The Location Decisions of Biodiesel RefineriesT. Randall Fortenbery, Steven C. Deller, and Lindsay Amiel

ABSTRACT. We examine the community character-istics that lead to locating a biodiesel plant. Utilizingdata that includes all biodiesel plants in the UnitedStates, we employ spatial econometrics to evaluatethose characteristics that lead to a plant siting. Whilepublic policies associated with biofuels may be en-dogenous to location decisions, we find them unim-portant except in the case of consumption mandates.Despite evidence of plant clusters, it does not appearthat operating plants are critical to the siting of a newplant. Results provide insights for community leadersrelative to actions that can increase the probability aplant will be built. (JEL O18, Q42)

I. INTRODUCTION

Despite significant growth in U.S. biofuelsproduction capacity, controversy exists con-cerning the impacts biorefineries have on in-dividual communities. As a result there are arange of reasons some plants face little localresistance and others fail to get local approval(Fortenbery and Deller 2008; Low and Isser-man 2009). The earliest domestic biorefiner-ies focused on the production of ethanol, withbiodiesel production lagging substantially. In2009, for example, the United States produced10.75 billion gallons of ethanol from plantsthat had a total capacity of about 13.5 billiongallons (Renewable Fuels Association 2010),while total U.S. biodiesel production was only490 million gallons from plants with a totalcapacity of about 2.7 billion gallons (NationalBiodiesel Board 2010).

Due to ethanol’s domination in the U.S.biofuels market, there have been several stud-ies examining the relationships between eth-anol plants and their host communities (see,e.g., McNew and Griffith 2005; Swenson2008; Fortenbery, Turnquist, and Foltz 2008;Flora 2008). The works of Sarmiento and Wil-

Land Economics • February 2013 • 89 (1): 118–136ISSN 0023-7639; E-ISSN 1543-8325� 2013 by the Board of Regents of theUniversity of Wisconsin System

son (2007), Haddad, Taylor, and Owusu(2010), Lambert and his colleagues (2008),and Low and Isserman (2009) are the mostgermane to this study in that they explicitlymodel the location decisions of ethanol plantsin the United States. Because of much lowermarket penetration, however, little work hasfocused on issues associated with biodieselplants. Such work is important for two rea-sons. First, the current federal Renewable Fu-els Standard (RFS) explicitly allows for bio-diesel to be counted as an advanced biofuel,meaning it can count toward the RFS. Thisprovides significant opportunity for biodieselto enter the fuel stream for commercial trucks.More importantly, understanding the locationcriteria surrounding biodiesel plants couldprovide important insight in understandingnext-generation biorefineries. Since corn-based ethanol has essentially reached its max-imum allowable contribution to the RFS,plants focused on cellulosic ethanol produc-tion and other advanced biofuels (bio–jet fueletc.) will need to be developed. Because theseplants will be developed as part of a compre-hensive energy policy, not as a rural devel-opment initiative, the issues surrounding plantlocation may be much more similar to thoseassociated with biodiesel plants, as opposedto earlier-generation ethanol plants.

Initially one might presume that impactswould not be all that different: both generateemployment and other benefits from the pro-duction of renewable transportation fuels, andthe positives and negatives associated withbiofuel manufacturing might be similar re-gardless of whether the fuel produced is a gas-oline or a diesel fuel substitute. From a

The authors are, respectively, professor and TomMick Endowed Chair, School of Economic Sciences,Washington State University, Pullman; professor, De-partment of Agricultural and Applied Economics,University of Wisconsin–Madison; and graduate re-search assistant, Department of Agricultural and Ap-plied Economics, University of Wisconsin–Madison.

89(1) Fortenbery, Deller, and Amiel: Location of Biodiesel Refineries 119

broader perspective it is important to note thatthe initial promotion of ethanol productionwas widely seen as a rural development policyaimed at helping corn farmers and rural com-munities capture more value-added dollars.The promotion of biodiesel, on the other hand,has been viewed more as an energy policy.There are, however, considerable differencesin the location decisions of ethanol versus bio-diesel plants. The greatest percentage of eth-anol producing capacity has been focused inthe upper Midwest, where the dominant feed-stock (corn) is readily available (Haddad, Tay-lor, and Owusu 2010).

The location of biodiesel plants has beenmore heterogeneous than that of ethanolplants, both in terms of geographical locationand the size of communities hosting plants.The purpose of this study is to understandwhat drives this difference. It could be due toa variety of factors, including the ability ofmost biodiesel plants to utilize various feed-stocks ranging from recycled cooking greaseto soybean oils, and the specific chemistry ofbiodiesel. Biodiesel can be introduced at anypoint along the fuel marketing chain, makingit less critical where plants actually locate.Perhaps most importantly, biodiesel has notbeen primarily viewed through the lenses ofrural economic development policy and in-creased agricultural value added at the com-munity level. Commercial biodiesel has his-torically been developed by entrepreneurs thatdo not necessarily have agricultural back-grounds. As such, the strong ties to productionagriculture that dominated early ethanol plantdevelopment do not apply to biodiesel, andmore importantly may not apply to next-gen-eration biofuels.

II. U.S. HISTORY OF BIODIESELPRODUCTION

The potential for a biodiesel fuel goes backto at least 1900, when German engineer Ru-dolf Diesel used peanut oil to power one ofhis engines at the Paris Exposition (Wu 1998).As noted above, however, growth in U.S. bio-diesel production has lagged that of ethanolproduction, in terms of both total output andas a percentage of petroleum based fuels.There was effectively no commercially pro-

duced biodiesel before 2001 in the UnitedStates, and by 2005 total biodiesel productionrepresented only 0.21% of the U.S. diesel fuelpool (U.S. Energy Information Administra-tion 2007). In 2007 total U.S. biodiesel pro-duction increased to 500 million gallons, andincreased another 200 million gallons in 2008(Weber 2009). Production in 2008 representedabout 1.2% of total U.S. distillate fuel con-sumption. While annual percentage increasesin U.S. biodiesel production have been im-pressive, measured in both total volume andas a percentage of diesel consumption, theypale in comparison to the relationship be-tween ethanol and gasoline.

Despite its slow start, the past several yearshave seen increased interest in alternative die-sel fuels to both control emissions and contrib-ute to a reduction of petroleum dependency(Durbin 2007). Current renewable fuels legis-lation mandates a total of 36 billion gallons ofrenewable fuel in the U.S. transportation fuelstream by 2022. Only 15 billion gallons of thatcan come from corn-based ethanol. Further, theEnvironmental Protection Agency (EPA) hasmandated that specific portions of the 36 bil-lion gallon renewable fuel total required by2022 be satisfied by biodiesel. In 2011, for ex-ample, the total biodiesel contribution was re-quired to be 0.8 billion gallons (U.S. Environ-mental Protection Agency 2010). This isexpected to increase over the next severalyears. In addition, biodiesel can count towardother advanced biofuels mandates underRFS2. This leaves considerable opportunityfor biodiesel to increase its contribution to theoverall domestic biofuel supply.

Biodiesel is made by a process called es-terification, in which an alcohol (usuallymethanol) is mixed with a catalyst such asvegetable oil to produce a methyl ester(Nobbe 1995). While there are many differenttypes of vegetable oil, most U.S. biodiesel ismade from soybean oil. This is due to both itsavailability and a $1 per gallon soybean oilblending credit (Williamson 2002). In addi-tion to vegetable oil, waste grease, like theyellow grease from deep fryers and restau-rants’ trap greases, can be used for biodiesel,but these recycled feedstocks tend to producelower yields (Fortenbery 2004).

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In addition to feedstock variety, the chem-ical properties of biodiesel and ethanol arefundamentally different and affect where theycan be introduced into the fuel stream. Bothcan be splash-blended, meaning the biofuel issimply mixed into the petroleum fuel bydumping it in, but there are some issues as-sociated with both the transport and distribu-tion of gasoline mixed with ethanol that arenot constraints for a biodiesel/petroleum die-sel mix. For example, neither ethanol nor anethanol/gasoline blend can be moved throughthe current pipeline distribution system. As aresult, ethanol is usually splash-blended withgasoline into a tanker truck just prior to deliv-ery to a retail location. Since the blending ofbiodiesel with petroleum diesel does not resultin a product incompatible with pipeline deliv-ery, biodiesel blending can occur anywherealong the fuel distribution network. Thus,from both input and output market perspec-tives, biodiesel plants are more flexible rela-tive to ethanol plants in terms of where theylocate.

Given the potential for biodiesel produc-tion to grow in response to the RFS, a morecomplete understanding of what contributes toa plant’s location decision and ultimate suc-cess is warranted. To that end, the objectivesof this study are to first identify potential com-munity characteristics that might contribute toan attractive location environment from aplant’s perspective, and then to estimate therelative importance of each of the identifiedcharacteristics. This will be useful in assess-ing the potential for a given community to at-tract a plant as production capacity increases,and to understand the community dynamicsthat lead to economic success.

Before turning to the theory of firm loca-tion, our model and the empirical results con-sider the simple spatial location of biodieselplants (Figure 1). Unlike ethanol plants,which are highly concentrated or clustered inthe Corn Belt of the Midwest, biodiesel plantsare more scattered across the United States.Indeed, commercial biodiesel plants are lo-cated at all four corners of the United States,from San Diego, California, to Bellingham,Washington, to Maine and southern Floridaand the very southern tip of Texas. Other thana concentration in Houston, Texas, commer-

cial biodiesel plants appear to be evenly dis-tributed across much of the United States.

III. SPATIAL DEPENDENCY INBIODIESEL PLANT LOCATION

To test for spatial dependency in the loca-tion of the biodiesel plants identified in Figure1, we computed the Moran’s I statistic, whichis a standard global statistic of spatial depen-dency (Anselin 1988).1 The test statistic ex-plicitly measures whether the pattern of plantlocations is spatially random or not. Spatialdependency, or spatial autocorrelation, is thespatial equivalent of autocorrelation in timeseries data. For the biodiesel data, the globalMoran’s I is 0.0143 with a Z score of 9.563.This provides strong evidence that there doesexist spatial dependency in the plant locationdata at a greater than 99.9% level of statisticalconfidence. Thus, the pattern observed in Fig-ure 1 is not spatially random. This depen-dency strongly suggests that aspatial statisti-cal modeling approaches will lead tosignificant model misspecification and biasedparameter estimates (Anselin 1988).

Given that biodiesel plant locations are notdistributed randomly across U.S. counties, theissue of spatial concentrations or clustersarises. As previously observed, there is astrong spatial clustering of ethanol plants, butfrom Figure 1 it is not clear whether there aresimilar clusters for biodiesel plants. To test forspatial clustering we calculated the Anselinlocal Moran’s I statistic (Anselin 1995) alongwith the Getis-Ord Gi* statistic (Getis andOrd 1992; Ord and Getis 1995). In general,we identify several spatial clusters. The spe-cific test results and a description of the clus-ters are presented in the Appendix.

1 The Moran’s I is defined asN

I =Σ Σ wi j ij

, where wij is a spatial weight ma-¯ ¯Σ Σ w (X − X)(X − X)i j ij i j

2¯Σ (X − X)i itrix defining the spatial proximity of Xi to Xj. Generally theweight matrix is defined on adjacency where wij takes on apositive value for counties that are adjacent and zero if non-adjacent.

89(1) Fortenbery, Deller, and Amiel: Location of Biodiesel Refineries 121

FIGURE 1Location of Biodiesel Plants

Modeling the Location of Biodiesel Plants

A general problem facing a biodiesel plantrelative to its location decision is that potentialcustomers and feedstock suppliers are scat-tered across a broad landscape. The firm facesa location choice that involves several consid-erations that will impact its ability to maxi-mize profits. A primary issue is the transportcosts associated with shipping inputs to thefirm and final product to consumer markets.In addition the firm’s location decision will beimpacted by such issues as land and laborcosts, taxes and quality of public services, andthe local regulatory environment, among oth-ers. The final site selected need not be the low-est transportation cost site if lower land andlabor costs, for example, are sufficient to off-set higher transportation costs. The locationdecision is often discussed as a two-step pro-cess: step one identifies areas where transpor-tation costs are minimized, and step two in-volves identifying a specific plant site.

Following Gabszewicz and Thisse (1986),McCann (2002), and Shaffer, Deller, and Mar-couiller (2004), the firm’s problem can be for-mally stated as

m

Π = P D (P )− f − vq(x )� i i i ii = 1 [1]

m ni i− t(s,s )D (P )− d(s,s )x ,� i i � i

i = 1 i = 1

where Π is profit; Pi is price charged at mar-ket, i = 1 . . . m; Di(Pi) is demand for the firm’sproduct at market i = 1 . . . m; si is spatiallocation of market i = 1 . . . m; t(s,si) is thecost of transporting one unit of output fromfirm location s to market location si; f is fixedcosts facing the firm to produce output; v isthe constant marginal cost of producing oneunit of output; xi is production inputs frommarket i = 1 . . . n; d(s,si) is the cost of trans-porting one unit of input xi from market lo-cation si to firm location s; and q(xi) is theoutput level of the firm.

In a spaceless world, t(s,si) = d(s,si) = 0,and the biodiesel firm has one decision, whatprice (Pi) to charge in each of its separate mar-kets. Once a specific market’s price has beenestablished, say P*, the amount sold in thatmarket is determined by its respective demandfunction Di(P*). In a spatial world wheret(s,si) ≠0 and d(s,si)≠ 0, the selection of loca-

February 2013Land Economics122

FIGURE 2Theoretical Location Decision Problem

tion has a direct impact on the effective priceof output. Here the consumer must endurehigher prices because of the cost of transpor-tation of the output: the greater the distancethe output is shipped the higher the effectiveprice of the output. This transportation costwill result in lower quantity demanded andtotal revenue for the firm.

This presents two problems. The first cen-ters on selecting a location (s) that minimizesthe cost of shipping raw materials, such assoybean oil, to the plant. The second is min-imizing output transport costs. From the pointof view of a biodiesel plant, the output marketis where biodiesel is blended with petroleumdiesel. Where the blended biodiesel is shippedis irrelevant to the firm. The plant’s revenueis maximized by offering the lowest delivery

or effective price possible, inclusive of trans-portation costs and local factors that influenceproduction costs.

To better understand the problem, considera situation where a biodiesel firm is lookingat three output markets (m = 3) as well as threeinput markets (n = 3), and the markets overlap(Figure 2). The firm is selecting a location(s*), somewhere between the three markets(s1,s2,s3), that will maximize profits. In thissimple example, the firm is shipping feedstockfrom three markets (d(s,si)) to a centrally lo-cated facility and then shipping product backto the same three markets (t(s,si)). Clearly thefirm will locate within the triangle defined bythe three hypothetical markets.

In the second stage of the location decision,communities often have some influence on the

89(1) Fortenbery, Deller, and Amiel: Location of Biodiesel Refineries 123

final outcome. Communities can offer high-quality infrastructure, skilled labor, buildinglocations, and generally high quality of lifecharacteristics and not only offer low-cost al-ternatives for the firm but, more importantly,offer a viable comparative advantage overother locations. This indicates communitiesmust be sensitive to the total cost of produc-tion in one community compared with othercommunities. Thus the community seeks tokeep transportation rates low or to offsethigher transportation costs by reducing thenontransportation costs through things likelower wages, inexpensive land, and tax con-cessions. Indeed, much of the empirical firmlocation literature is aimed at attempting togain a better understanding of how these sec-ondary factors influence firm decisions.

Empirically measuring the firm’s decisioncriteria outlined in equation [1] is difficult ifnot impossible without firm-specific data thatis generally not available to researchers. Asan alternative, Guimaraes, Figueiredo, andWoodward (2004) note that researchers haveturned to McFadden’s (1974) random utilityframework to provide both a theoretical andempirical foundation for their work. Here thefirm decomposes latent or unobserved profits( ) of location alternative i for firm n into∗Πinsystematic ( ) and stochastic ( ) compo-Π εin innents. The systematic profit is a function of kseparate characteristics describing a particularspatial site ( ), which have individualXinkweights ( ). The relationship can be ex-βikpressed as , where∗Π = Π + ε Πin in in in

K= β X .� in inkk = 1The stochastic elements of profit ( ) areεin

random variables that capture noise in the lo-cation decision-making process, such as a lackof complete information of all potential sitesand/or the ability to process, including incom-plete information. As outlined by Hunt, Boots,and Kanaroglou (2004) this framework allowsresearchers to specify a probability that a firmwill select an alternative from the choice set.The probability that firm n selects alternativesite i from a set of Cn alternatives equals theprobability that the latent profit of site i isgreater than or equal to the best latent profitof all other alternatives:

P (i) = P(Π + ε ≥ Π + ε )∀j ≠ i, j ∈ C . [2]n in in jn jn n

Note that the set of relevant alternatives is de-fined by the spatial boundaries of the relevantmarkets, or the triangle determined by thethree markets in Figure 2.

In order to estimate the relationship out-lined in equation [2] we must make some as-sumptions about the form of the stochasticelement of profits ( ). If we assume thatεinthese are independently and identically dis-tributed, the multinomial logit model ofMcFadden (1974) results. This model can beexpressed as

β Xin ineP (i) = , [3]n J

β Xjn jne�j = 1

which can be directly estimated. Carlton(1979, 1983) showed that the conditional logitmodel fits the random utility model, or in ourcase random profit model, of McFadden forfirm location.

As we saw in the spatial analysis of bio-diesel plant locations (Figure 1 and the Ap-pendix), there exists prima facie evidence ofsome clustering among plants. The spatialconcentrations, through the Anselin localMoran’s I and Getis-Ord Gi* estimation, how-ever, do not ensure that these biodiesel plantsare benefiting from agglomeration economiesas outlined by Porter (1990, 1995, 2000) andothers. Simply because firms are in close spa-tial proximity does not ensure that they arebehaving like an economic cluster. What wecan conclude, however, is that there is signifi-cant spatial dependency in the location of bio-diesel plants.

Estimation Methods

Because there is prima facie evidence thatthere is spatial dependency, traditional meth-ods of estimating location decision and/orconcentration patterns are suspect. Unfortu-nately, as noted by Robertson, Nelson, and DePinto (2009), most researchers elect to eitherignore the potential estimation problems oremploy simplistic “step-around” methods.More recently, however, two approaches that

February 2013Land Economics124

directly address spatial dependency have re-ceived considerable attention. The first is a re-cursive-importance-sampling (RIS) estimatorsuggested by Beron, Murdoch, and Vijverberg(2003) and Beron and Vijverberg (2004), andthe second a Bayesian strategy of MarkovChain Monte Carlo (MCMC) developed byLeSage (1999, 2000) and documented byLeSage and Pace (2009).2 For this study wehave elected to use the Bayesian strategy of-fered by LeSage. To test for consistency of ourresults we estimate a spatial logit along witha spatial tobit model of spatial concentration.

In the first model, is the binary depen-Yident variable for county i, which takes thevalue of one if a biodiesel plant is located inthe county and zero otherwise. Thus, the spa-tial logit or probit model is the most appro-priate. To estimate the spatial logit model, werely on the MCMC sampling method. Thismethod was chosen over others since it caneasily accommodate heteroskedastic distur-bances, and the logit model can be estimatedrelatively easily by changing the degrees offreedom. In order to employ the spatial Bayes-ian approach, however, a distribution for Ymust be established.

The value of the dependent variable prox-ies the profitability of a biodiesel plant locatedin one county relative to another county. It isassumed to be correlated with a latent, county-level unobserved utility associated with theexistence, or nonexistence, of a biodieselplant in a county. Because more profitableplants are expected to lead to more stable em-ployment prospects, a higher and more stabletax base to support the provision of local ser-vices (education for example), and a more se-cure business environment for other busi-

2 As noted by Franzese and Hays (2009), there are otherapproaches that have been suggested, such as a two-stepgeneralized method of moments estimator (Pinkse and Slade1998; Kelijian and Prucha 1998; Klier and McMillen 2005).More recently Schnier and Felthoven (2011) suggest that amethod offered by Train (2003) that utilizes a Cholesky ma-trix within a multinormial framework that they then applyto land use decisions. Indeed, much of the spatial discretechoice econometrics has developed in models of land use(e.g., McMillen 1989; Bockstael 1996; Bell and Irwin 2002;Irwin and Bockstael 2002; Wang and Kockelman 2009) andthe fisheries literature (e.g., Hicks and Schnier 2010; Smith2010; Schnier and Felthoven 2011). However, the RIS andBayesian are the most commonly used in applications.

nesses interacting with a biodiesel plant, acounty’s utility associated with a plant’s ex-istence is assumed proportional to a plant’sprofitability. If the value of the dependentvariable is equal to one, then the profitabilityof the biodiesel plant in that county is greaterthan in another county, thus the county’s util-ity is higher. The converse is true if the valueof the dependent variable is equal to zero. If

, then the profitability of the plant in theY = 0icounty is lower than in another county. Moreformally, let ,∗y = U − U ∀i = 1, . . . ,ni 1i 0iwhere is the utility if a biodiesel plant isU1ilocated in county i and is the utility if aU0ibiodiesel plant is not located in county i. If

, then a biodiesel plant would be located∗y ≥ 0iin the county and . If the biodiesel plantY = 1iselects to locate in county i, then that is aprofit maximizing location. If , then a∗y <0ibiodiesel plant would not be located in thecounty and . That is to say,Y = 0i

∗y = 1 if y ≥ 0,i i∗y = 0 if y <0,i i

∗and P(y = 1) = P(U ≥ U ) = P(y ≥ 0).i 1i 0i i

As a consequence, the conditional distributionfor the latent variable takes the followingform:

∗ −1y � TMVN(μ,Ω) s.t. μ = (I −ρW) Xβ,i n

and

−1Ω = [(I −ρW) ′(I −ρW)] ,n n

subject to the restriction that ,∗a ≤ y ≤ bwhere the bounds of the linear inequalitydepend upon the observed values of y. If

, then is bounded from above by zero;∗y = 0 yiif , then is bounded from below by∗y = 1 yizero. Using this definition of instead of the∗ybinary variables, it is possible to estimate themodel parameters and using the condi-β ρtional posterior distributions that arise for thespatial Bayesian autoregressive model withcontinuous dependent variables (LeSage andPace 2009).

For ease of notation, let .A = (I − ρW)nThen the joint posterior distribution of the pa-rameters is given by the following:θ = {β, ρ}

89(1) Fortenbery, Deller, and Amiel: Location of Biodiesel Refineries 125

1∗ ∗p(θ⎪y)∝⎪A⎪exp − (Ay − Xβ) ′(Ay − Xβ) π(β)π(ρ),� �

2

where is the prior distribution for andπ(β) βis the prior distribution for the param-π(ρ) ρ

eter. We will assign a normal prior distribu-tion to . Note that in both the nonspatial asβwell as spatial logit models, and cannot2β σεboth be identified. To solve this problem 2σεis generally set equal to one (LeSage andPace 2009). Thus, the prior distribution for

is assigned to be such thatβ β � N(c,T)and . The−1 ∗ −1c = (X ′X) X ′Ay T = (X ′X)

prior distribution for the parameter is a uni-ρform distribution on the interval [1/λ,1/λ]such that is the minimum eigenvalue of theλspatial weight matrix and is the maximumλeigenvalue.

The parameters specified above will be es-timated using the Gibbs sampling method.There are three main steps that must be com-pleted during each iteration of the procedure.First, β can be sampled from the followingconditional distribution: p(β⎪ρ0,y∗) ∝ N(c∗,T∗)such that the mean is given by ∗c = (X ′X +

and the covariance−1 −1 ∗ −1T ) (X ′Ay + T c )0 0 0matrix is given by .∗ −1 −1T = (X ′X + T )0

, , and are initial values for T, c, and−1T c ρ0 0 0ρ and can be set arbitrarily. In our model, theinitial values for and are zero and−1c T0 0

, respectively. Next, the values obtained−1210for are then used to estimate the value ofβ

. The conditional distribution for is givenρ ρby

1∗ ∗ ∗p(ρ⎪β ,y )∝⎪A⎪exp − (Ay − Xβ) ′(Ay − Xβ) .1 � �

2

Since the conditional distribution does nottake a known form, it must be evaluated usingthe Metropolis-Hastings algorithm (Hollo-way, Shankar, and Rahmanb 2002).

Finally, a sample from must be drawn.∗yAccording to Geweke (1991), sampling fromthe truncated multivariate normal distributionspecified above is the same as sampling fromthe n-variate normal distribution z � N(0,Ω)s.t. . is determined by us-ma − μ ≤ z ≤ b − μ zing the Gibbs sampling technique on the dis-tribution of , conditional on the values ofzi

. The sample for is obtained by setting∗z y− i

(LeSage and Pace 2009). A com-∗y = μ + zprehensive explanation of the approach is de-tailed by Geweke (1991) and LeSage andPace (2009).

The estimates from p(β⎪ρ0,y∗), p(ρ⎪β1,y∗),and p(y∗⎪ρ1,β1) constitute one iterationthrough the MCMC sampler. The estimatesfrom this iteration are used as the initial valuesin the following pass. The Gibbs samplingprocedure must be repeated until the values ofthe estimates converge.

In addition to a simple dummy variable forthe presence of a biodiesel plant, we also havedata on the number of biodiesel plants in agiven county. Thus, in the second model, Yiis the number of biodiesel plants in county iand takes the value of zero if there are no bio-diesel plants in the county. The data is trun-cated from below by zero; however, whilethere are simple attempts to develop spatialPoisson or ordered probit estimators (e.g.,Wang and Kockelman 2009), there are no spa-tial zero-inflated Poisson or ordered probit es-timators that we are aware of. Specifically,most U.S. counties do not have a biodieselplant, resulting in a spike on zero in the dis-tribution. To take advantage of the additionalinformation in the count data as well as toexplore the stability of the spatial logit mod-els, we employ the spatial tobit model. Thespatial tobit model is very similar to the spa-tial logit model described above.

In the spatial tobit model,

∗y = 1 if y ≥ 0,i i∗y = 0 if y <0,i i

∗and P(y = 1) = P(U ≥ U ) = P(y ≥ 0).i 1i 0i i

The main difference is in the sampling fromy∗ for the latent variable z. All else followsfrom the logit/probit estimator describedabove.

Model Specification and Data

To our knowledge no one has attempted tomodel the location decisions of biodieselplants, and as a result there is little direct em-pirical literature to draw upon in specifyingour model. There are, however, a handful ofstudies that examine the location decisions of

February 2013Land Economics126

ethanol plants (e.g., Sarmiento and Wilson2007; Lambert et al. 2008; Low and Isserman2009; Haddad, Taylor, and Owusu 2010). Un-fortunately, the technology that is involved inethanol production and distribution is funda-mentally different than that for biodiesel. Inaddition, ethanol production, as noted earlier,was looked to as a vehicle for rural develop-ment in areas where the actual feedstock wasproduced. This facilitated a clustering of eth-anol plants in corn producing regions.

Biodiesel plants have a much wider varietyof potential feedstocks, implying that even inthe case of contributing to economic devel-opment in feedstock rich areas, the spatialclustering could be quite different. Thus, in alocation model it is not clear how t(s,si) versusd(s,si) should play out for biodiesel plants.Second, as noted earlier, the chemistry of eth-anol as an additive is such that ethanol blend-ing in gasoline is more difficult than biodieselblending. These two conditions suggest thatbiodiesel plants may be more flexible in theirlocation decisions than are ethanol plants.3

In order to examine the drivers of biodieselplant location decisions, we employ county-level data for the year 2005. Much of the dataare drawn from the 2002 Census of Agricul-ture (U.S. Department of Agriculture 2004),the County and City Data Book published bythe U.S. Census Bureau (U.S. Census Bureau2005), or the Bureau of Economic Analysis’sRegional Economic Information System4

(BEA-REIS). The plant location data is de-rived from County Business Patterns, U.S.

3 Another problem with relying on the ethanol plant lo-cation literature as a foundation for our work is the mannerin which spatial dependency has been handled. Low andIsserman (2009) make similar observations about ethanolinput-output markets coupled with simple mapping analysisto draw their conclusions. Lambert et al. (2008) acknowl-edge serious spatial dependency in their data and address itby including a range of spatially descriptive variables ascontrols. Haddad, Taylor, and Owusu (2010) ignore the is-sue, and Sarmiento and Wilson (2007) address a very simpleproblem: how does spatial proximity between ethanol plantsinfluence future location decisions? Thus, through their ba-sic research, Sarmiento and Wilson (2007) address spatialdependency, but in their empirical work this dependency istreated simplistically.

4 U.S. Department of Commerce, Bureau of EconomicAnalysis, Regional Economic Information System, availableat www.bea.gov/regional/docs/reis2006dvd.cfm.

Census Bureau,5 and is 2007 data. The vari-ables hypothesized to influence biodieselplant location can be grouped into five blocks:input markets, output markets, local marketsocioeconomic characteristics, transportationinfrastructure, and policy options used to pro-mote biofuels.

To measure the impacts of input variables,the following proxies are employed:

• Number of soybean (or other oilseed) crushplants per 10,000 persons

• Number of restaurants and food service firmsper 10,000 persons

• Average farm sales• Acres of crops harvested• Farm share of total county income

The crush plant concentrations are aimed atcapturing the potential for oil-bearing cropsbeing processed within the county. The num-ber of restaurants and food service firms cap-tures the ability to supply the plant with re-cycled oils and grease from food preparation.The three measures of farm size are aimed atcapturing the potential input supply derivedfrom raw agricultural products. It is expectedthat the higher the concentration of each ofthese potential input sources the greater thelikelihood a biodiesel plant locates.

The output markets are measured by thefollowing:

• Number of tank farms per 10,000 persons• Number of trucking and busing firms per

10,000 persons• Number of fuel pipeline firms per 10,000 per-

sons

Because biodiesel is splash-blended, it can beadded to diesel at any point along the mar-keting chain, but most blending occurs at tankfarms. Trucking and busing firms are impor-tant consumers of diesel and thus representsignificant potential local biodiesel demand.Although biodiesel tends to be blended at tankfarms, technology exists to add it directly intothe pipeline distribution network. Hence thenumber of pipeline firms is designed to proxythis potential.

5 U.S. Department of Commerce, County Business Pat-terns, available at www.census.gov/econ/cbp/.

89(1) Fortenbery, Deller, and Amiel: Location of Biodiesel Refineries 127

We also include a range of local socioeco-nomic variables to capture not only potentialmarket demand but also the potential likeli-hood of support for biodiesel as an alternativefuel source. These variables include the fol-lowing:

• Population in 2005• Houses owner-occupied (percent)• Bachelor’s degree or higher (percent)• Workers who drove to work alone (percent)• Votes cast for president 2004 Republican (per-

cent)• Persons 5 years and over residing in same house

in 1995 and 2000 (percent)• Net nonfarm business job growth 2000–2004• Local government total revenue per capita• Local government revenue from the property

tax (percent)

Population captures the scale of the potentiallocal market, and percentage of houses owner-occupied is a proxy of wealth. The latter alsois a very simple measure of the NIMBY, or“not in my back yard,” phenomenon. In thecase of ethanol production there have beenseveral cases where local communities ac-tively campaigned against the operation of theplant. Concerns over odors and demandsplaced on local water supplies are often ref-erenced. Although the technology for bio-diesel is different from that for ethanol, mis-perceptions may result in homeownersdiscouraging such firms. Home stability is acomplementary measure of homeownershipand reflects the stability of the local commu-nity. Education level is again a proxy for thepotential willingness to embrace biodiesel asan alternative fuel source.6 The percentage of

6 We do not have a strong prior relative to the sign onthe education variable. If education is positively related toincome, and higher-income individuals disproportionallyrepresent the driving population of alternative fuel vehicles(they tend to be more expensive and are newer additions tothe private automobile fleet), then a positive relationshipmight be expected. Another possibility is that education cor-relates to a heightened awareness of environmental issues.To the extent that biodiesel is viewed as a “greener” fuelthan petroleum diesel, this might also lead to a positive re-lationship. Conversely, however, lower education levelslikely correlate to a higher percentage of blue collar workers.Since most of the jobs created by a biorefinery are blue collarin nature, the job creation aspect of a new plant may result

drivers who commute alone is also a potentialmeasure of demand. The percentage of totalvotes cast for President Bush in 2004 is a sim-ple measure of political leanings, which mayagain reflect the demand for alternative fuelssuch as biodiesel. We expect the coefficienton this variable to be negatively related toplant location for two reasons: (1) the currentdemocratic administration has been more ag-gressive in pushing bioenergy as a federal pol-icy than the previous administration, whichhad strong ties to the oil industry; and (2) con-cerns over potential climate change, and thedevelopment of bio-based fuels as a potentialresponse to CO2 emissions, tends to be moreprominent in the liberal as opposed to conser-vative political rhetoric and agenda. Net busi-ness growth captures the local economic per-formance of the county. We expect thathigher-growth counties will reflect a growingpotential market for biodiesel. The two localpublic finance measures are designed to cap-ture the role of taxes and tax burdens, in par-ticular the property tax, on firm formation.

There are two measures of the transporta-tion infrastructure:

• Distance to roads (meters)• Distance to rail lines (meters)

The distance of each county to major roadsand rail lines was calculated in ArcMap (ESRI2006) by finding the distance from each poly-gon (each county) to the nearest line (either arail line or major road). Due to the scale ofthe data, some small railroads and closely par-allel tracks are not included in the analysis.Major roads are defined as interstates, as wellas major highways.7 Higher values of both re-flect lower concentration of transportation in-frastructure and should be associated withhigher transportation costs.

There are a significant number of publicpolicy initiatives at both the federal and statelevels to encourage the formation of alterna-tive fuel sources including biodiesel. We ex-

in it being preferred in areas with lower education levelscompared to communities with higher average education.

7 See the National Atlas web site at www.nationalatlas.gov/maplayers.html?openChapters = chptrans#chptrans forthe shape files and metadata on the road and rail data usedin the analysis.

February 2013Land Economics128

pect that federal policies will have limited im-pact at the local level but promote overallindustry growth. In addition, because federalpolicies are the same across all U.S. counties,there are no spatial variations to capture.Hence we do not consider such policies. Sev-eral states, however, have been very aggres-sive in promoting alternative fuels and bio-diesel in particular. The problem is that localand state policies may be endogenous relativeto the plant siting decision. This makes intro-ducing explicit policy variables problematic.To explore the potential impact of state-levelbiofuel promotional policies we introduce twodummy variables to proxy policy. The first isone if there are local or state-level consump-tion mandates for biofuels, zero otherwise.The second is one if there are local or stateproduction incentives, zero otherwise.

IV. MODELING RESULTS

Results of estimating the Bayesian spatiallogit models described above are presented inTable 1, while the spatial tobit models are pro-vided in Table 2. For completeness we alsoreport the aspatial logit and tobit estimatedmodels. The models are specified in two dif-ferent ways: (1) with the variables that ac-count for public policies aimed at supporting(both consumption mandates and producer in-centives) biodiesel production, and (2) with-out the public policy variables. This lets ustest whether currently available policy tools,including supply and demand-side incentives,make a general contribution to our under-standing of biodiesel plant location decisions.Before turning to specific results, there aresome general observations about the overallresults that warrant discussion. First, the spa-tial parameter is not statistically significantρin any of the four models estimated using spa-tial methods.8 Despite the evidence from the

8 The weights (W) matrix is constructed based on row-stochastic weighting, which is in essence a simple adjacencymatrix. We have tested alternative specifications of the Wmatrix and find that our results are generally insensitive tothe W matrix specification. This is consistent with the workof Lesage and Pace (2010). They show that model estimatesand inferences are not significantly sensitive to differentspecifications of the spatial weights matrix when the mar-ginal effects in the model are properly calculated.

global and local Moran’s I and the Getis-OrdGi* statistic, the level of spatial dependencydoes not appear to be sufficient to affect thereliability of the logit or tobit estimators. Itmay be that the spatial dependency and clus-ters identified above are narrowly focused intoo few regions (e.g., Houston, Texas), or theaccess to rail transportation adequately cap-tures spatial dependencies. This supports thesecond observation that there is stability in in-dividual parameter results across the spatialand aspatial estimators. Third, there is stabil-ity in results across the logit and tobit models,lending credence to our results.

In general the results indicate that neitherthe number of crush plants nor the number ofrestaurants and food service firms in a countyimpacts the location of a biodiesel plant. Sur-prisingly, the number of tank farms also doesnot encourage a siting and, in fact, reveals anegative relationship with plant location.Thus, unlike ethanol plants, it appears thatbiodiesel plants are not sensitive to being lo-cated next to the market where their feed-stocks are produced, nor next to primary pe-troleum distribution centers. This may makesense since the splash-blending chemistry ofbiodiesel provides more flexibility than etha-nol in terms of where and how biodiesel entersthe fuel stream, thus the reliance on traditionalpetroleum distribution centers for blendingmay not be important. Further, the export mar-ket for unblended biodiesel is more significantthan for ethanol (thus a significant amount ofproduction does not need to go to a tank farmor any other U.S. facility for blending).

Population is statistically significant andpositive, implying a tendency for biodieselplants to locate in areas with relatively largepopulations. This challenges the idea that bio-diesel plants can or should be promoted as apure rural development strategy in the sameway as ethanol plants. Interestingly, however,both owner-occupied housing and educationare significant but with negative coefficients.Thus, greater wealth, as proxied by home own-ership, reduces the likelihood of a plant locat-ing in a county. This may be a result of theNIMBY effect, with location constraints beinggreatest around privately owned, single-familydwellings, as opposed to counties with a higherpercentage of rental properties. The negative

89(1) Fortenbery, Deller, and Amiel: Location of Biodiesel Refineries 129

TABLE 1Biodiesel Locational Analysis via Logit

Aspatial Logit Spatial Logit

Base Policy Base Policy

Intercept −3.147603(0.0221)

−3.751473(0.0080)

−1.513060(0.0118)

−1.874203(0.0049)

Number of crush plants per 10,000 persons 0.979318(0.1473)

0.869525(0.2074)

0.451941(0.1167)

0.387683(0.1584)

Number of tanker farms per 10,000 persons −0.333416*(0.0675)

−0.308620*(0.0898)

−0.160140*(0.0161)

−0.144110*(0.0443)

Number of restaurants and food service firms per10,000 persons

−0.004460(0.7147)

−0.004501(0.7183)

−0.002580(0.3406)

−0.003291(0.3047)

Number of trucking and busing firms per 10,000persons

0.006943(0.6778)

0.000230(0.9893)

0.002857(0.3584)

−0.000464(0.4859)

Number of pipeline transportation of crude oil firms per10,000 persons

0.010157(0.9818)

0.034694(0.9372)

−0.037700(0.4896)

−0.039544(0.4776)

Population 2005 0.000002*(0.0000)

0.000002*(0.0000)

0.000001*(0.0000)

0.000001*(0.0000)

Houses owner-occupied(percent)

−0.036966*(0.0076)

−0.043842*(0.0020)

−0.019310*(0.0031)

−0.022886*(0.0016)

Bachelor’s degree or higher(percent)

−0.031008*(0.0181)

−0.031214*(0.0178)

−0.016520*(0.0071)

−0.016000*(0.0086)

Workers who drove to work alone(percent)

0.066326*(0.0000)

0.071126*(0.0000)

0.033478*(0.0000)

0.036272*(0.0000)

Votes cast for president 2004 Republican(percent)

−0.014028*(0.0536)

−0.010128(0.1772)

−0.005940*(0.0469)

−0.004046(0.1353)

Persons 5 years and over residing in same house in1995 and 2000

(percent)

−0.014702(0.3161)

−0.007764(0.6061)

−0.008320(0.1278)

−0.004094(0.2882)

Net nonfarm business job growth 2000–2004 0.000001(0.9508)

0.000000(0.9977)

0.000000(0.4796)

0.000000(0.5002)

Local government total revenue per capita −0.000116(0.1090)

−0.000139*(0.0671)

−0.000056*(0.0514)

−0.000069*(0.0320)

Local government revenue from the property tax(percent)

0.001791*(0.0000)

0.003117(0.5205)

0.001009(0.3331)

0.001424(0.2800)

Average farm sales 0.000001*(0.0874)

0.000001*(0.0853)

0.000000*(0.0569)

0.000000*(0.0529)

Acres of crops harvested 0.000002*(0.0004)

0.000002*(0.0073)

0.000001*(0.0002)

0.000001*(0.0022)

Farm share of total county income 0.007032(0.9937)

−0.018915(0.9831)

−0.011290(0.4914)

−0.033275(0.4696)

Distance to roads(meters)

−0.000031(0.2704)

−0.000032(0.2486)

−0.000012(0.1506)

−0.000013(0.1206)

Distance to rail lines(meters)

−0.000020(0.1774)

−0.000021(0.1706)

−0.000009(0.0937)

−0.000010(0.0686)

Policy: mandates present — 0.535533*(0.0054)

— 0.277018*(0.0027)

Policy: incentives present — 0.126378(0.5126)

— 0.037696(0.3335)

Spatial parameter ρ — — 0.076654(0.1647)

0.057756(0.2451)

Note: Number in parentheses is the marginal significance level.* Significance at the 10% level.

sign on the college education variable couldindicate several things. One is another angleon the NIMBY effect, where more highly edu-cated communities (counties) are less willing

to promote or give access to manufacturingsuch as biodiesel. Another might be related tothe labor markets biodiesel producers are seek-ing. A higher level of education is likely related

February 2013Land Economics130

TABLE 2Biodiesel Locational Analysis via Tobit

Aspatial Logit Spatial Logit

Base Policy Base Policy

Intercept −2.927779(0.0320)

−3.421892(0.0144)

−3.312318(0.0188)

−4.011502(0.0074)

Number of crush plants per 10,000 persons 0.914394(0.2121)

0.818732(0.2694)

0.955980(0.1523)

0.843613(0.1755)

Number of tanker farms per 10,000 persons −0.312298*(0.0594)

−0.293539*(0.0760)

−0.391880*(0.0144)

−0.368772*(0.0277)

Number of restaurants and food service firms per10,000 persons

−0.013009(0.2578)

−0.013648(0.2445)

−0.013854(0.1583)

−0.016011(0.1245)

Number of trucking and busing firms per 10,000persons

−0.000073(0.9964)

−0.006006(0.7184)

0.001718(0.4612)

−0.005913(0.3822)

Number of pipeline transportation of crude oil firms per10,000 persons

0.004024(0.9925)

0.030724(0.9422)

−0.166159(0.4146)

−0.141191(0.4360)

Population 2005 0.000001*(0.0000)

0.000001*(0.0000)

0.000001*(0.0000)

0.000001*(0.0000)

Houses owner-occupied (percent) −0.042537*(0.0017)

−0.048801*(0.0004)

−0.051795*(0.0012)

−0.059111*(0.0003)

Bachelor’s degree or higher (percent) −0.012753(0.2944)

−0.012320(0.3110)

−0.017160(0.1167)

−0.016051(0.1345)

Workers who drove to work alone (percent) 0.062900*(0.0000)

0.067605*(0.0000)

0.076155*(0.0000)

0.082327*(0.0000)

Votes cast for president 2004 Republican (percent) −0.013282*(0.0532)

−0.009812(0.1622)

−0.015467*(0.0337)

−0.011521(0.0818)

Persons 5 years and over residing in same house in1995 and 2000 (percent)

−0.012895(0.3615)

−0.006774(0.6376)

−0.015019(0.1916)

−0.007263(0.3367)

Net nonfarm business job growth 2000–2004 0.000008(0.1975)

0.000009(0.1850)

0.000011(0.0857)

0.000011(0.0772)

Local government total revenue per capita −0.000089(0.2184)

−0.000113(0.1302)

−0.000111(0.1004)

−0.000140*(0.0566)

Local government revenue from the property tax(percent)

0.002042(0.6547)

0.003058(0.5135)

0.002314(0.3354)

0.003615(0.2572)

Average farm sales 0.000001*(0.0793)

0.000001*(0.0931)

0.000001*(0.0446)

0.000001*(0.0485)

Acres of crops harvested 0.000002*(0.0004)

0.000002*(0.0066)

0.000002*(0.0001)

0.000002*(0.0029)

Farm share of total county income −0.200794(0.8121)

−0.170611(0.8409)

−0.281747(0.3877)

−0.218296(0.4110)

Distance to roads (meters) −0.000021(0.3225)

−0.000022(0.3102)

−0.000030(0.1157)

−0.000032(0.1106)

Distance to rail lines (meters) −0.000019(0.1541)

−0.000019(0.1463)

−0.000026*(0.0504)

−0.000025*(0.0555)

Policy: mandates present — 0.504963*(0.0096)

— 0.589585*(0.0045)

Policy: incentives present — 0.074762(0.6784)

— 0.093404(0.3351)

Spatial parameter ρ — — 0.043299(0.1754)

0.036229(0.2140)

Note: Number in parentheses is the marginal significance level.* Significance at the 10% level.

to a higher average wage scale, placing upwardpressure on the costs of production. From ourtheoretical model of location choices, the geo-graphic location (step one in the location pro-cess) of a particular county may be ideal, but

the spatial isocost curves appear to rise (highercosts) with higher levels of education. In ad-dition, the labor demands of biodiesel plants,including technical jobs, do not require a col-lege degree, but perhaps skills more associated

89(1) Fortenbery, Deller, and Amiel: Location of Biodiesel Refineries 131

with technical schools. One way to test thislatter interpretation might be to replace ourmeasure of labor force education with a mea-sure of the supply of labor with a technical de-gree.

The percentage of drivers that drive towork alone is significant and positive, sug-gesting that it does capture some measure ofdemand potential and affects the location de-cisions of biodiesel plants.9 Political affilia-tion/attitudes do not seem to be important.Likewise, the age distribution and nonfarmjob growth do not appear to be important de-terminates in locating a plant.

Interestingly, the amount of local govern-ment revenue per capita does matter, and thesign of the coefficient is negative. This is con-sistent with theory that counties with higherlocal taxes causes the spatial isocost curve tomove up, making the site less attractive. It doesnot seem to matter whether local governmentsderive the majority of their funding from prop-erty taxes or other sources of funding (such asstate aids or the sales tax). While the locationof oilseed processing facilities was not impor-tant, both average farm sales and crop acrespositively impact on location decisions. Thismay simply indicate a greater willingness ofrural, farm-based communities to acceptvalue-added businesses that could potentiallyuse farm products as inputs, rather than somelocation decision that revolves around feed-stock procurement. The farm share of totalcounty income does not seem important.

Distance to roads measured by access tomajor highways does not matter, but rail ac-cess is important, and the greater the distanceto a rail siding the less likely a plant is tolocate. This makes sense for two reasons.First, if plants do not locate next to oilseedprocessing facilities, they are likely taking de-livery of vegetable oil by rail. This is by farthe cheapest way to move oil, and the abilityto utilize unit trains can greatly reduce the

9 It should be noted that most cars in the United Statesare not diesel, thus care must be taken in interpreting thisresult. But as consumers look for more highly fuel efficientvehicles, the potential future demand for diesel-poweredcars, and biodiesel in turn, could be significant in the nearfuture. The popularity of small-displacement, high-effi-ciency diesel cars in Europe may transfer to U.S. markets.

costs of transporting feedstocks (Fortenbery2004). Second, since biodiesel can be movedby rail in the same vessels used to haul petro-leum products, including diesel fuel, access torail services is critical in minimizing distri-bution costs of the refined product. This resultcomplements the results on education andhome ownership rates discussed above. Morehighly educated people and those that owntheir home may be less supporting of freightrail service in their communities; anotherform of the NIMBY effect may be at play.10

The two policy variables, simple dummiesif the state has implemented any incentiveprograms or renewable fuel mandates, suggestthat incentive programs do not influence thelocation decisions of biodiesel plants. Thepresence of mandates, however, is positivelyassociated with the presence and/or concen-tration of biodiesel plants. This result speaksto the viability of incentives, generally in theform of tax incentives or subsidies, to en-courage business activity: despite the popu-larity of such incentives, they seldom have thedesired results.

V. CONCLUSIONS

As we move toward developing the capac-ity to satisfy the biofuels objectives containedin RFS2, additional production capacity forbiodiesel will be necessary. In this study weprovide the first look at the important char-acteristics that lead to an attractive environ-ment for locating a biodiesel plant. As such,we provide insight to both communities andinvestors relative to the attractiveness of spe-cific communities (U.S. counties) whereplants may be located.

In general, we find that there is some spa-tial dependency associated with plant loca-tion. In other words there is some evidencethat biodiesel plants do tend to spatially clus-ter together in certain geographic locations.While we account for this spatial clustering inour formal modeling we cannot draw any in-ferences concerning whether these spatialclusters are true “economic clusters” in the

10 This would reflect a desire to be insulated from thenoise and traffic congestion associated with active rail lines.

February 2013Land Economics132

spirit of Porter (1990, 1995, 2000). This is thenext step in our line of inquiry.

We do find evidence that biodiesel plantsare not attracted to communities with highereducation levels and home ownership rates,but are attracted to ones that have access torail service and rely on agricultural productionas a significant enterprise. At the same time,however, biodiesel plants tend to be drawn tomore populous counties. Neither the process-ing of feedstock (i.e., presence of a crushplant) nor the presence of tank farms appearsto be particularly important in a plant’s sitingdecision. The results taken in tandem seem tosuggest that biodiesel plants are attracted tolarger output markets (consumers) rather thaninput supplies, but output markets that are lesswealthy. We think that the very last result onwealth may reflect NIMBY effects.

The results also provide some insight rela-tive to policy initiatives a community maywant to pursue in terms of attracting a plant.While endogeneity issues suggest some cau-tion in interpretation, it does appear that con-sumption mandates are more likely to impactpositively on plant location. Further, invest-ment in feedstock processing is less importantthan development of transportation infrastruc-ture (rail). Providing public support to thesesectors appears to be more important to aplant’s location decision than direct public in-vestment in biodiesel production. These find-ings are in contrast to the often-proposed localpolicy actions and suggest that communitiesneed to more carefully consider what typepolicy initiatives will have the greatest impacton a plant’s location decision.

APPENDIX: TEST RESULTS FORSPATIAL CLUSTERING OF BIODIESEL

PLANTS

We employ two tests to see if the characteristics ofa particular observation (i.e., number of biodieselplants in a given county) are similar or dissimilar toneighboring observations. The null hypothesis is thatthere is no association between the value observed ata location and values observed at nearby sites. Thealternative is that nearby sites have either similar(large positive values of the statistic) or dissimilar val-ues (large negative values).

Consider first the Anselin local Moran’s I (FigureA1),11 then the Getis-Ord Gi* (Figure A2).12 Thereappear to be several spatial clusters based on the Lo-cal Moran’s I, including parts of northern Illinois andsouthern Wisconsin, parts of Iowa along with Hous-ton, Texas, and several locations in California as wellas the Seattle, Washington, region. The dominantclusters appear to be in Houston, the southern half ofCalifornia, and the Seattle region. There appear to besmaller clusters in northern Colorado and southernWyoming, as well as the Atlantic Coast from NewYork City to southern New Hampshire. In addition,the other areas with biodiesel plants, such as much ofthe Appalachia region, parts of Missouri, Kansas, andOklahoma, along with the upper parts of Minnesotaand North Dakota, appear to be spatially isolated.

The results of the Getis-Ord Gi* (Figure A2) gen-erally complement and reaffirm the results of the An-selin local Moran’s I. Here the spatial concentrationis most evident in the Gulf region of Texas, whichincludes Houston, much of California, particularly theregion from San Francisco to San Diego, and the Se-attle region. There is also a weaker cluster from NewYork City north through Vermont. Notice, however,that the northern Illinois, southern Wisconsin, andIowa cluster identified by the local Moran’s I is notconfirmed by the Getis-Ord Gi* statistic. Also notethat there is a statistically significant lack of biodieselactivity in the western parts of the Great Plains alongwith parts of the northern Rocky Mountain rangethrough Montana.

If we examine the simple plotting of plant locations(Figure 2) coupled with the two spatial concentrationor clustering statistics (Anselin local Moran’s I, Fig-ure A1, and the Getis-Ord Gi*, Figure A2) it is clearthat biodiesel plants appear to be much more foot-loose in their locations than ethanol plants. In addi-tion, there appear to be perhaps five spatial clusters:Houston, Los Angles, San Francisco, Seattle, NewYork City, and southern New England, and perhapsthe Chicago area.

11 The Anselin local Moran’s I is computed as

, where ,

n

w� ijn¯X − X j = 1,j ≠ ii 2 2¯ ¯I = w (X − X) S = − Xi � ij i i2S n −1j = 1,j ≠ iiXi is the number of biodiesel plants within county I, and wijconsists of spatial weight matrix elements identifying adja-cent counties.

12 The Getis-Ord Gi* is computed as ∗G =i

where and X and

n n¯ nw X �X w� ij j � ij

2j=1 j=1 X� jj=12n n 2¯�, S= �(X)2 nn w � w� ij � ij� �S j=1 j=1�

n�1w are the same as above.

89(1) Fortenbery, Deller, and Amiel: Location of Biodiesel Refineries 133

FIGURE A1Spatial Clustering of Biodiesel Plants via Anselin Local Moran’s I

FIGURE A2Spatial Clustering of Biodiesel Plants via Getis-Ord Gi*

February 2013Land Economics134

Acknowledgments

Support for this work was provided in part by theWisconsin Agricultural Experiment Station under theHatch Act.

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