spatial assessment of the economic feasibility of short rotation coppice on radioactively...

Spatial assessment of the economic feasibility of short rotation coppice on

radioactively contaminated land in Belarus, Ukraine, and Russia.

I. Model description and scenario analysis

Marcel van der Perka,*, Jiske Buremaa, Hildegarde Vandenhoveb,Francois Goorb, Sergei Timofeyevc

aThe Netherlands Centre for Geo-ecology, Department of Physical Geography, Utrecht University, P.O. Box 80115, 3508 TC Utrecht, The NetherlandsbSCK-CEN, Boeretang 200, 2400 Mol, Belgium

cRIR, Fedyuninsk 16, Gomel, Belarus

Received 19 July 2002; revised 1 April 2004; accepted 5 May 2004

Abstract

The economic feasibility of short rotation coppice (SRC) production and energy conversion in areas contaminated by Chernobyl-derived137Cs was evaluated taking the spatial variability of environmental conditions into account. Two sequential GIS-embedded submodels were

developed for a spatial assessment, which allow for spatial variation in soil contamination, soil type, and land use. These models were applied

for four SRC production and four energy conversion scenarios for the entire contaminated area of Ukraine, Belarus, and Russia and for a part

of the Bragin district, Belarus. It was concluded that in general medium-scale SRC production using local machines is most profitable. The

areas near Chernobyl are not suitable for SRC production since the contamination levels in SRC wood exceed the intervention limit. Large

scale SRC production is not profitable in areas where dry and sandy soils predominate. If the soil contamination does not exceed the

intervention limit and sufficient SRC wood is available, all energy conversion scenarios are profitable.

q 2004 Elsevier Ltd. All rights reserved.

Keywords: Short rotation coppice; Radiocaesium; Geographical information systems; Spatial variability

1. Introduction

As a result of the accident in the Chernobyl Nuclear

Power Plant (NPP) in April 1986, large areas of Europe have

become contaminated by long-lived radionuclides such as

radiocaesium (137Cs, half-life 30.17 years) and radio-

strontium (90Sr, half-life 28.64 years). Particularly regions

in northern Ukraine, south-eastern Belarus, and south-

western Russia were affected by 137Cs contamination levels

above 1480 kBq m22 (see Fig. 1). Because of high activity

levels in food products, a significant part of the agricultural

lands in these territories has been left fallow (Saiko, 2001),

despite the fact that their agricultural potential with respect

to soil water and nutrient status has remained intact. In

contrast to restoration strategies aiming at a reduction of

external doses to humans and the radionuclide transfer from

soil to food products, alternative land-use options for the

revaluation of radioactively contaminated areas have

received little attention to date.

Short rotation coppice (SRC) for energy purposes may be

put forward as an alternative land-use option for radio-

actively contaminated farmland (Vandenhove et al., 2001).

In SRC cultivation, fast growing willows (Salix spp.) are

intensively managed and harvested for biomass in 3–5

years cutting cycles and a crop duration between 21 and 25

years. Fast-growing willow species can grow on a wide

variety of soils and are among the fastest and largest

biomass producers if optimally provided with water and

nutrients (Ledin, 1996). The harvested biomass is shredded

to wood chips that are incinerated or gasified for the

production of heat or electricity. Harvest is in winter when

farm labour is available and need for heat and electricity is

largest. Since SRC is a perennial crop, nutrients are

partly recycled by litterfall and, therefore, fertiliser

requirements are low. However, water demands are high.

0301-4797/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jenvman.2004.05.002

Journal of Environmental Management 72 (2004) 217–232

www.elsevier.com/locate/jenvman

* Corresponding author. Tel.: þ31-30-2535-565; fax: þ31-30-2540-604.

E-mail address: [email protected] (M. van der Perk).

http://www.elsevier.com/locate/jenvman

Specialised machinery for crop establishment, cultivation,

and harvest is not required, though preferred. The cultiva-

tion is not labour intensive, which is advantageous from a

viewpoint of external radiation doses to SRC farmers when

cultivating on radioactively contaminated land. Studies in

Sweden have shown that the contribution from radioactive

emissions from biomass fuels and ash products to the

total radiation doses is insignificant in areas with soil

contamination up to 2 kBq m22 (Hedvall and Erlandsson,

1996, 1997).

Recent research in radioactively contaminated areas in the

vicinity of Chernobyl has demonstrated that SRC production

may be profitable if the soil conditions are suitable

(Vandenhove et al., 2001; Goor et al., 2001). Vandenhove

et al. (2001) stated that only a relatively small part of Belarus

is suitable for SRC cultivation, because about 60% of the soils

in Belarus are of a sandy nature on which SRC yields are too

low. Yet, the environmental conditions that govern SRC

growth and 137Cs contamination may vary considerably in

space. Therefore, it is essential to allow for spatial variability

in SRC feasibility studies. Geographical information systems

(GIS) provide not only a useful opportunity for economic

evaluation of biomass production systems (see Liu et al.,

1992; Graham et al., 2000), but also a powerful tool to allow

for spatial variability of the environmental conditions in an

integrated manner (see for example Davydchuk, 1999; Goor

et al., 2001; Kolejka, 2002).

The aim of this study is to assess the economic feasibility

of SRC production and energy conversion in areas

contaminated by Chernobyl-derived 137Cs taking the spatial

variability of environmental conditions into account. For

this purpose, two sequential submodels that evaluate the

respective economic feasibility of SRC plantations and

energy conversion were developed and embedded in a GIS.

The GIS-embedded models were applied at two different

spatial scale levels. The first scale level is comprised of the

entire contaminated zone of Ukraine, Belarus, and Russia

(about 300,000 km2), which was modelled at a spatial

resolution of 1 km2. Fig. 1 shows the extent of the model

area, which stretches from 248030 E, 538050 N (western

Belarus) in the northeast corner to 348550 E, 508140 N

(northern Ukraine) in the southeast corner. To evaluate the

opportunities of SRC production as an alternative land-use

in the abandoned areas in the vicinity of Chernobyl,

the models were also applied at a more detailed scale for

a part of the Bragin district (352 km2), Belarus, south-west

of Bragin town (308170 E, 518 470 N) (see Fig. 2), which was

modelled at a spatial resolution of 250 £ 250 m2.

This paper focuses on the spatial variability occurring

between gridcells and which can readily be represented by a

GIS. In a second accompanying paper (Van der Perk et al.,

2004), the variability within gridcells is considered in more

detail using a stochastic approach.

2. Model description

2.1. General model structure

The economic feasibility of SRC plantation and energy

conversion is evaluated separately by two sequential GIS-

embedded submodels. Part of the output of the SRC

plantation evaluation submodel serves as input for the

energy conversion evaluation submodel. Fig. 3 shows the

general structure of the two submodels. The models have

been implemented in the spatio-temporal modelling

language of the PCRaster GIS (Wesseling et al., 1996).

Fig. 1. Soil contamination by radiocaesium in the model area of the entire contaminated zone of Ukraine, Belarus, and Russia (source: IAEA, 1991).

M. van der Perk et al. / Journal of Environmental Management 72 (2004) 217–232218

Fig. 3. General model structure of the GIS-based SRC production and energy conversion evaluation submodels.

Fig. 2. Bragin district, Belarus. The model area located south-west of Bragin town is indicated by a grey line.

M. van der Perk et al. / Journal of Environmental Management 72 (2004) 217–232 219

2.2. SRC plantation evaluation submodel

The SRC plantation evaluation submodel evaluates the

economic feasibility of SRC plantations for each gridcell

based on potential SRC production and the costs for SRC

production. The basic spatial input for the SRC submodel is

comprised of raster GIS maps of soil contamination by 137Cs,

land-use, and soil type. The soil contamination by 137Cs is

corrected for radioactive decay between the year to which the

input map of 137Cs contamination refers and the year of

prediction. The average annual SRC yield is estimated for

eight different generic soil types, which are based on soil

texture, peat content, soil moisture conditions, and salinity.

Since SRC is intended as an alternative for food crops in

contaminated areas, only agricultural lands are considered

suitable for SRC production. The land-use classes open

water, built-up areas and forests are excluded from the model

calculation as no-data values, because land-use change from

these types of land-use to SRC plantations is considered

unfeasible. Because the area of the SRC plantation is usually

greater than the size of a gridcell, the potential SRC

production is calculated within a user-defined square window

around each gridcell. The size of this window depends based

on the scale of SRC production and can be chosen based on,

for example, the maximum transport distance of SRC to the

plantation or the minimum distance between two SRC

plantations. SRC production is assumed to take place using a

crop rotation period of 3 years.

Legislation on external exposure to radiation and the

admissible 137Cs activity concentration in wood and

waste (in this case SRC ash after burning) give rise to

the condition that the predicted levels should not exceed

the intervention limits (CsProdMax and CsAshMax,

respectively). For the same reason, the soil contamination

by 137Cs should not exceed the intervention limit

(CsSoilMax).

The product contamination is calculated using the soil

contamination by 137Cs and soil to wood transfer factors

that depend on soil type. Although 137Cs transfer factors

tend in general to decrease over the years since initial

deposition (see Absalom et al., 1999), the transfer factors

are not corrected for this time effect. The transfer

factors thus refer to the year of prediction. The 137Cs

contamination of ash is calculated using a wood to ash

enrichment factor. Although the 137Cs contamination of

ash is directly proportional to the product contamination,

they are both evaluated with respect to their intervention

limits. Gridcells, in which the intervention limits for

contamination of soil, wood, or ash are exceeded, are

excluded as no data values.

The annual additional external dose received by a SRC

farmer is averaged over the areas within the window around

the gridcell where SRC can be potentially produced

considering the 137Cs intervention limits. The additional

external dose is calculated using an external dose rate factor

and the time needed for crop maintenance (2 out of 3 years)

and harvest (once per 3 years):

AnnualDose ¼WINDOWAVERAGE

ðMAXðPlantationArea £ 1=3 £ Th;

TimeAvailableÞ þ 2=3 £ Tm

£ PlantationAreaÞ £ FD £ Fcor £ Cs_soil;

WindowSizeÞ ð1Þ

where WINDOWAVERAGE and MAX are GIS operations,

AnnualDose is annual additional external dose received by a

SRC farmer (mSv y21), PlantationArea is the size of the

plantation (ha), TimeAvailable is the time available per

person during a 4-month harvest period (h y21), Th is the

time needed for harvest (h ha21 y21), Tm is the time needed

for crop maintenance (h ha21 y21), FD is a external dose

rate factor (mSv h21 per kBq m22), Fcor is a correction

factor for ploughing (– ), Cs_soil is the actual soil

contamination by 137Cs (kBq m22), and WindowSize is

the size of the square window around a gridcell (m). In

Eq. (1) only the soil contamination by 137Cs (Cs_soil) is

spatially variable, the other parameters are spatially

invariant.

The average annual proceeds from SRC production

(SRCprofit) (EUR ha21 y21) are calculated using:

SRCprofit ¼SRCYield £ CropCoverage £ PriceSRC

2 ProductionCosts ð2Þ

where SRCYield is the annual average SRC yield (oven-

dry t ha21 y21), CropCoverage is the part of the area that

is actually covered by the crop (–), PriceSRC is the fixed

price of SRC for which the entire SRC yield can be sold

(EUR t21), ProductionCosts is the average annual SRC

production costs per hectare (EUR ha21 y21). In Eq. (2),

the annual average SRC yield (SRCYield) is

spatially variable, the other parameters are constant over

space. The CropCoverage factor takes into account

the possible presence of other land cover types at a

finer scale than the model resolution, such as scattered

buildings, farmyards, gardens, hedges, ditches, ponds,

and streams. The production costs represents an

aggregated value for the different costs involved in SRC

production (plantation, machinery, labour, maintenance

and harvest costs).

The production of SRC is considered profitable if the

average yearly proceeds are greater than a user-defined

minimum profit (MinProfitSRC). This minimum profit takes

into account the possible opportunity costs, for example the

loss of proceeds from the previous land-use.

The main model output consists of the potential area

where SRC production is profitable, the average annual

profit from SRC production expressed per unit area to allow

comparison amongst scenarios that employ various planta-

tion sizes, and the minimum price for SRC for which SRC

production is profitable. The model output represents annual

averages for a 3-year SRC rotation period starting with


the year of prediction and assuming a crop duration of SRC

of 21 years. It should be noted that these figures assume a

distance between the SRC plantations greater than the user-

defined length of the window in which the SRC is supposed

to be produced. The total annual average SRC yield,

additional external dose rate received by a farmer cultivat-

ing SRC, and SRC product and ash contamination by 137Cs

are also stored as intermediate model results.

2.3. Energy conversion evaluation submodel

Energy conversion is considered to be economically

profitable if the annual average proceeds of energy sales at a

fixed price exceed the annual average costs of energy

conversion. It is assumed that energy conversion is only

possible if soil contamination by 137Cs does not exceed the

intervention limit and if sufficient biofuel (SRC) can be

produced within a given window around the location of the

energy conversion plant. Previous model studies have

shown that the costs for transport of SRC from the

plantation to the energy production plant are negligible

(Vandenhove, 1999a), so these costs are not taken into

account as a separate parameter. The amount of SRC needed

for the energy production plant (SRCuse) (t y21) is

calculated from the user-defined capacity, availability, and

operation time of the energy production plant, the potential

energy of the biofuel, and the efficiency of the energy

conversion:

SRCuse ¼ðCapacity £ OperationTimeÞ=

ðPotEnergyWood £ EffWHÞ ð3Þ

where Capacity is the capacity of the energy conversion

plan (kW), OperationTime is the operation time of the

conversion plant (h y21), PotEnergyWood is the potential

energy of wood (kWh t21), EffWH is an efficiency factor for

energy conversion from wood to heat (–). The annual

average costs for energy conversion encompasses the

capital costs (buildings and machinery), the operational

costs (labour and maintenance), the costs for SRC purchase

at a fixed price, and the additional costs for storage and

disposal of contaminated ash with 137Cs activity concen-

trations exceeding a user-defined value. The annual average

costs for storage and disposal of contaminated ash

(AvCostAsh) (EUR y21) is calculated using:

AvCostAsh ¼SRCuse £ Af £ ðCsAshSRC £ PriceCsAsh

þ ð1 2 CsAshSRCÞ £ PriceAshÞ ð4Þ

where Af is a SRC to ash conversion factor (weight ash/

weight SRC) (–), CsAshSRC is the part of ash that exceeds

the intervention limit for 137Cs (–), PriceCsAsh is the cost

of storage and disposal of ash that exceeds the intervention

limit for 137Cs (EUR t21), PriceAsh is the net cost of storage

and disposal of ash that does not exceed the intervention

limit for 137Cs. The part of the ash exceeding the

intervention limit is estimated from the part of the area

within the user-defined window around the energy conver-

sion plant where the SRC wood is contaminated to such a

degree that incineration leaves ash containing 137Cs levels

above the intervention limit.

The average profit of energy conversion (EnergyProfit)

(EUR y21) is:

EnergyProfit¼Energy£PriceEnergy2AvCostEnergy ð5Þ

whereEnergy is the annual energyproduction ( ¼ Capacity £

OperationTime) (kWh y21), PriceEnergy is the user-defined

price of energy (EUR kWh21), AvCostEnergy is the annual

average cost for energy conversion (see above) (EUR y21).

The main model output includes the area where energy

conversion is potentially profitable, the average annual

profit from energy conversion, the maximum price for SRC

and the minimum price of energy for which energy

conversion is profitable. These figures assume a distance

between the energy conversion plants greater than the user-

defined length of the window in which the SRC is supposed

to be produced. Like for the SRC production submodel, the

model output represents annual averages for a 3-year period.

3. Model input

3.1. General

We evaluated the economic feasibility of SRC pro-

duction in both study areas for the four scenarios taking the

spatial variability in soil conditions and 137Cs contamination

into account by using the GIS-based model described above.

Subsequently, we evaluated the four energy conversion

scenarios for each SRC production scenario, which resulted

in a total of 16 scenarios. The predictions were made for the

year 2000; so all model parameters that vary over time

(various costs, 137Cs transfer factors) apply to that year. The

model input for these scenarios are described in further

detail below.

3.2. Spatial model input

The basic spatial model input for the SRC production and

energy conversion models consists of a 137Cs soil

contamination map, a land-use map, and a soil map. This

spatial model input for the entire contamination zone of

Ukraine, Belarus and Russia was prepared at a spatial

resolution of 1 km2 and for the Bragin district at a spatial

resolution of 250 £ 250 m2. The maps for the entire

contaminated area were borrowed from data contained in

the RESTORE-EDSS (Van der Perk et al., 1999). The soil

map of this area was composed from three soil maps of the

different countries at an original scale ranging from

1:200,000 to 1:1,000,000. The 137Cs contamination map

was derived from the classified 137Cs maps for the three

countries in the Atlas of Caesium Deposition on Europe

after the Chernobyl Accident (De Cort et al., 1998) by


taking the back-transformed mean value of the log-

transformed class boundaries for each soil contamination

class. The soil type and land-use maps for the Bragin district

were prepared by the Research Institute of Radiology (RIR),

Gomel, Belarus. The soil contamination map of the Bragin

district was created by geostatistical interpolation using

ordinary point kriging of point measurements conducted by

RIR. The interpolated values were back-transformed to137Cs deposition values for 1986.

The land-use map and soil map were converted into

generic classification and map units. In most cases the

original attribute soil data were used to convert the original

soil maps into generic maps of soil properties, otherwise

external sources from the literature were used (e.g.

Dokuchaev Institute of Soil Science, 1986). The generic

soil classification differentiates the soil types based on soil

texture (three classes: sand, loam, and clay), soil moisture

conditions based on the presence of gley features in the soil

profile (two classes: dry and wet), and salinisation (two

classes: non-saline and saline). In addition, the generic soil

classification differentiates peat soils as a separate class if

the peaty topsoil layer (soil loss on ignition greater than

approximately 40%) is greater than 20 cm.

The SRC yields for the different generic soil types were

based on results from a SRC growth model (Jossart et al.,

1999) using local climate data. The highest SRC yields are

assigned to wet loam and peat, intermediate yields to wet

sand, loam, and clay, low yields to dry sand, and zero yield

to salty soils (Table 1a). The transfer factors for soil to wood

transfer of 137Cs to predict the SRC product and ash

contamination are assigned by soil texture class (Table 1b).

3.3. SRC production

We defined four scenarios of SRC production, which

differ mainly by the production scale, i.e. plantation area,

the degree of mechanisation (no machinery, local machin-

ery, European machinery especially designed or adapted for

SRC crop management and harvest), and the origin of the

willow plantlets (locally grown or bought from international

breeding companies). Therefore, the scenarios also differ in

the time needed for maintenance and harvest of SRC. In

general, the model inputs were based on data and

information collected in the framework of various inter-

national research projects, such as the RECOVER project

(Vandenhove, 1999a; Gommers et al., 2000; Vandenhove

et al., 2001), the PHYTOR project (Vandenhove, 1999b,

2000) and the RESTORE project (Van der Perk et al., 1999,

2001). Table 2 lists the values of the model parameters for

the four different SRC plantation scenarios. The average

annual production costs for the four scenarios of production

of biofuel for the energy production plants were estimated

based on the size of the plantation, the production method

and the origin of willow plantlets and were expressed per

unit area (Vandenhove, 1999b). These figures assume a crop

rotation period of 3 years and a crop duration of 21 years.

The window length for the calculation of the total SRC yield

was based on a ‘best-guess’ according to the size of the

plantation. Because of the difference in model resolution,

we assumed a crop coverage of 30% for the entire

contaminated area and a crop coverage of 60% for the

Bragin district. These values were assumed to be spatially

invariant. To enable comparison among the different regions

within the model area, the minimum profit to make the SRC

plantation profitable (MinProfitSRC) was assumed to be

zero for all scenarios. This implies that the opportunity costs

were also assumed to be zero.

The plantation scenario affects the 137Cs contamination

of the SRC product and the additional external dose

received by a farm labourer during SRC cultivation, because

a decrease in the surface area under cultivation results in an

increase of the influence of locations that are highly

contaminated by 137Cs. Moreover, the external dose is

also determined by the time spent in the plantation area,

which depends on the production methods used (manual or

mechanical) and the size of the plantation area. Table 3 lists

the intervention limits for 137contamination of soil, wood,

and ash, and for additional external dose rate. These values

have been applied for all scenarios.

Table 1b

Soil to wood 137Cs transfer factors as function of soil class

Soil texture class Transfer factor

(1023 m2 kg21)

Source

Peat 1.4 Back-transformed mean value

of log-transformed observed

transfer factor values for four

soil texture classes reported in

Vandenhove (1999a)

Sand 0.8

Loam 0.2

Clay 0.02

Table 1a

Average SRC yield as function of soil class

Soil class Average SRC yield

(oven-dry t ha21 y21)

Source

Peat 10.9 SRC yield estimated using a

growth model for seven soil

classes with respect to soil

texture and soil moisture

conditions reported in

Vandenhove (1999b)

Dry sand 5.4

Wet sand 8.1

Dry loam 8.5

Wet loam 10.9

Dry clay 7.1

Wet clay 8.2

Salty soils 0.0


3.4. Energy conversion

We defined four scenarios of SRC wood conversion to

energy, which differ mainly by the size of the energy

production plant, the type of plant (boiler or gasifier), and

the type of energy (heat or electricity). The scenarios for

conversion of SRC wood to energy represent a large,

medium and small-scale plant for heat production and one

small-scale plant for electricity production. Table 4 lists the

capacity of energy production, capital and operational costs,

the efficiency factor of wood to energy conversion, and the

energy price for the four scenarios. The operation time for

all energy production plants is assumed to be 1402 h per

year. This value was based on the average operation time of

16% in Belarus (Vandenhove, 1999a). For all energy

conversion scenarios, the minimum profit to make the

energy conversion profitable (MinProfitEnergy) was

assumed to be zero.

3.5. Other model input

The main non-spatial model parameters are the inter-

vention limit for product contamination by 137Cs and the

various costs for SRC production and energy conversion

and the SRC and energy prices. Table 5 lists the values,

descriptions, and references for these model parameter

values. Most of the parameters have been discussed in the

previous sections. In addition, the parameter PriceAsh was

assumed to be zero, which implies that the costs for

processing of ash that does not exceeds the intervention

limit for 137Cs are assumed to be cost-neutral.

4. Results

4.1. Entire contaminated area of Ukraine, Belarus,

and Russia

The SRC product contamination by 137Cs shows a

pattern that differs slightly from the soil contamination

patterns due to the differences in soil texture and thus soil-

to-wood transfer factors. If restrictions with respect to land

use and 137Cs contamination are taken into account, SRC

production is possible in 95.6% of the model gridcells. The

area where contamination levels in soil or SRC wood

exceed the intervention limits amounts to 7517 km2.

For all scenarios, the estimated additional external dose

received by a farmer cultivating SRC is between 0.1 and

0.2 mSv y21 in the majority of the model area, which is

Table 2

Model parameter values that vary for the four scenarios of SRC production

Scenario Description Average annual production

costs (EUR ha21 y21)

Window length for

calculation of total

SRC yield (m)

Time needed for maintenance

per production cycle of

3 years (hours ha21) (2 out of

3 years)

Time needed for harvest

per production cycle of

3 years (hours ha21)

(every third year)

1 SRC plantation of 1000

ha where the harvest

is mechanised (European

machinery) and the plantlets

are bought

301.1 50,000 0.4 2

2 SRC plantation of 100 ha where

the harvest is mechanised (local

machinery) and the plantlets

are bought

296.2 5000 0.8 4


the harvest is mechanised (local

machinery) and the plantlets are

produced at the farm

184.8 5000 0.8 4


the harvest is not mechanised and

the plantlets are produced at the

farm

211.2 1500 50 115

Table 3

Intervention limits for 137Cs contamination based on Belarussian legislation

and external dose above which SRC production is considered as not feasible

(source: Vandenhove, 1999a)

Parameter

name

Description Value Unit

CsSoilMax Intervention limit

soil contamination

by 137Cs

4800 kBq m22

CsProdMax Intervention limit for

SRC product contamination

by 137Cs

740 Bq kg21

CsAshMax Intervention limit for SRC

ash contamination

100,000 Bq kg21

ExtDoseMax Intervention limit for additional

external radiation dose from soil

contamination by 137Cs

1 mSv y21


relatively less contaminated by 137Cs (,37 kBq m2). This is

in accordance with Hedvall and Erlandsson (1996, 1997),

who stated that the contribution of radiation doses from

biomass fuels and ash products to the total radiation doses is

insignificant in areas with soil contamination up to

2 kBq m22. The estimated additional external dose

increases towards the more contaminated areas around

Chernobyl and north-east of Gomel (see Fig. 1). The

estimated additional doses remain below the intervention

limit of 1 mSv y21, since these areas were excluded from

SRC production. The additional external dose for

the medium scale production scenarios (2 and 3) is a factor

of about two lower than for scenario 1 (Fig. 4). This is

largely due to a smaller surface area of the plantation, which

results in a shorter residence time of the farm labourers at

the plantation. Due to the greater amount of time needed for

maintenance and harvest, scenario 4 results in the highest

additional external total dose with a maximum value of

0.95 mSv y21.

The spatial pattern of SRC yield (t ha21 y21) is shown in

Fig. 5. The SRC yield is largest in northern Ukraine about

370 km west from Chernobyl. Also in the Pripyat marshes in

Table 4

Model parameter values that vary for the four scenarios of energy conversion

Scenario Description Capacity

(kW)

Capital costs

(EUR y21)

Operational costs

(EUR y21)

Efficiency wood to energy

(–)

Energy price

(EUR kWh21)

A Large scale boiler for heat 28,000 1.2 £ 105 2.8 £ 105 0.75 0.0294

B Medium scale boiler for heat 3000 1.267 £ 104 4.0 £ 104 0.75 0.0294

C Small scale gasifier for heat 350 667 1000 0.75 0.0294

D Small scale gasifier for electricity 350 367 1000 0.28 0.035

Table 5

Model parameter values for the SRC plantation and energy conversion models

Parameter name Description Value Source

Ef Cs enrichment factor for SRC

ash (Bq kg21 ash/Bq kg21 wood)

50 All Cs is assumed to be contained in the ash.

Loss on ignition of SRC wood is assumed to be

98% (Vandenhove, 1999a)

FD External dose rate factor

(mSv h21 per kBq m22)

1.9 £ 1026 (Vandenhove, 1999a)

Fcor External dose rate correction

factor for plouhing (–)

0.4 Assumed ploughing depth ¼ 10 cm

(Vandenhove, 1999a)

TimeAvailable Time available per person (h)

during the 4 months harvest period

693 Estimated time during a harvest period

of 4 months (17 weeks) 80% of 5 days a week

8 h a day on the land ¼ 693 h

CropCoverage Part of the area that is actually

covered by the crop (–)

0.3–0.6 Assumption

PriceSRC Fixed price of SRC (EUR t21) 40 (Vandenhove, 1999a, Annex 1)

MinProfitSRC Minimum profit necessary to make

the plantation profitable (for example

opportunity costs) (EUR ha21 y21)

0 Assumption

OperationTime Operation time of the energy

conversion plant (h y21)

1402 16% of the amount of hours in

1 year (Vandenhove, 1999a)

PotEnergyWood Potential energy of wood (kWh t21) 5.11 £ 103 (Vandenhove, 1999a)

Abandoned Intervention limit for soil contamination

by 137Cs for energy conversion

plant (kBq m22)

1480 (Vandenhove, 1999a)

Af SRC wood to ash conversion factor

(weight ash/weight SRC)

0.02 (Vandenhove, 1999a)

CsAshmin 137Cs activity concentration of ash above

which special treatment (storage and

disposal) needed is needed (Bq kg21)


PriceAsh Net costs of storage and disposal of ash

with 137Cs activity concentrations below

CsAshmin (EUR t21)

0 Assumption

PriceCsAsh Costs of storage and disposal of ash

with 137Cs activity concentrations above

CsAshmin (EUR t21)


MinProfitEnergy Minimum profit necessary to make a

conversion plant profitable (EUR y21)

0 Assumption


Belarus areas with large yields are found. In both areas

peat and gleyic loamy soils predominate. The yield is lowest

in the northern part of the model area where dry sandy soils

are found.

Table 6 summarises the model results with respect to the

profitability of the four different SRC production scenarios.

It appears that the surface area where SRC production can

be profitable is smallest for scenario 1 and largest for

Fig. 4. Additional external dose received by farm labourers for the different scenarios of SRC production (mSv y21). (a) Scenario 1; (b) scenarios 2 and 3;

(c) scenario 4.


scenario 3. The profitable area of the latter scenario covers

nearly the entire area (98%) where SRC production is

possible on account of current land use and 137Cs

contamination. This means that in nearly the entire area

an SRC plantation in conformance with scenario 3 is

economically feasible. Scenario 1 results in the smallest

average and maximum profit per unit area in the entire

model area and scenario 3 in the largest average and

maximum profit. So, the large-scale SRC production

scenario is least profitable and the medium scale production

scenario with locally produced willow plantlets is most

profitable. Fig. 6 shows the spatial patterns of the profit

(EUR ha21 y21) for scenarios 1 and 3. The spatial pattern of

the profit from SRC production largely follows the pattern

of the SRC yield (Fig. 5). The profits are negative in the

areas where the SRC yield is low (for example in parts of the

Gomel region, Belarus, and the Bryansk region, Russia) and

in areas close to areas that are not suitable for SRC

production because restrictions with respect to land use or137Cs contamination (areas in the vicinity of Chernobyl).

The profits are positive in areas with large SRC yields. The

spatial pattern is smoothed to a varying extent for the

different scenarios as a result of the different window sizes

from which the SRC is acquired. Because scenario 1

represents the largest scale of a SRC plantation, the spatial

pattern for this scenario is most smoothed. The spatial

pattern of the profit for scenario 3 has a more spotty nature

because of the smaller plantation size, so that local soil

conditions influence the SRC yield to a greater extent.

In the areas where the largest SRC yields can be

achieved, the produced SRC can be sold for the lowest

minimum price per tonne. For scenario 3 this minimum

price amounts to 17 EUR t21 (see Table 6). The highest

minimum selling price for which SRC production is

profitable amounts to 34 EUR t21 and occurs in the areas

where the SRC yields are lowest.

Table 7 lists the areas where energy conversion can be

profitable for the scenarios of SRC production and energy

conversion. If SRC is produced according to scenario 3 or 4,

all energy conversion scenarios are profitable in the entire

model area, except for areas in which the soil contamination

exceeds 4800 kBq m22. However, if SRC is produced

according to scenario 1 or 2, there are areas where

insufficient SRC is available for energy production. This

particularly applies for the large-scale energy production

(scenario A). The lack of sufficient SRC occurs mainly in

low-yield areas and the area close to the highly contami-

nated areas around the Chernobyl NPP.

The spatial pattern of the profit (Fig. 7) is mainly

governed by the pattern of the ash contamination. In the

vicinity of the Chernobyl NPP, almost 100% of the

predicted 137Cs contamination levels of SRC ash are

Fig. 5. SRC yield in the entire contaminated area of Ukraine, Belarus, and Russia (oven-dry t ha21 y21).

Table 6

Model results for the different scenarios for SRC production for the entire contaminated area of Ukraine, Belarus, and Russia: surface area, average profit,

maximum profit, and lowest minimum selling price for which SRC production is profitable

Scenario Profitable area

(km2 and %)

Average profit

(EUR ha21 y21)

Maximum profit

(EUR ha21 y21)

Lowest minimum

price (EUR t21)

1 1.399 £ 105 km2 (49.5%a) 24 108 29

2 1.696 £ 105 km2 (60.1%a) 52 140 27

3 2.659 £ 105 km2 (94.1%a) 127 251 17

4 2.638 £ 105 km2 (93.4%) 108 225 19

a Percentages of the surface area suitable for SRC production (2.824 £ 105 km2).


greater than 1000 Bq kg21 above which special treatment

(storage and disposal) is needed. This percentage

decreases towards the boundaries of the model area

because the soil is less contaminated by 137Cs further

away from the Chernobyl NPP. However, the additional

costs for storage and disposal of contaminated ash are

generally less than 3.5% of the total costs, so the influence

of these costs on the profitability of energy conversion is

limited.

The maximum profit that can be achieved by energy

conversion is independent of how SRC is produced, because

we assumed the SRC to be acquired for a fixed price of

40 EUR t21. Table 8 presents the values of the profit, profit

per unit energy, the maximum SRC price and minimum

energy price for which energy conversion is profitable. The

model results shows that the maximum profit per energy

production plant can be achieved for the large-scale boiler

(scenario A), but the maximum profit per unit energy is

achieved for the medium-scale gasifier (scenario C). The

profit for the small-scale gasifier for electricity production

(scenario D) is low because of the poor efficiency of energy

conversion, which is not compensated by a higher added

Fig. 6. Profit from SRC production (EUR ha21 y21) in the entire contaminated zone of Ukraine, Belarus, and Russia. (a) Scenario 1; (b) scenario 3.

Table 7

Model results for the different SRC production and energy production scenarios for the entire contaminated area of Ukraine, Belarus, and Russia: profitable

areas for energy conversion for the different (percentages of the surface area suitable for SRC production (2.824 £ 105 km2))

Scenario A B C D

1 2.318 £ 105 km2 (82.1%) 2.400 £ 105 km2 (85.0%) 2.433 £ 105 km2 (86.2%) 2.421 £ 105 km2 (85.7%)

2 2.795 £ 105 km2 (99.0%) 2.818 £ 105 km2 (99.8%) 2.820 £ 105 km2 (100%) 2.819 £ 105 km2 (99.8%)

3 2.824 £ 105 km2 (100%) 2.824 £ 105 km2 (100%) 2.824 £ 105 km2 (99.9%) 2.824 £ 105 km2 (100%)

4 2.824 £ 105 km2 (100%) 2.824 £ 105 km2 (100%) 2.824 £ 105 km2 (100%) 2.824 £ 105 km2 (100%)


value for the energy produced. For scenario C the maximum

price for SRC is 2.5 times larger than the fixed price of

40 EUR t21 and the minimum energy price is about a factor

of 2 smaller than the fixed energy price of

0.0294 EUR kWh21, which implies that energy conversion

according to scenario C seems sustainable.

4.2. Bragin district

The model results for the modelled part of the Bragin

district for the same SRC production and energy conversion

scenarios as for the entire contaminated area of Ukraine,

Belarus, and Russia are summarised in Table 9 through 11.

Fig. 8 shows the soil contamination by 137Cs and Fig. 9

shows the predicted SRC yield for the areas that are not

excluded from SRC production because of soil or product

Fig. 7. Profit from energy production (EUR y 21) in the entire contaminated zone of Ukraine, Belarus, and Russia. (a) Scenario 1A; (b) scenario 3C.

Table 8

Model results for the different energy production scenarios for the entire

contaminated area of Ukraine, Belarus, and Russia: maximum profit per

energy conversion plant and per unit energy, and maximum SRC price and

minimum energy price for which energy conversion is profitable for the

four energy conversion scenarios

Scenario Maximum

profit

(EUR y21)

Maximum profit

per unit energy

(EUR MWh21)

Maximum

SRC price

(EUR t21)

Minimum

energy price

(EUR kWh21)

A 344,410 8.77 74 0.0206

B 27,088 6.44 65 0.0230

C 7638 15.56 100 0.0138

D 1966 4.01 46 0.0310

Table 9

Model results for the different SRC production and energy production

scenarios for the Bragin district: profitable areas for SRC production and

energy conversion (km2)

Scenario SRC Scenario A Scenario B Scenario C Scenario D

1 0 0 0 0 0

2 0 0 0 0 0

3 77.8 305.1 305.1 305.1 305.1

4 108.3 305.1 305.1 305.1 305.1


contamination by 137Cs. The areas where SRC can be

produced are relatively small and scattered over the model

area. The patchy pattern of the SRC yield leads to a

preference for small-scale SRC plantations. The SRC yield

in Bragin district (Fig. 9) is insufficient to make large scale

SRC production according to scenario 1 and 2 profitable. So,

scenario 3 and 4 are most profitable and the SRC produced

according to these scenarios can be sold for the lowest price.

Fig. 10 shows the spatial distribution of the profit for

scenario 3 and 4. Because of the plantation size for scenario

4 is smaller than for scenario 3, SRC can be produced at

more locations. Scenario 4 also yields the greatest profit

(Table 10).

Because the window size, from which SRC can be

acquired, is relatively large in comparison with the entire

model area, the spatial variation in the model results for the

different energy conversion scenarios is small. Therefore,

the profits for the different energy conversion scenarios areonly presented as single values. Like for the entire

contaminated area of Ukraine, Belarus, and Russia, the

large-scale boiler (scenario A) is most profitable in absolute

terms and the small-scale gasifier (scenario C) is most

profitable per unit energy (Table 11). In combination with

SRC production scenario 3 or 4 these energy conversion

plants can be established at any location within the model

Fig. 8. Soil contamination by 137Cs (kBq m22) in the Bragin district.

Fig. 9. SRC yield (t ha21 y21) in the Bragin district.

Fig. 10. Profit from SRC production (EUR ha21 y21) in the Bragin district.

(a) Scenario 3; (b) scenario 4.

Table 10

Model results for the different SRC production scenarios for the Bragin

district: profit from SRC production and the lowest minimum SRC price for

which SRC production is profitable

Scenario Average profit

(EUR ha21 y21)

Maximum profit

(EUR ha21 y21)

Lowest minimum

SRC price (EUR t21)

1 2276 2266 348

2 2122 22 40

3 44 109 25

4 81 225 19


area where soil contamination by 137Cs does not exceed the

intervention limit of 4800 kBq m22 (see Fig. 8). Obviously,

if SRC production is not profitable in the entire model area

(scenario 1 and 2), energy conversion is not profitable as

well for all energy conversion scenarios.

5. Discussion and conclusions

In this study, the potential economic feasibility of SRC

production and energy conversion in areas contaminated by

Chernobyl-derived 137Cs was evaluated by means of two

sequential GIS-based submodels. A number of explicit and

implicit assumptions underlie these models and model

exercises, some of which are discussed below.

The uptake of 137Cs by the willow coppice is modelled

using a straightforward transfer factor approach. The 137Cs

transfer factor is assumed to depend only on soil type.

Possible temporal variations (e.g. seasonal variations or

decreasing trends over the years) in 137Cs uptake are not

taken into account. Furthermore, the model does not account

for effects of possible countermeasures taken in contami-

nated areas on the 137Cs. The lack of quantitative

information on the effects of time and countermeasures on137Cs uptake by SRC is the major reason for omitting these

factors from the model. Moreover, spatial information

covering the entire model area about which counter-

measures have been taken are also lacking.

We used SRC growth model predictions using local

climate data (Jossart et al., 1999) to estimate SRC yields for

the various soil types. Field experiments in the areas near

Chernobyl suggest, however, that the actual SRC yields tend

to be lower than the growth model predictions (Vanden-

hove, 2000). This might have caused an overestimation of

the SRC yield, and thus also a slight overestimation of the

area where SRC production is profitable, particularly in the

north-western and north-eastern corners of the model area

where sandy soils predominate. The effect on the estimation

of the SRC profitability in the other parts of the model

area are assessed to be negligible, because in these areas

SRC is potentially available in abundance for all scenarios.

We assumed the absence of opportunity costs in the

scenario calculations, which means that we did not take

account of costs of transferring land to SRC production.

This may be realistic for the abandoned lands in the Bragin

district, but not for all agricultural lands in the model area of

Ukraine, Belarus, and Russia. We also did not allow for

taxes raised on SRC and energy sales and assumed that all

produced SRC is sold. If these assumptions were false, the

profits would decrease accordingly. The predicted profits

should therefore be interpreted as potential profits. To

evaluate the profits from SRC plantations in the case of

additional opportunity or other costs, these costs can easily

be subtracted from the profits presented in Fig. 6. In contrast

to the large-scale production, small-scale production seems

to remain profitable if opportunity costs are taken into

account.

In this study, the effects of contamination by 90Sr,

another important long-lived radionuclide that was released

by the Chernobyl accident, were not evaluated. The main

reason for this was the lack of appropriate digital spatial

data sets of soil contamination by 90Sr. The soil-to-wood

transfer factors of 90Sr may be one order of magnitude larger

than those for 137Cs, but the soil contamination by 90Sr is

about one order of magnitude lower than for 137Cs

(Vandenhove, 2000). By disregarding the effect of 90Sr

contamination, the potential area for SRC production may

have slightly been overestimated because in some areas in

the vicinity of Chernobyl, the intervention limit for 90Sr in

SRC wood or ash may be exceeded. However, the surface

area is probably relatively small because most areas where

the intervention limits for 90Sr are exceeded, have likely

been excluded from potential SRC production because of

exceedance of the 137Cs intervention limits.

The SRC production evaluation model implicitly

assumes that within the user-defined window around the

SRC farm location a user-defined part (crop coverage

factor) of the suitable agricultural land is transformed into

SRC plantation area. This implies that the average SRC

yield, product contamination, and external dose for each

SRC farm depend on which agricultural fields are actually

transformed into SRC fields. However, the SRC yield,

product contamination, and external dose are calculated as

a total or average value for all areas suitable for SRC

production within the window. Thus, the model disregards

the variation in yield, contamination, and dose, which may

result from differences in the spatial configuration of SRC

fields within the window. This variation leads to

uncertainties in the model results, which increase with

an increasing spatial variation in environmental conditions

Table 11

Model results for the different SRC production and energy production scenarios for the Bragin district: Profit per energy conversion plant and per unit energy

Scenario Scenario A Scenario B Scenario C Scenario D

EUR y21 EUR MWh21 EUR y21 EUR MWh21 EUR y21 EUR MWh21 EUR y21 EUR MWh21

1 0 0 0 0 0 0 0 0

2 0 0 0 0 0 0 0 0

3 338,644 8.63 26,471 6.29 7566 15.42 1771 3.61

4 336,707 8.58 26,262 6.24 7542 15.37 1706 3.48


or decreasing crop coverage factor. Therefore, these

uncertainties may be larger for the entire contaminated

zone than for the Bragin district. The spatial averaging

over the window size may have caused the simulated

external doses not to exceed the external dose limit of

1 mSv y21. It is likely that for some spatial configurations

of SRC fields and farm labour, the actual external dose

limit may be exceeded, particularly in the most contami-

nated areas near Chernobyl.

In fact, this problem of within-window variation also

occurs at the scale level of a single gridcell and also

contributes to uncertainties in the model results. Likewise,

the within-gridcell variation and so the uncertainties in

model results increases with increasing cell size and may be

larger for the entire contaminated zone than for the Bragin

district. The effect of within-gridcell variation of soil

contamination, soil-to-wood transfer factors, and SRC

yield on the assessment of economic feasibility of SRC

production and energy conversion is further explored and

evaluated in the second part of this paper (Van der Perk

et al., 2004).

It can be concluded that the scenario of small and

medium scale SRC production using local machines is

generally the most profitable of the four evaluated

scenarios. The areas near Chernobyl are not suitable for

SRC production since the contamination levels in SRC

wood exceed the intervention limit of 740 Bq kg21. That

is also why large-scale SRC production is not economi-

cally profitable in the Bragin district. In areas where dry

and sandy soils predominate, the SRC yields are too low

to make large scale SRC production profitable. This is

generally the case in parts of the Gomel region, Belarus,

and the Bryansk region, Russia. However, in contrast to

large scale SRC production, the results from the GIS-

based models showed that small and medium scale SRC

production is potentially profitable in these areas. If

sufficient SRC is available and soil contamination does

not exceed the intervention limit of 4800 kBq m22, all

energy conversion scenarios can be profitable. A large-

scale boiler for heat production is most profitable in

absolute terms and a medium-scale gasifier for heat

production is most profitable per unit energy. The costs

for disposal of contaminated ash are relatively small and

barely influence the profitability of energy conversion.

This study showed that SRC production and conversion

is potentially economically feasible in most farmland

areas in Ukraine, Belarus, and Russia, that are radio-

actively contaminated, provided that the scale of SRC

production and energy conversion is adapted to the

occurrence of the suitable environmental conditions with

respect to soil type, land use, and soil contamination and

their spatial variability. Political will and incentives, and

the insurance of a stable market price for energy in the

next years are necessary boundary conditions for an actual

implementation of SRC.

Acknowledgements

This study was partly funded by the PHYTOR project

(EC Contract No. IC15CT98 0213). The authors

acknowledge the valuable comments by three anonymous

reviewers on the manuscript.

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