavalapati d

a 315 Newins Ziegler Hall, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, PO Box 110410, USAbMallory Hall 358 N, Earth and Environmental Studies Department, Montclair State University, Montclair, NJ 07043, USAc357 Newins Ziegler Hall, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USAd313 Cheatham Hall (0324), Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State

University, Blacksburg, VA 24601, USA

a r t i c l e i n f o

Article history:

Received 22 February 2012

Received in revised form

19 June 2012

Accepted 29 July 2012

Available online 6 September 2012

Keywords:

Bioenergy

Landowners

Elasticities

Profit function

Policy simulation

* Corresponding author. Tel.: þ1 352 846 087E-mail addresses: asusaeta@ufl.edu (A

(J. Alavalapati).0961-9534/$ e see front matter ª 2012 Elsevhttp://dx.doi.org/10.1016/j.biombioe.2012.07.

a b s t r a c t

This article analyzed the impacts of emerging woody bioenergy markets on the behavior of

nonindustrial private forest (NIPF) landowners in the state of Florida, United States. A

seemingly unrelated regression (SUR) approach was used to assess the supply of

sawtimber, pulpwood, woody bioenergy and demand for labor. Data was gathered from

official statistical reports of the forestry sector in Florida spanning 1970 to 2006. The

econometric analysis suggested that consistent own price elasticities could be obtained

from the SUR approach. The results indicated that an increased price of woody bioenergy

implied an increase in the production of pulpwood and demand for labor. Results also

suggested that forest landowners might financially benefit from emerging bioenergy

markets.

ª 2012 Elsevier Ltd. All rights reserved.

1. Introduction A well-established woody biomass market can decrease the

Woody biomass for energy production has recently gained

attention in the United States (U.S.) because it contributes not

only to the economic development and growth and reduction

of the dependency on foreign energy markets, but also to the

mitigation of greenhouse gas (GHG) emissions [1e4]. It is

considered a better alternative than starch based energy

production as it has fewer risks associated with food security

and has higher net energy balance ratios and greater

greenhouse gas reduction potential [5,6]. Also, the removal

of excess biomass from forests for energy production

might decrease the risk of wildfires and pest outbreaks [7,8].

7; fax: þ1 352 846 1277.. Susaeta), lalp@mail.m

ier Ltd. All rights reserved018

probability of converting forestlands to other uses and allow

forest landowners to invest in treatments that in turn could

lead to improved forest health [9]. Incorporating socioeco-

nomic and environmental benefits might position woody

biomass as a viable option for energy production [3].

A woody biomass based bioenergy market development

could act as additional avenue source for landowner’s

stumpage, along with pulpwood and sawtimber industries.

Nonindustrial private forest (NIPF) landowners own around

70% of the private forestlands in the U.S. South [10] and are

expected to realize higher economic returns with the inclu-

sion of woody biomass as a forest product [11,12]. The sawmill

ontclair.edu (P. Lal), drcart@ufl.edu (D.R. Carter), jrra@vt.edu

.

b i om a s s an d b i o e n e r g y 4 6 ( 2 0 1 2 ) 4 1 9e4 2 8420

industry might benefit from bioenergy development due to

higher prices for secondary products such as sawdust and

chips demanded by bioenergy markets [13,14]. Potential

competition for raw material may exist between bioenergy

plants and wood processing plants in the U.S. South [15,16].

However, the empirical evidence from Europe has showed

that sawtimber markets would less likely be affected by new

bioenergy markets compared to pulpwood markets [13].

Furthermore, pulpmills might also benefit from bioenergy

market development as energy can be generated along with

the production of pulp and paper products [9].

NIPF landowners’ views and preferences about their

forests have evolved over time [17,18]. Primary management

decisions taken by NIPF landowners are influenced by non-

timber activities conducted in their lands such as aesthetics,

family legacy and privacy [10]. Factors that shape the future

choices of forest use depend on factors such as: policy and

economic environment; type of forests; and landowner

socioeconomic characteristics and attitudes [19]. Heteroge-

neity of NIPF landowners due to different forest management

objectives may affect their willingness to supply woody

biomass [20]. Although little information exists about NIPF

landowners’ attitudes towards woody biomass harvesting

behavior for bioenergy, recent literature helps in the identifi-

cation of critical factors affecting landowners’ willingness to

supply woody biomass for bioenergy. Price of biomass,

transaction costs incurred during biomass harvest and

removal, and existence of goals other than biomass manage-

ment in terms of biodiversity and recreation act as critical

motivational factors determining the availability of woody

biomass for bioenergy [21]. Joshi and Mehmood [22] reported

that landownerswithwildlifemanagement goals and younger

landowners with relatively large forestland areas are more

likely to supply biomass for bioenergy. Joshi and Mehmood

[23] segmented southern NIPF landowners into three cate-

gories (bioenergy friendly conservationists, multiple objective

landowners, and passive forest landowners) based on

management goals and willingness to supply woody biomass

for bioenergy. They found that landowners who manage their

forests not only for timber but for other benefits have the

greatest willingness to supply woody biomass.

Different approaches have been used to model NIPF land-

owners’ behavior. Previous work primarily focused on NIPF

landowners’ harvesting decisions and how these were related

to market conditions, timber characteristics and landowner

preferences. However, newer studies have incorporated non-

timber activities in the decision mix and how these in turn

can be used to predict landowners’ behavior [17,18]. Beach

et al. [24] and Pattanayak et al. [25] provide a reviewof different

microeconometric studies pertaining to NIPF management in

the U.S. In general, all these models explore the underlying

theoretical structure of landowner behavior within the profit

and/or utility maximization framework. Profit maximization

assumes than landowners maximize the discounted profits of

producing only timber over time while utility maximization

considers the value the benefits of non-market goods gener-

ated by forestlands along with the value of timber [17,24]. In

general, studies conducted in the U.S. South use the latter

approach and are mostly related to survey based studiesdre-

duced form regressions for binary choice models [24].

Two emblematic studies in the U.S. South using

a restricted profit function approach were conducted in the

nineties by Newman and Wear [26] and Wear and Newman

[27]. The former modeled the production behavior of indus-

trial and NIPF landowners using a restricted profit function

while the latter modeled regional forestry production to

assess short and long run production possibilities. However,

hardly any study has modeled landowner behavior in face of

woody bioenergy markets, under the profit maximization

framework, i.e., assessing the theoretical underlying structure

of the profit function for a stumpage market model that

includes woody bioenergy. In the Scandinavian market

context, a few studies such as Ankarhem et al. [28] and Geijer

et al. [29] havemodeled the effect of price of biomass on forest

landowners’ behavior through profit function maximization

approach.

It has been suggested that utility maximization can be an

alternative approach to model NIPF landowners’ behavior

[24,30]. While empirical evidence has shown that NIPF land-

owners recognize the tradeoffs between timber management

and non-timber amenities [26], they respond consistently to

market conditions such as prices, costs, and interest rates in

a way that is consistent with profit maximization [31]. In this

study, we considered profit maximization approach for an

illustrative forest landowner due to the following reasons:

around 60% of NIPF landowners harvest commercially trees

for their lands [10], economic benefits accruing from timber

are critical to encourage NIPF landowners to supply costly

non-timber benefits, and this economic framework is consis-

tent with long term economic outlook since majority of the

landowners are price takers and supply undifferentiated

forest products [32].

In this backdrop, we used a profitmaximizing approach for

forest landowners in three product markets comprising of

bioenergy, pulpwood, and sawtimber markets. We assumed

a short run landowner profit function, and analyzed its theo-

retical structure and investigated assumption of symmetry,

jointness in production, and separability of the landowners’

profit function. Non-inclusion of bioenergy markets in the

previous studies using profit maximization approach for

forest landowners can be largely attributed to lack of formal

woody bioenergy markets in the southern U.S. We recognized

this limitation and tried to mimic the existence of bioenergy

markets by utilizing forest and mill residues as a proxy for

woody bioenergy. Our rationale was based on the literature

suggesting that conventional forest products harvest residues

and mill residues can act as energy feedstocks [33,34].

Apart from testing the basic properties of profit maximi-

zation functions, we also simulated a policy scenario in which

we imposed a price support policy for bioenergy. Government

policy scenarios such as subsidies for woody biomass based

energy are expected to increase development of the bioenergy

industry by incentivizing the biomass supply through more

advantageous competitive prices [35]. Our policy simulation

scenario followed an implicit assumption that understanding

NIPF landowners’ behavior in light of bioenergy markets is

critical for the design of efficient policy instruments [36]. The

importance of this study is further accentuated by the fact

that policies that promote production of woody biomass for

energy use have not totally been developed [37]. Thus, this

b i om a s s a n d b i o e n e r g y 4 6 ( 2 0 1 2 ) 4 1 9e4 2 8 421

type of policy analysis will add to the research literature

focusing on nascent bioenergy markets and also assist in

effective policy design and implementation.

We used time series data for the state of Florida in our

study. The rest of the paper has been organized as follows. In

Section two we outlined the theoretical specification model

and econometric specification. In Section three we described

the data used in our study. In Section four we reported the

results of testing basic structure of profit functions for forest

landowners. In section five we reported the estimated elas-

ticities and discussed the findings of a policy simulation.

Finally, we presented the conclusions and future research

opportunities.

2. Theoretical model and econometricspecification

We developed a three sector marketmodel in Florida whereby

landowners can sell stumpage to wood fuel and power

industries along with traditional forest product industries

such as those engaged in sawtimber and pulpwood produc-

tion.We assumed a restricted generalized Leontief profit (GLP)

function to model the production behavior of NIPF landowner

which provides a second order approximation to an arbitrary

twice differentiable true profit function [38]. The Leontief

profit function has been widely adopted for econometric

analysis of supply and demand in forest sector [26,39]. We

chose this flexible functional form due to theoretical and

empirical criteria: it does not require having restrictions in the

parameter when linear homogeneity is imposed; and input

substitutability and supply and demand functions are easier

to determine compared to translog profit function [40e42].

The dual profit function p ( p, w, v)dwhere p and w are

respectively, the vectors of output and input prices, and v is the

fixed vector of inputs and outputsdsatisfies the following

properties [43]: non-negativity, non-increasing in w, non-

decreasing in p, convex and continuous function in ( p,w),

homogenousofdegree 1 in ( p,w) (constant returns toscale inall

factors), and differentiable in p and w. The dual specification

implies that thestructureofacost functioncanbe inferredfrom

profit function and vice versa [43]. In addition, the restricted

GLPF must be linear in parameters and have a functional form

that satisfies the appropriate regularity conditions over a range

of values for the independent variables, given a simple set of

inequalities restrictions on the unknown parameters [38].

We posited that a landowner requires labor and capital

inputs to produce three outputs: sawtimber, pulpwood and

woody bioenergy. Chip-and-saw was not included in our

analysis due to lack of historical data for this forest product.

The estimated short run profit function p(ws, wp, wb, wl, K )

thus became:

p ¼Xi

Xj

aijw1=2i w1=2

j þXj

akjwjKþ εp; i; j ¼ s;p;b; fl (1)

Wherews, wp, wb represented the output prices of sawtimber,

pulpwood and woody bioenergy, wl was the labor wage in

forestry, K was quasi-fixed capital in forestry, εp represented

the disturbance term for the profit function, and aij and akj

were the coefficients of the profit function to be estimated. As

it is difficult for forest landowners to adjust the timber

production in a short period of time, capital is typically

assumed as quasi-fixed input in the forest sector [44,45]d

price of capital ¼ 0. Operating cost of capital was also ruled

out. Although it was beyond the scope of our paper, some

studies have acknowledged the adjustment of capital over

time once a threshold is reached [27,46,47].

It was also assumed that NIPF landownersmaximized their

profits under perfect competition, an assumption that has

been empirically proved in context of the forestry sector

[48e50]. Symmetry was imposed, i.e., aij ¼ aji for all i and j.

Applying Hotelling’s lemma on the profit func-

tionsddifferentiating the profit function with respect to the

input and output pricesdwe obtained the supply and demand

function gradients, i.e., the short run supply of sawtimber ( ys),

pulpwood ( yp), and woody bioenergy ( yb), and the demand for

labor (eyl):

ys ¼Xi

asi

�wi

ws

�1=2

þaskKþ εsn i ¼ s;p;b; l (2)

yp ¼Xi

api

�wi

wp

�1=2

þapkKþ εpn i ¼ s;p; b; l (3)

yb ¼Xi

abi

�wi

wb

�1=2

þabkKþ εbn i ¼ s;p; b; l (4)

�yl ¼Xi

ali

�wi

wl

�1=2

þalkKþ εln i ¼ s;p; b; l (5)

Where εsn, εpn, εbn, and εln represented the disturbance terms

for the sawtimber, pulpwood, woody bioenergy and labor

equations, respectively. The subscript n of each disturbance

terms represented the number of time series observations per

equation. Disturbance terms were assumed to be additive and

normally iid, with E(εin) ¼ 0, Eðε0iεjÞ ¼ sij and E(ε0ε) ¼ U. All right

hand side variables were assumed to be exogenous. Following

Chambers [43], the supply functions are increasing in output

prices, the demand function is non-decreasing in input pri-

ces, supply and demand function are homogenous of degree

zero in input and output prices, and cross price effects are

equal.

The disturbances terms can be correlated as input and

output prices for supply and demand equations were taken

from the same dataset. Thus, Zellner’s seemingly unrelated

regression (SUR) approach was used to determine the supply

and demand equations because it yields asymptotically more

efficient estimators than traditional single equation least

squares estimators when disturbances are mutually corre-

lated [51]. By solving the system of equations, we obtained the

coefficients of the restricted profit function. Short run own

and cross price Marshallian elasticitiesd eii and eij, respec-

tively d was also determined from the system of equations

(Eqs. (2)e(5)). These elasticities were as follows:

eii ¼ �12

Piaij

�wj

wi

�1=2

Yii; j ¼ s; p; b; l; isj (6)

b i om a s s an d b i o e n e r g y 4 6 ( 2 0 1 2 ) 4 1 9e4 2 8422

aij

�wj

�1=2

eij ¼ 12

wi

Yii; j ¼ s; p;b; l; isj (7)

3. Data construction

The data used for the study were primarily compiled from

official statistical reports of the forestry sector in Florida

spanning from 1970 to 2006. Table 1 shows the input and

output variables used in the study and their respective sour-

ces. As historical time series information regarding woody

bioenergy prices was not available, forest residues (wood

chips and low value wood produced in the forests), and wood

residues (mill chips and other industrial products) were

utilized as a proxy since these products have been tradition-

ally used to generate electricity [58e62]. This is also reflected

by two largest facilities wood pellet production facilities in the

worlddCottondale, Florida and Selma, Alabamadwhich use

wood chips as main inputs [63]. Mill chips are a by-product of

other wood products such as lumber, thus the supply of

woody bioenergy would be somewhat a function of the supply

of lumber. We acknowledge this limitation and the implica-

tions for the results, however, the level of aggregation of the

historical data does not allow for gauging the proportion of co-

products in the woody bioenergy product.

The yearly hours of employment in the logging industry

was used as a proxy for the quantity of labor [54]. Pine pulp

chip prices were used as a proxy for woody bioenergy prices.

All prices were deflated to 1997 dollars. Table 2 shows the

descriptive statistics of the data.

4. Testing basic structure of profit function

Table 3 presents the coefficients, bootstrapped standard

errors (SE) and statistical significance of the system of equa-

tions. Bootstrapping (100 replications) was employed to obtain

the heteroskedasticity and autocorrelation consistent covari-

ance matrix. Although it has been suggested that bootstrap

standard errors and asymptotic standard errors may provide

similar results [64], we chose the former technique since these

estimates tend to perform better in small and moderate

Table 1 e Sources of information for input, outputs andprices.

Variables Variable/proxy description Source

Sawtimber ( ys) Sawlog production [52]

Pulpwood ( yp) Pulpwood production [53]

Wood fuels ( yb) Wood residues

(wood chips and mill residues)

[52,53]

Labor ( yl) Hours of employment in logging [54]

Sawtimber price (ws) Stumpage price for sawtimber [55]

Pulpwood price (wp) Stumpage price for pulpwood [55]

Wood fuel price (wb) Pine pulp chip prices [56]

Wage labor in

forestry (wl)

Earnings for logging camps

and contractors

[55]

Forestry capital (K ) Standing inventory [57]

samples than conventional asymptotic standard errors

[65e68]. Furthermore, bootstrap confidence intervals tend to

reflect the true nature of the sample distribution [69].

Regardless of the approach, it is not the scope of our research

to establish a comparison between both types of standard

errors. Only 45% of the coefficients were statistically

significant.

An R square proposed by McElroy [70] was used to test

overall model goodness of fit:

R2 ¼ 1�ε0�P�1 4I

�s

y0�P�1 4At

�y

(8)

Where y is the matrix of dependent variables, ε is disturbance

term matrix, S�1 is the residuals covariance matrix, I is the

identity matrix and At is a matrix that has transformed the

dependent variables into deviation from their means. The R

square can also describe an F statistic for the overall model

significance:

F ¼ R2

1� R2

nq�PikiP

iki � q(9)

Here n is number of observations, q the number of equations

andP

iki the total number of coefficients in the system of

equations. The estimated R2 and F values were 0.610 and 12.5

respectively. The F statistic was significant at p ¼ 0.01. The

independence of the disturbance terms was tested through

Breusch Pagan test, where null hypothesis of independence

was rejected at p ¼ 0.01, confirming the adequacy of the SUR

approach. The symmetry condition was tested by the unre-

stricted model i.e., aijsaji for all i and j, where Chi square

statistic at p < 0.001 indicated rejection of the null joint

hypothesis that unrestricted coefficients are equal in magni-

tude. Thus, the assumption of symmetry was justified in the

analysis. As expected, the profit function gradients were

positive in terms of prices of sawtimber, pulpwood, and

woody bioenergy and negative for labor at all sample points.

Likewise, own price elasticities were positive for outputs and

negative for inputs at all sample points.

Another crucial assumption of profit maximization is

convexity of the profit function. The convexity of the profit

function assumption was analyzed by examining whether

the Hessian matrix of the profit function evaluated at the

variable mean values is positive definite. The convexity

property was not satisfied as one of the four eigenvalues of

the Hessian matrix had non-positive values. Failure in

achieving convexityda frequent problem in the context of

profit function studies [71,72]dmight imply not having

strictly positive and negative gradients for supply of outputs

and demand for inputs, respectively [73]. Inconsistent profit

maximizing behavior [74], increasing returns to scale,

complementarity of input factors, low quality data and

model misspecifications [39] can also be attributed for failure

of convexity assumption. Furthermore, the underlying true

function properties might not be inherited by the approxi-

mating function [75]. In our particular analysis K is a quasi-

fixed input which means that complementarity of input

factors (the relationship between labor and capital in the

short run) cannot be investigated.

Table 2 e Descriptive statistics.a

Variable Unit Mean Standard deviation Minimum Maximum

ys Cubic decameter (dam3) 3520 1180 1240 5100

yp 6640 954 4790 8530

yb 1700 533 869 2900

yl Hours per year (h y�1) 2080 62.7 1950 2220

ws Dollar per cubic meter ($ m�3) 58.6 17.9 31.6 92.2

wp 8.21 0.593 6.84 9.98

wb 3.75 0.717 2.61 5.56

wl Dollars per hour ($ h�1) 58.6 17.9 31.6 92.2

K Cubic decameter (dam3) 198,000 11,900 164,000 209,000

a All values (and hereafter) are reported with three significant digits.

b i om a s s a n d b i o e n e r g y 4 6 ( 2 0 1 2 ) 4 1 9e4 2 8 423

A weaker form of convexity curvature is quasi-convexity

which implies that the profit function has convex level sets

[76]. A necessary and sufficient condition for quasi-convexity

in a profit function is that the number ( g) of non-negative

eigenvalues must be equal or greater than g�1 [75]. As only

one of the four eigenvalues was negative thus quasi-convexity

was achieved in our analysis. Additionally, the diagonal of

principal minors of the Hessian matrix were positive in 100%

of the sample points, suggesting that output supply and input

demand equations showed their expected own price elasticity

signs at all sample points. Based on these findings and

evidence from previous studies [39,74], we relaxed the

convexity condition imposed on the profit function. This

relaxation means that one should be cautious when inter-

preting our results. However, this relaxation is in sync with

studies such as Lau [77], who suggested that relaxation of this

assumption might be needed to incorporate effects of factors

such as increasing returns of scale on technology. Imposing

convexity in a Leontief profit function tends to be more

restrictive than other flexible functional forms such as the

normalized quadratic function because the second derivatives

of the profit functions depends on the estimated coefficients

Table 3 e Coefficients and standard errors (SE) of restricted pro

Sawtimber

Coefficient Estimate Bootstrapped SE

asp �465 434

asb �319b 169

asl 94.8 94.3

ask 0.0814a 0.00596

ass �12400a 1180

Woody bioenergy

Coefficient Estimate Bootstrapped SE

abs �319b 169

abp 337 845

abl 218 348

abk 0.0355a 0.00879

abb �4910a 1480

a Significantly different from zero at 1% level.

b Significantly different from zero at 10% level.

and variables [39]. Furthermore, it rules out the complemen-

tarity between pairs of inputs [72].

We also tested non-jointness in production which implies

that supply of a good depends on its own price change and not

by other goods’ prices [78,79]. Following Lau [79], a necessary

and sufficient condition for a production function to be non-

joint in inputs is vp2/vpipj ¼ 0 where pi and pj represent the

output prices. This implies testing whether asp, asb, apv are

significantly different from zero or testing if production of

sawtimber, pulpwood and woody bioenergy are independent

of each other. The Chi square ( p < 0.001) rejected non-

jointness in production for sawtimber, pulpwood and woody

bioenergy. Jointness in production might be attributed to

reasons such as increased cost complementarities among

outputs suggesting that separate production is less costly than

producing them together, generating diseconomies of scope

and causing potential inefficiency in the quality or quantity of

the output [80]. Similar to the agricultural sector, jointness in

production is a regular event in forestry due its multifunc-

tional nature, technological interdependence, allocatable

fixed factors such as land and non-allocatable inputs such as

labor [78,81e84].

fit function.

Pulpwood

Coefficient Estimate Bootstrapped SE

aps �465 434

apb 337 845

apl �607 669

apk 0.058a 0.0125

app �3180b 1950

Labor

Coefficient Estimate Bootstrapped SE

als 94.8 94.3

alp �607 669

alb 218 348

alk �0.0027 0.00601

all 2820b 1530

b i om a s s an d b i o e n e r g y 4 6 ( 2 0 1 2 ) 4 1 9e4 2 8424

We also tested for weak separability of input output tech-

nology, i.e., whether sawtimber, pulpwood and woody bio-

energy can be aggregated into an output index. Dual

properties of the profit function imply that the marginal rates

of substitution between outputs (inputs) are independent of

the level of inputs (outputs), suggesting that the effect of input

and output price variation can be reflected on the aggregated

input and output, respectively [43,85]. Following Newman and

Wear [26] and Shumway [86], the econometric restrictions of

no interaction between inputs and outputs and homotheticity

in output pricesdthe cost of producing any outputs are

independent of factor pricesdcan be given by asl¼ apl¼ abl¼ 0.

The Chi square statistic ( p ¼ 0.432) failed to reject the null

hypothesis of weak separability. These results indicated that

sawtimber, pulpwood andwoody bioenergy can be aggregated

as consistent wood product index that can be used to facilitate

decentralized landowners’ decision making regarding levels

of production.

5. Estimated elasticities and policysimulation

The estimated Marshallian elasticities and SE are shown in

Table 4. The elasticities were evaluated at the mean values

between 1997 and 2006. Our rationale to choose this time

period was that bioenergy markets are still immature,

particularly from the demand side [87]. Although power used

at wood processing plants has been historically generated by

small scale biomass based energy plants in the U.S. [88], it has

not been commonly distributed to the interconnected elec-

tricity network [60].

Consistent with economic theory, the own price elasticities

were positive for the three outputs and negative for the input

factor. All own price and cross price elasticities of the supply

and demand were inelastic. The own price elasticity of

sawtimber and pulpwood were significantly different from

zero, while forwoody bioenergywas not significantly different

from zero. The own price pulpwood elasticity value was

higher than the own price sawtimber elasticity value. This

might be explained by the fact that pulpwood can be obtained

from both large and small trees while sawtimber can be ob-

tained solely from larger diameter trees [27]. Larger trees are

required by sawmills for lumber while pulpmills are less

restrictive in terms of tree diameter as lower size trees can be

used to convert pulpwood into paper. The estimated

Table 4 e Marshallian elasticities (Elst) and standard errors (SEbioenergy and demand for labor, evaluated at the mean value

Elst wrt to Sawtimber SE Pulpwood SE

ws 0.0192a 0.000521 �0.110a 0.011

wp �0.0165a 0.000424 0.155a 0.019

wb �0.00691a 0.000313 0.0162a 0.002

wl 0.00426a 0.000335 �0.0609b 0.010

a Significantly different from zero at 1% level.

b Significantly different from zero at 5% level.

c Significantly different from zero at 10% level.

magnitude of own price supply of sawtimber elasticity was

lower than the values reported in previous studies such as

Newman andWear [26],Wear andNewman [27], andNewman

[89]. This difference could be attributed to reasons such as

differences in approaches employed (for example instru-

mental variable methods), data sources used and regional

variation (aggregated and cross sectional regional data).

The estimated elasticities values can be used to extrapolate

landowners’ response to product price changes. An increase

in the price of pulpwood and woody bioenergy results in

a decrease in the production of sawtimber. This implies that

sawtimber and pulpwood act as substitutes (negative cross

price elasticity). Substitutability is also observed between

sawtimber and woody bioenergy. In the short run, substitut-

ability between sawtimber and pulpwood has been commonly

found since it is easier for landowners, in physical terms, to

switch from producing sawtimber to pulpwood in response to

price changes [27]. This means that the forest landowners

might be inclined to transfer part of the sawtimber production

into pulpwood markets. Higher prices of pulpwood makes

forest thinningsda pre-harvest intervention in which some

smaller size trees are removeddmore financially attractive to

landowners instead of an undertaking a final harvest. This in

turn reduces and increases, respectively, the supply of

sawtimber and pulpwood [90]. In the long run sawtimber and

pulpwood generally act as complements [27]. Landowners are

encouraged to increase the production of outputs modifying

harvest decisions to be in line with higher values for both

products [27].

The positive sign of the cross price elasticity between

pulpwood and woody bioenergy indicates that both products

act as complements. This unexpected result suggests that

there may exist a joint production between pulpwood and

woody bioenergy, thus an increase in the price of pulpwood

results in an increase in the supply of wood residues (a source

for woody bioenergy) which is a by-product of the wood

supply [28]. The cross price elasticities of labor with outputs

are significantly different from zero. Price of labor has ex-

pected sign only for pulpwood production. Although the price

of labor has a positive effect on the sawtimber production, it is

almost negligible in magnitude (z0.0043).

One of the primary factors that determine use of woody

biomass for energy production is government policies [91].

Federal and state policies aimed towards increasing woody

biomass production are critical for the development of bio-

energy markets [92]. For example, the Energy Independence

) for the supply of sawtimber, pulpwood and woodys 1970e2006.

Woody bioenergy SE Labor SE

8 �0.469c 0.118 �0.0528a 0.00575

4 0.163c 0.037 �0.112a 0.00981

17 0.173 0.062 0.024a 0.00145

5 0.133a 0.023 �0.034b 0.00495

b i om a s s a n d b i o e n e r g y 4 6 ( 2 0 1 2 ) 4 1 9e4 2 8 425

Policy Act of 2007 (EISA) [93] established a renewable fuel

standard (RFS) of 136.3 hm3 by 2022, of which 79.5 hm3 must

be cellulosic biofuels. The American Clean Energy Security

(ACES) draft of 2009 [94] required retail electricity suppliers to

meet a percentage of their electricity generation from

renewable resources, up to 20% by 2020. The government

incentives till date have focused on production and

commercialization of bioenergy products. Only few incentives

exist for forest landowners [24,92]. It has been suggested that

forest landowners would prefer tax based policies than

subsidy support [92]. It was beyond the scope of this study to

determine the optimal financial incentives for NIPF land-

owners. However, the literature indicates that new woody

bioenergymarkets would likely result in higher product prices

[95].

In order to reflect such a price increase, we simulated two

price support policy scenarios by increasing price of woody

bioenergy by 50% and 75% with respect to the baseline price

levels, respectively. We chose these percentage increases to

factor in the potential competition for wood biomass between

wood based energy and pulpwood markets [96]. We used the

estimated equations to develop a baseline for prices and

quantities of sawtimber, pulpwood, woody bioenergy, and

labor using average data for production and input levels, pri-

ces and wages between 1997 and 2006. Next, the price of

woody bioenergy was increased by the respective percentages

in accordancewith policy scenarios. For the two scenarios, the

output supply and input demand equations resulted in new

equilibrium quantities for inputs and outputs of sawtimber,

pulpwood, woody bioenergy and labor. The new equilibrium

values, in turn were compared to the baseline values to gauge

the effect of policy interventions.

Table 5 shows the baseline and the policy scenario results.

Higher increases of price of woody bioenergy resulted in

a higher supply of pulpwood and woody bioenergy, as well as

increase in labor requirements (average increase of 0.90%,

18.9% and 1.18%, respectively). It also caused a small decreased

supply of sawtimber on an average of 0.37%. In both the policy

scenarios, an increase in the price of woody bioenergy stimu-

lated forest landowners, in turn enhancing the supply of

woody biomass for energy production. The positive cross price

elasticity sign between pulpwood and woody bioenergy sug-

gested a rightward shift of the supply of pulpwood. However,

the low magnitude of the cross price elasticity of pulpwood

with respect to woody bioenergy (z0.016) implied a small

Table 5 e Summary of policy simulation scenarios andtotal profits (p) in million dollars (MM$).

Variables Baselinec D50a,c Percentage

change (%)D75

b,c Percentagechange (%)

ys 4030 4020 �0.248 4010 �0.496

yp 6660 6710 0.751 6730 1.05

yb 1750 2030 16.0 2130 21.7

yl 2120 2140 0.943 2150 1.42

p 409 412 0.733 413 0.978

a New equilibrium quantities after an increase in wb by 50%.

b New equilibrium quantities after an increase in wb by 75%.

c Equilibrium quantities ys, yp, yb in dam3, and yl in h y�1.

increase in pulpwood production. Positive own price effects of

pulpwood and woody bioenergy, and negative cross price

elasticity of sawtimber with respect to the price of pulpwood

and woody bioenergy, resulted in a leftward shift of the supply

of sawtimber. An increase in the price of woody bioenergy

implied an upward shift of the demand for labor since both

products act as complements (positive sign of the cross price

elasticity between labor and price of woody bioenergy).

The profit function derived by econometric analysis was

used to estimate the landowners’ profits as well. Increased

price of woody biomass for energy resulted in a small increase

in landowners’ profits levels. On average, profitability

increased by z 1%. This result is in agreement with the

literature which suggests higher returns for landowners in

case where forest biomass diverted to traditional forest

products industries along with woody bioenergy conversion

plants [97,98]. Ankarhem [99] and Susaeta et al. [12] found that

economic revenues for landowners could increase by 64% and

10%, respectively, when landowners start diverting their

woody biomass for energy production along with sawtimber

and pulpwood. Our analysis did not consider demand forces

from sawmills, pulpmills and woody bioenergy industry.

Empirical studies have shown that adding the demand side

forces could result in higher profitability for forest landowners

[28], which suggests that our analysis might be conservative

as it could have a downward profitability bias.

6. Conclusions

We modeled forest landowner behavior in the face of bio-

energy markets in Florida using a restricted general Leontief

profit function. A test of non-joint production between

sawtimber, pulpwood and woody bioenergy resulted in rejec-

tion of non-jointness assumption, thus suggesting the exis-

tence of economies of scope. A test of separability between the

three forest product markets indicated that sawtimber, pulp-

woodandwoodybioenergy canbe aggregated into a consistent

product output index. The convexity property of the land-

owner profit function was not satisfied suggesting that incon-

sistent estimates of supply and demand functions are not

implausible. However, consistent economic information was

obtained from the supply and demand functions in terms of

their own price elasticities. As expected, the supply of outputs

in terms of price of outputs and own price elasticities had

positive signs while demand for inputs in terms of price of

inputs and own price elasticities had negative signs.

In general, all elasticities values were inelastic suggesting

that forest landowners are not much influenced by price

variations. This is in accordance with the literature, which

suggests that the forest products are inelastic in the short run

[25]. Cross price elasticities results indicated substitutability

between sawtimber and pulpwood and sawtimber and woody

bioenergymarkets. Woody bioenergy and pulpwood came out

as complements.

We simulated a price control policy instrument intended to

encourage the production of woody bioenergy. Results indi-

cated an increase in the production of wood energy and

pulpwood for both policy scenarios. Demand for labor showed

an increase as well. These findings are in sync with the fact

b i om a s s an d b i o e n e r g y 4 6 ( 2 0 1 2 ) 4 1 9e4 2 8426

that bioenergy markets might benefit rural economies by

stimulating employment [100]. Results also suggested that

forest landowners’ revenues would increase in face of woody

for energy markets. Although the increase in NIPF land-

owners’ profit may be considered small, greater economic

revenuesmay be expected with the development of bioenergy

markets [101]. New conversion technologies, construction of

bioenergy plants with larger capacities and adequate policy

instruments can be considered as feasible alternatives to

stimulate the production of woody bioenergy.

A potential shortcoming of our analysis was not to include

the non-timber benefits in the profit function. Thus, a more

complete assessment of this type of behavioral models would

be to incorporate endogenous factors such as non-timber

amenities. For example, non-timber benefits may be

modeled as a function of growing stock timber [26], empha-

sizing on the connection between age structure of the forest

capital and ownership [25]. Integrating our study findings into

spatial equilibrium models and assuming imperfect compe-

tition between timbermarkets remains an interesting area for

future research as well.

r e f e r e n c e s

[1] Aguilar F, Garrett HE. Perspectives of woody biomass forenergy: survey of state foresters, state energy biomasscontacts, and national council of forestry associationexecutives. J For 2009;107(6):297e306.

[2] Domac J, Richards K, Risovic S. Socio-economic drivers inimplementing bioenergy projects. Biomass Bioenerg 2005;28(2):97e106.

[3] Gan J, Smith CT. Co-benefits of utilizing logging residues forbioenergy production: the case for east Texas, USA. BiomassBioenerg 2007;31(9):623e30.

[4] Solomon BD, Barnes JR, Halvorsen KE. Grain and cellulosicethanol: history, economics, and energy policy. BiomassBioenerg 2007;31(6):416e25.

[5] Hill J, Nelson E, Tilman D, Polasky S, Tiffany D.Environmental, economic, and energetic costs and benefitsof biodiesel and ethanol biofuels. Proc Natl Acad Sci USA2006;103(30):11206e10.

[6] Wang M. The debate on energy and greenhouse gasemissions: impacts of fuel ethanol [Internet]. Argonne IL:Argonne National Laboratory, Energy Systems Division[cited 2011 Dec 20]. Available from: http://www.transportation.anl.gov/pdfs/TA/347.pdf; 2005 August. 35 pp.

[7] Neary DG, Zieroth EJ. Forest bioenergy system to reduce thehazard of wildfires: White Mountains, Arizona. BiomassBioenerg 2007;31(9):638e45.

[8] Speight MR. Forest pests in the tropics: current statusand future threats. In: Watt AD, Hunter MD, Stork NH,editors. Forests and insects. London: Chapman and Hall;1997. p. 207e28.

[9] Conrad JL, Bolding MC, Smith RL. Wood-energy marketimpact on competition, procurement practices, andprofitability of landowners and forest products industry inthe U.S. south. Biomass Bioenerg 2011;35(1):280e7.

[10] Smith W, Miles PD, Perry CH, Pugh SA. Forest resourcesof the United States. Saint Paul MN: U.S. Department ofAgriculture, Forest Service; 2009; 2007. 336pp. WOeGTRe78.

[11] Dwivedi P, Bailis R, Stainback GA, Carter DR. Impact ofpayments for carbon sequestered in wood products and

avoided carbon emissions on the profitability of NIPFlandowners in the US South. Ecol Econ 2012;78:63e9.

[12] Susaeta AI, Alavalapati JRR, Carter DR. Modeling impacts ofbioenergy markets on nonindustrial private forestmanagement in the southeastern United States. Nat ResourModel 2009;22(3):345e69.

[13] Aulisi A, Sauer A,Wellington F. Trees in the greenhouse: whyclimate change is transforming the forest products business[Internet]. Washington DC, U.S: World Resources InstituteReport [cited 2012 June 10]. Available from: http://www.wri.org/publication/trees-in-the-greenhouse; 2007. 72 pp.

[14] Bolkesjo TF, Tromborg E, Solberg B. Bioenergy from theforest sector: economic potential and interactions withtimber and forest products markets in Norway. Scand J ForRes 2006;21(2):175e85.

[15] Scott DA, Tiarks A. Dual cropping loblolly pine for biomassenergy and conventional wood products. Southern J App For2008;32(1):33e7.

[16] Wear D, Greis JG, Walters N. The southern forest futuresproject: using public input to define the issues. AshevilleNC: U.S. Department of Agriculture, Forest Service General;2009. 17 pp. SRSeGTRe115.

[17] Amacher GS, Conway MC, Sullivan J. Econometric analysesof nonindustrial forest landowners: is there anything left tostudy? J For Econ 2003;9(2):137e64.

[18] Conway MC, Amacher GS, Sullivan J, Wear D. Decisionsnonindustrial forest landowners make: an empiricalexamination. J For Econ 2003;9(3):181e203.

[19] Pouta E, Myyra S, Pietola K. Landowner response to policiesregulating land improvements in Finland: lease or searchfor other options? Land Use Policy 2012;29(2):367e76.

[20] Markowski-Lindsay M, Stevens T, Kittredge DB, Butler BJ,Catanzaro P, Damery D. Family forest owner preferences forbiomass harvesting in Massachusetts. For Policy Econ 2012;14(1):127e35.

[21] Tyndall JC, Schulte LA, Hall RB. Expanding the UScornbelt biomass portfolio: forester perceptions of thepotential for woody biomass. Small-Scale For 2011;10(3):287e303.

[22] Joshi O, Mehmood SR. Factors affecting nonindustrialprivate forest landowners’ willingness to supply woodybiomass for bioenergy. Biomass Bioenerg 2011;35(1):186e92.

[23] Joshi O, Mehmood SR. Segmenting southern nonindustrialprivate forest landowners on the basis of their managementobjectives and motivations for wood-based bioenergy.South J Appl For 2011;35(2):87e92.

[24] Beach RH, Pattanayak SK, Yang J, Murray BC, Abt RC.Econometric studies of non- industrial private forestmanagement: a review and synthesis. For Policy Econ 2005;7(3):261e81.

[25] Pattanayak SK, Murray BC, Abt RC. How joint is joint forestproduction? an econometric analysis of timber supplyconditional on endogenous amenity values. For Sci 2002;48(3):479e91.

[26] Newman DH, Wear DN. Production economics of privateforestry: a comparison of industrial and nonindustrialforest owners. Am J Agr Econ 1993;75(3):674e84.

[27] Wear DN, Newman DH. The structure of forestryproduction: short-run and long-run results. For Sci 1991;37(2):540e51.

[28] Ankarhem M, Brannlund R, Sjostrom M. Biofuels and theforest sector. In: Yoshimoto A, Yukutake K, editors. Globalconcerns for forest resource utilization; sustainable use andmanagement. Dordrecht: Kluwer Academic Publishers;1999. p. 259e67.

[29] Geijer E, Bostedt G, Brannlund R. Damned if you do, damnedif you do not- reduced climate impact vs. sustainableforests in Sweden. Resour Energ Econ 2011;33(1):94e106.

b i om a s s a n d b i o e n e r g y 4 6 ( 2 0 1 2 ) 4 1 9e4 2 8 427

[30] Lin W, Dean GW, Moore CV. An empirical test of utility vs.profit maximization in agricultural production. Am J AgrEcon 1974;56(3):497e508.

[31] Hultkrantz L. Forestry and the bequest motive. J EnvironEcon Manag 1992;22(2):164e77.

[32] Hanley N, Banerjee S, Lennox GD, Armsworth PR. Howshould we incentivize private landowners to “produce”more biodiversity? [Internet]. Stirling Economics Discussionpaper 2012.. University of Stirling, Stirling ManagementSchool. Available from: http://www.management.stir.ac.uk/research/economics/working-papers; 2012 Feb. 21 pp

[33] O’Laughlin J. The forestdbioenergydcarbon connection. In:Jain TB, Graham RT, Sandquist J, editors. Integratedmanagementofcarbonsequestrationandbiomassutilizationopportunities in a changing climate: Proceedings of the 2009national SilvicultureWorkshop; 2009 June 15-18; Boise, ID;Fort Collins CO: U.S. Department of Agriculture, ForestService, Rocky Mountain Research Station; 2010. p. 129e33.

[34] Perez-Verdin G, Grebner DL, Sun C, Munn IA, Schultz EB,Matney TG. Woody biomass feedstock supplies andmanagement for bioenergy in Southwestern Mississippi.Paper presented at: the 2007 southern forest economicsWorkshop; 2007 March 4-6. San Antonio, TX.

[35] Wyman C. Cellulosic ethanol: a unique sustainable liquidtransportation fuel. MRS Bull 2008;33(4):381e3.

[36] Alavalapati JRR, Hodges AW, Lal P, Dwivedi P, Rahmani M,Kaufer I, et al. Bioenergy roadmap for southern UnitedStates. Research Triangle Park NC: Southeast AgricultureForestry Energy Resources Alliance and Southern GrowthPolices Board; 2009. 127 pp.

[37] National Learning Center for private forest and rangelandowners [Internet]. Tennessee: sustainable forestry forbioenergy and biobased products [updated 2007 Nov 11;cited 2011 Nov 15]. Available from: http://www.forestandrange.org/Biomass/Modules/Module%201/Unit%201/Lesson%202.asap.

[38] Diewert WE. Functional forms for profit and transformationfunctions. J Econ Theory 1973;6(3):284e316.

[39] Williamson T, Hauer G, Luckert MK. A restricted Leontiefprofit function model of the Canadian lumber and chipindustry: potential impacts of US countervail and Kyotoratification. Can J For Res 2004;34(9):1833e44.

[40] Gunning TS, Sickles RC. A multi-product cost function forphysician private practices. J Prod Anal 2011;35(2):119e28.

[41] Hutchinson SD, Larkin SL, Lee DJ, Adams CM. Economicassessment of limited entry strategies in multi-speciesfisheries in south Florida. Gainesville FL: Food and ResourceEconomics Department, University of Florida; 2000. 13 pp.SP 00e1.

[42] Thompson GD, Langworthy M. Profit functionapproximations and duality applications to agriculture. AmJ Agr Econ 1989;71(3):791e8.

[43] Chambers RG. Applied production analysis: a dualapproach. 1st ed. Cambridge: Cambridge University Press;1988.

[44] Brannlund R, Kristrom B. Welfare measurement in singleand multimarket models: theory and application. Am J AgrEcon 1996;78(1):157e65.

[45] Constantino L, Townsend C. Modeling operating ratedecisions in the Canadian forest industries. Western J AgrEcon 1989;14(2):268e80.

[46] Bair LS, Alig RJ. Regional cost information for privatetimberland conversion and management. Portland OR: U.S.Department of Agriculture, Forest Service; 2006 Sep. 26 pp.PNWeGTRe684.

[47] Stier JC, Bengston DN. Technical change in the NorthAmerican forestry sector: a review. For Sci 1992;38(1):134e59.

[48] Murray BC, Prestemon JP. Structure and efficiency oftimber markets. In: Sills EO, Abt KL, editors. Forests ina market economy. Dordrecht: Kluwer AcademicPublishers; 2003. p. 153e76.

[49] Murray BC. Measuring oligopsony power with shadowprices: US markets for pulpwood and sawlogs. Rev Econ Stat1995;7(3):486e98.

[50] Wear DN, Parks PJ. The economics of timber supply: ananalytical synthesis of modeling approaches. Nat ResourModel 1994;8(3):199e223.

[51] Zellner A. An efficient method of estimating seeminglyunrelated regressions and tests for aggregation bias. J AmStat Assoc 1962;57(298):348e68.

[52] Johnson TG, Bentley JW, Howel MH. Historical trends oftimber output product output in the South. Asheville NC:U.S. Department of Agriculture, Forest Service; 2008 Sep. 70pp. SRSe138.

[53] Johnson TG, Steppleton CD, Howell MH. Trends in southernpulpwood production, 1953e2006. Asheville NC: U.S.Department of Agriculture, Forest Service; 2008 Sep. 53 pp.SRSe139.

[54] United States Bureau of Labor Statistics [Internet].Washington (DC): current employment statistics,Employment, hours and Earnings national [cited 2011 Nov20]. Available from: http://www.bls.gov/ces/.

[55] Howard JL. US timber production, trade, consumption andprice statistics 1965 to 2005. Madison WI: U.S. Departmentof Agriculture, Forest Service; 2007. 91 pp. FLPeRPe637.

[56] Timber Mart South. Timber mart south market newsletter:quarterly stumpage prices 1976-2005. Athens GA: Universityof Georgia; 2006. 4 pp.

[57] U.S. Department of agriculture, forest Service [Internet].Arlington (VA): Forest Inventory and Analysis NationalProgram. [Updated 2011 Nov 18; cited 2011 Nov 27].Available from: http://fiatools.fs.fed.us/fido/index.htm.

[58] Conrad JL, Bolding MC, Aust WM, Smith RL. Wood-to-energyexpansion, forest ownership changes, and mill closure:consequences for US south’s wood supply chain. For PolicyEcon 2010;12(6):399e406.

[59] Timmons D, Viteri C. Biomass energy from wood chips:diesel fuel dependence? Biomass Bioenerg 2010;34(9):1419e25.

[60] White EM. Woody biomass for bioenergy and biofuels in theUnited States e a briefing paper. Portland OR: U.S.Department of Agriculture, Forest Service; 2010 July. 45 pp.PNWeGTRe825.

[61] Hazel DW, Bardon RE. Evaluating wood energy users inNorth Carolina and the potential for using logging chips toexpand wood fuel use. For Prod J 2008;58(5):34e9.

[62] Perlack RD, Wright LL, Turhollow AF, Graham RL, Stokes BJ,Erbach DC. Biomass as feedstock for a bioenergy andbioproducts industry: the technical feasibility of a billion-ton annual supply. Oak Ridge TN: Oak Ridge NationalLaboratory; 2005 May. 78 pp. DOE/GOe102995e2135, ORNL/TMe2005/66.

[63] Marinescu M, Bush T. Wood to energy: use of the forestbiomass for wood pellets. Gainesville FL: School of ForestResourcesandConservationDepartment,FloridaCooperativeExtension Service, Institute of Food and AgriculturalSciences, University of Florida; 2009. 4 pp. FOR 207.

[64] Green R, Hahn W. Standard errors for elasticities:a comparison of bootstrap and asymptotic standard errors.J Bus Econ Stat 1987;5(1):145e9.

[65] Efron B. Bootstrap methods: another look at the jackknife.Ann Statist 1979;7(1):1e26.

[66] Goncalves S, White H. Bootstrap standard error estimatesfor linear regression. J Am Stat Assoc 2005;100(471):970e9.

b i om a s s an d b i o e n e r g y 4 6 ( 2 0 1 2 ) 4 1 9e4 2 8428

[67] McCullough BD, Vinod H. Implementing the singlebootstrap: some computational considerations. ComputEcon 1993;6(1):1e15.

[68] RaynerRK.Resamplingmethods for tests in regressionmodelswith autocorrelated errors. Econ Lett 1991;36(3):281e4.

[69] Schreuder HT, Williams MS. Reliability of confidenceintervals calculated by bootstrap and classicalmethods usingthe FIA 1-ha plot design. Fort Collins CO: U.S. Department ofAgriculture, Forest Service; 2000. 6 pp. RMRSeGTRe57.

[70] McElroy MB. Goodness of fit for seemingly unrelatedregressions: Glahn’ s r2y:x and Hooper’s r2. J Econometrics1977;6(3):381e7.

[71] Bernard JT, Bouthillier L, Catimel J, Gelinas N. An integratedmodel of Quebec- Ontario-US northeast softwood lumbermarkets. Am J Agr Econ 1997;79(3):987e1000.

[72] Diewert WE, Wales TJ. Flexible functional forms and globalcurvature conditions. Econometrica 1987;55(1):43e68.

[73] Somerville RA. Differentiable technology, the curvature ofthe profit function, and the response of supply to own-pricechanges. J Econ Educ 2007;38(2):222e8.

[74] Lopez RE. Estimating substitution and expansion effectsusing a profit function framework. Am J Agr Econ 1984;66(3):358e67.

[75] Lau LJ. Testing and imposing monotonicity, convexity andquasi-convexity constraints. In: Fuss M, McFadden DL,editors. Production economics: a dual approach to pretestsfor dual profit functions theory and applications. TheNetherlands: North Holland Publishing; 1978. p. 409e53.

[76] Diewert WE. Functional forms for revenue and factorrequirements functions. Int Econ Rev 1974;15(1):119e30.

[77] Lau LJ. A characterization of the normalized restricted profitfunction. J Econ Theory 1976;12(1):131e63.

[78] Shumway CR, Pope RD, Nash EK. Allocatable fixed inputsand jointness in agricultural production: implications foreconomic modeling. Am J Agr Econ 1984;66(1):72e8.

[79] Lau LJ. Profit functions of technologies with multiple inputand output. Rev Econ Stat 1972;54(3):281e9.

[80] Leathers HD. Allocable fixed inputs as a cause of jointness:a cost function approach. Am J Agr Econ 1991;73(4):1083e90.

[81] Nilsson FO, Hasund KP, Gren IM, Surry Y. Multifunctionalagriculture d what does the economic literature tell us? IntRev Environ Resour Econ 2008;2(4):281e319.

[82] Maier L, Shobayashi M. Multifunctionality: towards ananalytical framework [Internet]. Paris France: Organisationfor Economic Cooperation and Development [cited 2011 Nov10]. Available from: http://www.oecd.org/dataoecd/43/31/1894469.pdf; 2001. 159 pp.

[83] Lynne GD. Allocatable fixed inputs and jointness inagricultural production: implications for economicmodeling: comment. Am J Agr Econ 1988;70(4):947e9.

[84] Hof JG, Lee RD, Dyer AA, Kent BM. An analysis of joint costsin a managed forest ecosystem. J Environ Econ Manag 1985;12(4):338e52.

[85] Squires D. Fishing effort: its testing, specification, andinternal structure in fisheries economics and management.J Environ Econ Manag 1987;14(3):268e82.

[86] Shumway CR. Testing structure and behavior with first-andsecond-order Taylor series expansions. Can J Agr Econ 1989;37(1):95e109.

[87] Sheldon I, Roberts M. US comparative advantage inbioenergy: a Heckscher-Ohlin- Ricardian approach. Am JAgr Econ 2008;90(5):1233e8.

[88] Nicholls DL, Monserud RA, Dykstra DP. A synthesis ofbiomass utilization for bioenergy in the western UnitedStates. Portland OR: U.S. Department of Agriculture, ForestService; 2008 May. 48 pp. PNWeGTRe753.

[89] Newman DH. Shifting southern softwood stumpage supply:implications for welfare estimation from technical change.Forensic Sci 1991;89(2):31e7.

[90] Brannlund R, Johansson PO, Lofgren KG. An econometricanalysis of aggregate sawtimber and pulpwood supply inSweden. For Sci 1985;31(3):595e606.

[91] Aguilar FX, Saunders A. Policy instruments promotingwood-to-energy uses in the continental United States. J For2010;108(3):132e40.

[92] Shivan GC, Mehmood SR. Factors influencing nonindustrialprivate forest landowners’ policy preference for promotingbioenergy. For Policy Econ 2010;12(8):581e8.

[93] The energy independence and security act of 2007, Pub. L.No. 110e140, 121 Stat. 1492e1801; December 19, 2007.

[94] The American Clean energy act of 2009, HR 2454, 111thCong., 1st Sess; 2009.

[95] Abt RC, Abt KL. Potential impact of bioenergy demand onthe sustainability of the southern forest resource. J SustainFor 2012. Forthcoming.

[96] Galik CS, Abt RC, Yun W. Forest biomass supply in thesoutheastern United Statesd implications for industrialroundwood and bioenergy production. J For 2009;107(2):69e77.

[97] Munsell JF, Fox TR. An analysis of the feasibility forincreasing woody biomass production from pineplantations in the southern United States. BiomassBioenerg 2010;34(12):1631e42.

[98] Abt RC, Abt KL, Cubbage FW, Henderson JD. Current andpotential capabilities of wood production systems in thesoutheastern US. Biomass Bioenerg 2010;34(12):1629e30.

[99] Ankarhem M. Effects of increased demand for biofuels:a dynamic model of the Swedish forest sector. UmeaSweden: Department of Economics, University of Umea;2004. 22 pp. No 658.

[100] Faaij APC, Domac J. Emerging international bioenergymarkets and opportunities for socio-economicdevelopment. Energ Sustain Develop 2006;10(1):7e19.

[101] Mayfield CA, Foster C, Smith CT, Gan JB, Fox S.Opportunities, barriers, and strategies for forest bioenergyand bio-based product development in the Southern UnitedStates. Biomass Bioenerg 2007;31(9):631e7.