ARTICLE IN PRESS
Biomass and Bioenergy 28 (2005) 591–600
www.elsevier.com/locate/biombioe
Siting analysis of farm-based centralized anaerobic digester
systems for distributed generation using GIS
Jianguo Maa,, Norman R. Scotta, Stephen D. DeGloriab, Arthur J. Lembob
a
Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
b
Department of Crop and Soil Sciences, Cornell University, Ithaca, NY 14853, USA
Received 20 June 2003; received in revised form 29 November 2004; accepted 16 December 2004
Available online 27 March 2005
Abstract
There is growing interest in installing anaerobic digesters (ADs) on farms to use animal wastes as a biomass resource
for both economic value and environmental benefit. This potential expansion prompts the need for land suitability
assessment. In this paper, a GIS model is proposed for land-suitability assessment of potential energy systems featuring
an AD coupled with an energy generator. A variety of environmental and social constraints, as well as economic factors
are integrated in the model to help determine the optimal sites for installing such systems. The analytic hierarchy
process (AHP) method is employed to estimate the factors’ weights in order to establish their relative importance in site
selection. The model is then applied to Tompkins County, New York as a case study for demonstration. A siting
suitability map was produced to identify those areas that are most suitable for distributed bio-energy systems using
dairy manure. The results showed that this GIS-based model, by integrating both spatial data and non-spatial
information, was capable of providing a broad-scale and multidimensional view on the potential bio-energy systems
development in the area of study to account for environmental and social constraints as well as economic factors. The
model can be modified for evaluating other biomass resources.
r 2005 Elsevier Ltd. All rights reserved.
Keywords: Biomass; Anaerobic digestion; Distributed generation (DG); GIS; AHP
1. Introduction
Methane gas is produced naturally from dairy
manure in the absence of oxygen and released into
Corresponding author. Tel: +1 607 255 2012;
fax: +1 607 255 4080.
E-mail address: [email protected] (J. Ma).
the atmosphere. An anaerobic digester (AD)
energy system promotes methane production,
captures and converts it to electricity and heat
for on-farm use or sale to the local utility [1,2].
Biogas produced in AD primarily consists of
methane which is about 50–60% for a typical
plug-flow AD. The rest of the biogas includes
carbon dioxide (40–50%) and other trace gases
0961-9534/$ - see front matter r 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.biombioe.2004.12.003
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J. Ma et al. / Biomass and Bioenergy 28 (2005) 591–600
such as hydrogen sulfide and nitrogen [1,3]. AD
for combined heat and power (CHP) generation at
the farm level began in the United States in the
early 1970s and many projects were developed in
the 1980s. However, the digester industry has seen
a reduction to a small number of operating
systems today because of numerous reasons,
including poor design and high cost.
In recent years, an increasing awareness that
AD can help mitigate odor and help with nutrient
recovery along with economic benefits has stimulated renewed interest in the technology [2,4]. The
success of centralized/community AD project in
Europe, especially in Denmark and Germany, has
provided good examples. In addition, dairy farmers faced with increasing federal and state regulations on animal wastes are looking for ways to
comply. They are further encouraged by government support, such as the AgSTAR Program of
EPA which promotes the use of methane recovery
(biogas) technologies at concentrated animal feeding operations (CAFOs). In light of these developments, animal wastes have the potential to
become important renewable energy resources.
Because these resources tend to be highly sitespecific, it is important to know where they
are available in addition to numerical assessment.
A Geographic Information System (GIS), a
computer system capable of assembling, storing,
analyzing, and displaying geographically referenced information, is an ideal tool to address this
issue.
GIS has been used in a number of studies for
determining optimal locations for various development plans such as waste disposal sites, power
plants, and other applications [5,6]. A decision
support tool based on GIS was developed to
validate the siting of a new solar power plant
at Skhira, Tunisia [7]. Similarly, GIS-based information was developed in the UK to assist in
the decision-making process for locating wind
farm sites [8]. The purpose was to find sites
for wind farms that were both economically
viable and environmentally friendly. Research
performed in the UK using GIS showed how
resource mapping and analysis could be used
to identify sources of collectable farmyard
manure in order to determine the potential for
anaerobic digestion plants [9]. GIS-based decision
support systems (DSS) have also been developed to identify location of biomass and
evaluate economic costs for exploiting this
resource [10–12].
2. Problem definition
It is unrealistic to install an AD-energy-generator set on every dairy farm. Instead, the bioenergy system could be installed on farms or new
sites that satisfy a variety of environmental and
social constraints exhibiting favorable economic
and technical characteristics. Due to economies of
scale, large farms are preferred, especially CAFOs
which have more than 1000 dairy animal units or
more than 300 animal units if wastes are potentially discharged to state waters [13]. In addition,
some clustered small dairy farms may be interested in installing a centralized AD facility to
collect wastes within a certain distance of the
farms. Therefore, to promote the development of
dairy manure-based bio-energy systems, it is
essential to determine the suitable or best locations for such development. The electric and gas
utility companies and energy investors are also
interested in such information because it will guide
energy planning and investment in any local
region.
This research has two main objectives. The first
is to develop a mathematical model for use in a
GIS environment to locate bio-energy systems.
This model, integrating various environmental and
social constraints as well as economic and
technical factors, will help identify locations most
suitable for considering distributed bio-energy
systems. The second objective is to illustrate the
implementation of the model within a GIS context
by conducting a case study for Tompkins County,
New York.
3. Methodology
Locating optimal sites for power generation
facilities is a complex task involving many
environmental, economic, and social constraints
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J. Ma et al. / Biomass and Bioenergy 28 (2005) 591–600
3.1. Exclusive constraints
and factors. For example, residents consistently
prefer that facilities such as power plants or
CAFOs be located a certain distance from
residential areas [14]. Citing aesthetic impacts
and noise pollution as typical concerns, community reactions are often dubbed as not-in-mybackyard (NIMBY) syndrome [15]. In addition,
economic factors also play a very important role in
determining the viability of potential energy
development projects. With these restrictions and
other considerations on land use, the problem is
then to develop an appropriate land suitability
model (LSM) to determine the most suitable
sites for potential development from a holistic
perspective.
This LSM aims to derive a siting suitability
index, which is a quantitative measure of preference of land use for development and calculated
on the basis of both constraints and factors as
illustrated in Fig. 1. Different layers/grids of
geographic data are mathematically manipulated
in a GIS environment to obtain the suitability
index. The GIS software packages used in this
study include ArcViews and ArcInfos developed
by Environmental Systems Research Institute
(ESRI). The following sections provide details on
the variables and specific operations used in the
model.
Animal wastes have long been criticized as a
primary source of air and water pollution, raising
both environmental and social issues. Thus, the
nature of bio-energy systems using dairy manure
as a feedstock suggests that they must be properly
sited to avoid sensitive areas, such as wetlands,
residential areas, airports, etc. Within this model,
we define these sensitive areas as ‘‘constraints’’
meaning they are restricted from development of
dairy manure-based systems within the area. A
buffer zone is usually required for each of these
constraints to define the minimum distances of
development sites to the selected geographic
entity/feature. Different constraints correspond
to different widths of buffer zones.
A binary GIS grid is created for each constraint
feature, with cells falling within a constrained area
assigned ‘‘0’’ and cells falling outside the buffer
area assigned ‘‘1’’. A single grid, or final constraint
map, is calculated by multiplying all constraint
layers/grids together (Fig. 1).
For the ith cell in the final constraint map, its
value is calculated as follows:
Ci ¼
m
Y
C i;k ,
(1)
k¼1
Layers of Factors
Cell i
Layers of Constraints
Cell i
C1
w1F1
x
+
C2
w2F2
x
+
Cm
wnFn
=
C
593
=
x
F
||
suitability map
Fig. 1. Schematic diagram of land suitability model (LSM).
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J. Ma et al. / Biomass and Bioenergy 28 (2005) 591–600
594
where C i is the Boolean value (0,1) assigned to the
ith cell in the final constraint grid, C i;k is Boolean
value (0,1) assigned to the ith cell in the kth
constraint grid, m is the number of constraints
identified.
The multiplication of the Boolean constraint
grids result in the final constraint grid that will
define cells to be constrained as long as they have a
value of ‘‘0’’ in any one of the input layers. Only
the cells that have a ‘‘1’’ in each input layer will
have a non-zero value in the final result.
In addition to exclusive constraints, the model
considers certain selective factors that influence the
selection of a potential site. These factors are best
represented by distance. For example, the preferred criterion for AD systems would involve the
location of the site as closely as possible to existing
dairy farms for easy manure collection and
transportation. The GIS is used to aggregate the
factors into a single, final-factor grid (Fig. 1). A
higher value for a cell suggests higher land
suitability.
For the ith cell in the grid—final factor map, its
value is calculated as follows:
n
X
F i;j ,
(2)
j¼1
where F i is the value assigned to the ith cell in the
final factor grid, F i;j the value assigned to the ith
cell in the jth factor grid, and n the number of
factors identified.
To cancel the distortion caused by unit dimensions (due to the binary nature of the grids) and
also to assign larger values to cells that are closer
to the selected features, all distances are normalized as follows:
F stdi;j ¼
F i;j F i;j;max
ð1Þ,
F i;j;max
Fi ¼
n
X
wj F i;j ;
0pwi p1,
(4)
j¼1
where F i ; F i;j ; and n are same as above in (2), and
wj is the weight assigned to the jth factor.
3.2. Selective factors
Fi ¼
Furthermore, all factors are not equally important in influencing the selection of potential
sites. Therefore, it is necessary to assign appropriate weights to the factors reflecting their relative
importance. The method adopted in this study is
the analytic hierarchy process (AHP).
For the ith cell in the final factor grid, its value is
calculated as follows:
(3)
where is the F stdi;j is the normalized distance for
the ith cell in the jth factor grid, F i;j is the
originally calculated distance for the ith cell in the
jth factor grid, and F i;j;max is the maximum
distance for the jth factor grid.
3.3. Analytic hierarchy process (AHP)
Because quantitative ratings for the selective
factors are not available, it is difficult to assign
evaluations and weights. To address this issue, we
used AHP, which is a mathematic technique for
multicriteria decision making [16–17]. AHP helps
capture both qualitative and quantitative aspects
of a decision and provides a powerful yet simple
way of weighting selection criteria thus reducing
bias in decision making.
The first step in AHP process is to structure
the decision problem in a hierarchy as depicted in Fig. 2. The overall goal of the decision,
selecting a site, is at the top level of the hierarchy. The next level consists of the criteria/
factors relevant for this goal and at the bottom
level are the alternatives (i.e. many sites) to be
evaluated.
The second step is the comparison of alternatives and criteria. They are compared in pairs
with respect to each element of the next higher
level. For the relative comparison, the fundamental scales of Table 1 are used, allowing
users to express the comparisons in verbal
terms, which are translated to a quantifiable
scale.
The specific procedures are as following:
1. To create a pair-wise comparison matrix for
multiple factors, let Pij ¼ extent to which we
prefer factor i to factor j: Then assume Pji ¼
1=Pij :
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595
Selecting a Site
Distance to
Dairy Farms
Distance to
Transmission
Lines
Distance to
Natural Gas
Pipelines
Site 1
Site 2
Site 3
Distance to
Power Plants &
Substations
…………………..
Distance to
Roads
Site n
Fig. 2. Structure of hierarchy for AHP.
Table 1
Fundamental scale for pair-wise comparisons in AHP
3. consistency ratio (CR) is then calculated using
the formulae
Verbal scale
Numerical
values
CR ¼ CI=RI;
Equally important, likely or preferred
Moderately more important, likely or preferred
Strongly more important, likely or preferred
Very strongly more important, likely or
preferred
Extremely more important, likely or preferred
Intermediate values to reflect compromise
1
3
5
7
where RI is a known random consistency index
and can be obtained [18]. As a rule of thumb, a
value of CRp0.1 will be accepted. Otherwise a
re-voting of the comparison-matrix has to be
performed.
9
2, 4, 6, 8
2. Normalize a pair-wise comparison matrix:
a. Compute the sum of each column.
b. Divide each entry in the matrix by its column
sum.
c. Average across rows to get the relative
weights.
3.4. Land suitability map
A final land suitability grid is calculated by
multiplying the final constraint map and the final
factor map together. It has values ranging from 0
to 1 in each cell, with 1 representing most suitable
and 0 representing unsuitable.
For the ith cell in the grid—final suitability map,
the value is calculated as follows:
SIi ¼ C i F i ,
(5)
To evaluate credibility of the estimated weights,
one final step is recommended to measure consistency in pair-wise comparison. The consistency
ratio is calculated as following:
where SIi is the suitability index value calculated
for the ith cell in the final land suitability grid, C i is
Boolean value (0,1) assigned to the ith cell in the
final constraint grid, F i is the value assigned to the
ith cell in the final factor grid.
1. Calculate the eigenvector and the maximum
eigenvalue for each matrix.
2. Compute the consistency index (CI) for each
matrix by the formulae
4. Case study: Tompkins County, New York
CI ¼ ðlmax nÞ=ðn 1Þ.
The present study uses Tompkins County, New
York as the area to demonstrate this GIS-based
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J. Ma et al. / Biomass and Bioenergy 28 (2005) 591–600
model and its procedures. Tompkins County is
located in the western central part of New York
State. Although it is not a major dairy county, there
are sufficient dairy farms for research purposes. The
dairy sector in Tompkins County consists of 89
dairy farms and 9500 milk cows [19]. The study area
was selected primarily because it has the most
readily available GIS data compared to other
agriculturally dominated counties in New York
State. However, the methodology implemented here
for locating optimal sites for potential bio-energy
systems is broad enough to allow application to any
region, subject only to availability of data.
4.1. Constraints in land suitability assessment
Generally, a dairy manure-based bio-energy
system cannot be sited within a certain distance
of the following features: wetlands, streams,
critical environmental areas, flood plains, roads,
residential areas, and airports. In addition, safety
concerns prevent any construction near power
systems such as transmission lines, power plants,
etc. Areas of steep slope are also restricted for
development. In total, there are eleven constraints
identified in this study (Table 2).
After these constraints were identified, buffer
zones were created for each constraint to minimize
environmental impact. It was important to define
the width of various buffer zones, i.e. the minimum distance of development sites to those
selected geographic entities/features. However,
there are very few buffer distances criteria specified
by State governmental regulations. Most criteria
used in this study were based on regulations in
other states and also from research on locating
sites for landfills or low hazardous waste disposal
facilities [20–22].
A binary grid was created for each of the eleven
constraints, with pixels that fall within the buffer
zone of constraints assigned ‘‘0’’ and the rest ‘‘1’’.
To develop a constraint map for slope requires a
separate GIS process. A slope map was derived
from merged 7.5-min digital elevation models
(DEMs of 10-m cell size) using the standard slope
function defined by ESRI software. The slopes in
Tompkins County range from 0% to 79% and were
reclassified into slope categories. Slopes between 0%
and 15% were identified as acceptable topographic
gradient requirements for facility development for
engineering and runoff concerns and therefore,
coded as available area while land areas exceeding
a 15% slope were classified as restricted areas.
4.2. Factors in land suitability assessment
Five factors were identified to address the
preference of selecting a potential site to maximize
energy and economic benefits. These factors
include:
Distance from potential bio-energy system to
existing dairy farms: The potential bio-energy
systems are preferably sited as close to or on
Table 2
Constraints identified and their specifications for creating buffer zones
Attribute
Specifications
Wetlands and lakes
Critical environment areas
Streams
Airports
Flood plain
Slope gradient
Roads
Transmission lines
Natural gas pipelines
Power plants and substations
Residential areas
Sites falling within wetlands and a buffer zone of 100 m are avoided
Sites falling within such areas and a buffer zone of 500 m are avoided
Sites falling within streams and a buffer zone of 100 m are avoided
Sites falling within such areas and a buffer zone of 500 m are avoided
Sites falling within 100-year flood plains are avoided
Areas with slopes larger than 15% are avoided
Sites falling within 30 m of roads are avoided
Sites falling within a buffer of 200 m are avoided
Sites falling within a buffer of 100 m are avoided
Sites falling within a buffer of 200 m are avoided
A distance of 2000 m from high-density residential areas or urban residences
A distance of 1000 m from medium-density residential areas
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J. Ma et al. / Biomass and Bioenergy 28 (2005) 591–600
existing dairy farms as possible to minimize
transportation costs and environmental problems such as odor and nuisance. Ideally, most
of the bio-energy systems should be located on
existing farms if suitable. However, in some
cases it may be required to develop new sites or
centralized plants.
Distance from potential bio-energy system to
roads: Beyond the restricted buffer zone for
minimizing odor and view, the closer to roads,
the better to save transportation cost in the case
of collecting dairy manure to a central plant.
Distance from potential bio-energy system to
transmission lines: Beyond the buffer zone for
safety reasons, the closer to transmission lines,
the better to save interconnection costs when
excess farm-generated electricity is sold back to
the grid.
Distance from potential bio-energy system to
natural gas pipelines: Beyond the buffer zone for
safety reasons, the closer to natural gas pipelines, the better to save costs if farmers choose
the option of introducing cleaned biogas into
the pipeline instead of generating electricity and
heat.
Distance from potential bio-energy system to
power plants and substations: Same as transmission lines issue.
The second step is to determine weights of these
five factors by using the AHP method (Table 3).
The ‘‘distance of potential sites to existing dairy
farms’’ was the most important factor because it is
critical to save on construction and transportation
costs and minimize odor issues. Therefore, distance to existing dairy farms was rated moderately
597
more important ‘‘3’’ than the ‘‘distance to roads’’
and ‘‘distance to power plants and substations’’
(Table 1). Distance to farms was also rated very
strongly more important, ‘‘7’’, than the ‘‘distance
to transmission lines’’ and ‘‘distance to natural gas
pipelines’’. The ‘‘distance to roads’’ is considered
the next most important factor because transportation cost is a common variable in evaluating
economic benefits. Power systems are given lower
ratings because interconnection is currently more a
political issue rather than a technical one. Thus,
the other three factors were assigned lower
weights.
A distance map for each factor was developed
and stored in raster format with 10-m cell size. The
grid cell values represent the distance from the
selected features. To cancel the unit distortion,
distance values were normalized (see Eq. (3)) and
all factor grid cells have a value between 0 and 1.
5. Results
In this study, all layers were projected into the
Universal Transverse Mercator (UTM) system,
zone 18, NAD27. To better illustrate the LSM
procedures in actual GIS environment, two of
eleven constraints and two of five factors were
selected respectively to serve as examples shown in
Fig. 3. This composite map provides an explanation for Fig. 1 in real context.
Map a shows the spatial relationship between
the distribution of dairy cows and human residential areas. Not surprisingly, dairy farms are located
in areas where there is low human density. The
map also identifies dairy farms’ herd size. The
Table 3
Pair-wise comparison matrix
Dairy farms
Roads
PP and S
Transmission lines
Natural gas pipelines
Dairy farms
Roads
PP and S
Transmission
lines
Natural gas lines
Weights
1
1/3
1/3
1/7
1/7
3
1
1/3
1/5
1/5
3
3
1
1/3
1/3
7
5
3
1
1/3
7
5
3
3
1
0.476
0.269
0.139
0.071
0.045
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598
STD Dist to Dairy Farms
Dairy Farms (of Cows)
25-88
88-183
184-300
301-620
621-1230
County Boundary
Low Density Residential
Medium Density Residential
High Density Residential
0.0111
0.0111- 0.222
0.0222- 0.333
0.0333- 0.444
0.0444- 0.555
0.0555- 0.667
0.0667- 0.778
0.0778- 0.889
0.889- 1
No data
Wetland Buffer
Restricted
Available
No Data
N
8
(a)
0
E
W
16 Miles
8
(b)
S
a.
b.
Spatial distribution of dairy farms and human
population
(e)
Binary grid of wetlands with a buffer zone of
100 meter
c.
Binary grid of high density residential areas
with a buffer zone of 2,000 meters
d.
Final constraint map as a result of
multiplication of eleven constraint grids
e.
Normalized distances of potential sites to
existing dairy farms
f.
Normalized distances of potential sites to
transmission lines
g.
Final factor map as a result of summation of
five factor grids
h.
Land suitability index map
i.
Reclassified land suitability index map
Transmission Lines
STD Dist Trans Lines
0.0 - 111
0.111 - 0.222
0.222 - 0.333
0.333 - 0.444
0.444 - 0.556
0.556 - 0.667
0.667 - 0.778
0.778 - 0.889
0.889 - 1
No Data
High Density Residential
Restricted
Available
No Data
(c)
(f)
Country Boundary
Factor index
0.276 - 0.419
0.419 - 0.562
0.562 - 0.705
0.705 - 0.848
0.848 - 0.991
No Data
Constraints Map
Restricted
Available
No Data
Dairy Farms
Roads
County Boundary
Suitability Index (>0.872)
Loss or Not Suitable
Most Suitable
No Data
(g)
(d)
Suitability index
0 - 0.194
0.194 - 0.388
0.388 - 0.581
0.581 - 0.775
0.775 - 0.969
No Data
N
E
W
8
0
8
16 miles
S
(i)
8
0
8
16 miles
(h)
Fig. 3. Procedures and results of GIS-based LSM.
larger dots indicate greater number of dairy cows
on the farm and therefore a higher concentration
of waste. Maps b and c, respectively, display
wetlands with a buffer distance of 100 m and highdensity residential areas with a buffer distance of
2000 m. Multiplied with the other nine constraints
layer, a final constraint grid was created and is
shown as Map d. The areas coded as ‘‘0’’ are
reclassified as ‘‘restricted’’ while the areas coded as
‘‘1’’ are classified as ‘‘available’’, meaning these
areas meet all environmental constraints and are
eligible for further consideration.
Maps e and f show the results of factor grids
representing normalized distances from potential
project sites to existing dairy farms and transmis-
sion lines, respectively. The distance values are
normalized and therefore range from 0 to 1 and
the closer distances are assigned higher values.
With all five-factor grids created, they were
summed to generate a single factor map by using
their respective weights obtained from AHP
method (Map g). Because each of the five factor
maps has a grid value between 0 and 1 with a
weight less than 1, the final factor map is also a
grid with a value between 0 and 1.
Finally, the constraint map (d) and the factor
map (g) were multiplied to create the siting
suitability map (Map h). This GIS operation is
equivalent to cutting the ‘‘restricted’’ areas out of
the factor map. The suitability index value varies
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J. Ma et al. / Biomass and Bioenergy 28 (2005) 591–600
from 0 to 0.969. The areas with high suitability
index value will have relatively less environmental
impact, higher economic benefits, and easier access
to the existing power system. Thus, these areas are
more suitable for development. Because there are
more than enough candidate sites, it is desirable to
identify those sites with the highest suitability
index values that are sufficient to handle all waste
available.
To be economically feasible and technically
practical, a minimum herd size of 400 milk cows
is considered a minimum size to install an AD
system on a dairy farm based on previous studies
[23]. Therefore, for the number of milk cows in
Tompkins County, approximately 20 potential
sites would be enough to absorb the biomass—
waste generated by 9500 dairy cows within this
area. In addition, empirical studies have showed
that a minimum farmland of 1000 acres is needed
not only to install the bio-energy system, but also
to make compost, store liquids, and spread liquid
effluent on crop fields. Thus, approximately 20,000
acres of land with highest suitability index values
are probably needed in Tompkins County.
Based on the suitability index value, Map h was
further reclassified into two categories—‘‘less or
not suitable’’ (o0.872), and ‘‘most suitable’’
(X0.872). The statistics shows that the ‘‘most
suitable’’ areas on Map i have a total area of
22,940 acres and accordingly these areas are most
suitable for possible development of dairy manurebased bio-energy systems in the study area.
6. Discussion
A GIS model was developed to determine
optimal sites for potential bio-energy systems
using dairy manure as the feedstock in New York
State, with Tompkins County as a case study area.
The results not only indicate where renewable
energy resources are available based on distribution of dairy farms, but also show where the best
potential locations are to develop such systems in
the future. Those identified candidates are likely to
be the optimal locations for future development.
This study has both direct and indirect benefits
to New York State. The results will directly help
599
state/local governments, electric and gas utilities,
dairy farmers and other interested parties to
recognize the energy potential of dairy manure
and to serve as a guide for utilizing it. The model
developed in this study can also be applied to
investigate other types of biomass resources. Thus,
it will help promote the implementation of bioenergy in New York.
There are limitations that need to be addressed
and new directions that need to be explored in
future research. Since the electric grid is a
complicated network, the distributed power generation plants need to be located at strategic points
to best supplement the grid. This factor has not yet
been included into this optimization model. In
addition, the present study omitted the fact that
dairy farms are not totally independent of each
other. In the case of building a centralized system
among a group of small dairy farms, the spatial
relationship of these farms has to be considered in
the optimal siting analysis. GIS provides powerful
network analysis capabilities. Future research
using GIS to study potential bio-energy systems
from a network perspective should be explored.
Acknowledgements
The authors gratefully acknowledge partial
funding support for this study from the New
York State Energy Research and Development
Authority by P.O. 4841.
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