2011 Supply Chain Cost Analysis of Long-Distance Transportation of Energy Wood in Finland

8/10/2019 2011 Supply Chain Cost Analysis of Long-Distance Transportation of Energy Wood in Finland

1/16

Supply chain cost analysis of long-distance transportation

of energy wood in Finland

Timo Tahvanainen*, Perttu Anttila

Metla, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland

a r t i c l e i n f o

Article history:Received 5 October 2009

Received in revised form

28 October 2010

Accepted 1 November 2010

Available online 7 January 2011

Keywords:

Logging residue

Forest chip

Energy wood bundle

Railway transportation

Fuel terminal

Supply chain

a b s t r a c t

The increasing use of bioenergy has resulted in a growing demand for long-distancetransportation of energy wood. For both biofuels and traditional forest products, the

importance of energy efficiency and rail use is growing. A GIS-based model for energy wood

supply chains was created and used to simulate the costs for several supply chains in

a study area in eastern Finland. Cost curves of ten supply chains for logging residues and

full trees based on roadside, terminal and end-facility chipping were analyzed. The average

procurement costs from forest to roadside storage were included. Railway transportation

was compared to the most commonly used truck transportation options in long-distance

transport. The potential for the development of supply chains was analyzed using

a sensitivity analysis of 11 modified supply chain scenarios.

For distances shorter than 60 km, truck transportation of loose residues and end-facility

comminution was the most cost-competitive chain. Over longer distances, roadside

chipping with chip truck transportation was the most cost-efficient option. When the

transportation distance went from 135 to 165 km, depending on the fuel source, train-based transportation offered the lowest costs. The most cost-competitive alternative for

long-distance transport included a combination of roadside chipping, truck transportation

to the terminal and train transportation to the plant. Due to the low payload, the energy

wood bundle chain with train transportation was not cost-competitive. Reduction of

maximum truck weight increased the relative competitiveness of loose residue chains and

train-based transportation, while reduction of fuel moisture increased competitiveness,

especially of chip trucks.

2010 Elsevier Ltd. All rights reserved.

1. Introduction

Trucks strongly dominate the transportation of domestic

round wood in Finland (Table 1). Altogether, about 80% of the

round woodentersmillgates bytruck and about 16%by rail [1].

The remaining 3.5% is transported by waterways. During

the last decade, the share of waterways has decreased by

one third, while truck transportation has correspondingly

increased its share. In 2005 about 26% (17.9 Mm3) of the raw

wood used by the Finnish forest industry was imported. Ofimported raw wood, only 45% was transported by trucks and

over 23% by rail. Almost 32% (5.5 Mm3) was transported in

vessels, of which 2.2 Mm3 was in the form of wood chips. In

addition to round wood transportation,about800 kt of chipped

sawmill residues were transported by railway to pulp mills in

2005[2].

The average transportation distance by rail is three times

longer than that of truck transportation (Table 1). The cost per

* Corresponding author. Tel.: 358 50 443 2950; fax: 358 13 263 7111.E-mail address:[email protected](T. Tahvanainen).

A v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m

h t t p : / / w w w . e l s e v i e r . c o m / l o c a t e / b i o m b i o e

b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 3 6 0 e3 3 7 5

0961-9534/$ e see front matter 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.biombioe.2010.11.014
mailto:[email protected]://www.sciencedirect.com/http://www.elsevier.com/locate/biombioehttp://dx.doi.org/10.1016/j.biombioe.2010.11.014http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://www.elsevier.com/locate/biombioehttp://www.sciencedirect.com/mailto:[email protected]


2/16

cubic meter and kilometer is roughly half of the cost of truck

transportation[1]. However, railway and water transportation

also includes truck transportation from the forest to the

nearest loading terminal or water storage. For railway trans-

portation, the truck feed-in costs cover as much as 42% of total

transport costs.The amount of harvested forest biomass for energy

generation is still low compared to harvested industrial timber

(Table 2), but it is increasing rapidly. Energy wood harvesting

is strongly integrated into industrial timber harvesting; most

of the raw material for forest chips is collected from clear

cutting areas and also from thinnings, where both industrial

and energy wood is often harvested. The users of forest chips

are mainly local district heating or combined heat and power

(CHP) plants and the average transportation distances are

shorter than for industrial timber assortments. Trucks are

dominating energy wood transportation and for the time

being there are few large CHP installations that use railway

transportation for residue bundles, stumps or chips.The use of different technologies for forest fuel production

in Finland in the year 2007 has been estimated based on

a survey of energy wood suppliers, assuming the share of

railway transportation to be zero [4,5]. The most common

supply chain for producing energy chips from logging residues

and small-sized timber was based on roadside chipping and

road transportation using chip trucks. The estimated number

of chip trucks in use was 130 units, with a portion of the chip

trucks also used for transporting energy peat and industrial

wood residues. About 60 energy trucks were equipped for

transporting loose residues and stump wood. The bundling

systemis able to usestandardtimber trucksfor transportation;

seven timbertrucks areused fortransportation of energywood

bundles. The supply chains for both bundles and stumps

require a stationary crusher at the plant to prepare the fuel

before combustion. The number of stationary crushers in

energy plants was six[4,5]. Chipping or crushing can also take

place in a fuel terminal before transportation to the plant.Railway transportation in Finland was a state monopoly

until 2007, when private companies were allowed to offer

services for goods transportation. In summer 2007, the first

private company obtained a licence to operate with the

intention to start delivering cargo services for the forest,

mining and metal industries. The forest industry is the largest

client for railway transportation [6], and has criticized the

state monopoly for high costs and inadequate service. The

criticisms regarding service include the lack of specialized

wagons for hire and the scarcity of loading stations and

terminals suitable for wood transportation. Additionally, the

poor condition of infrequently used parts of the rail network

results in reduced maximum loads and speeds[7]. Comparedto truck transportation from forest to plant, railway trans-

portation is more complicated to organize. In order to mini-

mize equipment rentals, the minimum cargo is large (about

1000 m3), and the timetables for loading are tight and often

scheduled during weekends. These restrictions apply for

energy wood transportation as well. Another limiting factor is

the sparse railway network.

The general trend has been towards closing sparsely uti-

lised railway sections having little or no passenger traffic,

typically located in forested areas[7]. However, the revival of

the mining industry, increasing energy costs and demands for

improving the safety of road traffic are increasing the interest

in railway transportation in general. The closing of smallerproduction units in the Finnish pulp industry is leading to

longer average transportation distances for raw wood. New

large installations for using wood for energy production are

increasing the demand for fuel and competition between

different users. The demand for fuels is largest in southern,

western and central Finland, while the production potential is

located more in eastern and northern Finland. Disturbances in

local fuel supply and the need for balancing the regional

supply and demand during periods of peak consumption

require efficient systems for long-distance transportation of

biofuels.

Planned large-scale production of liquid biofuels and the

development of the so-called biorefinery concept may also

Table 2 e The sources of energy wood for forest chips andharvesting of industrial round wood in Finland in 2006[3].

Mm3

Thinnings: full trees and delimbed stems 0.70

Large stemwood 0.17

Logging residues 1.74

Stumps 0.46

Forest chip, total 3.06

Industrial roundwood, total 49.90

Table 1e Transportation of domestic round wood in Finland in 2005 [1], volumes over bark.

Domestic roundwood

Volume Share Distance Unit cost Unit cost

Mm3 % km m3 km m3

Trucks 33.27 80.1 105 0.054 5.68

Railway 6.85 16.4 293 0.030 8.92by road to railway 6.48 15.6 48 0.079 3.81

rail transportation 6.85 16.4 245 0.022 5.34

Water (floating barge) 1.45 3.5 311 0.027 8.35

Total long-distance transportation 41.54 100.0 143 0.044 6.31

Total by road 41.12 99.0 93 0.057 5.31

b i o m a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 3 3 6 0 e3 3 7 5 3361
http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://dx.doi.org/10.1016/j.biombioe.2010.11.014


3/16

increase the need for long-distance transportation of energy

wood. Furthermore, the growing demand for renewable

energy and fuels is fading the strict line between industrial

timber and energy wood. For both biofuels and traditional

forest products, the importance of energy costs, energy effi-

ciency, and environmental impact assessments are growing

[8], thus supporting the development of energy-efficient

means of transportation such as rail. The new methods andtechnology for the transportation of energy wood have to be

developed as a part of the whole logistic chain, because

activities at the beginning of the supply chain affect the

choices and costs in latter phases of the chain. The bundling

system is a good example: bundle wrapping in the beginning

of the supply chain results in cost savings in forwarding,

together with faster loading and unloading operations

throughout the whole chain.

The purpose of this study was to assess the costs of forest

chip procurement and the development potential of the

supply chains. A special emphasis was on railway trans-

portation, which was compared to the most commonly used

truck transportation options in long-distance transport. Thepotential for the development of supply chains was investi-

gated using a sensitivity analysis, on the basis of the cost

structures of the simulated supply chains. Another purpose

was to develop a GIS-based cost model for forest fuel supply,

which could be used to estimate supply costs and fuel avail-

ability at specific plants using forest resource data.

2. Materials

The CHP plants under consideration were located in Uima-

harju (62190N, 27540E) and Varkaus (62550N, 30130E); themain transportation routes are illustrated in Fig. 1. Road

network data were used as a basis for calculating the costs of

truck transportation. The Finnish Road Administration

maintains a national road and street database called Digiroad

[9]. Digiroad describes the geometry and physical features of

the roads including, for example, the functional class of a road

and turning restrictions.

The cost calculations of train transportation were based on

railway network data obtained from the National Land Survey

of Finland[10]. Locations of railway terminals were provided

by the Finnish Rail Administration [11]. The number ofrailway

terminals was 24. The land area in North Karelia is 17 780 km2,

of which 84% is forest land[12]. Based on the road and railwaydata, the railroad density in North Karelia is 30 m km2 and

the road density is 2.0 km km2.

A grid of forest data with an interval of 500 m between the

grid points was created. Based on CLC2000 land use per land

cover classification [13] and the boundaries of conservation

and Natura 2000 areas, points outside of forestry land were

removed from the analyses. Subsequently, the average

Finnish harvesting conditions were assumed for each point

(Table 3). The roadside storages for transportation simulations

were placed for each grid point onto the nearest Digiroad

network location. In Finland, logging residues are collected

mainly in spruce-dominated final felling sites. The total

number of simulated roadside storages was 69 743.

3. Methods

3.1. Supply chains

Ten different supply chains for forest chips were compared in

this study (Table 4). In addition, 11 modifications of these

supply chains were produced for sensitivity analysis. Thesimulations of the ten basic supply chains represent the most

commonly used supply chains with average cost and effi-

ciency parameters and their combinations with railway

transportation.

In chains 1-6 the fuel source is logging residues from clear

cutting areas, whereas in chains 7-10 energy wood consists of

small-sized full trees from thinning operations. Chains 1 and 7

represent the most common supply chain in Finland, where

the energy wood is chipped at roadside directly into the truck

and transported to the plant. In chains 2 and 8 the chip truck

delivers the chips first to the railway terminal, where chips are

loaded onto train wagons for long-distance transportation to

the plant. Residues (Chain 3) and full trees (Chain 9) aretransported as loose material straight to the plant andchipped

there in a stationary chipper. In chains 4 and 10 loose material

is transported to the railway terminal, where it is chipped and

transported in a chip wagon to the plant. Residue bundles are

transported in timber trucks straight from roadside storage to

the plant for end-facility comminution in chain 5, and via

railway terminal in chain 6.

3.2. Cost calculations

3.2.1. Distance-independent costs

In the calculations the costs were divided into those that are

independent of the transportation distance (Table 5) and coststhat depend on the distance. Felling-bunching costs, hauling

costs in thinnings[14,15], and hauling costs in clear-cuts[16]

were all based on models. Bundling, roadside chipping and

terminal crushing costs were based on cost models developed

by the Finnish Forest Research Institute (Metla) [14], which

combine the results of several public research projects and

non-publiccase studies. For roadside chipping options, loading

of the truck includes only the waiting time cost of the truck

during chipping into load space. Chipping costs at the terminal

were assumed to be 70% of the roadside chipping costs due

to more efficient equipment and larger chipping volumes.

Typical loading and unloading times (Table 6) for trucks were

used [17,18]. Loading and unloading times for train wagonsfollowed the corresponding efficiencies for trucks, except

loading of chip wagons, where chipping into load space was

replaced by chipping and loading using a front loader. The

preparation times in terminal operations were about the same

for a truck as for a train with tenwagons. Thecosts for loading

and unloading operations were calculated by multiplying the

consumed timeby hourlycosts of the equipment(Table 6).The

hourly costs for trucks [17] were multiplied by the average

cumulative increase in unit costs (m3 km) for truck trans-

portation in the Finnish forest industry in 2004 and 2005, was

about 9.4% [1,19]. The estimated organization costs varied

between1.5 and2.5 m3, depending on the multiplicity of the

procurement chain.



4/16

For the sensitivity analysis, improved supplychains were

produced. For bundle-based supply chains the truck and

wagon loads for bundled residues were increased by opti-

mizing the bundle dimensions in relation to the load space of

truck and train wagons. Shortening the bundles from the

average 3.11 m[18] into 3 m resulted in an increase in the

amount of bundles in the load from 60 to 72 bales and 17% by

volume. Train unloading times were reduced by 40% and

wagon costs by 15% for all train-based chains. Terminal chip-

ping costs for bundles were estimated at 50% of the costs of

roadside chipping of logging residues. The estimation is based

on efficient chipping (or crushing) machinery, larger chipping

volumes and better working conditions than in average road-

side storage. Chain 11 (opt) is a combination of optimized

bundle truck and optimized chip train supply chains.

Maximum truck payloads were based on Scandinavian

regulations, in which the maximum weight of a truck is 60

tonnes. This corresponds approximately to a 38 tonne

payload, depending on the truck type and how it is equipped[20]. In the sensitivity analysis, results were calculated for

trucks with a 40 tonne total mass limit and 25 tonne payload.

Truck load space was the limiting factor for all loose residue

and bundling options. The average basic density for logging

residues was 460 kg m3 (dry weight) and 398 kg m3 for full

trees from thinnings [18]. The density figures represent

average bundles delivered to the plant, where logging residue

bundles are spruce (Picea abies) dominant and full tree bundles

consist of a mixture of several tree species. For chipped

logging residues with an average 45% moisture content, the

limit for truck maximum weight reduced the payload from

54.0 m3 to 45.4 m3. Due to lower basic density and moisture

content (42%), the chipped full trees were able to fully utilize

Fig. 1e Location of the study area, studied CHP plants and main transportation routes in the study area. The grey area

represents the province of North Karelia. Coordinate system: ETRS-TM35FIN. Boundaries and railways: National Land

Survey of Finland, license MYY/179/06-V. Road network: The Finnish Road Administration/Digiroad 2006. Railway

terminals: The Finnish Rail Administration. Smaller picture: Boundaries: ESRI Data & Maps; National Land Survey of

Finland, license MYY/179/06-V.

Table 3e Average conditions in felling sites used inharvesting cost calculations.

Final Fellings Pre-commercial thinnings

Area, ha 1 Area, ha 2

Forwarding distance, m 150 Forwarding distance, m 200

Moisture of green

logging residue

(in forwarding), %

55 Moisture of fresh full

trees (in forwarding), %

55

Recovery of green

logging residues, %

70 Average full tree volume,

dm360

Accumulation of

industrial round

wood, m3 ha-1

266 Accumulation of

small-sized energy

wood, m3 ha-1

33



5/16

the maximum load space. The effect of drying the fuel from

45% moisture content to 35% was calculated for normal and

reduced (25 t) payloads of chipped logging residues. Changes

in the productivity of chipping due to drier raw material werenot taken into account.

3.2.2. Distance-dependent costs

There were three distance-dependent work phases: driving

a truck without a load, driving a loadedtruck,and transporting

by train. The GIS model involved finding out the fastest routes

from each of the generated roadside storage piles to theplants.

The first plant (pulp mill) is located in Uimaharju village in the

centre of North Karelia and the second plant (pulp and paper

mill) in the town of Varkaus, about 70 km from the south-west

border of North Karelia (Fig. 1).

The distances from the roadside storages to the plants

were calculated with Network Analyst extension in ArcGIS 9.2.

Subsequently, truck driving times were predicted with models

estimated for timber trucking[21]and driving the route two

times: empty and with a full load. The cost of driving (m3)

was calculated using Equation(1)as follows:

drivingcost hourly cost,

driving time

60truck load size

(1)

where hourly_cost hourly total cost of the truck,

driving_timedriving timein minutes, andtruck_load_size size

of a truck load (Table 6).

In the supply chains including train transportation, the

driving cost of a truck was calculated from each point to the

three closest railway terminals. Furthermore, train trans-

portation cost was estimated from each terminal to the plants.

The reason for calculating the costs via three terminals

instead of only the closest one was that the route via the

Table 4e The studied supply chain scenarios.

Supply chain Description Note

1 Chip truck Clear cutting e Chipping at roadside landinge Truck

transportation

Reference chain

2 Chip train Clear cutting e Chipping at roadside landinge Truck

transportatione Train transportation

3 Loose truck Clear cutting e

Loose residuee

Truck transportatione

Crushing at plant only to Uimaharju

4 Loose train Clear cutting e Loose residuee Truck transportatione

Chipping at station e Train transportation

5 Bundle truck Clear cutting e Bundlinge Truck transportatione

Crushing at plant

6 Bundle train Clear cutting e Bundlinge Truck transportatione Train

transportatione Crushing at plant

7 Chip truck (thinn) Thinning e Chipping at roadside landinge Truck

transportation

8 Chip train (thinn) Thinning e Chipping at roadside landinge Truck


9 Loose truck (thinn) Thinning e Loose residuee Truck transportatione

Crushing at plant

only to Uimaharju

10 Loose train (thinn) Thinning e Loose residuee Truck transportatione

Chipping at station e Train transportationSensitivity analysis:

11 Bundle truck chip train Clear cuttinge Bundlinge Truck transportation -

Terminal chipping - Train transportation

72 bundles in truck, wagon costs

reduced 15% and efficient terminal

operations

2 Chip train (opt) Clear cutting e Chipping at roadside landinge Truck


wagon costs reduced 15% and

efficient terminal operations

4 Loose train (opt) Clear cutting e Loose residuee Truck transportatione

Chipping at station e Train transportation



5 Bundle truck (opt) Clear cutting e Bundlinge Truck transportation e

Crushing at plant

72 bundles in truck, efficient

unloading at plant

6 Bundle train (opt) Clear cutting e Bundlinge Truck transportation e Train




1 Chip truck (40 t) Clear cutting e Chipping at roadside landinge Truck

transportation

40 t max truck weight

2 Chip train (40 t) Clear cutting e Chipping at roadside landinge Trucktransportatione Train transportation


5 Bundle truck (40 t) Clear cutting e Bundlinge Truck transportatione

Crushing at plant


6 Bundle train (40 t) Clear cutting e Bundlinge Truck transportatione Train



1 Chip truck (dry) Clear cutting e Chipping at roadside landinge Truck

transportation

35% MC

1 Chip truck (40 t, dry) Clear cutting e Chipping at roadside landinge Truck

transportation

40 t max truck weight and 35% MC



6/16

closest terminal does not always minimize the total cost. In

some cases, train transportation distance could be shortened

considerably if slightly longer truck transportation routes

were selected. Consequently, the cheapest route of the three

was selected for a certain point.

As empirical data and models for train costs were notavailable,asimplecostmodelforasinglewagonwasfitted.The

shape of the cost model follows the train transportation cost

curves of the train cost simulation model developed for the

forest industry [22]. In reference to [22], the average trans-

portationdistance was290 km andwhen the distance changed

to100 km the relativehauling costs changed byabout 20%. The

relative costs were transformed to absolute costs (m3 and

perwagon) byfittingthe curve to average train transportation

costs in the Finnish forest industry to the 2004and 2005 values

[1,19]. Subsequently, thecost of train transportationper wagon

(per wagon) was calculated by Equation(2)as follows:

train cost wagon 160 0:

89,distance (2)

where

distance train hauling distance (km) from a terminal to

a plant.

Furthermore, the cost of train transportation per volume

( m3) was calculated by dividing the wagon cost by the

wagon load volume.

3.2.3. Total cost

For each point in the grid, the total cost was computed by

totaling the distance-independent and distance-dependent

costs. Formap output thepoint data were interpolated by using

naturalneighboralgorithm [23].Subsequently,theareasoutside

forestry land were excluded from the resulting raster surface.

In order to model the dependence between transportation

distance and total costs, regression curves were fitted to the

point-level data. The curve form was decided based on visual

goodness of fit and coefficient of determination. The share of

truck and train transportation with a certain total trans-

portation distance varied, causing considerable variation in

Table 5e Summary of the different work phases related to the chains and the respective calculated costs in mL3.Distance-dependent phases are marked with f(d).

Work phase Supply chain

1 2 3 4 5 6 7 8 9 10

Felling - bunching 12.9 12.9 12.9 12.9

Bundling 7.6 7.6

Hauling 5.9 5.9 5.9 5.9 2.7 2.7 4.7 4.7 4.7 4.7Chipping at roadside landing 6.8 6.8 6.1 6.1

Driving without load f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d)

Loading of truck 1.6 1.6 3.0 3.0 1.6 1.6 1.5 1.5 3.0 3.0

Driving loaded f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d)

Unloading of truck 0.7 0.7 2.4 2.4 1.1 1.1 0.6 0.6 2.4 2.4

Chipping at station 4.8 4.3

Loading of train 0.5 0.5 1.6 0.5 0.5

Train transportation f(d) f(d) f(d) f(d) f(d)

Unloading of train 0.3 0.3 1.1 0.3 0.3

Crushing at power plant 1.7 1.7 1.7 1.7

Other costs 2.0 2.5 1.5 2.0 2.0 2.5 2.5 2.5 2.0 2.0

Total of distance-independent

costs, m317.1 18.4 14.6 18.9 16.6 19.8 28.4 29.1 26.7 30.1

Sensitivity analysis:

Work phase Supply chain

11opt 2opt 4opt 5opt 6opt 1 40t 2 40t 5 40t 6 40t 1 dry 1 40t dry

Felling - bunching

Bundling 7.6 7.6 7.6 7.6 7.6

Hauling 2.7 5.9 5.9 2.7 2.7 5.9 5.9 2.7 2.7 5.9 5.9

Chipping at roadside landing 6.8 6.8 6.8 6.8 6.8

Driving without load f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d)

Loading of truck 1.4 1.6 3.0 1.4 1.4 1.8 1.8 1.8 1.8 1.8 1.7

Driving loaded f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d) f(d)

Unloading of truck 0.9 0.7 2.4 0.9 0.9 0.9 0.9 1.1 1.1 0.9 0.8

Chipping at station 3.4 3.4

Loading of train 0.3 0.3 0.3 0.4 0.5

Train transportation f(d) f(d) f(d) f(d) f(d)Unloading of train 0.2 0.2 0.2 0.3 0.3

Crushing at power plant 1.7 1.7 1.7 1.7

Other costs 2.5 2.5 2.5 2.0 2.5 2.0 2.5 2.0 2.5 2.0 2.0

Total of distance-independent costs, m3 18.9 18.0 17.7 16.2 17.4 17.4 18.6 16.8 17.3 17.4 17.2



7/16

Table 6e

Parameter values used in calculating transportation costs. TruLS [Size of a truck load, TruLA [Preparation time of loaTruL[Timeto load or chip into a truck, TruUA [Preparation time of unloading a truck, TruCD[Hourly cost of a truck when drivingloading or unloading, WLS [Size of a wagon load, TrnLA [Preparation time of loading a train, TrnL [Time to load a wagon, Trna train, TrnU [Time to unload a wagon, BCL [Hourly cost of a bulldozer when loading or unloading a train.

Chain TruLS TruLA TruL TruUA TruU TruCD TruCL WLS TrnLA TrnL

m3 min load1 min m3 min load1 min m3 h-1 h1 m3 min train1 min m

1 Chip truck 45.4 18 1.5 20 0.4 79.8 49.2

2 Chip train 45.4 18 1.5 20 0.4 79.8 49.2 57.0 25 0.4

3 Loose truck 28.4 15 2.9 20 2.0 82.0 53.6

4 Loose train 28.4 15 2.9 20 2.0 82.0 53.6 57.0 25 0.4

5 Bundle truck 32.2 18 1.3 25 0.4 79.8 52.5

6 Bundle train 32.2 18 1.3 25 0.4 79.8 52.5 32.2 25 1.7

7 Chip truck (thinn) 54.0 18 1.5 20 0.4 79.8 49.2

8 Chip train (thinn) 54.0 18 1.5 20 0.4 79.8 49.2 57.0 25 0.4

9 Loose truck (thinn) 28.4 15 2.9 20 2.0 82.0 53.6

10 Loose train (thinn) 28.4 15 2.9 20 2.0 82.0 53.6 57.0 25 0.4

11 Bundle truck chip

train (opt)

37.6 18 1.3 25 0.4 85.3 52.5 57.0 25 0.4

2 Chip train (opt) 45.4 18 1.5 20 0.4 79.8 49.2 57.0 25 0.4

4 Loose train (opt) 28.4 15 2.9 20 2.0 82.0 53.6 57.0 25 0.4

5 Bundle truck (opt) 37.6 18 1.3 25 0.4 85.3 52.5

6 Bundle train (opt) 37.6 18 1.3 25 0.4 85.3 52.5 37.6 25 1.7

1 Chip truck (40 t) 29.9 18 1.5 20 0.4 79.8 49.2

2 Chip train (40 t) 29.9 18 1.5 20 0.4 79.8 49.2 57.0 25 0.4

5 Bundle truck (40 t) 29.9 15 2.9 20 2.0 79.8 53.6

6 Bundle train (40 t) 29.9 15 2.9 20 2.0 79.8 53.6 57.0 25 0.4

1 Chip truck (dry) 53.7 18 1.5 20 0.4 79.8 49.2

1 Chip truck (40 t, dry) 35.3 18 1.5 20 0.4 79.8 49.2


8/16

the total cost with the train-based chains. The maximum

feed-in truck transportation from roadside storage to railway

terminal was reduced to 30 km. Consequently, only points

having a maximum truck transportation distance of 30 km

from roadside storage to railroad terminal were considered

with train-based chains. Roadside storages within a 30 km

truck transportation distance to the railroad terminal covered

about 65% of all the roadside storages.

4. Results

4.1. Cost models

With the chains based only on truck transportation, cubic

curve form was found to give the best fit (see an example in

Fig. 2) for the model for estimating total costs. The coefficient

of determination was 1.00 for all the truck chains. For the

procurement chains based on both truck and train trans-

portation, linear function form was selected (see an example

inFig. 3). The coefficient of determination varied between 0.40and 0.90.

4.2. Total costs

The reference supply chain (Chain 1) chipping at roadside

storage and transportation of chips in full truck and trailer

units to the plant was the most cost-efficient alternative for

transportation distances between 60 and 160 km (Fig. 4). For

shorter distances, loose residue trucks combined with chip-

ping at the plant was the least expensive alternative. The total

costs of the supply chains with truck transportation were

much more sensitive to transportation distance than the

train-based chains, especially for loose residues and bundles,which were underutilizing the payload capacity of a truck.

Chipping at roadside storage and transportation as chips by

truck and train was the most competitive train option, when

the maximum truck transportation was limited to 30 km. The

total costs of the train transportation chain were lower than

the costs of the chipping at the roadside and truck option at

about a 160 km distance. Trucking of loose residues to the

railway terminal for chipping combined with train trans-

portation was more expensive than the chipping at the road-

side and train option, but the difference was rather small,about 1.5 m3. Due to the lower payload, the costs of bundle

trucks were higher than those of chip trucks. Train trans-

portation of bundles was the most expensive alternative.

For energy wood supply chains from thinnings, the total

costs were over 11 m3 higher compared to the same

transportation method for logging residues (Fig. 5). The truck

transportation cost curve for chips from full trees is slightly

less sensitive to distance than for chips made of logging

residues. The costs of transporting loose full trees exceeded

the costs of roadside chipping and truck combination at about

a 30 km distance. The most competitive train transportation

option based on chipping at the roadside offered the lowest

total costs of the studied thinning supply chains when thetransportation distance exceeded 145 km.

The spatial variation of the total supply chain costs for the

Uimaharju plant is illustrated in Fig. 6. For pure truck-based

transportation options like chain 1 (Chip truck) the costs are

increasing as a function of the total transportation distance.

For truck and train combinations, like chain 2 (Chip train), the

low-cost areas are located near the railway terminals. The

same pattern is clearly seen inFig. 7, where the lowest cost

supply chain options for Uimaharju are illustrated. At short

distances, the loose residue option with crushing at plant (3

Loose truck) results in the lowest total costs. Further away

chipping at the roadside landing and transportation by truck

is the cheapest. When the total transportation distance is long

Fig. 3e

Point-level data of 2 Chip train supply chain toUimaharju with the fitted linear curve. Transportation

distance includes both truck and train transportation, and

the maximum truck transportation distance is limited to

30 km.

Fig. 2e Point-level data of 1 Chip truck supply chain to

Varkaus with the fitted cubic curve.



9/16

15

17

19

21

23

25

27

29

31

33

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190

Transportation distance, km

Totalcost,m

-3

1 Chip truck

2 Chip train

3 Loose truck

4 Loose train

5 Bundle truck

6 Bundle train

Fig. 4 e Total costs of basic supply chains of logging residues as functions of transportation distance to Uimaharju.

15

20

25

30

35

40

45

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190


Totalcost,m

-3 1 Chip truck

7 Chip truck (thinn)

8 Chip train (thinn)

9 Loose truck (thinn)

10 Loose train (thinn)

Fig. 5e Total costs of supply chains of full trees from thinnings as functions of transportation distance to Uimaharju. The

curve of 1 Chip truck for logging residues is plotted as a reference.



10/16

but the distance to the nearest railway terminal is short, the

chipping at roadside followed by truck and train trans-

portation is the most cost-efficient alternative. The longer the

total distance, the larger the cost-competitive area around

a railway terminal.

In long-distance transport of logging residues from North

Karelia to Varkaus, the reference chain (1 Chip truck) was the

most cost-efficient until about 160 km. For longer trans-portation distances, chipping at the roadside combined with

truck and train transportation offered the lowest costs. For

most of the forest area in North Karelia, the truck and train

combination was the cheapest supply chain at Varkaus

(Fig. 8).

4.3. Improved supply chains

Optimization of bundle dimensions to fully utilize the load

space of a timber truck remarkably lowered the transportation

costs for bundle options. Additionally, when the efficiency of

unloading the truck was improved, the improved bundle truck

transportation option (5 Bundle truck (opt)) offered onlyslightly higher costs than loose residue truck until about

65 km (Fig. 9). At about a 65 km distance, the chip truck chain

beats both loose truck and optimized bundle truck, being the

most cost-efficient supply chain until about 120 km. When the

cost efficiency of train transportation was improved by 15%,

together with efficiency improvement in terminal operations,

the chipping at the roadside and train transportation option

(2 Chip train (opt)) became the most cost-efficient alternative

at about a 90 km distance. Chain 11, which combines the

bundle truck and chip train transportation became cheaperthan option 5 Bundle truck (opt) at a distance of 105 km, but

did not reach the cost of other train options.

In general, simple supply chains, like truck transport of

loose residues and chipping at the plant, offer the lowest total

costs at short transportation distances. For long distances, the

importance of packing density and the ability to use full load

capacity (t) become more important. Additional work phases

cause additional costs, which should be compensated by

improved efficiency in other phases in the supply chain. The

extra unloading and loading costs resulting from train trans-

portation are not fully compensated by lower transportation

costs at a 50 km distance, but at 200 km the total costs become

lower than in plain chip truck transportation (see chains 1 and2 inFig. 10). Bundling remarkably increases the roadside costs

for energy wood. However, haulage costs are significantly

Fig. 6e Total cost to Uimaharju with the chain 1 Chip truck scenario (left) and the chain 2 Chip train scenario (right).

Coordinate System: ETRS-TM35FIN. Boundaries and railways: National Land Survey of Finland, license MYY/179/06-V.

Road network: The Finnish Road Administration/Digiroad 2006. Railway terminals: The Finnish Rail Administration.

Non-Forestry Land: Finnish Environment Institute, partly Ministry of Agriculture and Forestry, National Land Survey,

Population Register Centre. Conservation areas: Metsahallitus, Finnish Environment Institute. Natura 2000 areas:

Finnish Environment Institute.



11/16

lowerthanforlooseloggingresiduesandfulltrees(seeTable5).

Consequently, more efficient loading andunloading anduse of

efficient end-facility crushing brings cost savings to these work

phases, and the distance-independent costs for the plain

bundle truck option are lower than for the chip truck-based

supply chain. The total costs remain higher than in the most

cost-efficient roadside chipping alternatives because of lower

payload.

4.4. Effect of reduced payload and moisture

When the trucks total weight was reduced to 40 t, corre-

sponding to 25 t payload, the weight was the limiting factor at

45% moisture content for all of the studied supply chains,

except for the loose residues. This strongly reduced the cost-

competitiveness of the chip truck-based supply chains.

Consequently, trucking of loose residues became the most

competitive option up to 120 km (Fig. 11). The reduced payload

for trucks improved the relative cost-competitiveness of rail-

road transportation. Roadside chipping combined with train

transportation remained the most competitive train trans-

portation option, offering the lowest costs for transportation

distances of 120 km or more. Bundle truck offered lower costs

than roadside chipping and truck transportation. Roadside

chipping combined with truck and train transportation (2 Chip

train (40 t)) became as expensive as roadside chipping and

truck transportation (1 Chip truck (40 t)) at an 80 km distance.

Lowering the moisture of chips from 45% to 35% before truck

transportation reduced the transportation costs by 0.8 m3

for a 70 km transportation distance. When using the 60 t

maximum weight, the corresponding cost difference was

slightly bigger. In general, the transportation costs with 40 t

trucks were higher and differences between supply chains

smaller compared to conditions, where 60 t total weights fortrucks is allowed. As the truck dimensionsand maximum load

volumes were not reduced compared to Scandinavian stan-

dards, the results cannot be generalized for areas having

different standards for truck dimensions.

5. Discussion

5.1. Overview of results

In Finnish conditions, the looseresidueoption withend-facility

chipping was the cheapest optionunder short distances, while

for medium distances the chipping at roadside together with

Fig. 7 e The cheapest procurement chains at Uimaharju

plant.

Fig. 8 e The cheapest procurement chains at Varkaus

plant.



12/16

truck transportation offered the lowest costs. Train trans-

portation became competitive when the transportation

distance exceeded145 km for chipped full treesand 160 km for

logging residues, when maximum truck transportation to the

train station waslimited to 30 km.When thetruckpayload wasreduced from 38 tonnes to 25 tonnes, train transportation

offered the lowest costs at a 90 km distance.

The threshold distance where the costs of the loose residue

option and chipping at the roadside were equal was about

65 km for logging residues and about 30 km for full trees,

which is in line with results by Asikainen[24], who stated that

distances up to 50 km are suitable for loose residue trans-portation. The difference between the two sources of forest

chips is mainly based on the higher efficiency of roadside and

15

17

19

21

23

25

27

29

31

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190


Totalcost,m-3

1 Chip truck

6 Bundle train (opt)

2 Chip train (opt)

4 Loose train (opt)

5 Bundle truck (opt)

3 Loose truck

11 Bundle truck chip train

Fig. 9 e Total costs of the optimized supply chains of logging residues as functions of transportation distance to Uimaharju.

The curves of chain 1 Chip truck and 3 Loose truck scenarios are plotted as reference.

Fig. 10e Total cost structure of selected supply chains for logging residues at 50 km and 200 km transportation distances

(scenarios: 1 Chip truck, 2 Chip train, 3 Loose truck, 4 Loose train, 5 Bundle truck and 15 Bundle truck (opt)). Bundling costs

are included in roadside cots in supply chains 5 and 15 (opt).



13/16

terminal chipping of full trees compared to chipping of logging

residues, and partly due to differences in basic densities and

moisture contents of the fuels. At zero stumpage prices, theroadside cost for thinning wood was about 11.3 m3 higher

than for logging residues.

Chipping at roadside and truck transportation to the plant

is the most common supply chain for logging residues and

small-sized trees in Finland[5,25]. It seems to offer the lowest

supply costs in quite a wide range of transportation distances,

being relatively competitive in wide range (30e200 km) of

distances. The results presented here are based on optimal

conditions, when a separate chipping unit and full-sized truck

with full loads are used, which may overestimate the cost-

competetiveness of roadside chipping. The challenge of this

kind of hot supply chain is how to avoid un-productive

waiting hours of a chippere

truck combination. The work flow,including loading and unloading procedures and chipping

itself, as well as the technology (chippers, self-unloading

trucks) for roadside chipping supply chain are mature and

tested. Thus, they probably include less development poten-

tial than some other supply chains.

The truck weight limit reduced the maximum volume for

chips from logging residues by 20%. Lowering of the moisture

content during storage offers an opportunity for reducing

costs in chip truck transportation. Reduction of moisture

content from 45% to below 35% allows for the use of the full

load space capacity of 60 tonne chip trucks with the fuel

parameters used in these calculations. The corresponding

reduction of distance-dependent costs would be about one

euro per m3 for a 100 km transportation distance. Drying may

reduce the efficiency of chipping by slowing down the feeding

speed of the chipper and increased storing time also causeshigher biomass losses [26]. On the other hand, drier chips

tolerate longer storing times in later phases in the delivery

chain. The fuel quality of drier chips is also better; dry fuel

allows for a higher boiler capacity during peak consumption

and causes less freezing-induced problems in feeding

systems.

The use of roadside chipping combined with train trans-

portation sets high requirements for chip storage facilities at

railway terminals. The surface should be paved to allow effi-

cient re-loading and to prevent stones and other impurities

from mixing with the fuel. A constantly used chip storage area

should preferably be covered to avoid rain and snow during

storing. The feasible storage time of moist chips is still onlya few weeks to avoid the risk of composting. Storing as chips

at roadside storage is not feasible due to material losses,

impurities and extra loading costs on top of the typical road-

side chipping procedure, in which the chipper blows the chips

straight into the trucks load space.

5.2. Improving the overall efficiency of supply chains

Delivery as chips is well suited to small plants that cannot

afford costly end-facility chipping. Loose residue supply

chains are the most cost-efficient for short distance trans-

portation from the forest to fuel terminals equipped with

stationary or mobile chippers or crushers, and into plants

15

17

19

21

23

25

27

29

31

33

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190


Totalcost,m

-3

1 Chip truck

1 Chip truck (40 t)

2 Chip train (40 t)

3 Loose truck

5 Bundle truck (40 t)

1 Chip truck (dry)

1 Chip truck (40 t,dry)

Fig. 11e Total costs of supply chains of logging residues as functions of transportation distance to Uimaharju, when the

truck maximum weight is reduced to 40 tonnes. The curves for scenarios for chain 1 Chip truck, the corresponding chain

with dried chips (1 Chip truck (dry)) and Chain 3 Loose truck are plotted as reference.



14/16

equipped with an end-facility comminution unit. Trans-

portationof loose material increases traffic andrequires larger

storing area, and slow unloading times cause logistical prob-

lems, especially in large bioenergy installations. Large plants

are often located in city areas and typically have no place for

fuel storage or even for end-facility comminution. Also,

comminution may not be accepted in urban areas because of

noise and dust problems.Terminal comminution of stumps usually takes place

before long-distance transportation to increase the load

density and to hasten the operations in the plant. Loose resi-

dues and stumps canbe transported in similar trucks andboth

fuel sources are well suited to logistic systems based on local

fuel collection terminals. Thus, loose residue transportation

may become more popular in Finland because of increasing

useof stump wood. Some extra savings using the loose residue

to terminal supplychaincould be gained by transportingsmall

roadside storage piles during summer and autumn to larger

fuel terminals to avoid the cleaning costs of rarely used forest

roads. More efficient drying in large, open terminal storage

facilities could also bring some extra benefits.Bundling increases efficiency in forwarding and decreases

the need for storage area compared to loose residues. Bundles

are fast to load and unload and they can be transported with

normal timber trucks, which leads to better possibilities for

avoiding empty truck driving than the use of trucks designed

only for energy wood transportation. Current residue bundles

require the most heavy mobile chippers or end-facility

comminutionattheplant,whichhinderstheiruseinsmalland

mediumsize plants. Plastic bundling stringsespecially require

efficient chipping to avoid problems on conveyors at the plant

that can be caused by loose pieces of strings. Compact and

easy-to-handle bundles can reduce moisture content during

storing, although decaying of bundle strings may cause prob-lems during repeated loading and unloading [18,27]. The

overall efficient handling characteristics make bundles suit-

able forterminal operations, resulting in lowest terminal costs

(chipping, unloading and loading) compared to all studied

supply chains (Fig. 10). If the bundle truck bears the same load

as the chip truck, the bundle truck chain is competitive with

the roadside chipping chain in cost efficiency. Improvements

in efficiency can still be gained by optimizing bundle dimen-

sions in respect to transportation units. In addition, some

improvement can likely be gained in bundling productivity by

lowering the bundler investment costs and possibly also by

increasing the density of a single bale.

Combining the bundling of residues together with chippingat the railway terminal and long-distance transportation as

chips in railway wagons may be an interesting option for

further development. The terminal chipping costs would

probably be lower than those presented here, because it is

faster to feed bundles into the chipper than it is to feed resi-

dues. New bundler technology designed for thinnings is

already in the demonstration phase [28], which opens new

possibilities for energy wood logistics.

5.3. Implications of results

The growing needfor biofuels and the geographical imbalance

between energy wood supply and demand will presumably

increase the need for long-distance transportation of wood

fuels within a few years. For long distances, railroad, together

with waterways, offer the most cost-competitive and energy-

efficient transportation means. Proper infrastructure of

railway terminals for storing, comminuting and loading

together with optimized technology and integration with

industrial timber logistics can remarkably reduce the logistic

costs of wood fuel supply. Also, the growing lack of skilledtruck drivers and demands for improving the safety of road

traffic are creating additional pressures to move timber

transport from roads to railroads. A major obstacle to

increased use of railway transportation for energy wood in

Finland is currently the lack of railway connections and

terminals at energy plants. Thus, when designing any new

large installations for wood energy production, railway and

waterway connections are highly recommended to secure

cost-efficient fuel supplies.

The terminal phase in the supply chain causes extra costs

compared to the straight chain from forest into plant; an extra

stop causes extra unloading and loading of fuel and usually

the total transportation distance increases. The fuel terminalcan be located near the end-users (distribution terminal) or in

areas where the fuel sources are located (fuel collection

terminal). Similar to Johansson et al. [27], the present study

found that supply chains straight from the forest into the

plant resulted in lower costs than those using the terminal

and a combination of different transportation means for short

and medium distances. However, in long-distance trans-

portation the use of railway terminals for collecting fuels

seems to reduce the supply chain costs. Terminals also

provide possibilities for new logistical cost reductions due to

specialized heavy-duty technology. Cost and energy-efficient

railway transportation is well suited to the demands for

reducing the ecological footprint in timber transportation[8],and the growing fuel prices are likely to improve the

competitiveness of railway transportation against more

energy-intensive means of transport.

Terminals are needed for processing fuel sources like

stump wood and recycled wood and for producing mixed

biofuels like wood-straw and wood-peat mixtures before

deliveries to power plants. Large energy generation units and

increased competition forfuel resources are boosting the need

for buffer storage for securing the fuel supply during peak

consumption[29]and disturbances in energy wood markets.

This is likely to lead to the increased use of terminals. When

terminals are already needed for securing the fuel supply, it

would be more logical to consider which terminal-basedlogistic chains are the most efficient for forest fuels, instead of

just comparing terminal chains with the most cost-efficient

supply chains.

5.4. Conclusions

This study suggests that fuel collection terminals linked to

railway transportation have great potential as alternatives to

truck transportation for distances even under 100 km, if any

(buffer) storageis needed beforefinal delivery to the plant. The

train-based supply chains still include remarkable develop-

ment potential. Terminals also offer new business opportu-

nities for improving the fuel quality and overall reliability of



15/16

the supply chain, and possibilities to divide and direct various

qualities to the right consumers. Increasing transport

volumes and synergies between fuelwood and industrial

timber transportation open possibilities for new business

models in terminal operations and wood supply services.

The results of this study are only tentative and simulations

are based on average Finnish conditions. Because of the

missing empirical data, several cost factors were based onassumptions. This especially concerns the cost factors of

railway transportation, the overhead costs of different supply

chains, the costs of terminal operations (e.g., the costs of

heavy mobile crushers), and the share of empty truck driving

in energy wood transportation. The costs of train-based

supply chains were based on the assumption that already

existing stations could be used as loading terminals. Thus, the

investment costs for terminals were not included. Even with

the same productivity and cost parameters, the result can be

different in other areas having different logistic infrastruc-

ture, distribution of wood resources and energy plants, wood

species (e.g., varying densities) and natural environment.

Also, the dimensions and maximum load capacity of theavailable trucks can vary significantly.

Supply costs and the availability of fuel can be successfully

estimatedwith the logisticalmodeldeveloped in thisstudy.The

model can be further developedusing several methods to better

utilize the services of GIS; forexample, the transportation time

sub-models could be developed to utilize the Digiroad data

more efficiently, so that time estimates are calculated for each

road section of the route and road classification data is used as

an independent parameter, instead of using only the total

distance between roadside storage and plant. Truck fuel

consumption represents a large share of transportation costs.

Thus, fuel consumption models, based on road classification

andcurrent load, would helpto produce morepreciseestimatesof transportation costs and CO2 balances of different supply

chains. Finally, the roadside costs of energy wood could be

estimated using harvesting removal (m3 ha1), size of harvest-

ing area, and average tree volume (in thinnings) as an inde-

pendent value instead of using the average stand conditions.

Acknowledgements

The authors would like to thank Mr. Juha Laitila from Metla for

providing the cost calculators for the study, and Dr. David

Gritten and Ms. Brenna Lattimore for the linguistic revision ofthe manuscript. We also thank Prof. Timo Pukkala, Prof. Antti

Asikainen and Mr. Heikki Parikka for their valuable comments

on the manuscript. The article was finalized with the support

of GLOENER project, which was funded by the Climbus

Research Programme in the Finnish Funding Agency for

Technology and Innovation, Tekes.

r e f e r e n c e s

[1] Kariniemi A. Puunkorjuu ja kaukokuljetus vuonna 2005.Metsatehon Katsaus 2006;19:4. Summary: Timber harvesting

and long-distance transportation in 2005.

[2] Torvelainen J. Harvesting and transportation of roundwood. In: Peltola A, editor. Finnish Statistical Yearbookof forestry 2006. Finnish Forest Research Institute; 2006.p. 195e212.

[3] Ylitalo E. Wood consumption. In: Peltola A, editor. FinnishStatistical Yearbook of forestry 2007. Finnish Forest ResearchInstitute; 2007. p. 247e75.

[4] KarhaK. Supply chains and machinery in the production of

forest chips in Finland. In: Savolainen M, editor. Book ofProceedings. Bioenergy 2007, 3rd International bioenergyConference and Exhibition, 36. Finland: FINBIO Publications;2007. p. 367e74. 3rde6th September 2007. JyvaskylaPaviljonki.

[5] KarhaK. Metsahakkeen tuotantokalusto Suomessa 2007 jatulevaisuudessa. Metsateho. Katsaus 2007;28:4 [Summary:Production machinery for forest chips in Finland in 2007 andin the future].

[6] Iikkanen P, Siren J. Rautatiekuljetusten kilpailukykySuomessa. Liikenne- Ja Viestintaministerion Julkaisuja 2005;44:64 [Summary: Competitiveness of railway transportationin Finland].

[7] Vahaliikenteisten ratojen tulevaisuusselvitys - liiteraportti.In: Viitasaari H, PoyskoT, editors. Ratahallintokeskus

Muistiot; 2005. p. 126 [In Finnish].[8] Lindholm E-L, Berg S. Energy requirement and

environmental impact in timber transportation. Scand JForest Res 2005;20:184e91.

[9] Digiroad.http://www.digiroad.fi/en_GB/.[accessed 5February, 2009].

[10] Topographic database.http://www.maanmittauslaitos.fi/en/default.asp?id488[accessed 5 February, 2009].

[11] The Finnish Rail Administration (RHK) e Ratahallintokeskus.http://www.rhk.fi/in_english/.[accessed 5 February, 2009].

[12] Peltola A, Ihalainen A. Forest resources. pp. 33-74. In:Peltola A, editor. Finnish Statistical Yearbook of forestry2006. Finnish Forest Research Institute; 2006. p. 435.

[13] Finnish Environment Institute. CLC2000 Finland. FinalReport; 2005. 66.

[14] Laitila J. Cost and sensitive analysis tools for forest energyprocurement chains. Forestry Studies 2006;45:5e10.

[15] Laitila J, Asikainen A, Nuutinen Y. Forwarding of whole treesafter manual and mechanized felling bunching in pre-commercial thinnings. Int J Forest Eng 2007;18:29.

[16] Ranta T. Logging residues from regeneration fellings forbiofuel production e a GIS-based availability and supply costanalysis. Doctoral Thesis, Lappeenranta University ofTechnology; 2002. 180 p.

[17] Ranta T, Rinne S. The profitability of transportinguncomminuted raw material in Finland. Biomass Bioenergy2006;30:231e7.

[18] Korpilahti A. Oksapaalien autokuljetus. Metsatehonraportti2004;169:25 [In Finnish].

[19] Orn J, Vakeva J. Puunkorjuu ja kaukokuljetus vuonna 2004.

Metsatehon Katsaus 2005;4:4. Summary: Timber harvestingand long-distance transportation in 2004.

[20] Peltola J. Puutavara-autojen rakenteen vaikutusomamassaan. Metsatehon Raportti 2004;176:22 [InFinnish].

[21] Nurminen T, Heinonen J. Characteristics and timeconsumption of timber trucking in Finland. Silva Fennica2007;41(3):471e87.

[22] Pennanen O, Lehtonen J-M, Lonka T, Saranen J. Puutavaranraideliikenteen kustannuksiin nakyvyytta. MetsatehonKatsaus 2006;17:4 [Summary: Simulation model for railtraffic costs].

[23] Sibson RA. Brief Description of natural neighborInterpolation. In: Interpolating multivariate data. New York:

John Wiley & Sons; 1981. p. 21e36.

http://www.digiroad.fi/en_GB/http://www.maanmittauslaitos.fi/en/default.asp?id=488http://www.maanmittauslaitos.fi/en/default.asp?id=488http://www.maanmittauslaitos.fi/en/default.asp?id=488http://www.rhk.fi/in_english/http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://dx.doi.org/10.1016/j.biombioe.2010.11.014http://www.rhk.fi/in_english/http://www.maanmittauslaitos.fi/en/default.asp?id=488http://www.maanmittauslaitos.fi/en/default.asp?id=488http://www.maanmittauslaitos.fi/en/default.asp?id=488http://www.digiroad.fi/en_GB/


16/16

[24] Asikainen A. Design of supply chains for forest fuels. In:Sjostrom K, editor. Supply chain management for paper andtimber industries; 2001. p. 179e90.

[25] Hakkila P. Developing technology for large-scale production offorest chips. Wood energy technology programme 1999-2003.Technology Programme Report 6/2004. Tekes; 2004:99.

[26] Pettersson M, Nordfjell T. Fuel quality changes duringseasonal storage of compacted logging residues and young

trees. Biomass Bioenergy 2007;31:782e92.

[27] Johansson J, Liss J-E, Gullberg T, Bjorheden R. 2006.Transport and handling of forest energy bundles -advantages and problems. Biomass Bioenergy

2007;30:334e41.[28] JylhaP, Laitila J. Energy wood and Pulpwood harvesting from

young stands using a Prototype whole-tree bundler. SilvaFennica 2007;41(4):763e79.

[29] Gronalt M, Tauch P. Designing a regional forest fuel supply

network. Biomass Bioenergy 2007;31:393e402.


2011 Supply Chain Cost Analysis of Long-Distance Transportation of Energy Wood in Finland

Documents

Transcript of 2011 Supply Chain Cost Analysis of Long-Distance Transportation of Energy Wood in Finland