• Open Access

GHG emissions of forest-biomass supply chains to commercial-scale liquid-biofuel production plants in Finland

Authors


Abstract

Commercial-scale liquid-biofuel production utilizing forest-based biomass would require feedstock supply from a large geographical area. Feedstock composition, supply chains' arrangements, and the resulting greenhouse gas (GHG) emissions are location dependent, and case-specific assessments are needed if one is to guarantee the fulfillment of GHG reduction requirements by a specific biofuel product. This work assessed GHG emissions derived from the feedstock supply and transportation chain to three possible commercial-scale biodiesel plant locations in Finland (Rauma, Porvoo, and Kemi) at site-specific level. The supply of 7.2 PJ yr−1 (approximately 1 million m3solid) of forest biomass (harvesting residues, stumps, and small-diameter energy wood) was assessed for each location, including four distinct scenarios for truck and railway transportation and two scenarios for biomass availability. Biomass availability and transportation-network assessments were conducted through utilization of geographical information system methods, and the GHG emissions were assessed by means of life-cycle assessment. The results showed that the GHG emissions of the supply chains can be effectively reduced through use of railway transportation from distant supply areas. Furthermore, even though the supply-chain GHG emissions differed by up to 30% between the case-study locations, the GHG emissions of the feedstock supply chain, from roadside stores of uncomminuted biomass to comminuted biomass delivered to the plants, were relatively low (2–4%) when compared with the GHG emissions of fossil diesel.

Introduction

Replacing fossil fuels with biomass-based alternatives is considered a viable option for reduction in greenhouse gas (GHG) emissions, and thus for mitigating global climate change. Because of feedstock diversity, the different technological pathways, and uncertainty of the actual GHG-related performance of biomass-based fuels, various certification and sustainability programs have been introduced to ensure GHG reductions, including those of the Renewable Energy Directive (RED) in the European Union (EU) (European Commission, 2009), and the Renewable Fuels Standard (RFS) in the United States (US Congress, 2007; EPA, 2010). The RED includes a legally binding requirement of a 10% share of renewable energy in road-transportation fuels in all EU member states by 2020. For Finland as a member state of the EU, the national targeted share of renewable energy in transportation fuels is 20%, in view of the double counting of certain fuels1 (Ministry of Employment & the Economy, 2011a).

In Finland, so-called second-generation biofuels play an important role in reaching of the national renewable-energy targets (Heinimö et al., 2011; Ministry of Employment & the Economy, 2011b), and three plans have been presented for large-scale production of second-generation liquid biofuels, all of which are based on gasification of solid forest biomass and Fischer–Tropsch (FT) synthesis. Possible plant locations include Rauma (61°07′0″N, 21°28′0″E), in southwestern Finland (UPM-Kymmene Oyj, 2011); the Ajos harbor area, in Kemi (65°40′0″N, 24°33′0″E), in northern Finland (Metsäliitto, 2011); and Porvoo (60°18′0″N, 25°32′0″E), in southern Finland (Stora Enso Oyj, 2010). The average production capacity of each of the three plants is approximately 250 000 t yr−1 of liquid biofuels, and the estimated total use of biomass raw material in each plant is 14.4 PJ yr−1 (approximately 2 million m3solid).

In addition to requirements pertaining to the share of renewable energy in transportation fuels, the RED introduced binding criteria for GHG savings with liquid biofuels. The GHG savings that need to be reached by liquid biodiesel are 35% relative to the value of fossil diesel fuel, 83.8 gCO2 equivalent MJ−1 (gCO2eq MJ−1) (European Commission, 2009). For comparison, the requirements in the US RFS are 50% savings from a comparative value of 93.1 gCO2eq MJ−1 (Khatiwada et al., 2012). Also, the RED GHG savings requirements will become stricter, with a required level of 60% savings coming into effect in 2018 for new installations. The RED also presents default GHG savings figures for liquid biofuels, which are 95% and 93% for FT diesel made from waste wood and farmed wood, respectively.

Several studies have assessed the GHG savings and reductions in greenhouse impacts that can be achieved by production and use of FT diesel from forest residues in Nordic conditions in place of fossil diesel fuel (Mäkinen et al., 2006; Kirkinen et al., 2007; Holmgren & Hagberg, 2009; Soimakallio et al., 2009; Antikainen et al., 2011). The estimated GHG savings or reduced greenhouse impact in these studies ranges from roughly 20% to 90% on a 100 year timescale. The large variation in results between individual studies is due to differences in the GHG emissions of the electricity used in the production processes considered, plant specifications, and estimates of the carbon stock changes in the soil that are due to collection of forest biomass. The consortia behind the three FT-diesel projects in Finland have also presented GHG savings estimates for their products, in the 85–88% range (UPM-Kymmene Oyj, 2009; Vapo Oy & Metsäliitto, 2010; NSE Biofuels Oy Ltd, 2011; UPM-Kymmene Oyj, personal communication). Lignocellulose-based second-generation biofuels, such as FT diesel, generally offer greater GHG emission reductions than do conventional biofuels made from sugar, starch, and vegetable oil (European Commission, 2009; Hoefnagels et al., 2010).

However, the results of sustainability assessments depend on the assumptions that are made when the studies are conducted, including those related to geographical location and raw-material supply. Therefore, instead of generic or default GHG savings values, case-specific assessments are called for by many researchers (Quirin et al., 2004; Larson, 2006; Holmgren & Hagberg, 2009; Soimakallio et al., 2009).

A liquid-biofuel plant consuming 14.4 PJ yr−1 of forest biomass would increase the demand for solid wood fuels for energy purposes in Finland by 8% from today's 176 PJ yr−1 (Finnish Forest Research Institute, 2012) and consume almost seven times as much forest biomass as Alholmens Kraft in Pietarsaari, which is currently the largest consumer of forest biomass in Finland (Laitila et al., 2010). Given the large demand for forest biomass created by a large biofuel production facility, the GHG emissions of raw materials' transportation and supply chains must be addressed accordingly. Kariniemi et al. (2009) estimated that the operations covered by this study (long-distance transportation, comminution, and associated handling operations) account for approximately 50% of the total supply-chain GHG emissions of similar forest-biomass supply, from cutting of the trees to the final wood chips inside the end-user facility's gates. The EU RED (as well as the RFS in the United States) also states that the GHG emission calculations for biofuels must (among various other things) include emissions from the transportation and processing of raw materials.

The purpose of this study was to assess the raw-material supply chain's GHG emissions for a large-scale user of forest biomass in Finnish conditions, such as a second-generation liquid-biofuel production plant. Three case studies of possible plant locations – for Porvoo, Rauma, and Ajos (in Kemi) – are presented. The differences in the total supply-chain GHG emissions among the three case-study locations that are due to local forest biomass's availability and possibilities for transportation by road and railway are assessed. The effects of logistics arrangements on the supply-chain GHG emissions are also assessed by means of four distinct truck and railway transportation scenarios. Two scenarios of raw-material availability are also assessed.

The forest-biomass raw-material categories considered for this study include harvesting residues from final fellings (HR), spruce (Picea abies) stumps from clear-cuts (ST), and small-diameter energy wood from early thinning or first thinning (EW). In addition, by-products of the pulp and paper industry, such as bark, would be utilized in the FT-diesel plants (UPM-Kymmene Oyj, 2009; NSE Biofuels Oy Ltd, 2011; Vapo Oy & Metsäliitto, 2011). In this study, it is assumed that at least 50% of the total expected use of each plant will be covered by HR, EW, and ST from domestic inland sources, making the amount supplied in the case studies 7.2 PJ yr−1 (approximately 1 million m3solid).

Materials and methods

Biomass availability assessment

The shares of HR, ST, and EW in the raw-material supply are assumed to be equal to the proportions of these in the total techno-economic energy wood potential within the supply area in question. The assessment is based on municipality-level potential estimates (398 municipalities) provided by the Finnish Forest Research Institute2. The properties of comminuted (chipped or crushed) biomass fractions are presented in Table 1.

Table 1. The properties of comminuted harvesting residues, stumps, and small-diameter energy wood
 Heating value (GJ m−3solid)Moisture content (%)Density, dry (kg m−3solid, dry matter)Density, wet (kg m−3solid, wet matter)
  1. a

    Kärhä et al. (2010a).

  2. b

    Hakkila (1978).

  3. c

    Karttunen et al. (2010).

Harvesting residues7.49a47a425b802
Stumps7.67a37a435b690
Small-diameter energy wood7.63c36c420c656

The total techno-economic availability potentials for HR, ST, and EW in Finland are 48.6, 19.4, and 46.8 PJ yr−1, respectively, totaling 114.8 PJ yr−1. In general, of the three forest-biomass categories, HR is the cheapest, followed by ST, whereas EW is the most expensive (Laitila et al., 2010): the actual use of HR by existing plants, such as local combined heating and power plants, is greater relative to the techno-economic potential than is the use of ST and EW. However, because the supply of HR and ST is tied to the supply chains of industrial roundwood, which the companies behind the three FT-diesel consortia dominate in Finland, with a 75% market share (Ministry of Agriculture & Forestry, 2001), it is assumed that all forest-biomass fractions are available to the consortia regardless of their present use. It is also assumed that, to match the demand of a large-scale biodiesel plant in Finland, all categories of forest biomass must be included in the raw-material supply. In addition to limitations related to site selection, which are accounted for in the techno-economic potential calculations (Laitila et al., 2008; Anttila et al., 2009), various other factors too may limit the actual availability of forest-biomass fuels to a specific user. These include (but are not limited to) local competition, forest-owners' willingness to sell the type of biomass in question, lack of information about suitable harvestable stands, prohibitive harvesting and transportation costs that result from remote location, and lack of the subsidies that are often required for economically feasible supply of forest biomass for energy purposes (Laitila et al., 2008; Maidell et al., 2008; Kärhä et al., 2010a; Karhunen et al., 2011). Therefore, two scenarios of biomass availability to the plants were included in this study. In the 100% availability scenario, the availability of forest biomass to each plant was assumed to be equal to the techno-economic potential. In the 50% availability scenario, the total effect of the various additional availability limitations was estimated to reduce the amount of biomass available at each supply point by 50%.

In this study, the geographical reference area, Finland, was divided into a geographical 4 × 4 km grid, which was estimated to be detailed enough to produce geographically accurate results on the scale of this study. The biomass potential was allocated to the geographical grid as presented by Jäppinen et al. (2012).

Road-network assessment

The biomass supply points (midpoints of the 4 × 4 km grid) were linked directly to the nearest road. For definition of the supply areas around the plants and railway loading points, the shortest possible driving route to each point was calculated with ArcGIS software. The route calculations and road classification were based on the Finnish national road and street database, Digiroad (Finnish Transport Agency, 2010). The life-cycle inventory dataset (GaBi Databases, 2012) that was used in the GHG calculations utilizes a three-level road classification to take road types into account with respect to the GHG emissions of truck transportation. Accordingly, road segments were classed into three distinct road types on the basis of the maximum speed limit for each road segment (Jäppinen et al., 2012). In this study, if a biomass supply point was inside the supply areas, all of its biomass was considered to be collected. This resulted in the total amount of collected biomass being slightly different from the desired total amounts. Therefore, the supplied amounts were adjusted to be exactly the desired amounts, by means of correcting factors. A km PJ−1 value was calculated for all three road types for all of the cases, and these values were then multiplied to yield the distances that would be driven on each road type for supply of the desired amounts of biomass.

Supply chains

The supply chains of forest biomass and the scope boundaries of this study are presented in Fig. 1.

Figure 1.

Supply chains and boundaries of the study.

The supply chains are described below:

Truck

In this study, Truck refers to a direct truck supply chain used in supply areas located in the immediate vicinity of the plants. HR and EW are chipped and blown directly into chip trucks with mobile chippers at the roadside. After transportation to the plants, the chips are unloaded with the truck's own unloading systems (by a chain unloader or via opening container walls) and moved to a storage pile with a wheel loader. Moving of a mobile chipper between the roadside storage locations is included in the calculations. ST material is picked up at the roadside with the truck's own crane and, as loose stumps, transported by a stump truck to the plants, where it is unloaded and crushed with a mobile stump crusher. These are the most common supply-chain methods for HR, EW, and ST in Finland (Hakkila, 2004; Kärhä, 2011).

Railway

In this study, Railway refers to the chain used when biomass is transported to the plants from distant supply areas, which are around the railway loading locations. The first stages in the Railway supply chain between the roadside storage sites and railway loading spots are identical to the early stages in the Truck supply chain around the plants. At the railway loading location, the material is loaded onto a train with a wheel loader and transported to the plants, where it is unloaded and moved to a storage pile with a wheel loader.

Emission calculations

The emissions of the biomass transportation chains were calculated with GaBi LCA software and the results are given as CO2 equivalents (CO2eq). The calculations included CO2, N2O, and CH4, with global warming potential factors of 1, 296, and 23 (European Commission, 2009). The unit processes of the emission calculations and their key parameters are summarized in Table 2.

Table 2. The unit-process descriptions, key parameters, and resulting GHG emission values used in the emission calculations, with distance- and/or road type-dependent emissions marked as f(d,r)
PhaseDescription of unit process and key parametersGHG emissions
  1. a

    GaBi Databases (2012).

  2. b

    Strandvall (2006).

  3. c

    The payload of the mobile chipper is assumed to be 50% of the total weight of 32 t (LHM Hakkuri Oy, 2012).

  4. d

    Based on fuel-consumption data presented by Rinne (2010).

  5. e

    Rinne (2010).

  6. f

    Based on the fuel consumption of crushing as presented by Kärhä et al. (2010b) and fuel consumption of loading given by Rinne (2010).

  7. g

    Cargo space of 153 m3 (Palander et al., 2011) and density of loose ST in a stump truck of 0.19 m3solid m−3loose (Korpinen et al., 2008).

  8. h

    Cargo space of 127 m3 (Karttunen et al., 2012) and cargo space utilization rate of 83% (Jäppinen et al., 2012).

  9. i

    Korpinen et al. (2010).

Production of diesel fuelDiesel mix at refinery (5.75% bio-components), average for the EU-27 countriesa320 gCO2eq kgdiesel−1
Transportation of diesel fuel used by trucks, chippers, crushers, and wheel loaders

Truck (60 t total capacity, EURO 4)a

Payload: 40.0 tb

f(d,r)
Transportation of diesel fuel used by diesel trains

Rail-transportation cargo – diesela

Payload: 726 t

f(d)
Transportation of mobile chipper between roadside storage locations

Truck (28–34 t total capacity, EURO 4)a

Payload: 16.0 tc

f(d,r)
Loading/unloading of ST on/from truckTruck's own crane0.05 gCO2eq MJ−1 d
Chipping of EW and HR at the roadside storage siteMobile drum chipper0.91 gCO2eq MJ−1 e
Crushing of ST at a railway loading location or in the plant yard (includes loading of the crusher with a crane)Mobile stump crusher0.50 gCO2eq MJ−1 f
Transportation of loose ST by stump truck

Truck (60 t total capacity, EURO 4)a

Payload: 16.7 tg

f(d,r)
Transportation of chips by a chip truck

Truck (60 t total capacity, EURO 4)a

Payloadh for EW and HR: 27.7 and 33.8 t

f(d,r)
Loading/unloading of comminuted material on/from a truck or trainWheel loadera

HR: 0.0084 gCO2eq MJ−1

EW: 0.0068 gCO2eq MJ−1

ST: 0.0071 gCO2eq MJ−1

Rail transportation of comminuted material

Rail-transport cargo, diesel/electrically driven, 1000 t total capacitya

Load weighti for EW, HR, and ST: 378, 461, and 397 t

f(d)
Electricity used in railway transportation

Average: Average power-grid mix: 1–60 kV, Finlanda

Coal: Coal power, Finlanda

Hydro: Hydropower, Finlanda

82 gCO2 eq MJel−1

270 gCO2 eq MJel−1

3.9 gCO2 eq MJel−1

In addition to the supply-chain operations presented in Fig. 1, the production and supply chains for diesel fuel used in trucks, diesel trains, chippers/crushers, and wheel loaders were included in the emission calculations. Diesel fuel is transported from a refinery in Porvoo (60°19′00″N, 25°30′00″E) to the plants and railway loading locations. Chipping includes loading the chipper and blowing the chips onto a truck or a storage pile. Unloading of chips from a truck or train includes emptying the cargo space and moving the chips into a storage pile. The calculations related to railway transportation assumed a train with a total cargo volume of 1440 m3 because this has been estimated to be the most cost-efficient train size for transporting comminuted forest fuels in Finnish conditions (Korpinen et al., 2010). The conversion factor between loose and solid volumes of comminuted biomass was 2.5 (1 m³solid = 2.5 m³loose) (Hakkila, 2004; Kärhä et al., 2011). All truck- and train-transport emission calculations included empty returns. The GHG emissions from railway transportation were assessed for electric trains, using three types of electricity, and diesel trains. The types of electricity used by the electric trains were average power in the Finnish power grid (the basic scenario), hydropower, and hard coal condensing power. The dominant railway transportation company in Finland, VR Group, uses 100% hydropower (VR Group, 2011), whereas coal condensing power is typically considered to be a marginal electricity source in Finland (Kara, 2005). The emission data for electricity production take into account the whole production and supply chain (GaBi Databases, 2012). Building of transportation infrastructure (roads and railways) and the manufacturing and end-of-life handling of machines and transportation equipment were excluded from the emission calculations.

Case studies

The research included three case studies of forest-biomass supply of 7.2 PJ yr−1 to Porvoo (PRV); Rauma (RMA); and Ajos, in Kemi (AJO). Four logistics scenarios for each case-study location were assessed (see Table 3).

Table 3. Case-study scenarios in the supply of 7.2 PJ yr−1 of forest biomass to the plants
ScenarioProportion (amount) delivered to the plant by truck (Truck supply chain)Proportion (amount) delivered to the plant by railway (Railway supply chain)
33% by truck33.3% (2.4 PJ)66.7% (4.8 PJ)
50% by truck50.0% (3.6 PJ)50.0% (3.6 PJ)
67% by truck66.7% (4.8 PJ)33.3% (2.4 PJ)
100% by truck100% (7.2 PJ)0% (0 PJ)

The maximum and minimum shares of truck and railway transportation in the scenarios considered correspond to the FT-diesel plant consortia's estimates (UPM-Kymmene Oyj, 2009; Vapo Oy & Metsäliitto, 2010; NSE Biofuels Oy Ltd, 2011), except the scenario ‘100% by truck', in which only the Truck supply chain is used.

Two railway loading locations were selected to serve each of the three plant locations, in accordance with the following criteria: (i) the loading place is planned to serve as a wood terminal in 2018 (Iikkanen & Sirkiä, 2011), (ii) there is no energy-production facility with over 0.36 PJ yr−1 forest-biomass consumption within 10 km by road (Laitila et al., 2010), (iii) the railway loading locations must be at least 150 km by road from the plants, and (iv) the loading spots must be at least 50 km by road from Finnish borders and the coast (thus enabling raw-material supply to the railway loading locations from all directions).

The railway loading locations Parkano (PKO) and Pieksämäki (PIE) were selected to serve the plants in PRV and RMA, and the loading locations of Kemijärvi (KJÄ) and Kontiomäki (KON) were chosen to serve the AJO plant (see Fig. 2). In this study, it was assumed that equal amounts of biomass would be delivered from the two railway loading locations to the plants (again, see Fig. 2). In the supply scenarios that included railway transportation, direct truck transportation was prioritized such that if the supply areas around the plants and loading spots overlapped, the supply points were allocated to the direct truck-transportation supply chain, and thus the supply areas around the railway loading locations were extended accordingly in other directions (as shown in Fig. 2). All three plant locations – PRV, RMA, and AJO – and their biomass supply areas were assessed independent of each other. Thus, the overlapping of the supply areas around PRV and RMA in the 100% by truck – 50% biomass availability – scenario (see Fig. 2) was not taken into account as a further limitation to biomass availability to these plants.

Figure 2.

Plant locations, railway loading locations, and the biomass supply areas for each case-study scenario for 7.2 PJ yr−1 forest biomass supply.

Results

Supply areas

The plant locations, railway loading locations, and biomass supply areas for each case-study scenario are presented in Fig. 2 and the supply areas' radii in Table 4.

Table 4. Supply-area radii of the plants and railway loading locations
 Supply-area radiusa, in kilometers, with 100% availability of biomass (and 50% availability)
Amount suppliedb and study scenario
Location1.2 PJ yr−1, 67% by truck1.8 PJ yr−1, 50% by truck2.4 PJ yr−1, 33% by truck3.6 PJ yr−1, 50% by truck4.8 PJ yr−1, 67% by truck7.2 PJ yr−1, 100% by truck
  1. a

    The supply area's radius represents the longest driving distance that must be covered from a plant or a loading location to a biomass supply point if the annual demand for biomass is to be met.

  2. b

    Sum of EW, HR, and ST from the supply area.

PRV66 (86)76 (102)86 (118)102 (142)
RMA70 (95)84 (116)95 (134)116 (167)
AJO110 (156)137 (188)156 (214)188 (254)
PIE25 (37)31(45)37 (51)
PKO34 (50)42 (61)50 (71)
KON43 (63)53 (78)63 (89)
KJÄ68 (99)83 (122)99 (141)

The availability of raw material is poorer around the AJO plant location than in the more southerly locations of RMA and PRV. If all biomass were supplied to the plants with the direct Truck supply chain, a 7.2 PJ yr−1 supply would require a supply area of 188, 116, and 102 km radius for 100% availability for AJO, RMA, and PRV, respectively. With 50% biomass availability, the corresponding supply-area radii grow to 254, 167, and 142 km. The shares of the various categories of raw material within the supply areas around the plants and railway loading locations are presented in Table 5.

Table 5. Average proportion of harvesting residues (HR), small-diameter energy wood (EW), and stumps (ST) within the supply areas around the plants and railway loading locations
LocationShare of HR–EW–ST (%–%–%)
PRV49–27–24
RMA47–33–21
AJO26–69–5
PIE40–41–19
PKO44–37–19
KON43–47–10
KJÄ22–75–3

GHG emissions

The total GHG emissions of the various supply chains and the GHG emissions per unit of energy (CO2eq MJ−1) delivered to the plant locations are presented in Fig. 3.

Figure 3.

GHG emissions of the supply chains for 7.2 PJ yr−1 of comminuted forest biomass to Porvoo (PRV), Rauma (RMA), and Ajos (AJO) with 100% and 50% availability of forest biomass relative to the techno-economic potential. Left axis: tCO2eq yr−1, right axis: gCO2eq MJ−1. The emissions of truck and railway transportation include loading and unloading, and comminution includes moving of chippers between roadside storage sites. The maximum values of error bars for electric trains (ET) represent coal power, and the minimum values represent hydropower.

For all three plant locations (PRV, RMA, and AJO), with both 100% and 50% biomass availability, the least GHG-emitting supply-chain scenarios were those in which only 33% of the raw material was delivered to the plants by direct truck transportation, assuming that electric trains powered by hydropower or average electricity were used. With 100% biomass availability, the emissions in the least GHG-emitting scenarios were 12 210, 12 050, and 13 970 tCO2eq yr−1 (1.70, 1.67, and 1.94 gCO2eq MJ−1) for PRV, RMA, and AJO, respectively, when electric trains using average power were used for railway transportation. With a 50% limitation in biomass availability, the corresponding GHG emissions were 13 760, 13 720, and 16 780 tCO2eq yr−1 (1.91, 1.91, and 2.33 gCO2 eq MJ−1). In the scenarios in which only direct truck transportation was used, the supply-chain emissions for PRV, RMA, and AJO were 14 780, 15 170, and 19 360 t CO2eq yr−1 (2.05, 2.11, and 2.69 gCO2eq MJ−1) with 100% biomass availability, respectively. With a 50% limitation in biomass availability, the corresponding values were 17 610, 18 670, and 24 260 tCO2eq yr−1 (2.45, 2.59, and 3.37 gCO2eq MJ−1).

With a 50% limitation to the availability of biomass, all scenarios that included railway transportation produced less GHG emissions than the scenarios in which only direct truck transportation was used, regardless of whether diesel or electric trains were used or of the type of electricity used for the electric trains.

In comparison of the plant locations to each other, the results show that in the least GHG-emitting supply scenario (33% by truck, 100% availability), the GHG emissions for a 7.2 PJ yr−1 biomass supply are 14% higher (0.24 gCO2 eq MJ−1 greater) for AJO than for PRV and 16% higher (0.27 gCO2eq MJ−1 greater) than for RMA. With a 50% limitation in biomass availability, the difference is 22% (0.42 gCO2eq MJ−1) between AJO and both RMA and PRV.

Discussion

The results of this study show that the GHG emissions of large-scale forest biomass supply can be effectively reduced through the use of railway transportation, especially if electric trains running on low-GHG electricity are used. The relative GHG emission savings achieved through railway transportation are greater if feedstock availability around the plant is lower. From a broader perspective, the results of this study also indicate that the impact of differences in local feedstock availability on the GHG emissions of the final energy product can be significantly reduced with efficient logistics solutions. In addition to reducing emissions, railway transportation provides a way of utilizing the loading sites for buffer storage and increases the security of supply of biomass related to, for example, disturbances to energy-wood markets (Gronalt & Rauch, 2007). These issues may become increasingly important in the future, as competition for the same biomass resources is expected to rise (Hillring, 2006; Heinimö & Junginger, 2009; Sims et al.,2009).

To meet current EU RED sustainability criteria, the GHG emissions created in the production and use of biofuels may be 54.5 gCO2eq MJ−1 today and 33.5 gCO2eq MJ−1 in 2018 (European Commission, 2009). The GHG emissions created in the transportation and supply chains dealt with in this study ranged from 1.5 to 3.4 gCO2eq MJ−1, or 3–6% of the current emission limits, and 4–10% in 2018, depending on the plant location, the amount of biomass supplied by rail, the type of electricity used in the trains, and any limitations in biomass availability. Therefore, the effects of choices made in arrangement of the feedstock transportation logistics for a commercial-scale liquid-biofuel production facility in Finnish, or similar, conditions have a relatively minor influence on the total GHG emissions in comparison with fossil fuels.

In addition to GHG savings requirements, the EU RED presents a 95% default GHG savings value for FT diesel made from waste wood (such as the feedstock assessed in this study). A 95% reduction from the fossil-fuel comparison value of 83.8 gCO2eq MJ−1 would result in total production-chain emissions of only 4.2 gCO2eq MJ−1. As the processes included in this study have been estimated to account for approximately 50% of the feedstock supply chain's total emissions (Kariniemi et al., 2009), without even the process efficiency at the plant or any changes in the carbon stock of the forest land being taken into account, the default savings values presented in the EU RED seem to be too optimistic, as stated also by Soimakallio & Koponen (2011). This highlights the need for case-specific assessments that take local conditions into account in assessment of the GHG reductions associated with any given biofuel product. It must be noted that the study described here concentrated only on location-specific forest-biomass raw-material supply operations, and the results of this study do not represent the total GHG emissions of a specific final product. The final GHG emissions and savings gained, if any, depend on numerous other factors also, such as changes in the carbon stocks of the forest land and process-technology choices (Palosuo et al., 2008; Repo et al., 2011; Soimakallio & Koponen, 2011; Kilpeläinen et al., 2012; Schultze et al., 2012).

This study addressed two scenarios for availability of forest biomass: 100% and 50% availability relative to the techno-economic potential of the forest biomass. The 100% availability scenario, representing the maximum theoretical availability, can be considered unrealistic: a single user would utilize all of the biomass potential available, and nothing would be left for other users within the supply areas, such as local heating and power plants. In 2011, the use of the biomass fractions assessed in this study in Finland came to 50 PJ (Finnish Forest Research Institute, 2012), corresponding to 44% of the maximum techno-economic potential. Accordingly, the 50% availability scenario is more realistic. The assessments performed in this study also applied the assumption that all forest-biomass fractions (harvesting residues, stumps, and small-diameter energy wood) were available to the plants, regardless of their present use. This may not be the case in practice, as, for example, local heating plants may be able to pay more for the same feedstock. As a consequence, the possibility of using industrial roundwood as feedstock for either local heating and power plants or liquid-biofuel plants cannot be ruled out.

Numerous commercial-scale liquid-biofuel production facilities using lignocellulosic feedstock are currently in the planning phase (IEA, 2008; Bacovsky et al., 2010; European Commission, 2012), and raw-material supply and its logistical challenges are considered to be major constraints on the way toward more widespread commercial deployment of second-generation liquid biofuels, as Sims et al. (2010) point out. Therefore, future research should be conducted for other potential plant locations and their supply areas, as well as for other feedstock combinations, including the potential for imports by sea or land. The effect of load sizes in truck and railway transportation and that of local transportation infrastructure should also be addressed in further work in view of the specific location under consideration.

  1. 1

    The contribution made by biofuels produced from wastes, residues, nonfood cellulosic material, and lignocellulosic material shall be considered to be twice that of other biofuels (Directive 2009/28/EC).

  2. 2

    The methodology for the calculations of potential in relation to harvesting residues and stumps is presented by Laitila et al. (2008) and that related to small-diameter energy wood by Anttila et al. (2009).

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