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b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 2 ) 1e1 4Available online at whttp: / /www.elsevier .com/locate/biombioeGIS-based location suitability of decentralized, medium scalebioenergy developments to estimate transport CO2 emissionsand costsThomas Kurka*, Chris Jefferies, David Blackwood

University of Abertay Dundee, Bell Street, Scotland DD1 1HG, UKa r t i c l e i n f o

Article history:

Received 29 August 2011

Received in revised form

30 July 2012

Accepted 6 August 2012

Available online xxx

Keywords:

Bioenergy

CHP

GIS

Distributed generation (DG)

Spatial modeling

Site suitability modeling* Corresponding author. Tel.: 44 01382 3085E-mail addresses: [email protected]

Please cite this article in press as: Kurkdevelopments to estimate transport COj.biombioe.2012.08.004

0961-9534/$ e see front matter 2012 Elsevhttp://dx.doi.org/10.1016/j.biombioe.2012.08.a b s t r a c t

This paper presents a transferable and adaptable GIS-based approach to identify suitable

locations of medium scale CHP bioenergy plants. Location suitability of bioenergy plants is

of particular importance in planning decentralized bioenergy generation, as both biomass

feedstock supplies and heat demand have to be considered in location selection when heat

and electricity want to be utilized. A generic GIS model was developed to identify most

suitable locations of CHP bioenergy plants based on regional supply, demand and prox-

imity. Furthermore, the paper provides a simple approach to allocate biomass feedstock

supplies to bioenergy plants and to estimate transport costs and CO2 emissions. The GIS-

based approach was applied in a Scottish region (Tayside and Fife) to identify locations

for 10 decentralized, medium scale bioenergy plants based on which regional biomass

feedstock supplies were allocated and road transport-related CO2 emissions and costs were

estimated. The paper concludes that the approach can assist in developing and imple-

menting a long-term sustainable and integrated strategy for decentralized bioenergy

generation, which includes heat utilization in addition to electricity production and which

can be further developed to take account of broadly diversified conventional and renewable

energy generation in a region.

2012 Elsevier Ltd. All rights reserved.1. Introduction Change (Scotland) Act 2009 (asp 12) ensures that the netBioenergy, the energy obtained from biomass, is considered to

have the potential to supply a significant portion of global

primary energy over the next century. On a European scale

biomass will potentially contribute 17.2% of the EU heating

and cooling mix and 6.5% of electricity consumption in 2020

[1]. Currently, there is also a strong policy drive at the global,

national and regional level to address the effects of energy-

based Greenhouse Gas (GHG) emissions, particularly CO2emissions, as it relate to global warming and climate change

[2]. In Scotland legislation has been established to ensure

compliance with the Kyoto Protocol. For instance, the Climate45; fax: 44 01382 308117, [email protected] (T

a T, et al., GIS-based lo

2 emissions and cost

ier Ltd. All rights reserved004Scottish emissions account for the year 2050 is at least 80%

lower than the baseline with an interim target of at least 42%

lower than the baseline by 2020.While biomass transportation

will add to CO2 emissions, the Royal Commission on Envi-

ronmental Pollution [3] believes that bioenergy emissions are

almost completely offset. For this reason, a number of actions

are currently being taken or planned to develop medium and

large scale bioenergy plants within Scotland. Important

initiatives and drivers include the Renewables Obligation (RO)

2002e2037; Feed in Tariffs (FITs) April 2010 to 2021 (20yrs);Renewable Heat Incentive: (started 2011), the UK Renewable

Energy Strategy (2009); the Scottish Biomass Action Plan.. Kurka).

cation suitability of decentralized, medium scale bioenergys, Biomass and Bioenergy (2012), http://dx.doi.org/10.1016/

.

mailto:[email protected]:[email protected]/science/journal/09619534http://www.elsevier.com/locate/biombioehttp://dx.doi.org/10.1016/j.biombioe.2012.08.004http://dx.doi.org/10.1016/j.biombioe.2012.08.004http://dx.doi.org/10.1016/j.biombioe.2012.08.004

b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 2 ) 1e1 42(2007); the Scottish Low Carbon Economic Strategy (2010) and

Scotlands Renewable Heat Action Plan (2009). As a result,

a high number of medium and large scale biomass plants

(electricity-only and Combined Heat and Power (CHP)) have

been proposed across the country, but the future demand of

proposed plants is significantly higher than current indige-

nous supply. The long term availability of biomass and bio-

energy supply is a barrier that Scotland and other EU Member

States will experience in attempting to achieve 2020 targets

and beyond. Thus, a major objective is to ensure that bio-

energy resources and technologies can be mobilized and

utilized in the most efficient and sustainable manner possible

to guarantee the long term transition to bioenergy [4]. For

instance, the employment of a CHP plant can increase its

overall efficiency [5]. The Scottish Government considered

this aspect in its policy position, which prioritizes biomass for

renewable heat [6], whereas in addition the Wood Fuel Task

Force recommended that CHP plants should be at an appro-

priate scale to use the heat most effectively [4]. In terms of

biomass feedstock sourcing, long distance transportation of

biomass resources, such as from Canada to Europe, can

reduce the economic attractiveness of the bioenergy devel-

opment in the long-term [7]. Locating bioenergy plants close to

the supply of biomass feedstock can offer a sustainable way

for bioenergy production as transport distances decrease,

which again results in significant transport CO2 emissions and

costs reductions. In regard to installing CHP plants and heat

utilization, close proximity to heat demand is equally essen-

tial as heat for industrial processes or district heating cannot

be transported over long distances without significant heat

loss [8]. To address both necessities for proximity, Geograph-

ical Information System (GIS)-based location suitability can

assist. There have been studies in the past looking into GIS-

based assessments to identify sites for renewable systems.

For instance Baban and Parry [9] and Aydan, Kentel and

Duzgun [10] developed approaches to assist in locating wind

farms in the UK and Turkey respectively. In a number of

studies GIS was used to identify biomass resource potential

with some also analyzing associated economic costs

[8,11e16]. Perpina et al. [17] provided a GIS-based method-

ology for determining optimal locations for bioenergy plants

with a network analysis to calculate transport costs.

Conversely, Ma et al. [18] proposed a GIS-based model for

land-suitability assessment to identify bioenergy locations

with integrated environmental and social constraints, as well

as economic factors weighted by employing the analytic

hierarchy process (AHP) to establish their relative importance.

The research presented incorporates and builds on

elements of these previously conductedworks in the field. The

paper focuses on sourcing regionally and in particular on CO2emissions and costs as a result of transporting biomass

feedstock from suppliers to bioenergy plants by road. The

objectives of the research are threefold: The first objective is to

develop a generic GIS model to locate most suitable locations

for decentralized, medium scale bioenergy systems based on

supply, demand and proximity. The second objective is to

provide a simple approach to allocate biomass feedstock

supply to bioenergy plants and based on this estimate trans-

port costs and CO2 emissions. Finally the third objective is to

implement model, biomass feedstock allocation process andPlease cite this article in press as: Kurka T, et al., GIS-based lodevelopments to estimate transport CO2 emissions and costj.biombioe.2012.08.004transport CO2 emissions and costs estimation by conducting

a case study for Tayside and Fife in Scotland.2. GIS model-based methodology to identifythe most sustainable locations for decentralizedmedium scale bioenergy developments

Naturally, transport CO2 emissions and costs evaluations are

most influenced by the locations of bioenergy plants relative

to the sources of biomass feedstock. A GIS-based model was

built to support decisions on the location of bioenergy plants.

The model integrates key factors relating to biomass feed-

stock supply and heat demand locations and quantities,

existing bioenergy developments and distances to these sites.

This required a methodology comprising the following stages:

data collection and assumptions, GIS modeling and location suit-

ability analysis and allocation of supplies to the medium scale bio-

energy plants.

2.1. Data collection and assumptions

Data required for input into the GIS model depend on data

source and availability and can be obtained by desktop studies

or surveys (phone and/or a face-to-face interviews etc) with

industry values adopted as benchmark figures in case no

relevant information should be available. Table 1 illustrates

the type of data, which could be collected.

2.2. GIS modeling and location suitability analysis

A GISmodel which integrates collected and assumed data can

be built to identify most suitable locations for medium scale

bioenergy CHP developments. This identification of potential

site locations is a prerequisite to assess the two distance-

dependent criteria transport CO2 emissions and costs for the

decentralized development. First, all supply and heat demand

data collected has to be categorized into appropriate biomass

feedstock supply or heat demand types such as for instance

sawmills or universities. This categorization can be based on

the spreadsheet application Microsoft Excel. Each category

spreadsheet is then imported separately to GIS andmapped in

form of an individual layer. Data gathered about the existing

medium and large scale bioenergy plants is imported to GIS in

the samemanner. Also, point of reference and additional data

like regional base maps are imported, i.e. each GIS layer

contains the dataset of one spreadsheet.

The second step of the methodology involves modeling

with the ArcView 9.3 ModelBuilder to identify most suitable

site locations in case study areas based on proximity, biomass

feedstock supply and heat demand. ArcView 9.3 Mod-

elBuilder is an application, which can be used to create, edit,

and manage models. Models are workflows that string

together sequences of spatial analysis tools, feeding the

output of one tool into another tool as input. As could be seen

below (Fig. 1), layers (circular elements), which contain data-

sets and processes of tools (rectangular elements) from the

ArcView 9.3 toolbox are connected to produce a new output.

Based on these outputs other tools can be applied until the

desired result is achieved.cation suitability of decentralized, medium scale bioenergys, Biomass and Bioenergy (2012), http://dx.doi.org/10.1016/

http://dx.doi.org/10.1016/j.biombioe.2012.08.004http://dx.doi.org/10.1016/j.biombioe.2012.08.004

Table 1 e Example of input data for GIS model.

Data group Role of datagroup

Type of data (case study) Individual layers(case study)

Type of datastructure

(case study)

Source of spatialdata (case study)

Notes (case study)

Major biomass feedstock

suppliers

Scoring layers Name, address, coordinates for coordinate

reference system (British National Grid,

OSGB 1936 (EPSG:27700), Scale Factor:

0.99960127), feedstock type and quantity

available for bioenergy sector and current

destinations

Wood waste

re-processors

Vector point Own data gathering

and processing

Logs/chips/pellets

producers


and processing

Sawmills Vector point [23] Relevant data were

extracted from the

original spatial dataset

and modifications and

additions were made

Major heat customers Scoring layers Name, address, coordinates for coordinate



0.99960127), heat demand quantities

Primary schools Vector point [23]

Secondary schools Vector point [23]

Colleges Vector point [23]

Universities Vector point [23]

Major hospitals Vector point [23]

Malt distilleries Vector point [23]

Grain distilleries Vector point [23]

Industrial points Vector point [23]

Bioenergy plants competing

for biomass feedstock

and/or heat customers

Screening layers Name, address, coordinates for coordinate



0.99960127), feedstock requirements

(type and quantity), heat customers

and quantities provided

Existing bioenergy

plants (medium-scale)


and processing

Existing bioenergy

plants (large-scale)

N/A Own data gathering

and processing

Large-scale plants did

not exist, but were

proposed in case study

area

Major regional roads Screening layers Coordinates for coordinate reference

system (British National Grid, OSGB 1936

(EPSG:27700), Scale Factor: 0.99960127)

Major regional roads Vector line [23] Relevant data were

extracted from the

original spatial dataset

and modifications and

additions were made

Points of reference Illustrative purpose Coordinates for coordinate reference

system (British National Grid, OSGB 1936

(EPSG:27700), Scale Factor: 0.99960127)

Base map of

Scotland

Vector polygon [23]

Settlements

in Scotland

Vector polygon [23]

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Sawmills

Kernel Density

Euclidean Distance

Extraction

Extraction

Combination of model sub-strings

Normalization

Normalization

Case studyarea

1

2

43

5

Fig. 1 e Example of a scoring model string with steps marked.

b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 2 ) 1e1 44The model we built for this research comprised of two

groups of layers, which we called screening layers and scoring

layers. When combining these layers, a layer to whichwe refer

to as location suitability layer can be produced. Based on this,

most suitable locations can be identified and allocation of supplies

to the medium scale bioenergy plants can be undertaken as

a separate step.

2.2.1. Scoring modelCombined layers of biomass feedstock suppliers and heat

customers illustrate the suitability score of locations. The so-

called scoring layers are used to identifymost suitable locations

based on the distance to potential heat customers and

biomass feedstock suppliers and the quantities of biomass

feedstock supply and heat demand. The information for the

GIS model strings consists of data gathered during the data

collection process as described in 2.1. For each supplier or heat

customer a model string with two parallel sub-strings is

required to reflect distance on the one hand and demand or

supply quantities on the other hand. The Euclidean Distance

(ED) tool calculates straight-line distance, whereas the Kernel

Density (KD) tool is applied to consider the quantities per

individual site or building.

TheKD tool (step 1 in Fig. 1) delivers density values basedon

heat demand or supply quantities within defined search

radiuses. These radiuses take into account that realistically

demand for the generated heat and supply for the plants only

takes place within a certain zone around plants. Conversely,

the ED tool (step 2 in Fig. 1) also considers locations outside of

these zones and delivers distance-based values for the

remaining case study area. TheKDand the ED tools are applied

for each of the scoring layers, which have to be combined to

obtain the final suitability score of a location. Unlike the KD

tool, which provides only values for locations within defined

zones, the ED tool enables this combination of layers by

delivering a value for every location in a case study area.

After applying both, the ED and the KD for the two different

sub-model strings, aGISpolygon representinga case studyarea

extractsdata toproceedwith datawithin a case studyarea only

(step 3 in Fig. 1). This step is optional and required only if the

base maps used include areas exceeding a case study area.

Normalization is required (step 4 in Fig. 1) in order tomake the

resulting grids with different unit dimensions comparable and

to aggregate them into a single scoring grid. For this purposePlease cite this article in press as: Kurka T, et al., GIS-based lodevelopments to estimate transport CO2 emissions and costj.biombioe.2012.08.004the Single Output Map Algebra function is applied with the

same scale (range from zero to one) for all grids. A higher value

for a cell (50 m 50 m) indicates a higher suitability score andthe closer a cell to the selected grid, the higher the value

assigned. ED and KD values are normalized as follows:

ED : Fnormij Fij Fij;max

Fij;max1

KD : Fnormij Fij

Fij;max

where Fnormij is the normalized distance for the ith cell in the jthfactor grid, Fij is the originally calculated distance for the ithcell in the jth factor grid, and Fij,max is the maximum distance

for the jth factor grid.

Following normalization, a Single Output Map Algebra step

combines the two model sub-strings (step 5 in Fig. 1). At this

stage, twice the weight is assigned to the ED grid, as the

distances on a regional scale are assumed to be of more

importance than the quantity densities within the defined KD

search radiuses. This combination and weighting are under-

taken through multiplication. The resulting grids are then

aggregated to a single scoring grid showing the degree of

suitability of every location in a case study area.

2.2.2. Screening modelA sub-model towhichwe refer to as screeningmodel is built to

exclude unsuitable areas from a case study region by applying

Boolean logic. The first model sub-string is based on the

regional major roads network. Depending on regional acces-

sibility and economics of road transport, proximity to major

roads can be of differing influencing importance to transport

CO2 emissions and costs. To make allowance for this, an

appropriate regional-depending buffer zone around major

roads canbe incorporated into theGISmodel. Locationswithin

this buffer zone are considered suitable and locations beyond

the zone unsuitable. In an intermediate step the polygon

resulting from buffering is converted into a raster allowing

proceeding with a reclassification with the Boolean value one.

Consequently, all areas beyond the buffer zone are assigned

a value of zero indicating unsuitability. These three steps are

repeated for the two remaining screening layers model sub-

strings, for which it is assumed that new bioenergy plants

cannot be built within an appropriate buffer zone aroundcation suitability of decentralized, medium scale bioenergys, Biomass and Bioenergy (2012), http://dx.doi.org/10.1016/


b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 2 ) 1e1 4 5existing bioenergy developments, which can be of different

size in a case study area. The reason for this assumption is that

demand for themajority of heat produced at locations in close

proximity to existing plants, which also utilize generated heat,

is expected to be limited. Therefore, buffer zones around the

existing medium and large bioenergy plants can be incorpo-

rated with different model sub-strings and buffer zones for

each of the two types of scale. Contrary to the major roads

model sub-string, the resulting binary grid that assigns each

raster cell either a value of one or a value of zero and classifies

them into suitable and unsuitable locations is reversed, as in

this case the locations beyond the buffer zone are suitable and

vice versa. A Single Output Map Algebra step combines the

three resulting binary grids, followed by an extraction step to

proceedwith datawithin a case study area only. The produced

screening grid allows exclusion or cutting-out of unsuitable

locations of the scoring grid as described below (2.2.3).

2.2.3. Location suitability layerFinally, the screening grid with binary cell values and the

scoring grid with cell values between zero and one for every

location in a case study area are combined bymultiplication to

create a location suitability layer. In principle, the Single Output

Map Algebra tool cuts out unsuitable locations of the scoring

map. The produced location suitability layer allows identifica-

tion and illustration of most suitable locations for bioenergy

plants in a case study area based on proximity, biomass

feedstock supply and heat demand.

2.2.4. Most suitable locations identification modelBased on the location suitability layer, the second part of the

GIS modeling comprises of various model strings, which

number depends on the quantity of bioenergy plants to be

developed. The quantity and size of bioenergy plants can be

determined by the actual amount of biomass feedstock,

which can be sourced regionally in a case study area. Data

about the actual amount of biomass feedstock should be

available after the data collection process as described

previously (2.1).

The first step for each model string is to identify the

location with the highest suitability score by applying the

Single Output Map Algebra tool. After extraction to proceed

with the identified location only, the produced raster is con-

verted into a point to illustrate the location in the map. To

allow continuation of the process and to identify the suitable

location with the next highest score, the identified location

has to be excluded or cut-out of themap. In consistencewith

the screening model string for existing bioenergy plants, the

same distance for buffering has to be applied. An interme-

diate step converts the generated polygon into a raster. This

allows reclassification and results in a binary grid, which

again classifies suitable and unsuitable locations in the

remaining case study area. Combination of this produced grid

with the previous location suitability layer ensures that both

the original scoring and screening grids are considered. In

other words, the new identified location is cut out of the

previous location suitability layer to create the next one. In

descending order, the same process continues until all most

suitable locations for the medium scale bioenergy plants are

identified.Please cite this article in press as: Kurka T, et al., GIS-based lodevelopments to estimate transport CO2 emissions and costj.biombioe.2012.08.0042.3. Allocation of supplies to the medium scale bioenergyplants

The identification of the most suitable locations for bioenergy

plants in a case study area and gathered data about suppliers

as illustrated in 2.1 allows available supplies of biomass

feedstock to be allocated following the proximity principle.

Starting with the plant location with the highest suitability

score, the ED tool can be applied for this location to facilitate

the identification of the closest suppliers. Then, the quantities

of biomass feedstock from the closest supplier are allocated to

this plant followed by the supplies from the second closest

supplier and so on. This sequence continues until the biomass

feedstock required for the operation of this plant is satisfied.

In this process a spreadsheet can be of assistance to keep the

overview and also to calculate the transport CO2 emissions

and costs per supply. The same supply allocation process is

repeated for the plant location with the second highest suit-

ability score and then in descending order for the remaining

suitable locations until all the plants feedstock demands are

satisfied. This allocation process provides the values for the

overall transport CO2 emissions and costs calculation.

2.4. Calculation of transport CO2 emissions and costs

Distances and benchmark figures for the carbon intensity and

costs of different modes of transporting biomass feedstock

have to be taken into account in determining CO2 emissions

and costs associated with the transportation of biomass

feedstock to the bioenergy plants. Benchmark figures for both

transport CO2 emissionsand costs are available from literature

and other relevant information sources. Based on these figures

and the allocation of supplies process described in Section 2.3,

the total transport CO2 emissions and costs can be calculated.3. Practical application of the methodologyin Tayside and Fife/Scotland

The methodology described in Section 2 was applied in a case

study area comprising of Tayside (including Angus, Perth and

KinrossandDundee) andFife inScotland to locatemost suitable

locations for decentralized, medium scale bioenergy plants.

3.1. Scenario description

Wood pellets with a water mass fraction of 8% and waste

wood re-processed to industrial softwood chips with a water

mass fraction of 20% were selected as biomass feedstock for

the case study area. These materials have a relatively high

Lower Heating Values (LHVs), which are typically about

17.5 GJ t1 and 15.2 GJ t1, respectively [19] and were assumedto be regionally sourced from the case study area. In terms of

bioenergy conversion technologies, the direct combustion

technology was assumed to be installed at the decentralized

sites. This technology is known to produce a high net effi-

ciency for energy generation with approximately 80e90%

boiler efficiency for industrial Combined Heat and Power

(CHP) systems fired with biomass [20]. One of the case studies

in a report about the situation of biomass CHP technologies incation suitability of decentralized, medium scale bioenergys, Biomass and Bioenergy (2012), http://dx.doi.org/10.1016/


Scoring Layers

Screening Layers

Location Suitability

Layers

Fig. 2 e Case study GIS model.

Table 2 e Kernel density search radiuses for scoringlayers in case study area.

Layer KD search radiuses

Primary schools 3000 m

Secondary schools 5000 m

Colleges 10,000 m

Universities 10,000 m

Major hospitals 10,000 m

Malt distilleries 10,000 m

Grain distilleries 10,000 m

Industrial sites 10,000 m

Sawmills 25,000 m

Logs, pellets and chips producers 50,000 m

Wood waste re-processors 50,000 m

b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 2 ) 1e1 46Finland, Denmark and Sweden evaluated a CHP plant in

Kuhmo/Finland with 4.9 MW electrical, and 12.9 MW of

thermal output for district heating [21]. Typical sized CHP

plants firing biomass feedstock in Finland have an electrical

output ranging from 1 to 5 MW [21]. The biomass resource

used for the Kuhmo plant is made up of industrial wood

residues and forest chips, which are sourced regionally from

nearby sawmills and wood waste re-processors. The plant is

operating with fluidized bed combustion boiler technology

with a total efficiency of 88%. For the case study reported in

this paper it was assumed that the same kind of installations

will be built in Tayside and Fife each with a biomass feedstock

demand of 65 kt y1. In this area district heating and CHP areunderused, with utilization of waste heat from industrial and

electricity generating processes a fairly new concept. Biomass

feedstock availability for bioenergy generation is expected to

be scarce in this area, if all proposed developments will be

built in the future. Additionally, as in many other regions, in

the case study area typically large proportions are already

dedicated to alternative markets.

3.2. Data collection and assumptions

The case study data required for input into the GIS model

was obtained by desktop studies and phone/email or face-

to-face interviews, while industry values were sourced and

adopted as benchmark figures for transport CO2 emissions

and costs. Heat demand data was centered on the current

heat consumption of large industrial, commercial or insti-

tutional buildings, which included universities, colleges,

schools (primary and secondary), grain and malt distilleries,

industrial point sites and major hospitals within the study

area. Data about significant biomass suppliers were

centered on the current quantity of woody biomass obtain-

able from wood waste re-processors, logs/chips/pellets

producers and sawmills within the study area and beyond.

These types of buildings and suppliers were chosen in the

case study area because in a previous study [22] they have

proven to be the main consumers of heat and suppliers of

biomass resources, respectively. The Heat Map of Scotland

[22] provided data already available in GIS shape file format

[23], which was exported to Microsoft Excel for modifica-

tions and additions to complete the data required as

described in 2.1.

Some assumptions had to be made for both heat demand

and biomass feedstock supply. For the universities and colleges

within Tayside and Fife, it was assumed that the data about the

energyconsumedwas foroneof themaincampusesand thatall

students were full time equivalent. The energy demand was

calculatedper student basedon thenumberof students in2008/

2009 [24]. The benchmark figure adopted for the total energy

consumption per Scottish university/college student (FTE) was

6264 KWh y1 [25]. This general benchmark figure was chosendue a lack of information with regard to the total heat

consumption per individual university/college. For the same

reason, a general benchmark figure for schools for the total heat

consumed per pupil (2583 KWh)was adopted [26]. For grain and

malt distilleries, energy demand was based on the assumption

that the energy demandper liter of alcohol produced is equal to

the energy demand per liter of whisky produced. ThePlease cite this article in press as: Kurka T, et al., GIS-based lodevelopments to estimate transport CO2 emissions and costj.biombioe.2012.08.004benchmark figure (9.36 MWh m3 of alcohol) was provided byone of the malt distilleries interviewed. Since data on the floor

area of each hospital in Scotlandwas not available, the number

of beds in the identifiedmajor hospitals was used as ameasure

of their size. Based on this assumption, a benchmark figure

(25,740 KWh) for heat demand per hospital bed was used to

calculate the heat demand of major hospitals [26].

Assumptions were also made to estimate the biomass

feedstock supply from wood waste re-processors sawmills

and logs/chips/pellets producers. For some groups of

suppliers, it was necessary to estimate the number of

employees, because the amount of biomass feedstock

supplies was calculated based on this number. Relevant datacation suitability of decentralized, medium scale bioenergys, Biomass and Bioenergy (2012), http://dx.doi.org/10.1016/


Fig. 3 e Example of a kernel density grid map e sawmills in the case study area.

Fig. 4 e Example of an Euclidean distance grid map e sawmills in the case study area.

Please cite this article in press as: Kurka T, et al., GIS-based location suitability of decentralized, medium scale bioenergydevelopments to estimate transport CO2 emissions and costs, Biomass and Bioenergy (2012), http://dx.doi.org/10.1016/j.biombioe.2012.08.004

b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 2 ) 1e1 48about logs/chips/pellets producers in Tayside and Fife and

50 km beyond its regional borders were collected. Depending

on the level of this data assumptions about the number of

employees had to bemade and the average biomass feedstock

output per employee was calculated based on data from

conducted surveys. The available supplies per site were esti-

mated using both figures. Gathered data about total outputs of

sawmills helped to calculate an average amount available for

the bioenergy sector from sawmills. The database included all

sawmills within the Tayside and Fife region and within

a 25 km radius beyond the regions borders. Data about the

available amounts of biomass feedstock from major wood

waste re-processors were obtained from a survey conducted

in the case study area and 50 km beyond. The reason for

a wider radius for this type of suppliers was the larger quan-

tities of biomass feedstock produced at these sites and

affected by that, the more advantageous economics for

transportation assumed. Finally, additional data about exist-

ing plants and the network of major roads in the case study

area was gathered by a desktop study.

3.3. GIS modeling and location suitability analysis

In total, eight heat demand layers, including primary and

secondary schools, colleges,universities,majorhospitals,malt

and grain distilleries and industrial points; and three biomass

feedstock suppliers layers, including sawmills, logs/chips/Fig. 5 e Example of a scoring grid layer m

Please cite this article in press as: Kurka T, et al., GIS-based lodevelopments to estimate transport CO2 emissions and costj.biombioe.2012.08.004pellets producers and wood waste re-processors, were map-

ped.Additionally, data aboutmajor regional roadsandexisting

plants, basemaps of the region and Scotland, as well as points

of referencesuchascitiesweremapped for spatial analysisand

illustrative purposes. Modeling allowed the identification of

the most suitable decentralized bioenergy plants locations.

Fig. 2 shows a simplified illustration of the developed GIS

model. It shows 11 scoring layers (top left), 3 screening layers

(bottom left) and the model strings to identify the 10 most

suitable plant locations in the case study area (right).

3.3.1. Scoring modelThe scoring model for the case study was built following the

processes described in Section 2.2.1. The search radiuses for

the KD tool were defined for the scoring layers in the case

study as illustrated in Table 2.

Depending on the type of layer, the KD population field for

each layer was either heat/energy demand or biomass feed-

stock quantity.

Following the steps outlined (Section 2.2.1) the model was

built to undertake intermediate steps (KD (Fig. 3), ED (Fig. 4)

and scoring grid layers (Fig. 5)) for each layer, before the latter

were combined into a final scoring grid layer map (Fig. 6).

3.3.2. Screening modelThe screeningmodel for the case studywas built following the

processes described in Section 2.2.2. There were no existingap e sawmills in the case study area.

cation suitability of decentralized, medium scale bioenergys, Biomass and Bioenergy (2012), http://dx.doi.org/10.1016/


Fig. 6 e Example of a final scoring grid layer map e all scoring layers in the case study area.

b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 2 ) 1e1 4 9large scale plants in Tayside and Fife when data was gathered.

Therefore the associated model sub-string was not necessary

and only the binary grids taking account of the regional major

road networks and existing medium scale plants were

combined to form the screening grid layers map (Fig. 7).

3.3.3. Location suitability layerAgain, following the methodology described above (Section

2.2.3), the location suitability layer (Fig. 8) indicating most

suitable locations for medium scale bioenergy plants in the

case study area based on proximity, biomass feedstock supply

and heat demand was produced.

3.3.4. Most suitable locations identification modelThe location suitability layer served as a basis for identifying

the 10 most suitable locations in the case study area. As out-

lined above (Section 3.1) biomass feedstock demand for one of

the plantswas assumed to amount 65 kt y1. The total amountof biomass feedstock from suppliers available for the bio-

energy industry in the case study area, estimated as part of

Section 3.2, amounted 632,788 t y1. In accordance with thisregional and import-independent sourcing of biomass feed-

stock, the input demand of 10 plants could almost bemet. The

most suitable locations of these plants in the case study area

were identified and mapped following the methodology

described in Section 2.2.4 (Fig. 9).Please cite this article in press as: Kurka T, et al., GIS-based lodevelopments to estimate transport CO2 emissions and costj.biombioe.2012.08.0043.4. Allocation of supplies to the medium scale bioenergyplants

In accordance with the steps described in Section 2.3, the

supplies were allocated to the 10 plants. The suppliers closest

to the plants were identified by using the ED tool. A spread-

sheet facilitated the allocation process and based on the

distances measured between suppliers and plants, transport

costs and CO2 emissions were calculated for each plant indi-

vidually and in total for the case study. Table 3 illustrates an

example of this process for one plant.

3.5. Calculation of transport CO2 emissions and costs

In a report produced for the Department for Environment

Food and Rural Affairs (Defra) and the Department of Energy

and Climate Change (DECC) [27], outlining the guidelines to

GHG emissions conversion factors for company reporting, the

carbon intensities of different modes of transporting biomass

resources were presented. According to this report the carbon

dioxide intensity for road transportation of biomass is

86 g t1 km1. As only road transport was considered asa mode of transporting biomass feedstock, this figure served

as a benchmark value used for the evaluation of CO2 emis-

sions within the context of the case study. Based on this

benchmark value and the allocation of supplies process, thecation suitability of decentralized, medium scale bioenergys, Biomass and Bioenergy (2012), http://dx.doi.org/10.1016/


Fig. 7 e Example of a screening grid layers map e all screening layers in the case study area.

b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 2 ) 1e1 410transport CO2 emissions for the case study amounted

3525 t y1. Similarly, the road transport costs were calculated:E4tech [28] in a report to DECC, evaluated biomass prices in

the heat and electricity sectors in the UK. In this report it was

stated that the average UK road transport costs for wood chips

with a water mass fraction of 25% are 0.55 V t1 km1

(currency conversion from GBP at a rate of 1.2 V per GBP, 27

Jun 2012). This benchmark figure was used for the case study

to represent biomass and again combined with the results of

the allocation of supplies process made up the total transport

costs, which amount 22,625,485 V y1.4. Results and discussion

By implementing the GIS modeling as outlined in Section 3,

areas were determined where biomass feedstock availability

and heat demand are highest, and based on this most suitable

locations for 10 medium scale CHP bioenergy plants were

identified in the case study area (Fig. 9). Not surprisingly, the

locations of the bioenergy developmentswere identified in the

two biggest cities of the area. The first reason for this is that

the assessed buildings with the highest heat demand can be

found there and secondly, the majority of the case studys

large biomass feedstock suppliers also tend to be based inPlease cite this article in press as: Kurka T, et al., GIS-based lodevelopments to estimate transport CO2 emissions and costj.biombioe.2012.08.004those two cities. The 10 bioenergy plants could produce

49MWelectrical, and 129MWof thermal output. Based on this

suitability of plant locations the transport CO2 emissions and

costs were calculated, which amount 3525 t y1 and22,625,485 V y1, respectively.

With a net electricity and heat use efficiency of 88%, the 10

mediumscale developments can provide an efficient supply of

bioenergy and as a result increase the overall energy and

carbon savings compared to, for example less efficient

electricity-only plants of similar or larger size. Large scale CHP

plants as an alternative to the decentralized medium scale

bioenergy plants also have the capability to utilize and deliver

all generated heat to potential userswith the aim of improving

the plants efficiency. However, this would mean large

concentrated heat generation, for which it can be more diffi-

cult to find heat end-users. In this case a large amount of heat

concentrated at one specific location is required to be

matched with equally concentrated end-user demand in

vicinity to the same location if maximum efficiency is desired.

In the case study for example, it is unlikely that for such

a relatively high amount of heat equally much demand would

exist in close proximity to a large scale development.

However, close proximity to heat customers is desired,

because transporting of heat between heat source and end-

user is associated with high infrastructure cost. Thecation suitability of decentralized, medium scale bioenergys, Biomass and Bioenergy (2012), http://dx.doi.org/10.1016/


Fig. 8 e Example of a suitability layer map e case study area.

b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 2 ) 1e1 4 11spatially diversified medium scale plants are more likely to

match demand for parts or ideally all of the smaller amounts

of heat produced at each individual site. The GIS model in

particular addresses this aspect, as it considers both biomass

feedstock supply and heat demand to identify the most suit-

able locations for the medium scale developments. This again

considerably increases the likelihood of matching heat supply

of plants with end-user demand entirely.

When the methodology was applied in the case study area

two or more cells did not have the same suitability score. In

case this should occur, the cells with the same scores should

be equally considered as plant locations. The allocation of

biomass feedstock is only affected by this if the locations in

question are in close proximity to each other and thus

compete for the same supplies. Then, an equal and fair supply

distribution has to be considered with allocating biomass

feedstock from the various suppliers in turns for example. In

this connection the likelihood of a supplier being exactly the

same distance away from two or more locations with the

same suitability score is relatively low.

For GISmodeling it has to be considered that amending the

radiuses in the screening model (buffer zones around existing

bioenergy plants considering heat demand) and the scoring

model (KD radiuses for maximum distances of suppliers and

heat demand) can change the results and therefore should be

appropriate and realistic for a case study area.Please cite this article in press as: Kurka T, et al., GIS-based lodevelopments to estimate transport CO2 emissions and costj.biombioe.2012.08.004For further work in regard to GIS modeling, it could be

considered to convert biomass feedstock supply data into

energy units. This would mean that the same unit can be

used for supply and demand, which would reduce the

number of normalization steps required and result in

a simplification of the GIS model. Additionally, an electricity

grid layer could also be added to the GIS model to take grid

connectivity into account. Furthermore, the application of

GIS extensions, such as the ArcGIS Network Analyst and the

ArcGIS 3D Analyst could be considered. The latter extension

can assist in improved illustration of GIS layers and spatial

analysis results to stakeholders. The ArcGIS Network Analy-

st can improve the identification of most suitable locations,

as well as the calculation of transport costs and CO2 emis-

sions resulting from the allocation of supplies process,

because it allows calculating distances based on road trans-

port routes, rather than on straight-line distances to plants.

Modeling based on straight-line distances has to be kept

however due to the model strings for heat customers and the

presumed straight-line distribution of heat to neighboring

premises.

Another issue to take into account is the type of biomass

feedstock used. In the case study, general benchmark figures

for both CO2 emissions and costs due to biomass trans-

portationwere applied. However, the type of biomass can vary

and influence both costs and emissions indirectly, as thecation suitability of decentralized, medium scale bioenergys, Biomass and Bioenergy (2012), http://dx.doi.org/10.1016/


Fig. 9 e Example of a final suitability map e 10 most suitable plant locations in the case study area.

b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 2 ) 1e1 412combustion of a biomass type with a higher LHV requires

lower quantities of biomass feedstock to generate the same

amount of energy. Less biomass feedstock requirements

reduce logistical needs, which again lead to reduced transport

CO2 emissions and costs. The GIS model can be extended byTable 3 e Example for supplies allocation and calculationof transport costs and CO2 emissions for a bioenergyplant in the case study area.

Supplier Suppliedamount(t y1)

Transportdistance(km)

Transportcosts(V y1)

CO2 emissions(kg y1)

Supplier 1 11,088 1.88 11,525.04 1795.57

Supplier 2 25,000 2.59 35,742.00 5568.5

Supplier 3 2112 9.99 11,655.91 1815.96

Supplier 4 374 11.09 2288.89 356.6

Supplier 5 20,000 12.73 140,550.24 21,897.32

Supplier 6 1500 14.9 12,337.20 1922.1

Supplier 7 374 17.27 3564.53 555.34

Supplier 8 125 18.84 1299.68 202.49

Supplier 9 187.5 20.64 2136.24 332.82

Supplier 10 355 21.01 4116.92 641.4

Supplier 11 3168 22.03 38,528.15 6002.57

Supplier 12 717.5 21.65 8574.70 1335.91

Total 65,000 272,319.50 42,426.58

Please cite this article in press as: Kurka T, et al., GIS-based lodevelopments to estimate transport CO2 emissions and costj.biombioe.2012.08.004a weighting step to incorporate these differences in supply

materials and LHVs. Furthermore, the application of more

case-specific benchmark figures and consultations with

regional market actors and stakeholders about actual

amounts of biomass feedstock supply and heat demand can

contribute to increased accuracy of the GIS model results.

Data gathering could also include the residential sector for

district heating considerations. Furthermore, procedures

could be established, which allow incorporation of planned

supply and demand developments and a continuous updating

of input data rather than working with point-in-time snap-

shot data.

An aspect to reflect on is the availability of supplies. In the

case study it was assumed that available amounts for the

bioenergy sector from the different suppliers can theoretically

be allocated to the identified plants entirely. In reality

however, proportions of these amounts can already be dedi-

cated to other existing bioenergy plants or competitive

markets such as the wood panel industry in the region. Hence

these amounts have to be subtracted from the total quantity

of regional supplies. This aspect was not considered in this

research, because it was assumed that installation of a rela-

tively high number of medium sized plants will significantly

change existing markets, logistics and sourcing of biomass,

which would eventually lead to a new structure of supplies

distribution altogether in the case study area. A number ofcation suitability of decentralized, medium scale bioenergys, Biomass and Bioenergy (2012), http://dx.doi.org/10.1016/


b i om a s s a n d b i o e n e r g y x x x ( 2 0 1 2 ) 1e1 4 13new bioenergy plants of various sizes are currently proposed

there. However, it can be argued that building of large scale,

low efficient electricity-only biomass plants among them can

be questionedwhen keeping the scarcity of biomass feedstock

in mind. These developments would highly rely on imports.

From a GIS-modeling perspective even if imports are desired,

they can be incorporated in the presented approach by

widening the KD radiuses of the scoring model and by

including suppliers further away from the case study area.

Alternatively, in a regional and import-independent sourcing

scenario more or less available biomass feedstock supply

could simply result in a reduced or increased number of bio-

energy plants. To compensate for limited resources for

biomass-fueled installations, bioenergy can also be generated

by alternative technologies such as anaerobic digestion. This

diversification of bioenergy technologies approach would

require a policy change and a more ambitious commitment

toward energy self-sufficiency in the case study area.

The presented work can also build the basis for future

research in regard to comprehensive sustainability assess-

ments and sustainable energy generation strategies. Gener-

ally, the criteria used to evaluate the sustainability of

bioenergy developments are divided into: technical,

economical, environmental and social [29]. In terms of further

GIS modeling work some environmental criteria could be

taken account of in the screening model. As suggested by

Fiorese and Guariso [8] andMa et al. [18] unsuitable areas such

as protected areas, wetlands and lakes or streams, where

bioenergy plants cannot be built, can be excluded through

additional screening layers.

For strategic decision-making toward bioenergy develop-

ment participatorymulti-criteria decisionmaking approaches

involving regional key stakeholders can be employed, as they

are known to address the complexities of a broad range of

stakeholders perspectives, goals and objectives [29]. By

following these approaches decision-making toward

a sustainable bioenergy generation strategy can be signifi-

cantly improved in Tayside and Fife and in similar regions.5. Conclusion

This study provides a transferable and adaptable approach

for identifying suitable locations for medium scale CHP bio-

energy plants, for allocating biomass feedstock and for esti-

mating transport CO2 emissions and costs. Location

suitability of bioenergy plants is of particular importance as

both biomass feedstock supplies and heat demand have to be

considered in location selection when heat and electricity are

utilized. The generic GIS-based model developed to identify

most suitable locations based on supply, demand and prox-

imity facilitates planning of decentralized bioenergy genera-

tion. The approach was applied to identify locations for 10

decentralized, medium scale bioenergy plants spatially

diversified throughout a Scottish region (Tayside and Fife)

based on which regional biomass feedstock supplies were

allocated and road transport-related CO2 emissions and costs

were estimated. The results indicate where biomass feed-

stock is available and heat demand is highest in the case

study area. Furthermore, it also shows where most suitablePlease cite this article in press as: Kurka T, et al., GIS-based lodevelopments to estimate transport CO2 emissions and costj.biombioe.2012.08.004locations likely are to develop future bioenergy systems in

this case study area, in which bioenergy development is still

at an early phase due to insufficient political commitment,

funding and technological expertise at both the production

and operation stages of biomass processing and at the bio-

energy plants. The presented work can assist in developing

and implementing a long-term sustainable and integrated

strategy for decentralized bioenergy generation, which

includes heat utilization in addition to electricity production

and which can be further developed to take account of the

broadly diversified conventional and renewable energy

generation in Scotland and in other regions.r e f e r e n c e s

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GIS-based location suitability of decentralized, medium scale bioenergy developments to estimate transport CO2 emissions an ...1. Introduction2. GIS model-based methodology to identify the most sustainable locations for decentralized medium scale bioenergy developments2.1. Data collection and assumptions2.2. GIS modeling and location suitability analysis2.2.1. Scoring model2.2.2. Screening model2.2.3. Location suitability layer2.2.4. Most suitable locations identification model

2.3. Allocation of supplies to the medium scale bioenergy plants2.4. Calculation of transport CO2 emissions and costs

3. Practical application of the methodology in Tayside and Fife/Scotland3.1. Scenario description3.2. Data collection and assumptions3.3. GIS modeling and location suitability analysis3.3.1. Scoring model3.3.2. Screening model3.3.3. Location suitability layer3.3.4. Most suitable locations identification model

3.4. Allocation of supplies to the medium scale bioenergy plants3.5. Calculation of transport CO2 emissions and costs

4. Results and discussion5. ConclusionReferences

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CasoEstudio-ArticuloIngles2.pdf