ORIGINAL ARTICLE

Co-operative forest fuel procurement strategy and its saving effectson overall transportation costs

PETER RAUCH, MANFRED GRONALT & PATRICK HIRSCH

BOKU University of Natural Resources and Applied Life Sciences, Feistmantelstrasse 4, AT-1180 Vienna, Austria

AbstractMinimum procurement cost is an essential element for the competitiveness of the forest fuel supply chain. This papercompares one co-operative procurement strategy with several non-co-operative strategies by measuring the cost gap. For astudy region consisting of five Austrian provinces, forest fuel supply potential and transportation costs were investigatedconcerning 28 newly built combined heat and power (CHP) plants. In the case of co-operation, the minimum totaltransportation cost was derived by solving the corresponding transportation problem. In non-co-operative supply chains,CHP plants compete for forest fuel. This case was illustrated by analysing three different clearly non-co-operativeprocurement strategies, because CHP plants guard their real supply sources as business secrets. The minimum procurementcost for all CHP plants is provided by the co-operative strategy. It comprises a total transportation cost of j17 million and anaverage procurement distance of 122 km. Co-operation between CHP plants lowers forest fuel transportation costs by 23%on average and reduces average transportation distances by 26%. The resultant cost-cutting potential stresses theimportance of co-operation between CHP plants in order to allocate forest fuel supplies efficiently. Establishing partnershipsand working alliances for forest fuel procurement therefore has important management implications for achieving efficiencyin forest fuel supplies and strengthening the competitiveness of wood-fuel-based energy production.

Keywords: combined heat and power (CHP) plant, co-operation, forest fuel, procurement, transportation costs.

Introduction

In Austria, 100 wind turbines, 150 biogas plants

and 60 forest fuel power plants had been built by the

end of 2006. Since 2008, these plants have produced

1700 GWh of green energy per year (Fischer, 2005),

with between 900 and 1100 GWh per year coming

from wood fuel-fired plants. The supply of latter

plants, mainly combined heat and power (CHP)

plants, with forest fuel presents a challenging task

owing to the immaturity of the forest fuel market, as

well as natural disturbances. Wood fuel procurement

planning has been based on studies that evaluated

the potential supply volume in Austria or its

individual provinces (e.g. Jonas, 2000; Jonas &

Haneder, 2001; Streißelberger et al., 2003). These

studies keep in mind that yearly increments and

wood reserves, accumulated as a result of under-

utilization in the past, cannot be harvested as a

whole because of technical and economic

limitations. However, although not declaring it

explicitly, the authors assume that every forest owner

will utilize timber within a couple of years, if this can

be done in a profitable way. On the contrary, actual

research on the behavioural patterns of Austrian

forest owners shows that an increasing number of

owners value their forests as a place to spend leisure

time and do not want to harvest the timber (Hogl

et al., 2005). Bohlin and Roos (2002) report similar

trends for Swedish forest owners. Furthermore,

most of the calculated forest fuel potential comes

from small-scale forests, and small-scale forest own-

ers tend to set the harvest time according to own

investment needs. Ignoring these restrictions pro-

duces excessively high supply potentials for forest

fuels (Wenzelides & Hagemann, 2007). Therefore,

providing wood feedstock for Austrian CHP plants

turned out to be more crucial as well as more cost

intensive than previously assumed. Wooden feed-

stock coming directly from the forest is referred to

Correspondence: P. Rauch, BOKU University of Natural Resources and Applied Life Sciences, Feistmantelstrasse 4, AT-1180 Vienna, Austria. E-mail:

peter.rauch@boku.ac.at

Scandinavian Journal of Forest Research, 2010; 25: 251�261

(Received 22 June 2009; accepted 9 March 2010)

ISSN 0282-7581 print/ISSN 1651-1891 online # 2010 Taylor & Francis

DOI: 10.1080/02827581003766967

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here as forest fuel, whereas by-products from saw-

mills or scrapwood are not included. Primary forest

fuel is the most expensive feedstock that CHP plants

fire, but according to the Austrian Green Energy

Act, current feed-in tariffs are rising along with the

primary forest fuel proportion on total feedstock.

Austria finds itself in a favourable import situa-

tion, as several neighbouring states are densely

forested and provide a high forest fuel potential

(Dieter et al., 2001; Karjalainen et al., 2004; Bauer

et al., 2006), since their forest fuel-based energy

production is comparably low (Rauch et al., 2007).

With the exception of Germany, harvesting costs

(Fagernas et al., 2006), as well as transportation and

labour costs (Hoogwijk et al., 2009), of export states

(Czech Republic, Slovakia, Hungary, and Slovenia)

are lower. Hence, import prices usually equal

domestic prices, but imports can cover greater

transport distances.

To assess forest fuel procurement costs for CHP

plants, the available market potential of forest fuel

has to be estimated for each supply district. For

North Trondelag County (Norway), Bjornstadt

(2005) estimates the potential annual supply of

forest fuel split into marginal cost categories. Results

show that at actual market prices only about one-

quarter of the potential can be produced profitably.

Therefore, quantifying the portion of forest fuel

potential that can be supplied profitably at market

prices is an essential task. A method for the calcula-

tion of woody biomass potential for fuel in Sweden

was developed by Parikka (2000) using sample plot

data from the National Forest Survey and ecological

restrictions, such as soil moisture or vegetation

classes. It can be shown that the commonly used

average fuel potential reduction from 14% to 18%

due to ecological restrictions is not sufficient, if the

calculations take place on a regional or district level

where much higher reduction percentages are possi-

ble (e.g. up to 40% reduction for stumps and harvest

residues; Skogsstyrelsen, 2008). Junginger et al.

(2001) provide an example of how to consider the

above-mentioned constraints, as well as how to

include competition with alternative users. First,

the gross technical potential (GTP) is estimated,

but the amount of fuel that can be supplied is

restricted (e.g. by physical constraints, such as steep

slopes or wet soil) by the availability of harvesting

equipment or labour, as well as fragmented supply

areas. Taking into account these constraints leads to

the net supply potential (NSP), which they calculate

as GTP times the percentage of volume that can

be realistically supplied. To calculate the net avail-

able fuel potential, all of the amounts utilized by the

competing applications have to be subtracted from

the NSP. The corresponding calculations for the

forest districts of the province of Salzburg are

provided by Gronalt and Rauch (2007).

Comprehensive literature exists on the assessment

of different harvesting, chipping and transportation

technologies, with respect to supplying a single plant

(e.g. Noon & Daly, 1996; Allen et al., 1998; Parikka,

2000; Asikainen et al., 2001; Ranta, 2002; Wittkopf

et al., 2003; Hakkila, 2004; Junginger et al., 2005;

Johansson et al., 2006). In addition, a more recently

developed operational forest fuel logistics model

includes daily variations in moisture content of

delivered woodchips, as well as weather conditions

that slow down the logging operations (Mahmoudi

et al., 2009). However, achieving efficiency in

biomass supply requires one to view the supply

chain from the point of production to the supply of

a plant as a whole (Allen et al., 1998). A good

procurement network design is a promising possibi-

lity for reducing costs (Chan & Chung, 2004). The

forest fuel procurement network can be seen as a

network of several supply nodes (forest districts) and

demand nodes (CHP plants), which are connected

by different transport modes. Erikson and Bjorheden

(1989) developed a linear programming model to

minimize the procurement cost for a single CHP

plant, which is supplied by seven forest regions via a

central terminal. Recently developed models include

the supply of several CHP plants via various trans-

port modes on a regional or state level (Gunnarsson

et al., 2004; Gronalt & Rauch, 2008). Latter linear

programming and mixed integer programming mod-

els assume co-operation among all power plants

supplied in the form of optimized fuel allocation

and are not limited by contractual restrictions or

preference of independence in fuel supply. Linear

programming models are also used to locate and size

CHP plants according to fuel procurement costs, as

well as plant costs (Freppaz et al., 2004; Frombo

et al., 2009). Analyses of transportation costs of

forest fuels in Denmark documented that resource

allocation was not optimal and resulted in higher

transportation distances (Moller & Nielsen, 2007).

Carlsson and Ronnqvist (2005) provide an exam-

ple for co-operative wood procurement of two

Swedish pulp producers, who optimize the allocation

of sawmill chips to pulpmills in order to minimize

the total transportation cost (TTC). They state that

this co-operation reduces TTC, but give no exact

figures on the cost gap. The cost reduction of co-

operative fuel procurement compared with actual

applied non-co-operative supply strategies has still

not been sufficiently investigated. Therefore, a co-

operative forest fuel procurement strategy will be

tested against three non-co-operative strategies. The

present study focuses on measuring the cost gap

between co-operative and non-co-operative forest

252 P. Rauch et al.

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fuel supply and calculates corresponding cost figures

for a study region in Austria.

Materials and methods

For a study region consisting of five Austrian

provinces, forest fuel supply potential and TTC are

investigated for 28 newly built CHP plants. In the

case of co-operation, the minimal TTC is derived by

solving a linear programming problem. Since the real

procurement areas were business secrets, three

different strategies reflecting the managers’ decisions

were used to form a picture of non-co-operative

supply chains competing for forest fuel. Compar-

isons of competing versus co-operating transporta-

tion costs demonstrated the direct cost-cutting

effects of co-operation. Consequently, several con-

secutive steps were applied to evaluate the potential

effect of co-operation in forest fuel procurement for

the study region (Figure 1).

Estimating the available fuel potential

To estimate the forest fuel supply situation in the

study region, the method described in detail by

Gronalt and Rauch (2007) was applied. According

to their administrative forest districts, the five

Austrian provinces were divided into 38 forest fuel

procurement areas. For each procurement area, the

available potential was estimated, keeping in mind

that there are legal, technical and economic restric-

tions, as well as restrictions on the part of forest

owners. Additional forest fuel must be harvested to

meet the demands of newly built CHP plants. The

actual forest fuel supply for existing heating plants is

usually based on long-term contracts that can be

viewed as fixed. Therefore, the potential of forest

fuels available for the newly anticipated demand was

calculated. In Austria, as in most central European

countries, forest fuels are produced from tree tops

and poor-quality logs, and as such they are mainly

by-products of clear-cutting or commercial thinning.

The amount harvested depends mainly on the

demand for sawlogs and veneer logs. For the study

region (Figure 2) containing the largest newly built

CHP plants, the net available potential (NAP) of

forest fuel was calculated as follows:

The calculation of the available market forest fuel

potential relied on the procedure designed by

Junginger et al. (2001). The data of the Austrian

Wood Inventory 2002 (Bundesamt und Forschungs-

zentrum fur Wald, 2004) as well as some extra data

analyses (R. Buchsenmeister, unpublished results,

2006), were used as a calculation basis. The GTP

was calculated for so-called production forests,

which excludes forests with ecological or legal

harvest restrictions. The GTP was calculated sepa-

rately for each district.

GTP�X7

a�1

AWIa

�Portion of forest fuel harvested by

applying silvicultural measurea: (1)

First, the sum of actual unused wood resources

[Austrian Wood Inventory (AWI)] accumulated as a

result of underutilization in the past and the com-

plete utilization of yearly increments was calculated,

and then reduced according to the harvested portion

of forest fuel for prevalent silvicultural measures (in

total there are seven silvicultural measures as listed

in Table I; a denotes an index variable that stands for

a particular silvicultural measure). The AWI data

were available for all silvicultural measures on a

district basis; only the possible volume of harvest

residues had to be calculated separately. Forest fuels

derived from harvest residues are limited to harvest-

ing locations, where the entire tree is hauled by a

yarder, and limbing and slashing are performed

centrally at landings, resulting in large piles of

residue at the roadside. Contrary to Nordic coun-

tries, other residues are not considered sources of

forest fuels because of excessive operational costs

GTP

NAP

NSP

Strategy N1

Minimal TotalTransportation Cost

LP

yes

no

Cost Gap between Co-operative and Non-

Co-operative Procurement Strategies

Strategy N3

Strategy N2

AWI

Technical / EconomicalRestrictions

Mobilization Rates

Total Transportation Cost 1

Total Transportation Cost 2

Total Transportation Cost 3

CHP‘s Demand

Co-operation?

Legal / EcologicalRestrictions

Figure 1. Overview of the applied methodology. AWI�Austrian

Wood Inventory 2002; GTP�gross technical potential; NSP�net supply potential; NAP�net available potential; LP�linear

programming model; CHP�combined heat and power; Strategy

N1�non-co-operative strategy no. 1.

Co-operative forest fuel procurement 253

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(Wittkopf et al., 2003). In Austria this is mainly due

to legal restrictions limiting clear-cutting to a total

area of 2 ha.

NSP�GTP�Volume due to technical or

economical restricted area (2)

Harvesting volumes of areas that cannot be

harvested in a technically safe manner (e.g. slopes

too steep), or where the harvesting costs are too

high, were assessed by a detailed evaluation of

Austrian production forests and slope classes, based

on the rough data of the Austrian Forest Inventory

2000�2002 (R. Buchsenmeister, unpublished re-

sults, 2006). These volumes were then subtracted

from the GTP. Furthermore, the lowest quality log

categories (trash), which are usually left behind, can

be used as forest fuel. The resulting supply potential

is the net supply potential (NSP):

NSP�X3

k�1

NSPk: (3)

However, the NSP is not available on the market

as a whole, because not every forest owner wants to

harvest it. Therefore, the NSP was first split into

three parts, according to three different mobilization

measures (cutback of accumulated wood reserves,

increasing utilization of annual increment and utili-

zation of trash).

NAP�X3

k�1

NSPk�Mobilization ratek: (4)

Next, specific mobilization rates for these different

parts of the NSP can be introduced. The mobilization

rates reflect the percentage of the NSP of a specific

mobilization measure, where k is the index of mobi-

lization measures, which will presumably be har-

vested annually in the near future. The mobilization

rates were separately estimated for each province by

regional experts (Table II). The NSP was then

reduced according to the mobilization rates of the

specific measures, and the NAP was thus calculated.

To gain an overview of the procurement situation, the

districts’ forest fuel demand was subtracted from the

NAP to calculate the region’s excess supply, which is

denoted as free potential. The total fuel demand was

investigated for each newly built CHP plant, and the

portion of primary forest fuel, which each CHP plant

fires, was determined. Most Austrian CHP plants

exclusively use primary forest fuel to obtain the

highest renewable energy feed-in tariff.

The NAP was calculated for two more scenarios

reflecting the forecast uncertainty of mobilization

rates. The pessimistic scenario showed only half of

the estimated mobilization rate. In the optimistic

scenario, the mobilization rate was assumed to

increase by 50%. Undersupply occurs in the pessi-

mistic scenario showing which plants will have an

Figure 2. Austria and the study region (hatched area).

Table I. Primary forest fuel to be harvested under different

silvicultural measures as a proportion of the total harvest volume.

Silvicultural measures

Forest fuel (% of total

harvest volume)

Thinning 70

Regeneration cut 5

Final felling 5

Clearing out 20

Precommercial thinning 100

Complete utilization of yearly increment 20

Harvest residues 25

Table II. Mobilization rate of different measures.

Mobilization measures

Mobilization rate

(% of total potential)

Cutback of accumulated wood reserves 50

Increasing utilization of yearly increment 20

Using trash 20

254 P. Rauch et al.

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insufficient fuel supply, if domestic forest fuel harvest

decreases. The pessimistic scenario reflects situations

in which forest owners lack interest in harvesting

forest fuels. In contrast, the optimistic scenario

indicates which supply districts cannot sell all of their

forest fuel. The optimistic scenario could occur if

forest owners were motivated to harvest more.

Total transportation cost

Forest fuel procurement cost mainly consists of the

timber price for forest fuel at the landing, the

chipping cost and the transportation cost. Co-operation

between CHP plants can help to optimize resource

allocation to minimize transportation cost, which

contributes to about one-third of the total procure-

ment cost. Transportation cost from a district to a

specific CHP plant was based on the transportation

cost rates according to the cost accounting of certain

Austrian forest-based industries. Using these data, a

transportation cost model was set up to sum up the

loading and unloading costs, costs for transport and

toll costs. Entry points were set up for each supply

district (e.g. where the forest road network is

accessible via the superior road network). Transpor-

tation distances were measured from each entry point

to each CHP plant with a Geographical Information

System (GIS). According to common practice, it was

assumed that half of the forest fuel volume was

transported in chipped form and the other half

unchipped. The transportation volumes represent

the results of the co-operative or non-co-operative

procurement strategies. In addition, the average

procurement distance (APD) was calculated as

the volume of weighted average transport distance:

where s�index of forest fuel procurement strategies

(LP, N1, N2, N3), i�index of forest districts, and

j�index of CHP plants. Vercammen (2001) uses

this as an expression for the average distance over

which an average unit of forest fuel is trucked.

Besides the 38 Austrian supply districts, the four

neighbouring countries (Germany, the Czech Re-

public, Slovakia and Hungary) were also included as

potential suppliers. Total transportation cost (TTC)

sums up the trucking cost for transporting and

loading or unloading forest fuel:

TTCs�X42

i�1

X28

j�1

Transported volumesij

Volume per truck

�Average transport distanceij

�Kilometer cost truck

�Loading and unloading cost: (6)

Procurement strategies

The different procurement strategies will now be

discussed. First, non-co-operative strategies, which

aim to reflect typical economic behavioural patterns

of CHP plants, are presented. The second approach

shows how an overall and co-operative procurement

strategy is able to save transportation costs. A linear

programming model formulation is proposed to

enable the advantages of the co-operative strategy

to be quantified and calculated.

Non-co-operative forest fuel procurement strategies

The actual procurement areas of CHP plants are

business secrets, therefore the procurement strate-

gies of CHP managers were modelled using algo-

rithms for their procurement activities. Three

different non-co-operative procurement strategies

were applied describing realistic behaviour of a

manager supplying a single CHP plant. An algo-

rithm that defines rules on how to combine CHP

plants’ demand and districts’ supply to estimate the

procurement area for a particular CHP plant as

realistically as possible represented each strategy.

The first non-co-operative procurement strategy

(N1) uses the CHP plant’s total operation costs

(TOCs) as a decision-making basis. TOCs consist of

the transportation costs of wood fuel that depend on

the supply area and the operating costs of the plant.

The operating costs depend on the plant size and

production processes, and they were set as reported

by Caputo et al. (2005). Owing to economies of

scale, larger plants have lower operating costs per

unit of fuel fired. First, the algorithm selects the

lowest TOC value in a matrix, where the rows

represent the CHP plants, and the columns, the

supply areas. If the demand of the CHP plant with

APDs�

�P42

i�1

P28

j�1 Transported volumesij � Average transport distanceij

�P42

i�1

P28

j�1 Transported volumesij

; (5)

Co-operative forest fuel procurement 255

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the lowest TOC value exceeds the supply of the

respective supply area, the unfulfilled demand has to

be calculated and the column is blocked. If supply

exceeds demand, the row is blocked and the new

supply is calculated. In the next step, the second

lowest TOC value, which may also be associated with

another CHP plant than the primarily selected one, is

identified, and the algorithm proceeds with the steps

described above. The algorithm is completed when all

demand is fulfilled. The procedure is comparable to

the minimum cost method that is commonly used

as heuristic for the transportation problem. This

non-co-operative procurement strategy assumes that

managers attempt to utilize the supply regions

that offer the lowest TOC values first.

The second non-co-operative procurement strat-

egy (N2) uses a concept of market power for

modelling the actual procurement areas. It is as-

sumed that the CHP plant with the largest demand

also has the most market power and can first choose

its supply areas. The CHP plant makes this decision

based on transportation cost. After the CHP plant

with the most market power has fulfilled its demand,

the second largest can decide on its supply areas.

The procedure continues until the demand of all the

CHP plants has been fulfilled.

The third non-co-operative procurement strategy

(N3) uses the quotient of the supply volume of a

district and its transportation distance to the CHP

plant as an indicator of the district’s supply attrac-

tiveness instead of TOC. The rest of the procedure is

the same as in N1. This algorithm reflects the

attitude of managers that supply districts with a

combination of high supply potential and low

transportation distance are the most attractive ones.

Co-operative procurement strategy

A linear programming model is formulated to mini-

mize the total forest fuel transportation cost by

simultaneously allocating a district’s fuel potential to

CHP plants in a cost-optimized manner. This model

is well known as the transportation problem. The

model includes 42 supply areas (38 domestic districts

and four neighbouring countries’ regions), as well as

28 CHP plants. According to amortization needs,

CHP plants were assumed to be operating at max-

imum capacity. Since the choice of a supply strategy

had no impact on fixed or variable (operating, labour,

maintenance and ash transport/disposal costs) plant

costs, these were not considered in the model.

First, the notation is described, followed by the

objective function and constraints. I is the set of

supply sources (supply districts), and J is the set of

CHP plants. Index i is used for sources, and index j

for CHP plants.

Decision variable

First, continuous variables representing the trans-

portation flow of forest fuels from supply districts to

plants were defined: xij�the amount of fuel trans-

ported from district i to CHP plant j in loose cubic

metres (lcbm).

Data

The following notation is used: cij�the transporta-

tion cost for fuel supply from district i to CHP plant

j (t lcbm�1), dj�the annual forest fuel demand of

CHP plant j (lcbm), and si�the annual volume of

forest fuels available in district i (lcbm).

In terms of the above notation, the model can be

formulated as follows (the objective is to minimize

the TTC for all CHP plants):

minXi � I

Xj � J

cij �xij ; (7)

subject toXi � I

xij �dj � j � J; (8)

Xj � J

xij 5 si � i � I; (9)

xij ]0 � i � I and � j � J: (10)

The model minimized the forest fuel transportation

cost for 28 CHP plants in the study region (7).

Constraints (8) ensure that the demand of all the

CHP plants was fulfilled by direct supply from the

forest districts. Forest fuel supply from the forest to

the CHP plants was restricted to the forest

fuel potential of the district (9). Constraints (10)

set all decision variables non-negative, since actual

material flows can only be zero or positive.

The model was implemented and solved with

Standard-Solversoftware Xpress. All three non-co-

operative procurement strategies were implemented

with Microsoft Excel using Visual Basic as the macro

language for programming the calculation procedure

according to the defined rules.

Results

Available fuel potential

The demand of CHP plants and NAP was calculated

on a district basis (Table III). The difference

between the NAP and CHP plants’ demand in a

district provides the free potential that is available to

be transported outside the district, or that is missing

in the district to satisfy its demand.

256 P. Rauch et al.

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The forest fuel demand of each of the 28 CHP

plants is shown in Figure 3, in which the five largest

CHP plants need approximately 2 million lcbm; that

amounts to 45% of the total forest fuel demand.

In Table IV the districts’ NAP and the demand of

CHP plants for the districts of the provinces provides

an overview of the supply situation on a more

aggregated level. The results for the three mobiliza-

tion scenarios (pessimistic, realistic and optimistic)

are included here. For further analysis, it can be

assumed that the domestic undersupply can be over-

come by imports from neighbouring countries (Rauch

et al., 2007).

Performance figures of the different procurement strategies

The calculation results in Table V depict the fuel

allocation according to certain procurement strate-

gies. Assessing the allocated supply volumes with

transportation costs, as well as transportation dis-

tances, reveals the TTC and the average procurement

distance for each procurement strategy. Furthermore,

three different fuel market scenarios were tested: a

realistic scenario with NAP as calculated, a pessimis-

tic scenario in which mobilization rates are only

half of the rates in the realistic scenario, and finally

an optimistic scenario, which assumes mobilization

rates to be 50% higher than in the realistic scenario.

Solving the linear programming model with the

supply potentials of the pessimistic scenario indi-

cates the districts where CHP plants will experience

the most procurement problems, if mobilization

rates drop (Figure 4).

Furthermore, it can be shown which supply

districts will not be able to sell all of their NAP, if

mobilization rises (Figure 5) as modelled in the

optimistic scenario.

Discussion

The calculation of the forest fuel demand for each

district reveals that CHP plants were built, on the

one hand, mainly in areas with a high population

concentration and, on the other hand, at existing

forest industry-based plants. In addition, forest fuel

demand is concentrated in the eastern provinces

(Burgenland, Vienna and Lower Austria). The

western provinces (Salzburg and Upper Austria)

face a nearly balanced demand and NAP ratio,

whereas the eastern provinces need imports to cover

their fuel demands. As a result, the calculation of

regional forest fuel NAP and demand indicates the

importance of ensuring forest fuel imports from

neighbouring countries, as well as undertaking

further steps to promote forest measures to harvest

more domestic forest fuel. However, the use of

forest fuel is also rising in countries that are currently

forest fuel exporters, mainly in Germany and the

Czech Republic. Therefore, their export potential

could decrease in the long term. The increasing

domestic mobilization rates are commonly seen as

the most promising mitigation strategy for declining

forest fuel imports. Nevertheless, results of the

scenario assuming an optimistic mobilization show

that even an increase of 50% in domestic forest fuel

harvest would just lower, but not overcome, import

dependency. If mobilization falls by about 50%, as

was assumed in the pessimistic scenario, the pro-

vinces of Salzburg and Upper Austria will become

forest fuel importers. Both provinces directly adjoin

Germany, the country with the highest forest fuel

potential of all of Austria’s neighbouring countries

(Dieter et al., 2001; Karjalainen et al., 2004; Bauer

et al., 2006). Therefore, they have a competitive

advantage over the eastern provinces.

Table III. Net available potential (NAP), demand from combined

heat and power (CHP) plants and free potential (NAP � Demand)

for districts of the Province of Lower Austria.

District

Demand

(lcbm)

NAP

(lcbm)

Free potential

(lcbm)

Amstetten 826,200 47,000 �779,200

Baden 150,000 33,000 �117,000

Ganserndorf 35,100 8,000 �27,100

Horn 0 38,000 38,000

Korneuburg 0 17,000 17,000

Krems 75,000 46,000 �29,000

Lilienfeld 0 56,000 56,000

Melk 0 41,000 41,000

Mistelbach 0 9,000 9,000

Neunkirchen 0 69,000 69,000

Sankt Polten 96,768 47,000 �49,768

Scheibbs 105,000 62,000 �43,000

Waidhofen/Thaya 264,000 73,000 �191,000

Wiener Neustadt 39,300 51,000 11,700

Wien-Umgebung 154,200 37,000 �117,200

Zwettl 16,800 70,000 53,200

Lower Austria 1,762,368 704,000 �1,058,368

Notes: lcbm�loose cubic metres.

Figure 3. Annual forest fuel demand (loose cubic metres) of the

study region’s combined heat and power (CHP) plants.

Co-operative forest fuel procurement 257

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The implemented calculation of forest fuel poten-

tial required some explanation for the forest practi-

tioners involved in this study, since they were

accustomed to the results of older studies, which

promised higher potentials. Nevertheless, after some

discussion, the NAP results were commonly agreed

on as representing the available market forest fuel

potential of a district, which reflects regional forest

fuel market conditions and the experience of pro-

curement managers more accurately.

The comparison of non-co-operative procurement

strategies with a co-operative approach illustrates the

benefits of co-operation and is useful for deriving

management implications. In terms of cost, the

optimal procurement strategy for all CHP plants is

provided by the co-operative strategy and has a TTC

of t17 million, as well as an average procurement

distance of 122 km. The TTC of strategy N3 is only

5% higher, and the average procurement distance is

only 8% higher. The non-co-operative procurement

strategy N1 leads to 20% higher costs and to a 34%

higher procurement distance. The non-co-operative

procurement strategy N2 results in higher perfor-

mance figures of 45% and 37%, respectively.

Compared with the average figures of the three

non-co-operative strategies, co-operation between

CHP plants lowers forest fuel transportation costs

by 23% and reduces average procurement distances

by 26%. This corresponds with the results of

Palander and Vaatainen (2005), who noted a 20%

reduction in truck transport costs by interenterprise

co-operation in roundwood procurement of three

large timber industries. One of the major drawbacks

of all non-co-operative procurement strategies is that

human behaviour is not as rational as it is assumed

in the calculations. Furthermore, competition and

concerns about security of supply, or supply short-

fall, influence a manager’s decisions concerning

forest fuel procurement. Therefore, realistic trans-

portation costs could be even higher than the worst

non-co-operative strategy assumes.

Scenario analyses illustrate that in the case of

decreasing mobilization (pessimistic scenario), five

CHP plants in south-eastern Austria will face a

Table V. Average procurement distance (APD) and total forest fuel transportation cost (TTC) as a result of co-operation or non-co-

operative (N1, N2, N3) procurement strategies and the average of non-co-operative procurement strategies (AVG N) under different

mobilization scenarios.a

Mobilization scenario

Realistic Pessimistic Optimistic

TTC (M t) APD (km) TTC (M t) APD (km) TTC (M t) APD (km)

Co-operation 17.1 122 21.3 154 11.6 96

N1 20.5 164 25.7 210 13.7 125

N2 24.9 167 31.5 216 17.1 132

N3 17.9 132 22.1 163 12.1 103

AVG N 21.1 154 26.3 195 14.3 121

Notes: arealistic�as actually assumed by regional experts; optimistic�harvested forest fuel volume increases by 50%; pessimistic

�harvested forest fuel volume decreases by 50%.

lcbm�loose cubic metres.

Table IV. Demand from combined heat and power (CHP) plants, net available potential (NAP) and free potential (NAP � Demand) under

different mobilization scenarios.a

NAP (lcbm) Free potential (lcbm)

Province

Demand

(lcbm) Pessimistic Realistic Optimistic Pessimistic Realistic Optimistic

Salzburg 502,620 312,500 486,000 722,500 �190,120 �16,620 219,880

Lower Austria 1,762,368 351,000 704,000 1,057,000 �1,411,368 �1,058,368 �705,368

Upper Austria 528,580 220,000 511,000 860,000 �308,580 �17,580 331,420

Vienna 600,000 3,000 6,000 10,000 �597,000 �594,000 �590,000

Burgenland 1,009,000 72,000 143,000 213,000 �937,000 �866,000 �796,000

Sum study area 4,402,568 1,132,000 1,850,000 2,862,500 �3,270,568 �2,552,568 �1,540,068

Notes: arealistic�as actually assumed by regional experts; optimistic�harvested forest fuel volume increases by 50%; pessimistic

�harvested forest fuel volume decreases by 50%.

lcbm�loose cubic metres.

258 P. Rauch et al.

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tremendous undersupply, and two of them will

obtain no forest fuel at all. In Lower Austria the

largest CHP plant will be lacking 10% of its demand,

whereas two other CHP plants will have a supply

lack of approximately 50%. In the case of co-

operation among all CHP plants, average procure-

ment distance will rise by 26%, and TTCs will rise

by 25%. In contrast, the optimistic domestic fuel

mobilization scenario will lead to an oversupply of

forest fuel. One district in the province of Salzburg

will not be able to sell 50% of its NAP, and another

one will sell none of its NAP. Imports from Germany

will not be needed in the optimistic scenario.

Increasing domestic mobilization of forest fuel will

lower the average procurement distance by 21%, and

TTC even decreases by 32% in the co-operative

procurement strategy. Nevertheless, as efforts by

various governmental and non-governmental orga-

nizations supporting small-scale forestry to boost

harvest activities have thus far not been very

successful, the realistic or pessimistic scenario seems

more likely.

Co-operation among all 28 CHP plants would

seem to be unrealistic. However, co-ordinating forest

Figure 4. Procurement situation in the pessimistic mobilization rate scenario (harvested inland forest fuel volume decreases by 50%).

combined heat and power (CHP) plants that cannot fulfil their demand are marked as follows: 1�up to 10% of their demand is not fulfilled;

2�40�60% is not fulfilled; and 3�more than 90% is not fulfilled. 0�plants with total fulfilment of their demand. D�Germany; CZ�Czech Republic; SK�Slovakia; H�Hungary.

Figure 5. Procurement situation in the optimistic mobilization rate scenario (harvested inland forest fuel volume increases by 50%). 0�supply districts that are able to sell 100% of their NAP; 1�those that can sell only 50%; 2�those that can sell none of their NAP. D�Germany; CZ�Czech Republic; SK�Slovakia; H�Hungary.

Co-operative forest fuel procurement 259

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fuel procurement activities is likely within a small-

sized group. For smaller CHP plants, co-operation

with a large CHP plant could mean giving up their

own supply organization. A main concern is that

supply co-operation will work well provided market

conditions are more or less stable. However, during a

fuel shortage period, the co-operation partner hold-

ing the supply organization could be favoured.

Implementing a co-operative supply strategy is easier

if the supplied CHP plants are owned by the same

company, or if co-operation partners are on a par

with each other and maintain their own supply

organizations.

Another issue is that co-operation benefits may

not be distributed equally between co-operation

partners. Such is the case if one partner receives

a bigger share of cost-cutting effects than the

other(s). Carlsson and Ronnqvist (2005) report on

co-operation between two Swedish companies ex-

changing sawmill chips according to the results of a

monthly optimization that minimizes transportation

costs. Transportation costs were then compared with

those resulting when no co-operation had taken

place. Implemented compensation mechanisms are

not cited, but the exact calculation of each partner’s

benefits could be used to estimate the difference in

benefit and balance it (e.g. by adjustment

payments).

In conclusion, the resultant cost-cutting potential

stresses the importance of co-operation between

CHP plants to allocate forest fuel supply efficiently.

Establishing partnerships and working alliances for

forest fuel procurement therefore has important

management implications for achieving efficiency

in forest fuel supplies and strengthening the compe-

titiveness of wood-fuel-based energy production.

Acknowledgements

The authors would like to thank the program

‘‘Energy System of the Future’’, an initiative of the

Federal Ministry for Transport, Innovation and

Technology (bmvit), for financially supporting major

parts of this research.

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