international and domestic uses of solid biofuels under ... · it was linked to the renewable...

Applied Energy 131 (2014) 139–157

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Applied Energy

journal homepage: www.elsevier .com/locate /apenergy

International and domestic uses of solid biofuels under differentrenewable energy support scenarios in the European Union

http://dx.doi.org/10.1016/j.apenergy.2014.05.0650306-2619/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +31 302537645; fax: +31 302537601.E-mail address: [email protected] (R. Hoefnagels).

Ric Hoefnagels a,⇑, Gustav Resch b, Martin Junginger a, André Faaij c

a Energy & Resources, Copernicus Institute of Sustainable Development, Utrecht University, 3584 CS Utrecht, The Netherlandsb Vienna University of Technology, Energy Economics Group, A-1040 Vienna, Austriac Energy and Sustainability Research Institute, University of Groningen, Nijenborg 4, NL-9747 AC Groningen, The Netherlands

h i g h l i g h t s

� A GIS based intermodal biomass transport model was developed for the European Union.� It was linked to the renewable energy model Green-X updated with biomass trade.� Scenarios of renewable energy deployment in the EU27 to 2020 were assessed.� Domestic biomass resources will remain the largest source of bioenergy (over 90%).� But increasing amounts of solid biomass will be traded (up to 506 PJ in 2020).

a r t i c l e i n f o

Article history:Received 25 November 2013Received in revised form 3 May 2014Accepted 22 May 2014Available online 8 July 2014

Keywords:Biomass tradeRenewable energy targetBioenergySolid biofuelGIS

a b s t r a c t

This article describes the development of a geographic information systems (GIS) based biomass transportanalysis tool BIT-UU used in combination with the European renewable energy model Green-X. BIT-UUcalculates cost and GHG emissions from lowest cost routes, using intermodal transport (by road, rail,inland waterways and sea) between origins of supply and demand destinations. With the developed bio-mass trade module in Green-X, the role of bioenergy can be evaluated in the larger context of renewableenergy deployment. The modeling framework takes into account the current and future energy policies atEU and country levels, competition with alternative sources of renewable energy (e.g. photovoltaic, wind)and sectors (electricity, heat, transport fuels) as well as competition between EU member states for thesame biomass resources. Scenario projections to 2020 are used to demonstrate the developed modelingframework. According to these scenarios, biomass from domestic supply remains the most importantsource of bioenergy (91–93% in 2020). However, the role of traded solid biomass will become increasinglyimportant. With a business as usual scenario, assuming continuation of current renewable energy policiesto 2020, the binding renewable energy targets will not be achieved, but trade of solid biomass will stillincrease up to 451 PJ in 2020. In the scenario that meets the conditions to achieve the 20% renewableenergy target in 2020, traded solid biomass is projected to increase to 440 PJ if sustainability criteriaare applied (minimum GHG saving) and 506 PJ without these sustainability regulations. Because importsof solid biomass from outside the EU are projected to become larger than intra-EU trade in the scenarios,the scenarios show the importance of improving the representation of extra-EU biomass sources and tradein the modeling framework.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

The substitution of fossil energy carriers by renewable sources ofenergy has become one of the key challenges to mitigate greenhousegas (GHG) emissions and to improve energy supply security by

diversification and reducing dependencies on imported fossil energycarriers. Especially in the European Union (EU), where memberstates have agreed on binding targets for renewable energy genera-tion, as laid down in the renewable energy directive (RED) 2009/EC/28 [1]. These country specific targets are aimed to increase the shareof renewable energy in total gross final energy consumption in theEU to 20% in 2020. The trajectories of how EU member states projectto achieve these binding targets have been published in the NationalRenewable Action Plans (NREAPs) in 2011. According to the NREAPs,

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1 Both the international solid biomass trade module and the BIT-UU model weredeveloped within the Re-Shaping project [23], a projected supported by the‘‘Intelligent Energy Europe’’ program of the European Commission, Executive Agencyfor Competitiveness and Innovation, and co-funding received through IEA BioenergyTask 40 [24].

2 PRIMES is an agent based, price driven energy system model 35 countries inEurope and widely used in European energy assessment and outlook studies by theEuropean Commission [29].

140 R. Hoefnagels et al. / Applied Energy 131 (2014) 139–157

bioenergy will remain the largest source of renewable energy in theEU to 2020. In 2020, over 54% of renewable energy is projected to begenerated from biomass resources [2] providing almost 12% of grossfinal energy consumption in the EU [3]. Therefore, bioenergy gener-ation has to increase with 44% between 2010 and 2020 with 90%growth in biobased electricity, 22% in production of heat from bio-mass and 129% in biofuels [2].

Residential biomass energy systems mainly use domestic bio-mass resources. However, the largest growth is anticipated in indus-trial sectors such as combined heat and power (CHP) or electricitygeneration in co-firing or dedicated electricity plants. These sectorshave become increasingly dependent on imported biomass, in par-ticular wood pellets [4–6]. Imports of wood pellets in the EUincreased from 1.9 Tg in 2009 to 4.6 Tg in 2012 and are foreseen toincrease to 15 Tg to 30 Tg in 2020 depending on economic factors,policies and the availability of domestic biomass resources [4–6].Bioenergy is a renewable, but not infinite source of energy and withthe increasing demand and international trade of bioenergy, safe-guarding the production and use of biomass in a sustainable wayhas become increasingly important [7]. Imports of biomass fromthird countries or from other EU member states, could improvethe cost-effectiveness of renewable energy generation as well assupply security. On the other hand, it could as well shift importdependencies from fossil resources towards bioenergy [8]. Althoughmany EU member states consider increasing imports of biomass, sofar few EU member states have quantified the expected amounts ofimported biomass. Likewise few countries have indicated possiblecountries to import from in their national renewable action plans(NREAPs) [9]. This can be explained by uncertainties in the volumesavailable, costs, the impact of sustainability criteria, the demand inother sectors and competing importing regions and the competitionof bioenergy with other renewable energy technologies.

To assess the potential role of biomass at the EU level, an integralanalysis is required that encompasses the complexities of interna-tional biomass supply and biomass demand markets. These includeconsequences of policy choices and the application of policy instru-ments, the dynamic characteristics of the future biomass supplypotentials and costs, international competition for resources withfood, feed and fiber markets and developments in agriculture andbioenergy conversion systems as well as developments in otherrenewable energy systems. Only few studies assess bioenergy tradeflows triggered by possible fluctuations in domestic supply anddemand of bioenergy sources [10]. Furthermore, these studies tendto generalize the representation of costs, energy requirements andGHG emissions of logistic operations [11] and lack infrastructurespecifications and geo-spatial features [12]. Studies that do includelogistic operations of biomass in more detail are often limited tospecific regions and production systems [13–17] or individual ship-ping routes [18–20]. Transport is a crucial component of the bio-mass supply chain and can add substantially to the total cost andGHG performances depending on the geographic locations of supplyand demand as well as available transport networks [12], e.g. seaports or inland waterways in land locked regions. So far, the mostcomprehensive evaluation of bioenergy in the EU with biomasstrade is provided in van Stralen et al. [2]. Van Stralen et al. [2] usedthe biomass potentials available from the Biomass Futures project[21] and the RESolve models to assess the feasibility of bioenergyin the NREAPs and possible biomass trade. Biomass trade in RESolveis based on distances between countries in Europe and the relatedfixed handling and variable transport costs of road, rail or shortsea shipping. Biomass supply from outside Europe is aggregated ina single supply node [22].

The main objective of this article is to describe the structure,methodology and related data requirements of a geographicexplicit modeling framework to assess supply, demand and tradeof solid biomass in the EU27. To demonstrate the modeling

framework developed, scenarios of renewable energy support pol-icies and GHG sustainability criteria are assessed for the EU27 to2020. For this purpose, Green-X, a partial equilibrium model ofthe (renewable) energy sector, has been extended with an interna-tional biomass trade module. The cost and GHG emissions fromlogistic operations to move biomass from the sources of biomasssupply to end use destinations, including handling, storage, pre-processing and transport have been calculated using the Geo-graphic Information Systems (GIS) based Biomass IntermodalTransportation (BIT-UU) model1 [18,23,24]. Consequently, the bio-mass trade module in Green-X model combined with the BIT-UUmodel enables the evaluation of renewable energy scenarios takinginto account biomass trade as well as sector, commodity and countryspecific GHG criteria of biomass logistics specific to the biomasstrade routes.

2. Methodology

2.1. Characterization of Green-X

Green-X is a dynamic toolbox originally developed to analyzehow cost-effective deployment of renewable electricity and CHPin Europe can be organized [25], but the current version alsoincludes a detailed characterization of transport and heat sectors,their potentials and as well as renewable energy policy descrip-tions at member state level using country profiles [26]. The modelcovers the EU27, but can be extended to other countries, e.g. Croa-tia, Norway and Turkey. With Green-X, future deployment ofrenewable energy can be evaluated with energy policy instruments(e.g. quota obligations, feed-in tariffs, tax incentives, investmentincentives and emission trading). Future scenarios can be manipu-lated, e.g. to meet the binding renewable energy targets to 2020 ina cost efficient way [25,27]. Green-X calculates the deployment ofrenewable energy at country and technology level and associatedcapital expenditures, consumer expenditures and fossil fuel andGHG avoidance compared to the reference scenarios on a yearlybasis in detail up to 2020 and with outlooks to 2030.

Green-X includes detailed representations of renewable energypolicies which have been updated to 2011 based on the renewableenergy country profiles in Teckenburg et al. [26]. The impact of pos-sible combinations and interaction of policy instruments can beevaluated taking into account the dynamics of technological learn-ing and technology diffusion, final energy demand and energy prices.Technology diffusion, following S shaped curves, can be altered bythe parameterized mitigation of non-economic barriers. Technolog-ical learning and the technology diffusion characteristics, as appliedin Green-X, are discussed in detail in Hoefnagels et al. [28].

Projections of the conventional (fossil) energy generation mix,the final and primary energy demand and related CO2 intensitiesby sector and fossil fuel prices are exogenously applied to theGreen-X model. For reasons of consistency with EU energy assess-ment studies and outlooks, the PRIMES2F2 Baseline [29] andPRIMES High Renewables scenario projections have been used(Table 4). The PRIMES High Renewables scenario is, amongst others,used in the Energy Roadmap 2050 [30]. Input parameters specific torenewable energy systems (RES) are based on Green-X. Biomasstrade parameters are specified in this analysis.

R. Hoefnagels et al. / Applied Energy 131 (2014) 139–157 141

2.2. Model set-up for biomass trade specifications

To calculate biomass trade endogenously in the Green-X model-ing framework, an intra-European biomass trade module wasadded to allow each individual member state to compete for thebiomass resource potentials available in the EU27 (Fig. 2). Sec-ondly, biomass sustainability restrictions can be specified includ-ing threshold values for minimum GHG savings. The GHG savingpotential depends on the source of biomass, the supply chain andend conversion type. The GHG saving performance of CHP plantsis often better than stand-alone electricity generation. It is there-fore possible that biomass can meet the restrictions applied if usedfor CHP, but not for standalone electricity as calculated endoge-nously in Green-X. Furthermore, small scale generation of heatand electricity (<1 MW) can be exempted from these criteria inthe Green-X model. Fig. 1 shows the model set-up of the extendedGreen-X model with intra-European solid biomass trade and withsustainability criteria.

Green-X is comprised of a series of input databases and scenarioinformation and framework conditions linked to the Green-X cal-culation tool (see Fig. 1, arrow (a)). The Green-X biomass databaseincludes the potentials and cost of biomass supply at country levelin Europe as well as possible wood pellet supply via third countryimports. The biomass trade module developed in this study usesthe same biomass database, but for each tradable commodity(excluding mainly gaseous biomass), time period and possibleroute of intra-European biomass trade, the biomass trade moduleadds the cost and GHG emissions implied by the logistic operationsrequired (b). Green-X calculates the combined cost and revenues

Green-X inputs

….

BIT-UU

Road networkRail networkInland waterwayShort sea shipp

Origins & destin

BIT-UU geographi

Green-X

Base input information

Scenario information

Biomass Biomass resource

potentials and costs per country, commodity and

year

BIT-UU input data

Logistics performance data

Costs, fuel requirements, load capacities,

efficiencies

Country specific inputs

Fuel prices, labor cost, rail freight rates

Locations Origins and destinations

Intermodal networkShort sea shipping network anInland waterways: Trans-Tools

– VI (JRC) Rail freight network: Trans-

Road network: Trans-ToTerminals: Trans-Tool

NUTS2 biomass potentials aForestry cover (EUROHerbaceous crops (RE

Short rotation coppice (R

(a)

(d)

(f)

(e)

Fig. 1. Overview of the modeling approach, components

for each possible bioenergy resource and conversion system com-binations allowed within the policy framework conditions. Thesescenario conditions change over time and include for example sup-port incentives for heat and electricity generation and non-eco-nomic barriers that hamper the deployment of renewable energytechnologies. For non-EU resources of biomass, the supply poten-tials are split by country of origin, reflecting differences in feasibleimport volumes, costs and specific GHG emissions that are associ-ated with land use change, harvesting and transport to the EU. Theextended Green-X model can model competition of biomass supplyand demand at the country level as well as competition betweenEU member states affected by country and sector/technology spe-cific support incentives for heat and electricity generation andcompeting renewable energy technologies (c).

The additional costs and GHG emissions associated with bio-mass trade are specific per biomass commodity and specificbetween countries. The cost and GHG emissions of logistic opera-tions required are calculated with the GIS based BIT-UU model.BIT-UU is an analysis toolset developed with ESRI’s ArcGIS Net-work Analyst extension [31] using geographic databases (f) com-bined with spreadsheet based input assumptions (e). The BIT-UUmodel uses the OD cost matrix analysis tool of ESRI’s ArcGIS Net-work Analyst extension [31] to calculate the least cost routes andassociated GHG emissions for all possible origins of biomass supplyto all destinations of biomass demand (g) and added to the domes-tic biomass supply input data (d). The BIT-UU model includesintermodal transport networks representing actual transport net-works of road, rail, inland waterways and sea and possible transferhubs (intermodal terminals).

sing

ations

c data

Country specific biomass supply curves Costs, GHG emissions

Origin – destination matrices

Costs, distances, fuel requirements, routes

Results Renewable energy deployment, costs,

benefits, final and primary energy mix (2006 – 2030)

datad ports: (RRG) ECMT Class II

Tools (JRC) ols (JRC) s (JRC)

nd distribution STAT) FUEL) EFUEL)

Legend

Input data

Biomass trade module inputs

Final results Green-X

(b)

(g)

(c)

and data inputs for solid biomass trade in Green-X.


2.3. Biomass supply

2.3.1. EU Biomass supply and locationsResources of biomass for bioenergy included in Green-X assume

actual production, import and use categories and costs to 2030,also referred to as ‘implementation-economic biomass potential’[32,33]. A detailed discussion of the biomass resource potentialsin Green-X compared to other resource assessments is providedin the supplementary information and in Hoefnagels et al. [28].The future cost of the different biomass feedstock categories asused in the model reflect correlations between developments incrude oil prices and feedstock-specific production costs. Fig. 2shows the Green-X potential for bioenergy that stems from solidbiomass in the EU27 per feedstock type as used in this analysis.Sources of solid biomass in Green-X include energy crops (AP),agriculture residues (AR), forestry products (FP), forestry residues(FR) and the biodegradable fraction of municipal solid waste(BW). The total supply potential of domestic biomass in the EU27is estimated to increase from 6.5 EJ in 2005 to 9.7 EJ in 2020.Growth in bioenergy supply is mainly driven by dedicated energycrops, of which the total supply increases with 66% between 2010and 2020. The strongest growth is assumed for lignocellulosicenergy crops (short rotation coppice, miscanthus, switchgrass).The average cost and cost ranges of lignocellulosic biomass supplyper feedstock category are provided in Table 5.

Because biomass in Green-X is specified to the country level, thedatabase is combined with the geographic biomass potentials atthe NUTS2 level of dedicated energy crops (both herbaceous andshort rotation coppice) from the REFUEL project5 [36–38] to esti-mate the distributed supply within each country. For forestry prod-ucts and residues, the fraction of forest cover at the NUTS2 levelrelative to the forest cover per country is used to estimate the geo-graphic distribution using EUROSTAT data [39]. The geographicdistribution per biomass type is shown in Fig. 4.

2.3.2. Non-EU biomass supply potentialThe future supply potential of extra-EU supply of solid biomass

in the scenarios is based on actual trends of wood pellet productionin major supply regions, press releases and market prognoses andscenario studies, mainly [34,35]. The breakdown of the total supplypotential of extra EU imports is shown in Fig. 3.

In 2010, wood pellets to Europe where mainly supplied by Can-ada, the Southeastern region of the U.S., Northwest Russia [4].Although demand from competing regions in Asia (e.g. in South

0

2000

4000

6000

8000

10000

12000

2010 2015 2020

Solid

bio

mas

s -p

oten

tial i

n te

rms

ofpr

imar

y en

ergy

(PJ

y-1) Extra-EU forestry

imports (pellets)BW - BiodegradablewasteFR - ForestryresiduesFP - ForestryproductsAR - AgriculturalresiduesAP - Agriculturalproducts

Fig. 2. Break down and development of resource potential for biomass in Green-Xbetween 2010 and 2020 (excluding biogas and imports of biofuels).

Korea and Japan), might become increasingly important in thefuture [40], these exporting countries are expected to remainexport-oriented to Europe up to 2020. Furthermore, additionalsupply from short rotation coppice is anticipated from countriesthat have pulp wood plantations established in South America(Brazil, Uruguay) and Africa (West Africa, Mozambique). In North-west Russia, developments in wood pellet supply markets havebeen erratic, but might still grow substantially with large privateinvestments [4]. Furthermore, a large supply potential of agricul-ture products and agriculture residues has been identified inUkraine [37]. Currently, only wood pellets from agriculture resi-dues are exported from Ukraine (sun flower husk are exportedfrom Ukraine to Poland [41]) and the potential of energy cropsremains untapped.

In total, the potential supply of extra-EU biomass supply for theEU27 is projected to increase to 560 PJ y�1 primary biomass in2020, equivalent to 32 Tg y�1 wood pellets. It should be noted thatthe supply potential, as considered in the scenarios in this article, isrelatively high compared to other studies. For example, in Lamerset al. [42], the total supply potential of extra-EU solid biomassavailable for the EU27 is anticipated to increase to 17.5 Tg in2020. Also Goh et al. [4] show that, in a business as usual scenario,the supply potential of solid biomass might be substantially lower(269 PJ in 2020).

The estimated cost of solid biomass imports to Europe are basedon CIF ARA spot prices of wood pellets at 125 €2010 Mg�1 (net cal-orific value: 18 MJ kg�1) [41]. After correction for inflation to thecost base year of Green-X (2006), solid biomass import prices areassumed to be 6.3 € GJ�1 in 2010 increasing to 7.2 €2006 GJ�1 in2020 as a result of increasing crude oil prices in the scenarios.Future price effects from market developments, the availability ofbiomass resources, competing demand, learning and supply chainoptimization, e.g. the use of advanced pre-treatment methods[43] or larger ships [18] are not covered in the scenarios.

2.4. Biomass logistics

2.4.1. Feedstock supply chainsThe operations required to move biomass from the field or for-

est to end users depend on the type of biomass, e.g. herbaceous orwoody biomass, the origin of supply, e.g. saw dust available at saw-mills or straw available at field sides and distance to the end user.Fig. 5 shows the biomass supply chains considered in this analysis.Herbaceous biomass sources (straw, switchgrass, miscanthus) arebaled at field side and transported by truck to end users if useddomestically (see Fig. 5, arrow (a)). If herbaceous biomass isexported internationally, bales are transported to a central gather-ing point (CGP) and pelletized before long distance transportation(c). Forestry biomass is chipped in the forest and transported bytruck to end users, if used domestically, or transported to a CGPand either transported as chips (b) or pelletized before long dis-tance transportation if exported to other countries (c). The costand GHG emissions of the logistic operations between field sideand the CGP and domestic end users are calculated with an Exceltool. Long distance, international transportation is calculated withthe BIT-UU model.

2.4.2. Biomass pre-processingThe techno-economic performance parameters of biomass pre-

treatment (chipping, pelletization) are depicted in Table 1. Theenergy requirements and related GHG emissions for pretreatmentare derived from the reports of the JRC [44] for willow crops andthe European Commission [45]. Chipping cost are based onHamelinck et al. [19]. The cost for pelletization are based on Thekand Obernberger [46], but corrected for inflation (Eurozone1.00 €2002 = 1.09 €2006).

0

100

200

300

400

500

600

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020Supp

ly p

oten

toal

of e

xtra

-EU

sol

id b

iom

ass

sour

ces

for i

mpo

rt to

the

EU (P

J yr

-1)

South America, AP (eucalyptus)Africa, FR (rubber tree), APUkraine, AP (short rotation coppice)North-West Russia, FPNorth-West Russia, FRSouth-East US, FP (pine)South-East US, FRCanada, FP (mountain pine beetle)Canada, FR

Fig. 3. Break down of supply potentials of forestry imports from abroad (extra-EU sources available for import to the EU27) [4,24,34,35].

Green - X database (supply)Microsoft ExcelBIT - UU model (ESRI ArcGIS Network Analyst)

Truck transport to

CGP (50 km)

Loading at CGP

Truck transport to

CGP (50 km)

Drying/ pelletisation

Unloading atdestination

Int. transport

Distribution to end user

Loading at CGP

Unloading atdestination

Int. transport


(a)

(b)

(c)

Biomass supply

Chipping/ baling (field

side)

Truck transport to destination

(50 km)


Fig. 4. Geographic distribution of biomass supply. Supply fractions per NUTS2 region are calculated relative to the total supply at the country level of herbaceous crops andshort rotation coppice from the REFUEL project and forestry cover per country in Europe from EUROSTAT [37,39].

Herbaceous crops Short rotation coppice Forestry biomass Supply NUTS2 relative to the total per country

0% - 2%

3% - 6%

7% - 12%

13% - 20%

21% - 29%

30% - 60%

61% - 100%

Supply NUTS2 relative to the total per country0% - 2%

3% - 5%

6% - 10%

11% - 19%

20% - 36%

37% - 68%

69% - 100%

Forestry cover NUTS2 relative to the total per country0% - 2%

3% - 6%

7% - 10%

11% - 16%

17% - 28%

29% - 56%

57% - 100%

Fig. 5. Lignocellulosic biomass feedstock supply chains.


2.4.3. International transportationThe BIT-UU runs in ESRI’s ArcGIS Network Analyst extension

and calculates the lowest cost routes between origins and destina-tions using an intermodal transportation network. Transport net-work layers (networks) in the BIT-UU model are derived fromTRANS-TOOLS (‘‘TOOLS for TRansport Forecasting ANd Scenariotesting’’), a decision support model for transport impact analyses[47]. TRANS-TOOLS includes major roads, rail (freight) and 6 clas-ses of inland waterways (IWW) in Europe. Short sea shipping

(SSS) has been added to the BIT-UU model using the short sea ship-ping network data from RRG [48]. The network model uses 4 differ-ent types of network data for each mode of transport (road, rail,IWW, SSS):

� Transport network links.� Transport network nodes.� Terminal connectors.� Terminal nodes.

Table 1Cost and energy requirements of biomass pre-treatment.

Parameter Unit (output) Wood chips Wood pellets Pellets (wood proc. residues)a

Scale Mg/h 10 10 10Load factor 90 0.91 0.91Capital M€ 0.2 1.06 0.62

€/Mgb 0.2 13.7 8.07O&M €/Mg 0.4 6.2 0Electricity MJ/Mg 1260 720Diesel MJ/Mg 50 36 36Heat MJ/Mg 4235 847Total costc

2010 €/Mg 1.7 – 2.2 51 – 53.9 42.8 – 45.92020 €/Mg 2 – 2.5 53 – 56.3 44.8 – 48.3

a Reduced energy requirements and capital due to the physical properties of the feedstock (mainly sawdust).b Discount rate = 7%.c The production cost differ per country due to variable energy prices.


Network links represent existing transport networks (e.g. roadsor rail lines) and attribute fields (the specifications that can beadded to each individual link in the network dataset, e.g. distance,maximum allowed speed, tolls [49]) used to calculate the variablecost and GHG emissions per mode of transport. Transport networknodes are access/egress locations at network link locations, e.g.highway entrees or rail and inland waterway terminals. Fig. 6 dem-onstrates how different transport network link layers are con-nected via terminal nodes and terminal connectors. Terminalconnectors connect transport network nodes with transferterminals where biomass can be transloaded between differenttransport modalities, e.g. from truck to ship. The terminal connec-tors include attribute fields to calculate the cost and GHG emis-sions of loading and unloading of biomass. Terminal nodesrepresent intermodal facilities where biomass can be transloadedbetween different modes of transport. Terminal nodes are assumedto be located in the geographic centers of NUTS3 regions becausethe actual locations of the intermodal terminals are not includedin the TRANS-TOOLS database.

The intermodal transport route, depicted in Fig. 6, shows bio-mass transport by truck to an intermodal terminal facility whereit is transferred to a ship, that has relatively low variable transportcost compared to a truck. Finally, biomass is transferred back to atruck to reach the destination. At longer overland transport dis-tances (>100 km), such an intermodal transport route is often costeffective over unimodal transport (single transportation mode),despite the additional fixed cost to transfer biomass betweendifferent modes of transport [12,19].

Fig. 6. Intermodal transport network concept

2.4.4. Transport input parametersThe cost and performance assumed for biomass transport per

type of transport mode and transloading between each modetransport in the BIT-UU model are depicted in Table 2. Labor cost,based on hourly cost in Europe [51], excise duties and value addedtax (VAT), based on transport statistics [52], are country specific(covered in Hoefnagels et al. [23]). The total specific cost of trans-port are in turn different in each country. For inland navigation(IWW), four ship types are included that depend on the size ofthe waterway following the ECMT (European Conference of Minis-ters of Transport) classification [53].

2.5. Cost and GHG emissions

The costs of solid biomass production and logistic operationsrequired to supply biomass to final conversion capacity are calcu-lated using Eq. (1). Because no by-products are generated for thefeedstock supply systems included in this analysis, Eq. (1) alsoapplies to the GHG calculation before conversion to final energy(heat, electricity).

Cfd ¼ Ccult þ Ch þ Cproc1 þ CT1;2 þ Cproc2 þ CT3 ð1Þ

With:Cfd = cost of biomass feedstock supplyCcult = cost for cultivationCh = cost of harvest and collectionCT1,2 = cost of transportation between the biomass supplysource and CGP or end user if used domestically (50 km)

in the BIT-UU model, adapted from [50].

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CT3 = cost of intermodal transportation between the CGP andend user (variable transport and fixed handling cost)Cproc1 = cost of processing at field side (e.g. baling, chipping)Cproc2 = cost of processing at CGP (pelletization).

For each NUTS2 region of biomass supply and each NUTS1region of demand, the cost and GHG emissions of feedstocksupply are calculated with the BIT-UU model. Based on the NUTS2geographic distribution of biomass, the weighted average trans-port cost from origin countries are calculated for each countryand type of biomass. These countries to NUTS1 results are pro-cessed into country-to-country matrices by assuming that 50%will be supplied to the NUTS1 destination with the lowest costof supply and 50% will be distributed equally among the destina-tions per country assuming average supply cost.

The GHG reduction performance relative to the fossil referencesystems for electricity and heat are calculated consistent with themethodology of the European Commission [45]. The performancedepends on the actual conversion efficiency and the type of bio-energy system (electricity, heat or CHP) as calculated endoge-nously in Green-X. For CHP systems, allocation by exergy isapplied to calculate the system performance with the fraction ofexergy in electricity set at 1.0. The fraction of exergy in usefulheat generation is calculated with the Carnot efficiency asexplained in detail in Annex I of COM(2010)11 [45]. The fossil fuelcomparators used in this analysis to calculate the GHG emissionsof the biomass feedstock supply chains and fossil reference sys-tems used to calculate the GHG reduction performance are con-sistent with the parameters proposed by the EuropeanCommission [45] as shown in Table 3.

3. Scenarios

In this article, 4 scenarios are included that differ in renewableenergy policy and are either without or with (‘bsc’) sustainabilitycriteria for solid biomass (Table 4).

3.1. Renewable energy support

The business as usual (BAU) scenarios assume a continuation ofthe current national renewable energy support mechanisms andpolicies with prevailing non-economic barriers. These non-eco-nomic barriers include administrative deficiencies (e.g. a high levelof bureaucracy), diminishing spatial planning, problems associatedwith grid access, possibly missing local acceptance, or even thenonexistence of proper market structures [57]. In Green-X, this isparameterized in low diffusion settings of the diffusion curves.The diffusion rates are derived from an econometric assessmentof past diffusion rates of renewable energy technologies. Thestrengthened national support (SNP) scenarios assume non-eco-nomic barriers to be mitigated meaning that the maximum growth

able 3HG emission values of fossil fuels and fossil reference systems used to calculate theHG saving performance of bioenergy generation [44,45].

Energy source/reference Total (direct + indirect) GHG emissions(g CO2-eq MJ�1)

Diesel/MDOa 87.4Heavy fuel oil (HFO) 85.0Electricity mix EU27 (fossil)b 198.0Heatc 77.0

a Marine diesel oil (MDO) is assumed to have similar emissions to diesel.b For consistency reasons, the fossil electricity mix, used to calculate the GHG

avings from biobased electricity generation, is also used in the GHG calculations ofiobased supply chains.c Reference to calculate the GHG savings from biobased heat generation.

Table 4Overview of the scenarios and key scenario assumptions.

Scenario name BAU BAU-bsc SNP SNP-bsc

National renewable energy policies Business as usual Business as usual Strengthened national support Strengthened national supportFossil energy deployment Baseline (PRIMES) Baseline (PRIMES) High Renewables (PRIMES) High Renewables (PRIMES)Sustainability constraints for biomass No Yes No YesNon-economic barriers mitigated No No Yes Yes


rate of renewable energy deployment will be equal to best practicesobserved from the econometric assessment [28]. Secondly, therenewable energy policy instruments and levels of support areincreased or changed to meet the overall EU target of 20% renewableenergy in gross final energy consumption in 2020.

3.2. Sustainability criteria

At the EU level, binding sustainability criteria have been set forliquid biofuels in the Directive 2009/28/EC [1] related to biodiver-sity, risks for carbon stock changes and agriculture managementpractices. Up to now, solid and gaseous biofuels used for the pro-duction of heat and electricity are exempted from EU wide bindingsustainability criteria. In this analysis, the impact of EU wide sus-tainability criteria for solid biomass is assessed for a GHG thresholdvalue in ‘bsc’ scenarios. In these scenarios, minimum GHG savingsof 60% are required for solid biomass uses in electricity and heatgeneration which is consistent with the GHG savings from biofuelsand bioliquids produced in plants that started production in 2017[1] and will potentially also be applied to electricity and heat gen-eration from solid biomass [58].

The GHG threshold, as applied in the cases with sustainabilitycriteria, takes into account emissions from biomass cultivation,processing, international trade and end use efficiencies asexplained in Section 2.5. Thereby, possible carbon releases fromforest harvesting (carbon debt) or land use change emissions aretaken into account in calculating the GHG balance of the wood pel-let supply chains from outside the EU. In this context, Fig. 7 showsthe assumed potentials for solid biomass imports to the EU27 aswell as specific LCA GHG emissions by geographic origin. It should

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ern

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Potential 2020

LCA GHG emissions(specific)

Fig. 7. Feedstock potentials for 2020 and related GHG emission

be noted that the underlying assumptions are highly uncertain,mainly as a result of data limitations.

4. Results

4.1. Feedstock supply cost

Table 5 summarizes the average cost and ranges of feedstocksupply at farm gate, the total cost of biomass if used domesticallyand the total supply cost if exported to other EU member states.These include the cost of pretreatment (chipping, baling, pelletiza-tion), transport and handling. The farm gate cost of wood process-ing residues (FR5) are cheapest (0.9–2.4 € GJ�1), but are assumed toneed pelletization to improve the physical characteristics of saw-dust. Because the cost for pelletization add substantially to thetotal cost of biomass supply (over 3 € GJ�1), domestic supply ofwood chips from forest residues (FR2) can be supplied at lowercost if used domestically. The average cost of wood chips are there-fore cost effective over wood pellets for intra-European biomasssupply. For example, wood chips from forestry products (FP1) areon average 5.9 € GJ�1 compared to 8.1 € GJ�1 for wood pellets fromthe same feedstock in 2010. The upper cost levels of supply chainsare the result of long distance, expensive transport routes betweenEU member states that will never be selected in the Green-X sce-narios, e.g. pellets from miscanthus cultivated in Sweden andexported to Spain (17.4 € GJ�1 in 2020). Pelletization of agricultureresidues and grassy crops is cheaper (average 1.5 € GJ�1) than for-estry biomass (average 3.1 € GJ�1) because drying is only requiredfor woody biomass.

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s for assumed solid biomass import streams to the EU27.

Table 5Supply chain cost of domestic and Intra-European supply per biomass feedstock type (€ GJ�1 primary biomass).

Feedstock Transportedas

Year Feedstock(farm gate)a

Domesticb Exportc

Av. Range Av. Range Av. Range

AP4 (SRC willow) Chips 2010 6.3 5.7 – 7.1 7.1 6.4 – 7.7 9.1 6.8 – 12.82020 8.1 7.3 – 9.0 8.9 8.0 – 9.7 10.9 8.4 – 14.4

Pellets 2010 6.3 5.7 – 7.1 11.2 9.4 – 13.92020 8.1 7.3 – 9.0 13.1 11.1 – 15.8

AP5 (miscanthus) Bales/pelletsd 2010 8.2 6.9 – 9.3 9.0 7.9 – 10.0 11.5 8.9 – 14.82020 10.4 8.8 – 11.8 11.3 9.9 – 12.6 13.9 10.8 – 17.4

AP6 (switch grass) Bales/pelletsd 2010 8.2 6.9 – 9.3 8.2 5.2 – 9.3 10.7 6.6 – 13.72020 10.4 8.8 – 11.8 10.2 6.5 – 11.7 12.8 7.9 – 16.1

AR1 (straw) Bales/pelletsd 2010 3.0 2.7 – 3.6 3.8 3.5 – 4.3 6.8 4.9 – 9.52020 3.7 3.3 – 4.5 4.7 4.3 – 5.2 7.6 5.6 – 10.4

FP1 (forestry products – current use (wood chips, log wood) and FP2 (forestry products –complementary fellings (moderate))

Chips 2010 5.2 4.2 – 6.1 5.9 5.1 – 6.6 8.1 5.8 – 12.2

2020 5.9 4.8 – 6.9 6.7 5.7 – 7.5 8.9 6.5 – 13.1Pellets 2010 5.2 4.2 – 6.1 10.2 8.3 – 13.0

2020 5.9 4.8 – 6.9 11.1 9.2 – 14.0

FP3 (forestry products – complementary fellings (expensive)) Chips 2010 7.6 6.4 – 8.5 8.3 7.4 – 9.0 10.5 8.2 – 14.32020 8.6 7.3 – 9.7 9.4 8.3 – 10.2 11.7 9.3 – 15.5

Pellets 2010 7.6 6.4 – 8.5 11.0 10.0 – 11.8 12.6 10.8 – 15.12020 8.6 7.3 – 9.7 12.2 11.1 – 13.1 13.8 11.9 – 16.4

FR2 (forestry residues – current use) and FR3 (forestry residues – additional) Chips 2010 2.6 1.1 – 4.4 3.4 2.0 – 4.7 5.5 2.5 – 10.12020 3.0 1.3 – 5.0 3.8 2.2 – 5.4 6.0 2.8 – 10.8

Pellets 2010 2.6 1.1 – 4.4 7.6 5.2 – 11.02020 3.0 1.3 – 5.0 8.1 5.6 – 11.7

FR5 (additional wood processing residues (sawmill, bark) Pelletse 2010 1.6 0.9 – 2.4 4.5 3.9 – 4.9 6.0 4.6 – 8.42020 1.9 1.0 – 2.8 4.9 4.1 – 5.4 6.4 4.9 – 8.9

FR6 (forestry imports from abroad)f Pellets 2010 6.32020 7.2

a Farm gate cost excluding processing (baling/chipping/pelletization). The feedstock cost vary per country.b Processing (baling/chipping/pelletization and transport of 50 km by truck).c Intra-European transport, based on lowest cost routes between countries. Emissions depend on distance and used transport modes (ship, rail, truck).d Bales for domestic use, if traded internationally, bales are transported by truck to a pelletization plant (50 km). Pellets are exported.e No chips available (part of this stream exists of saw dust).f The estimated costs are based on CIF ARA spot prices of wood pellets at 125 €2010 Mg�1 (net calorific value: 18 MJ kg�1).

Table 6Greenhouse gas emissions related to the total supply chain of domestic and Intra-EU supply per biomass feedstock type in Green-X (g. CO2-eq MJ�1 primary biomass).

Feedstock category Transportedas

Cultivationa Domesticuseb

International transportc

2010 2020

Average Range Average Range

AP4 (SRC willow) Chips 2.0 2.7 8.2 3.0 – 16.9 7.4 2.9 – 13.7Pellets 2.6 12.9 16.7 13.1 – 22.9 16.2 13.0 – 20.6

AP5 (miscanthus) Bales/pelletsd 3.6 5.0 13.0 9.1 – 19.5 12.4 9.0 – 17.1AP6 (switch grass) Bales/pelletsd 3.6 5.0 13.0 9.1 – 19.5 12.4 9.0 – 17.1AR1 (straw) Bales/pelletsd 0.5 1.7 9.4 4.9 – 17.1 8.7 4.8 – 14.3FP1 (forestry products – current use (wood chips, log wood) Chips 1.0 1.7 7.2 2.0 – 15.9 6.4 1.8 – 12.7

Pellets 1.0 11.3 15.2 11.6 – 21.3 14.6 11.4 – 19.0FP2 (forestry products – complementary fellings

(moderate))Chips 1.0 1.7 7.2 2.0 – 15.9 6.4 1.8 – 12.7

Pellets 1.0 11.3 15.2 11.6 – 21.3 14.6 11.4 – 19.0FP3 (forestry products – complementary fellings

(expensive))Chips 1.0 1.7 7.2 2.0 – 15.9 6.4 1.8 – 12.7

Pellets 1.0 11.3 15.2 11.6 – 21.3 14.6 11.4 – 19.0FR2 (forestry residues – current use) Chips 0.0 0.7 6.2 1.1 – 14.9 5.4 0.8 – 11.7

Pellets 0.0 10.4 14.2 10.6 – 20.3 13.7 10.5 – 18.1FR3 (forestry residues – additional) Chips 0.0 0.7 6.2 1.1 – 14.9 5.4 0.8 – 11.7

Pellets 0.0 10.4 14.2 10.6 – 20.3 13.7 10.5 – 18.1FR5 (additional wood processing residues (sawmill, bark) Pelletse 0.0 5.6 9.5 5.9 – 15.5 8.9 5.7 – 13.3

a Emissions for cultivation and harvesting are allocated to primary wood products. The emissions for cultivation of forestry residues are therefore 0. Emissions for pre-processing (chipping) are covered in domestic and international uses.

b Transport of 50 km by truck.c Intra-European transport, based on lowest cost routes between countries. Emissions depend on distance and used transport modes (ship, rail, truck).d Bales for domestic use, if traded internationally, bales are transported by truck to a pelletization plant (50 km). Pellets are exported.e No chips available (part of this stream exists of saw dust).


Table 7Renewable energy deployment in the renewable energy scenario projections in the EU27 of Green-X compared to the deployment of renewable energy in the EU27 NREAPs for2020 [59].

2010 2020

BAU BAU-bsc SNP SNP-bsc NREAPa

Electricity (PJ y�1)Biomass 379 707 708 945 946 834

Biogas 88 199 199 232 237 230Solid biomassb 227 419 420 584 581 558Biowasteb 64 90 90 128 128Bioliquidsb 46

Hydro 1274 1344 1344 1352 1352 1306PV and solar thermal electricity 129 424 424 392 394 372Tide and wave and geothermal 26 44 44 48 49 63Wind 646 902 902 1763 1780 1779Total renewable electricity 2455 3420 3421 4500 4522 4339Total gross final consumption 11,895 13,593 13,593 13,051 13,051 12,708

Share 20.6% 25.2% 25.2% 34.5% 34.6% 34.1%Share biobased 3.2% 5.2% 5.2% 7.2% 7.3% 6.6%

Heating and cooling (PJ y�1)Biomass 2711 3290 3290 3700 3682 3763

Biogas 49 113 113 125 128 187Solid biomassb 2,538 3,014 3,014 3,354 3,334 3,391Biowasteb 123 164 164 220 220Bioliquidsb 185

Heat pumps, geothermal, solar thermal 154 272 272 492 506 841Total renewable heating or cooling 2865 3562 3562 4192 4189 4672Total gross final consumption 23,502 23,873 23,873 21,869 21,869 21,796


Transportc (PJ y�1)First generation biofuels 491 460 460 431 430 677Second generation biofuels 0 91 91 152 152 97Imported biofuels 125 287 287 479 479 462Total renewable biofuels 616 838 838 1062 1062 1236Total gross final consumption 14,617 15,542 15,542 14,328 14,328 13,078

Share biobased 4.2% 5.4% 5.4% 7.4% 7.4% 9.5%

Total (PJ y�1)Total renewable energy 5936 7820 7821 9754 9772 10,247Of which biobased 3706 4835 4836 5706 5690 5833Total gross final consumption 50,015 53,008 53,008 49,248 49,248 49,794


a NREAP data is derived from the Renewable Energy Projections Table 1 (Additional Energy Efficiency Scenario), 10, 11, 12 [59].b Solid biomass in the NREAP also covers organic wastes. Renewable electricity, heating and cooling from liquid biomass are not covered in Green-X.c Excluding renewable electricity transport.


The GHG emissions of the feedstock supply chains at farm gate(cultivation) and including transport for domestic uses and rangesif exported to other EU member states are depicted in Table 6.Wood pellets from short rotation coppice (willow) have the highestemissions, mainly resulting from pelletization and transportation ifused internationally. However, the upper limit of the GHG rangesin Table 6 (22.9 g CO2-eq MJ�1) still meets the GHG saving thresh-old value of 60%, as imposed in the BAU-bsc and SNP-bsc scenarios,if used for electricity generation with a net conversion efficiency of30%.

4.2. Renewable energy deployment

The scenarios evaluated in this study are compared with thenational renewable energy deployment as provided in the NREAPs(Table 7). A larger set of Green-X scenario results and sensitivitycases is provided in Ragwitz et al. [9]. With a continuation of cur-rent support policies, the BAU scenarios show that the share ofrenewable energy in gross final energy consumption in the EU27will remain below 15% in 2020 if no further actions are taken byEU member states. Some countries, including Austria, Swedenand Finland, are projected to meet their targets with current

policies in the BAU scenarios [9], but other member states showlarge gaps between the national renewable energy targets andthe results of the BAU scenarios. These include, amongst others,Italy, Belgium, the Netherlands and the UK, that have becomemajor importers of solid biomass from outside the EU [5].

Note that the gross final energy demands in the BAU scenarios,based on the PRIMES reference scenario, is higher than in the finalenergy demand in the NREAPs. The PRIMES High Renewables sce-nario that underlies the gross final energy demand projections inthe SNP scenario projections, is more consistent with the NREAPs.Also, the biobased share in gross final energy supply in the SNP sce-nario (11.6% biobased) as well as the bioenergy contributions inelectricity, heat and transport are within similar ranges to theNREAP (11.7%). Green-X does however project larger amounts ofadvanced biofuels (152 PJ y�1 in SNP compared to 97 PJ y�1 in theNREAPs), but this will not affect the total amount of lignocellulosicbiomass substantially compared to the demand for heat andelectricity.

At member state level, the differences between the projections ofthe individual member states in the NREAPs and the Green-X calcu-lations are larger as shown in Fig. 8 for the projected growth inrenewable energy generation (electricity, heat, 2nd generation

-40%

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0)BAU BAU-bsc SNP SNP-bsc NREAP*

(*) NREAP including organic waste

Fig. 8. Projected changes of renewable energy generation from solid biomass (electricity, heat, second generation biofuels) in 2020 relative to 2010 in the Green-X scenariosand the NREAPs [59].

0

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Extra EU imports

Agriculture residues (T)

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Energy crops (E)

Energy crops (H)

Forestry residues (T)

Forestry residues (E)

Forestry residues (H)

Forestry products (T)

Forestry products (E)

Forestry products (H)

Total primary biomassdemand BAU

Fig. 9. BAUbsc scenario: primary biomass demand in the EU27 (excluding wastesand biogas), per feedstock category and end use sector (electricity (E), heat (H),transport (T)) compared to the total primary biomass demand in the BAU scenario(line).

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Forestry residues (E)

Forestry residues (H)

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Forestry products (H)

Total primary biomassdemand SNP

Fig. 10. SNPbsc scenario: primary biomass demand in the EU27 (excluding wastesand biogas), per feedstock category and end use sector (electricity (E), heat (H),transport (T)) compared to the total primary biomass demand in the SNP scenario(line).

200

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bio

mas

s (P

J y-1

)

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Extra-EU biomassimports


transport fuels). The key difference between the NREAPs and Green-X SNP scenario results that affects solid biomass trade, is the growthin renewable electricity generation in larger member states. Forexample, Germany projects 21% increase in renewable energy gen-eration from solid biomass between 2010 and 2020 compared to41% in the SNP scenarios. In contrast, France and the UK expect largercontributions from solid biomass (40% in France, 86% in the UK)compared to 29% in France and 68% in the UK in the SNP scenarioresults. The difference is larger in smaller member states (e.g. Bel-gium and Cyprus), but the impact on EU levels are smaller.

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2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

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Fig. 11. Solid biomass consumption from biomass traded within the EU or importedfrom non-EU resources in the EU27 according to the scenarios.

4.3. Solid biomass demand

According to the Green-X projections, primary biomass demandin the EU27 could increase with 26% from 3.98 EJ in 2010 to 5.01 EJin the BAU-bsc scenarios in 2020 (Fig. 9) and increase with up to47% (to 5.78 EJ) in the SNP-bsc scenario in 2020 (Fig. 10). Forestry


products remain largely used for heating purposes in householdsand are for a large extent supplied form domestic resources. Thisresource category is difficult to mobilize for other markets due to

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01020304050


Fig. 12. Domestic production, import from non-EU countries and net import from EU coproduction and net domestic consumption in 2010 (markers).

the decentralized supply. Furthermore, large amounts of thesewood products are used by the producers, i.e. private ownersthemselves or traded on informal markets [60].

Z PT BE HU SK BG GR LT LA SI EE IE LU CY MT

Domestic biomass production


Import from non-EU countries


Net domestic consumption

SNP SNP-bsc 2010


Net import EU countries

Import

Export

untries in the Green-X scenario projections for 2020 compared to domestic biomass

0%10%20%30%40%50%60%70%80%90%

DE FR SE UK PL FI IT ES AT RO DK NL CZ PT BE HU SK BG GR LT LA SI EE IE LU CY MT

Frac

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of d

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ticex

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the

tota

l pot

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l Exploitation of the domestic potential

BAU BAU-bsc SNP SNP-bsc

Fig. 13. Exploitation of domestic biomass supply potentials in the Green-X scenario projections for 2020.

Table 8Biomass imports in the BAU-bsc and SNP-bsc scenarios in 2015 and 2020.

BAU-bsc SNP-bsc

2011a 2015 2020 2015 2020

Total imported biomass PJ y�1 140 161 356 161 426Of which intra-EU PJ y�1 82 46 106 45 122Of which extra-EU PJ y�1 58 115 250 117 304

Total imported biomass inWPEb

Tg y�1 7.8 8.9 19.8 9.0 23.6

Of which intra-EU Tg y�1 4.5 2.5 5.9 2.5 6.8Of which extra-EU Tg y�1 3.2 6.4 13.9 6.5 16.9

Key importing countriesof EU biomass

DK: 44%, IT: 16%, SE:9%, Others: 30%

AT: 36%, DE: 30%, RO:8%, Others: 19%

AT: 44%, DE: 13%, RO:11%, Others: 22%

DE: 30%, AT: 27%, RO:22%, Others: 17%

DE: 25%, AT: 17%, RO:15%, Others: 30%

Key importing countriesof non-EU biomass

UK: 28%, NL: 25%, BE:12%, Others: 35%

DE: 46%, UK: 11%, BE:9%, Others: 26%

DE: 50%, UK: 10%, BE:10%, Others: 23%

DE: 49%, FR: 9%, UK: 9%,Others: 27%

DE: 51%, UK: 8%, IT: 8%,Others: 27%

a Wood pellet trade, based on EUROSTAT statistics of wood pellet imports (CN code 44013020) [61].b Wood pellet equivalent (18 MJ kg�1).


4.4. Solid biomass supply and trade

Domestic supply of biomass remains the main source ofbioenergy generation in the EU27 in all scenarios. However, therole of internationally traded biomass sources is projected tobecome increasingly important as a result of growing biomassdemand (Figs. 9 and 10). Fig. 11 depicts the trends in bioenergytrade in the EU27 according to the scenarios. In total, intra-EUand extra-EU trade of solid biomass is projected to increase tobetween 373 PJ (BAU-bsc) and 506 PJ (SNP) equivalent tobetween 21 Tg and 28 Tg wood pellets. The strongest growth isprojected for imports of solid biomass from outside the EU27.Compared to current imports of wood pellets to the EU27(4.6 Tg in 2012), imports of solid biomass are projected tobecome 3.2 times larger (15 Tg WPe) in the BAU-bsc scenario to4.7 times larger (22 Tg WPe) in the SNP scenario in 2020. Intra-EU trade of solid biomass is highest in the SNP-bsc scenario(122 PJ in 2020, 6.8 Tg WPe), but the differences between the sce-narios remain small up to 2020. In the scenarios, in which sus-tainability regulations are assumed, total imports of extra-EUbiomass are 18% (SNP-bsc scenario) to 24% lower (BAU-bsc sce-nario) compared to the same scenarios without biomass sustain-ability criteria resulting from the exclusion of resources that donot meet the GHG saving performance.

Fig. 12 depicts the domestic production, extra-EU imports andnet intra-EU imports (import–export) of biomass per EU memberstate according to the scenario projections for 2020. Countries areranked based on the net domestic consumption in the scenarios.Domestic biomass production and consumption show consistentpatterns between individual member states. Overall, biomass

consumption is about 20% higher in the SNP and SNP-bsc scenarioscompared to the BAU and BAU-bsc scenarios.

Germany, France and Sweden remain the largest consumers ofbiomass for bioenergy, consuming 44% of the total primary bio-mass demand in the EU27 in 2010 and 40–44% in 2020.

By 2020, key exporting regions of EU biomass include centraland eastern European countries (Bulgaria, Czech Republic, Hun-gary, Poland, Slovakia, Slovenia) where cheap resources of biomass,such as straw, are abundantly available and forestry rich countriesin the Baltic states (Estonia and Latvia). Key regions of intra-EUbiomass imports are Germany, with largest imports from Polandand the Czech Republic (mainly agriculture residues) and Austria,with largest imports from Slovenia and Hungary (forestry productsand agriculture crops).

The exploitation of domestic biomass resources in the scenariosfor 2020 are depicted in Fig. 13. The total exploitation of the eco-nomic-implementation potential of domestic biomass resourcesis highest in forestry rich countries including Finland and Sweden,but remains below 86%. In Germany, 71–76% of the domesticpotential is projected to be exploited in 2020 demonstrating thatit is more cost effective to import biomass than using more expen-sive domestic resources such as forestry products or energy crops.Similarly, the total exploitation of the supply potential of non-EUbiomass sources is projected to be 47% in the BAU-bsc scenarioto up to 69% in the SNP-bsc scenario in 2020.

4.5. Biomass trade routes

Total intra-EU and extra-EU imports of solid biomass to theEU27 for the BAU-bsc and SNP-bsc scenarios are summarized in

BAU-bsc 2015 BAU-bsc 2020

Fig. 14. Net biomass trade flows BAU-bsc scenario.6F. (EU member states are clustered in 15 regions to improve readability: Baltic States (Estonia, Latvia, Lithuania), 2.Benelux (Belgium, Netherlands, Luxembourg), 3. Spain, Portugal, 4. Bulgaria, Romania, 5. Czech Republic, Slovakia, 6. Ireland, United Kingdom, 7. Cyprus, Greece, 8. Italy,Malta, 9. Austria, Slovenia, 10. Denmark, Sweden, 11. Finland, 12. Hungary, 13. France, 14. Poland, 15. Germany (The Green-X model does provide results for each country andbiomass type if more detailed analyses are required).).

SNP-bsc 2015 SNP-bsc 2020

Fig. 15. Net biomass trade flows SNP-bsc scenario. (EU member states are clustered in 15 regions to improve readability: Baltic States (Estonia, Latvia, Lithuania), 2. Benelux(Belgium, Netherlands, Luxembourg), 3. Spain, Portugal, 4. Bulgaria, Romania, 5. Czech Republic, Slovakia, 6. Ireland, United Kingdom, 7. Cyprus, Greece, 8. Italy, Malta, 9.Austria, Slovenia, 10. Denmark, Sweden, 11. Finland, 12. Hungary, 13. France, 14. Poland, 15. Germany. (The Green-X model does provide results for each country and biomasstype if more detailed analyses are required).)


Table 8 and compared to imports of wood pellets in 2011 accordingto EUROSTAT [61]. Net intra-EU trade of solid biomass in Green-Xis projected to be 1.1 Tg in 2011 (Fig. 11) compared to 4.5 Tg in2011 for trade of wood pellets according to EUROSTAT import sta-tistics. These results show that Green-X underestimates intra-EUtrade flows of solid biomass. The difference becomes larger if alsoother types of solid biomass trade would be considered (e.g. woodchips and fuelwood) [6]. Due to the underestimation of current

intra-EU trade of solid biomass in Green-X, results for intra-EUimports of solid biomass in 2015 (2.5 Tg) are still below currentwood pellet trade figures.

The projected intra-EU and extra-EU biomass solid biomasstrade flows are depicted for the BAU-bsc scenario in Fig. 14 andfor the SNP-bsc scenario in Fig. 15. The non-EU origins in Fig. 14and in Fig. 15 represent the aggregated imports from all non-EUregions available in this analysis including North America, South


America, Africa, Russia and Ukraine. Almost all EU member statesare projected to import solid biomass from outside the EU in2020, but the main importing countries of extra-EU solid biomassare Germany, France, the UK, Belgium and Italy. Total imports ofextra-EU solid biomass to the Netherlands, the second largestimporting country of non-EU biomass in (25% of total extra-EUwood pellet imports), is projected to remain relatively constant(0.9 Tg WPe in 2020 in the SNP-bsc scenario). In comparison, totalsolid biomass import to the Netherlands in 2011 was 1.4 Tgaccording to IEA Task 40 statistics [62] and 0.8 Tg in 2011 accord-ing to EUROSTAT. With Germany, currently a net exporting countryof wood pellets, becoming the largest importing country of extra-EU biomass according to the scenarios, these model projectionsindicate that the role of biomass trade in individual member statesmight be overestimated (Germany) and underestimated (the Neth-erlands) by the Green-X model.

5. Discussion

5.1. Methodology and input data

The scenarios in this article are used to demonstrate the capa-bilities of the modeling framework and the results of the scenarioprojections for biomass trade should therefore be considered indic-ative. The scenario assumptions for biomass supply are limited toone set of fixed, predefined biomass supply and costs for Europeanand non-European biomass because Green-X does not include aland use and forestry module. Effects of technological learning inharvesting and transport, scale and possible imbalances in supplyand demand could affect biomass feedstock prices substantially,but are not taken into account. Therefore, the results do not coverthe uncertainties inherent to future biomass supply and costs andrelated ranges of biomass trade. For European biomass sources, it isrelevant to evaluate the possible competition between the addi-tional supply potentials in Central and Eastern Europe that mightbecome available at competitive cost to imports of solid biomassfrom extra-EU countries if agriculture sectors are to be reformedin these regions, for example by targeted agriculture policies.

Furthermore, the predefined biomass supply mix, cost and land,in particular of energy crops, also influences the deploymentpotentials of end conversion options available to the Green-X opti-mization scenarios that could be sub-optimal. For example, landused for the production of 1st generation energy crops cannot beused for the production of lignocellulosic crops. The REFUEL project[36,37] has demonstrated that different land use, agriculture man-agement practices and crop choices, can have a large impact on thecost and supply potential of dedicated crops and agriculture resi-dues available for bioenergy generation. Note though that up to2020, electricity and heat remain the main markets of lignocellu-losic biomass largely supplied from forestry products, forestry res-idues and agriculture residues that are cheaper than dedicatedenergy crops. Therefore, the scenarios are only marginally affectedby land use and crop allocation. Beyond 2020, diffusion of 2nd gen-eration biofuels and growing competition for lignocellulosic bio-mass sources, increases the relevance for energy crops andtherefore the importance of optimal crop selection procedures.

The results of this study show that biomass imports from out-side the EU are larger than intra-European biomass trade. The cur-rent representation of non-EU biomass in the Green-X model ishowever highly aggregated compared to intra-EU biomass trade.Lamers et al. [42] have assessed the supply potential of solid bio-mass from outside Europe for multiple types of biomass (wood pel-lets, agriculture residues) from multiple regions under differentsustainability criteria and scenarios. Similarly, the Green-X bio-mass trade module could be further extended with more regions

of non-EU biomass supply as well as different types of biomassand the implied cost and GHG emissions.

With respect to the BIT-UU model, the cost and GHG emissionsof biomass logistics are the result of the assumed performances ofthe different modes of transport (road, rail, inland waterways andsea) and assumed net load capacities (including empty returns) aswell as pretreatment options and resulting bulk energy densities ofraw biomass and transported intermediates. This study assumesthat woody biomass is transported after processing into woodchips or biomass pellets. More advanced pretreatment options,including torrefaction [43] and pyrolysis oil [63] as well astransport of liquid biofuels result in higher bulk energy densitiesand improved handling properties and reduces transport cost.Extension of the BIT-UU model with transport of advanced inter-mediates and biofuels would provide insight in possible optionsto reduce cost and improve the GHG performance of biomass sup-ply chains.

The cost and GHG emissions of biomass supply chains areassumed to be similar for all end use sectors. In reality, the supplychains of bulk, industrial markets, e.g. co-firing in coal power plants,CHP and district heating are different from residential, decentralizedmarkets of wood pellets. Wood pellets for residential and decentral-ized uses of wood pellets are often sold in economy bags (500–1000 kg) or small bags (15–25 kg) via retailers [64] whereas large(coal) power plants are often located close to sea terminals or inlandwaterways for efficient fuel supply and cooling purposes and handlewood pellets in bulk. Current imports from overseas locations aretherefore mainly used for electricity generation in Northwest Eur-ope rather than small scale decentralized uses. Because large indi-vidual power plants can add substantially to the demand of solidbiomass, it is important to add these locations and logistic networkconnections to the model as applied in Lamers et al. [42]. Similarly,the representation of cost and GHG implied by distribution to decen-tralized end users should be improved to model competitionbetween domestic supply and imports from intra-EU and extra-EUresources.

5.2. Bioenergy deployment and trade

The Green-X model results for bioenergy deployment in heat,electricity and transport sectors of the SNP and SNP-bsc scenariosmeet the fulfillment of the 20% renewable energy targets in 2020and can be compared with the NREAPs submitted by EU memberstates in 2011. At the EU level, the SNP scenario projections for bio-based energy generation with Green-X match with the renewableenergy trajectories of the NREAPs. At EU member state level how-ever, there are some major differences between the Green-Xresults and expectations from individual member states and otheravailable studies. For countries that already rely for a large extenton imported biomass sources, including the UK and the Nether-lands, solid biomass electricity generation appears to be conserva-tive. This can be partly explained by the substitution approach inthe Green-X. The approach used in Green-X builds on general dif-fusion theory that new commodities or technologies generally fol-low an S-shaped pattern [28]. For co-firing or conversion of coalpower plants, the assumed substitution rate might be too low or,possibly, the diffusion approach does not reflect the substitutionpathway of this technology correctly. It should be noted howeverthat policies relevant for electricity generation from solid biomassin these countries have been subject to changes recently. Forexample, the Netherlands has set a maximum contribution ofrenewable energy generation from co-firing to 25 PJ in 2020(6.9 TW h) [65]. This is still above the projected electricity genera-tion from solid biomass in the Green-X SNP scenario (4.5 TW h in2020), but below the anticipated share of co-firing in the NREAPof the Netherlands (8.4 TW h in 2020) and model projections from


ECN [66]. For the UK, Green-X projects that electricity generationfrom solid biomass will increase to 11.2 TWh in 2020 comparedto 20.6 TW h in the NREAP and 25.5 TWh in the NPOL scenario ofLamers et al. [42]. It is therefore expected that far more woody bio-mass will be imported by the UK to fuel large coal power plantsthat are or will be converted to biomass, e.g. Drax [42].

In contrast, bioenergy deployment in Germany, the key import-ing region of biomass according to the Green-X scenarios, appearsto be too optimistic. In 2010, Germany was still a net exportingcountry of wood pellets (500 Gg in 2010) [41] and wood pelletsare only used for heating systems supported via tax exemptionsor investment grants [67]. The German feed-in tariff system inthe Renewable Energy Act (EEG) only supports small scale biomassCHP systems (up to 20 MW electricity). Electricity generation fromsolid biomass is projected to increase to 31.5 TW h in 2020 in theSNP scenario compared to 24.6 TW h in the NREAP of Germanyand 22.1 TW h in the NPOL scenario in Lamers et al. [42] for2020. Note however that biomass imports to Germany in theGreen-X projections are mainly used for heat generation (86% ofimported biomass is used for heat generation in the SNP scenariofor Germany in 2020). Potentially because (international) supplychains of solid biomass used for decentralized heat markets arenot properly addressed in the current modeling framework. Alsoin the NREAP of Germany imports of biomass are anticipated. Ger-many expects that about 400 PJ of total biomass (electricity, heat,transport) needs to be imported in 2020 [9] compared to 188 PJin the SNP scenario in 2020. Also for Austria, imports of biomassare anticipated in the NREAP with imports of solid biomass fromneighboring countries (29 PJ in 2020) and import of vegetable oilsfrom countries in the Danube region (19 PJ in 2020) [9].

5.3. Sustainability criteria

At the EU level, binding sustainability criteria have been set forliquid biofuels in the Directive 2009/28/EC [1] related to biodiver-sity, risks for carbon stock changes and agriculture managementpractices. Up to now, solid and gaseous biofuels used for the pro-duction of heat and electricity are exempted from EU wide bindingsustainability criteria. At the national level, some individual mem-ber states have set binding criteria for solid biomass with theirrenewable energy support schemes, including Belgium and theUnited Kingdom [68]. Also in the Netherlands, sustainability crite-ria are considered [65]. Furthermore, voluntary schemes have beendeveloped in close relation with energy utilities, e.g. Green GoldLabel (GGL), NTA8080, Laborelec), or are still under development,e.g. the Initiative Wood Pellet Buyers/Sustainable Biomass Partner-ship (IWPB/SBP) [68,69].

Sustainability certification systems, both national and voluntaryhave their own individual scope in terms of markets and geogra-phy, criteria and indicators. Inconsistencies and possible conflictsbetween different sustainability certification systems could havea disruptive effect on biomass markets, especially for internationaltrade of solid biomass. The European Commission is therefore pro-posing binding, harmonized sustainability criteria at the EU levelthat might include minimum GHG saving targets of 60% [58], sim-ilar to the BAU-bsc and SNP-bsc scenarios in this article. Theimpact of the additional cost and GHG restrictions in the BAU-bsc and SNP-bsc scenario projections remains limited comparedto the scenario projections without these criteria applied (BAU,SNP). Van Stralen et al. [2] found significant effects of sustainabilitycriteria on the deployment of bioenergy in the EU27 to 2020 andbeyond decreasing with over 14% compared to the reference sce-nario. The impact of sustainability criteria in van Stralen et al. [2]applies mainly to biofuels by reduced supply of 1st generationenergy crops. Sustainability criteria for biofuels are not assessedexplicitly in this article.

The impact of sustainability criteria on the total cost and supplyof biomass is difficult to quantify and there are few studies thathave addressed these topics. Smeets et al. [70] assessed the impactof sustainability criteria on the procurement cost and supply poten-tial of short rotation coppice production in Ukraine and Brazil. Withsustainability criteria applied, the weighted average cost couldincrease with 0.7 € GJ�1 in Brazil and 0.3 € GJ�1 in Ukraine, mainlyas a result of additional labor cost. Sikkema et al. [72] assessedthe impact of sustainable forest certification on the cost and supplyof forestry biomass for bioenergy purposes. Although part of thesupply potential cannot meet the sustainability criteria, the impacton supply cost remains small. Furthermore, the assumed cost ofwood pellet imports from outside the EU are based on current woodpellet prices of which the major share is already certified [72].

6. Conclusion

This article describes the development of a modeling frame-work to assess the role of biomass and international biomass tradefor bioenergy in context of renewable energy deployment scenar-ios for the EU27. With the extension of international biomass trade,the Green-X model fits multiple purposes. It can be used byresearchers and policy makers, both at national levels and Euro-pean level, to evaluate current and future policies to 2020 andbeyond. It serves to assess renewable energy deployment andrelated GHG emission reduction pathways, implied cost andrequired resources. Furthermore, the model enables the identifica-tion of possible weak spots or opportunities in sustainable use ofdomestic and imported biomass resources. For investors andindustrial stakeholders, results allow for identifying businessopportunities or investment risks in biomass supply, logistic facil-ities and bioenergy conversion systems.

In all scenarios, domestic supply of biomass remains the largestsource of bioenergy in the EU27 to 2020, but traded biomass is pro-jected to become increasingly important, increasing from 2% in2010 to 7% to 9% in 2020. Intra-EU trade is projected to increaseup to 122 PJ in the SNP-bsc scenario in 2020, equivalent to 6.8 Tgwood pellets. The strongest growth is foreseen for extra-EU bio-mass imports that are projected to become 266 PJ (15 Tg WPe) inthe BAU-bsc to 389 PJ (22 Tg WPe) in the SNP scenario or equiva-lent to 3.2–4.7 times the imports of wood pellets in 2012.

It is important to note that the model-based scenarios only assesspossible effects of the demand side with changes in support policiesand mitigation of non-economic barriers. Future biomass supplyand cost are highly uncertain, especially the interaction betweenextra-EU cost and supply versus the cost and supply of domesticresources in the EU27. With a single fixed set of biomass cost andsupply, as used in this analysis to demonstrate the capabilities ofthe extended Green-X model, the results of biomass supply demandand trade in this article should therefore be considered illustrative.

The spatial explicit approach to represent biomass trade routesin the renewable energy model Green-X demonstrated in this arti-cle forms a basis for further research. There are key topics toimprove the modeling framework and to assess the potential roleof international biomass supply, demand and trade. These includethe improvement of the economic potentials, dynamics and geo-graphic resolution of biomass supply in the EU, the potentialsand logistics of biomass supply outside the EU and the additionof other demand sectors such as biobased chemicals.

Appendix A

A.1. Biomass resource potentials

Biomass resources for bioenergy can be categorized in threemain categories: biomass residues and organic wastes, surplus for-

Theoretical potentialTechnical potentialMarket/Economic potentialEcologically Sustainable potentialImplementation potential

Fig. A.1. Overlap between theoretical, technical and market potentials with chartareas illustrating the relative amounts of biomass potentials [33].


estry and biomass produced via cropping systems (dedicatedenergy crops) [73]. For each of these categories, key factors havebeen identified that determine the total potential, but future esti-mations of the biomass resource potential, both from domesticsources within the EU and available from outside the remain highlyuncertain. These include policy and market conditions as well asdevelopments in agriculture, forestry and their related markets(e.g. food, feed, timber, pulp products) [74]. Studies that assessresource potentials of biomass basically distinct three types ofpotentials in hierarchic order [74–76]:

� Theoretical potentials (the supply potential only limited by bio-physical conditions).� Technical potentials (part of the theoretical potential limited by

technical and non-technical constraints as well as competitionwith other land uses). If environmental constraints are includedto the technical potential, e.g. biodiversity, soil and water, thefraction is typically referred to as sustainable potential.� Market (economic) potentials (part of the technical potential

that is economically feasible to produce and mobilize undermarket conditions, e.g. fossil fuel prices, other renewable tech-nologies, CO2 prices).

When also institutional and social constraints and policy incen-tives are also covered to estimate the amount of biomass that can

0

200

400

600

800

1000

1200

Prim

ary

biom

ass

(PJ

yr-1

)

2010 2020 NREAP 2020

2010 Green-X: 2.0 EJ REFUEL: 2.2EJ (1.1 -4.2.0 EJ) 2020 Green-X: 3.3 EJ REFUEL: 4.2EJ (1.8 -6.8.0 EJ)

Total EU27:

Fig. A.2. Supply potentials of dedicated energy crops in the EU27 for Green-X and availaenergy crop supply mix to Green-X and REFUEL low yield crops only and high yield cro

be implemented in a certain time frame, it is defined as the imple-mentation potential [33]. Fig. A.1 illustrates the hierarchic orderand overlap between the different types of biomass potential.The horizontal and vertical axes are dimensionless, but could e.g.represent ecological criteria or supply costs versus the potentialbiomass availability. Ideally, the role of bioenergy should beassessed taking into account the variability and uncertainties fromenvironmental, socio-economic and technical conditions thatdetermine and constrain the potential of biomass that is availablefor bioenergy purposes. However, biomass in Green-X requirespredefined commodity specific implementation-economicpotentials [32]. This implies that assumptions have to be madeon policy incentives, e.g. subsidies for energy crop cultivation,types of agriculture management and harvest technologies applied.The main implication of this approach, with crop cultivation notinternalized in the Green-X model calculations, is that Green-Xcannot optimize for crop type production. In the REFUEL project,de Wit et al. [37,77] demonstrated that selecting high yield croptypes in Europe, i.e. energy grasses and short rotation coppice,increases the economic potential of dedicated energy cropssubstantially.

A.2. Energy crop potentials in the EU27

To show the consequences of the assumptions underlying theimplementation-economic potentials of biomass resources inGreen-X, the potentials are compared to the biomass potentialsof the REFUEL project [36,77] and the biomass potential estimatedby individual EU member states in Table 7 of the NREAPs. Fig. A.2depicts the potential of dedicated energy crops in the EU27 forGreen-X and NREAPs (columns) and REFUEL (markers). The mark-ers for REFUEL show the potential for the same crop mix asassumed in Green-X produced on available arable land. The cropmix in Green-X is depicted in Fig. 6 and includes rapeseeds,sunflower, maize and wheat (corn and whole plant), short rotation

ble estimates in the NREAPs (columns) and in REFUEL (markers) assuming the sameps only ranges.


coppice willow, miscanthus, sweet sorghum, etc. The negativeerror bars show the range if only low yield energy crops (oil crops)are produced on available land. The positive error bars show thepotential if only high yield (grassy) crops are produced on availableland. Results of REFUEL are based on the base scenario and excludepotentials of cultivation of lignocellulosic crops on pasture land(2009) since this option was not considered within Green-X.

The comparison of Green-X potentials and REFUEL potentials ofdedicated energy crops shows that the higher estimated potentialsin 2030 in REFUEL (138 Mtoe) compared to Green-X (84 Mtoe) aremainly due to differences in Central and Eastern European Coun-tries (CEEC). In France, Spain and Italy and the UK, the estimatedpotentials are more conservative in REFUEL compared to Green-X. In REFUEL, the estimated potential for energy crops in CEECincreases from 33 Mtoe in 2010 to 79 Mtoe in 2030 (57% of theEU27 potential). In Green-X, the potential in CEEC countriesincreases from 17 Mtoe in 2010 to 29 Mtoe in 2030 (34% of theEU27 potential).

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