Ethanol production in biorefineries using lignocellulosic feedstock – GHG performance, energy balance and implications of life cycle calculation methodology

Journal of Cleaner Production 83 (2014) 420e427

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Ethanol production in biorefineries using lignocellulosic feedstock e GHG performance, energy balance and implications of life cycle calculation methodology € rjesson b, Per-Anders Hansson a, Serina Ahlgren a Hanna Karlsson a, *, Pål Bo a b

Department of Energy and Technology, Swedish University of Agricultural Sciences, P.O. Box 7032, SE-75007 Uppsala, Sweden Environmental and Energy Systems Studies LTH, Lund University, P.O. Box 118, SE-221 00 Lund, Sweden

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 December 2013 Received in revised form 10 July 2014 Accepted 11 July 2014 Available online 19 July 2014

Co-production of high-value biobased products in biorefineries is a promising option for optimized utilization of biomass. Lignocellulosic materials such as agricultural and forest residues have been identified as attractive alternative feedstocks because of their high availability and low resource demand. This study assessed the greenhouse gas (GHG) performance and energy balance of ethanol co-production with biogas and electricity in biorefineries using straw and forest residues. Two calculation methods were used: Method I (ISO), which applied the international standard for life cycle assessment, and Method II, which applied the EU Renewable Energy Directive (RED) methodology. These methods differed in allocation procedure, functional unit and system boundaries. Analysis of the importance of significant methodological choices and critical parameters showed that the results varied depending on calculation method, with co-product handling and the inclusion of upstream impacts from residue harvesting explaining most of the differences. Important life cycle steps were process inputs in terms of enzymes and changes in soil organic carbon content due to removal of residues. Ethanol produced from forest residues generally gave lower GHG emissions than straw-based ethanol. The GHG savings for both feedstocks were 51e84% relative to fossil fuel. Omission of upstream impacts from residue recovery in agriculture and forestry in the RED method means that it risks overlooking important environmental effects of residue reuse. Furthermore, the default allocation procedure used in the RED method (energy allocation) may need revision for biorefineries where multiple products with different characteristics are co-produced. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Biorefinery Lignocellulosic materials Life cycle assessment Ethanol Biogas Calculation methodology

1. Introduction Ethanol production is predicted to double globally in the coming decade (OECD-FAO, 2012). The growth will mainly be driven by newly introduced policies in e.g. the US and EU aimed at reducing dependency on fossil fuels and greenhouse gas (GHG) emissions and promoting domestic and rural markets (CRS, 2013; EC, 2009). The Swedish transport sector is currently 92% fossil-based and the main biofuels in use are biodiesel (54% of total biofuels), ethanol (34%) and biogas (12%) (SEA, 2013). The Swedish biofuel market is largely dependent on imports, as two-thirds of the ethanol and practically all biodiesel are either produced from imported feedstock or imported as fuel (SEA, 2012).

* Corresponding author. E-mail address: [email protected] (H. Karlsson). http://dx.doi.org/10.1016/j.jclepro.2014.07.029 0959-6526/© 2014 Elsevier Ltd. All rights reserved.

Biofuel production currently uses approximately 65% of the vegetable oil produced in the EU, 50% of Brazilian sugarcane and 40% of US maize (OECD-FAO, 2012). However, in recent years the sustainability of using conventional food crops for biofuel production has been questioned. The main concerns are competition with food production (Escobar et al., 2009) and GHG emissions due to direct and indirect land use changes (Fargione et al., 2008; Searchinger et al., 2008). As an alternative, lignocellulosic materials have been identified as promising feedstock for sustainable biofuel production (Tilman et al., 2009). Swedish potential yield of non-harvested forest residues (tops and branches, not including stumps) is estimated to be 24.0e53.2 TWh per annum (SFA, 2008) and straw supply 3.3 TWh per annum (Nilsson and Bernesson, 2009). The cellulose and hemicellulose in lignocellulosic material can be converted to sugars through hydrolysis and these sugars can then be further fermented to ethanol. However, sugar production

H. Karlsson et al. / Journal of Cleaner Production 83 (2014) 420e427

from lignocellulosic materials is more complex than the wellknown process of starch hydrolysis involved in producing ethanol from e.g. maize and wheat. To enable efficient hydrolysis, pretreatment to modify the structure and chemical composition of the material is required (Zheng et al., 2009). Several alternatives for hydrolyzing cellulose and hemicellulose exist, of which chemical and enzymatic hydrolysis are regarded to be the most promising (Binod et al., 2011). The advantages of lignocellulosic biomass include its abundance (Balat, 2011) and the possibility of decreasing competition with food production (Escobar et al., 2009), environmental footprint (Kamm et al., 2007) and costs (JRC, 2007). In recent studies, biofuel production from lignocellulosic biomass has shown promising GHG and energy balances compared with first-generation biofuels (Williams et al., 2009; Wiloso et al., 2012). Technological and economic challenges still remain before large-scale commercial use (King et al., 2010), but lignocellulosic materials are predicted to be the main feedstock for ethanol production in the near future (Mussatto et al., 2010). The biorefinery concept, defined by International Energy Agency (IEA) Task 42 on Biorefineries as: “…the sustainable processing of biomass into a spectrum of marketable products and energy”, is gaining increased interest. Co-production of commodities such as energy, food and chemical products in biorefineries can be a viable commercial and practical option in future optimized utilization of biomass (IEA, 2009; Kamm et al., 2007). Co-production of ethanol and biogas is beneficial in Sweden, since both fuels are used as transportation fuels. Further, the biofuel yield of sequential ethanol fermentation and biogas digestion has shown to be higher than separate production systems, (Dererie et al., 2011). Although lignocellulosic ethanol in general is considered to have a lower environmental impact than first-generation ethanol, it is important to evaluate fuels produced from lignocellulosic material using a systems approach. The life cycle assessment (LCA) method allows the environmental impact of products and services to be evaluated from cradle to grave (ISO, 2006a,b). LCA is commonly used to assess the environmental impact of biofuels. However, there are still some methodological issues to be resolved, e.g. the results can vary significantly not only due to biofuel production system differences, but also depending on methodological choices € rjesson and Tufvesson, 2011; Gnansounou et al., 2009). This (Bo creates uncertainty and prevents comparison between different feedstocks and technologies. Several authors have called for more coherent LCA practice, e.g. Wiloso et al. (2012) and Singh et al. (2010). The EU Renewable Energy Directive (RED) (EC, 2009) includes a mandatory target for biofuel use in the transport sector of 10% of total energy consumption by 2020 including GHG reduction targets from a fossil fuel reference. The method to calculate the GHG performance of fuels is based on the LCA methodology with standardized procedures for system boundaries, functional unit and allocation. The RED also contains sustainability criteria for liquid and gaseous biofuels that must be met in order for the biofuel to count towards the target. To stimulate the production of biofuels from lignocellulosic materials in the EU these types of fuels are promoted by counting double towards the 10% target (Ahlgren, 2012). By implementing reduction targets the calculation method induced by the RED is highly influential in the European biofuel market. In addition, more biofuels are predicted to be produced from lignocellulosic material and increasingly in multi-output biorefinery systems. Hence, the applicability of the RED method in comparison with other LCA methods to these production systems is of interest. The aim of the present study was to analyze the GHG performance and energy balance of ethanol co-production with biogas

421

and electricity in a biorefinery. Two methods were used, one applying the international standard of life cycle assessment, referred to as Method I (ISO), and one applying the RED (EC, 2009) method, referred to as Method II (RED). Two lignocellulosic materials relevant for the Swedish context, straw and forest residues were considered. In addition, sensitivity analyses were performed to examine the importance of significant methodological choices and critical parameters, with the objective of contributing to the discussion about use of LCA in assessing the environmental performance of energy production from lignocellulosic biomass and co-production in biorefineries. 2. Methodology and assumptions Life cycle assessment was used to evaluate two large-scale hypothetical biorefinery systems, one utilizing straw as feedstock (straw scenario) and one using forest residues (forest residue scenario). Technical data on process performance and input requirements were taken from technical/economic studies by Ekman et al. (2012) and Barta et al. (2010). The technical specifications of the biorefinery processes are presented in Table 1. The processes consisted basically of a pre-treatment followed by simultaneous hydrolysis and fermentation (SSF) and anaerobic digestion. The dewatered, lignin-rich digestate residue from the process was combusted to produce heat and electricity. The straw biorefinery was assumed to be located in southern Sweden and forest residue biorefinery in northern Sweden. Straw supply considers straw from grain and oilseeds, whereas the technical model was based on wheat straw (Ekman et al., 2012) and the forest residues scenario used softwood (Barta et al., 2010). Biorefinery performance refers to near-term commercial technologies and the Swedish situation. Two calculation methods were used. In Method I (ISO), ethanol was considered to be the main product and the multi-functionality was treated by system expansion, following the recommendations in the ISO standard for LCA (ISO, 2006b). In Method II (RED), the calculation procedures in the RED were used, including the energy allocation approach (EC, 2009). Both methods are further explained below. The scenarios were analyzed using a well-to-tank perspective, i.e. end use of the fuels was not included. However, the wellto-tank perspective was considered sufficient, since the aim was to compare the scenarios, for which energy content was a sufficient FU. The energy balance was calculated as primary fossil energy over ethanol production use in the production processes. Factors for fossil fuel emissions and primary energy differ between the methods. In Method II (RED), values of 83.8 g CO2eq MJ
1 and 1.16 MJ MJ
1 are used for all fossil fuels (EC, 2009; IEE, 2012), while Method I (ISO) uses 89.2 CO2eq MJ
1 and 1.19 MJ MJ
1 for

Table 1 Technical specifications and yearly production in the straw and forest residue scenarios.

Annual feedstock Pre-treatment Hydrolysis Anaerobic digestion Biogas upgrading Ethanol production Biogas production Electricity production Heat production a b

Ekman et al. (2012). Barta et al. (2010).

Strawa

Forest residuesb

120 000 t DM Dilute acid (H2SO4) Enzymatic Mesophilic, continuous stirred tank Amine absorption 945 TJ 55 TJ 98 TJ 328 TJ

200 000 t DM Steam and SO2 Enzymatic Mesophilic, continuous stirred tank Pressure swing adsorption 1050 TJ 761 TJ 155 TJ 895 TJ

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diesel and 87.6 CO2eq MJ
1 and 1.17 MJ MJ
1 for petrol (JRC, 2011a, 2011b). 2.1. System description and functional unit Collection of feedstock, transportation to the biorefinery and biorefinery inputs such as enzymes, chemicals and nutrients were included in both calculation methods. Infrastructure, machinery and buildings were excluded, as were storage of substrates and end use of the fuels. Fig. 1 summarizes the system studied and the system boundaries used in the two methods. In Method I (ISO), impacts on the surrounding system were included by expanding the system so that co-products replaced conventional products with the same function (ISO, 2006b), and impacts on the agricultural and forestry system were accounted for by using a land use reference with no straw or forest residue harvesting. In the latter case, the differences in nutrient and carbon balance compared with the reference system were taken into account, but no impact from cultivation was allocated to the straw or forest residues. Ethanol was the main product and the FU was set to 1 MJ based on the lower heating value (LHV) ethanol co-produced in a biorefinery with the coproducts biogas, electricity and heat. The biogas was assumed to be upgraded and ready to use as vehicle fuel. The forest residue scenario produced relatively large amounts of biogas, so this scenario was also analyzed with the FU 1 MJ upgraded biogas. The heat generated was not utilized because this would require the biorefinery to be located close to a large heat sink. In Method II (RED), no single main product needed to be selected, as all the output products were energy products and partitioning was based on energy content. The FU was set to 1 MJ (LHV) high-value fuel (ethanol, biogas or electricity). Apart from harvest operation impacts, further impacts from biomass harvesting were not included, in accordance with the recommendations in the RED. The RED recognizes seven land use categories: forest land, grassland, cropland, wetlands, settlements, other land and multi-annual crops. Soil organic carbon (SOC) changes due to land use changes are included if the land use change can be characterized as changing from one category to another (Ahlgren, 2012). Consequently, SOC due to removal of straw and forest residues should not be accounted for according to the RED.

Table 2 Avoided technologies in the substitution in Method I (ISO). Product

Replaces

Amount

Biogas Electricity Heat Ethanol

Gasoline Natural gas electricity e Petrol

1 MJ biogas replaces 1.01 MJ petrola 1 MJ replaces 1 MJ electricityb e 1 MJ ethanol replaces 1 MJ petrol1

a

JRC (2011a). GWP and energy data (Gode et al., 2011), power plant efficiency 58% (Uppenberg et al., 2001). b

2.2. Methods to handle multi-functionality Both the feedstock production system and the biorefinery are multi-output processes. Choice of method to use in LCA to handle multi-output situations is not straight-forward. In studies of bioenergy systems, choice of method has been shown to have a strong € rjesson, 2009; Gnansounou et al., 2009; impact on the results (Bo Luo et al., 2009). The ISO standard prioritizes methods to deal with multi-functionality in the following order: Avoiding allocation by increasing the level of detail in the study (subdivision) or the use of system expansion; partitioning based on physical relationships between the products; and partitioning based on other relationships between the products, such as economic value (ISO, 2006b). Avoiding allocation by subdivision was not feasible in the present case, since the processes in the biorefineries were highly interlinked, so system expansion was used in Method I (ISO), in line with the ISO standard. The environmental impact of the main product was calculated as the emissions from the main production system minus the avoided emissions from the production systems replaced by the co-products on the market (Table 2). This form of system expansion is sometimes called substitution, a term also used in this study. In Method I (ISO), biogas was assumed to replace petrol and excess electricity was assumed to replace natural gasbased electricity. Utilization of excess heat was assessed in a sensitivity analysis. Petrol was used to illustrate the effect of replacing conventional fossil fuels with a biofuel. On the Nordic electricity market, natural gas-based electricity is the technology with the highest variable costs (Widerberg and Wråke, 2009) and is likely be the first type of electricity production to be out-competed on the market.

Fig. 1. Overview of the system studied using the system boundaries of (left) Method I (ISO) and (right) Method II (RED).

H. Karlsson et al. / Journal of Cleaner Production 83 (2014) 420e427

In Method I (ISO), an alternative fate for the feedstock of being left at the field or forest site rather than being harvested and used in a biorefinery was considered. The impact of biomass removal was accounted for by using a land use reference with no straw and forest residue harvesting considering the SOC changes and nutrient compensation. In Method II (RED), energy allocation is based on LHV and excess electricity can be handled in two ways. Credit (decreased emissions) is accounted for in some cases, although not when the electricity is produced from a co-product, e.g. the de-watered lignin-rich residue in this study. Then the principle of allocation based on energy content also applies for electricity (point 16e18 in Annex V of EC (2009)). Heat has no LHV and is therefore not accounted for (EC, 2010).

423

3.4. Transportation of feedstock For all road transport, a lorry with load capacity 33 t and a maximum load volume of 110 m3 was assumed. Data on diesel use during transport and properties of the lorry were taken from NTM (2010). The total diesel use for transport, including the empty return, was 0.066 MJ kg
1 DM and 0.20 MJ kg
1 DM for the straw and forest residue scenarios, respectively (Table 3). 3.5. Biorefinery inputs

Diesel use for collection and handing of the straw up to unloading from storage was estimated using data from Nilsson € rjesson and Tufvesson (2011) to be 0.27 MJ kg
1 (1997) and Bo DM straw (DM denotes dry matter). For forest residues collected as loose residues in northern Sweden, diesel use for collection, forwarding, loading, unloading and comminution was estimated using data from Lindholm et al. (2010) to be 0.21 MJ diesel kg
1 DM harvested. The straw harvest was assumed to be 2 t ha
1. Depending on crop variety, 50e85% of the biological straw yield was assumed to be harvested, with biological straw yield calculated using the straw/ grain (Nilsson and Bernesson, 2009) and average grain yield (period 2007e2011) (SBA, 2012). Yield of logging residues was set at 26.3 t ha
1, with approx. 65% tops and branches (Lindholm et al., 2010).

Table 4 shows the biorefinery inputs of enzyme and nutrients. All nitrogen added was assumed to be in the form of ammonia, phosphorus as diammonium phosphate and sulphur as sulphur dioxide. Yeast was assumed to be continuously cultivated in the biorefinery (Barta et al., 2010; Ekman et al., 2012). The enzyme dose was set to 12.4 g kg
1 DM and 8.2 g kg
1 DM for the straw and forest residue scenario, respectively, assuming 4 g enzyme product 100 g
1 for 90% cellulose conversion (straw scenario) and 3 g enzyme product 100 g
1 cellulose for 80% cellulose conversion (forest residue scenario). These doses were based on cellulose conversion in maize stover (Novozymes A/S, 2012) and the cellulose content of the pretreated feedstock. The cellulose content in pretreated straw was assumed to be 31% and in forest residues 27% (40% cellulose in untreated forest residues (Stephen et al., 2012) and 69% of the initial cellulose remained after pretreatment (Z. Barta, personal communication 2014)). Basing enzyme dose on maize stover has limitations, as the actual dose depends on multiple factors such as feedstock type, processing conditions (Novozymes A/S, 2012), pre-treatment method (Linde et al., 2008; Yu et al., 2011) and enzyme mixture (Berlin et al., 2007). The impact of changing the enzyme dose was assessed in a sensitivity analysis.

3.2. Nutrient compensation

4. Results

The ash remaining after incineration was assumed to be returned to cultivation to recover some of the nutrients (particularly phosphorus and potassium, but also magnesium and particular micronutrients). Therefore, nutrient compensation included only the nitrogen lost during combustion. In Method I (ISO), all nitrogen removed with straw and forest residues was assumed to be replaced using mineral fertilizer. Nitrogen content in straw is approx. 0.5% of DM for cereal straw and 2.3% of DM for oilseed straw (Phyllis2, 2012). Approx. 17% of the straw used was assumed to be oilseed straw and the rest cereal straw. Average nitrogen content was set at 8.4 g N kg
1 DM, equivalent to approx. 17 kg ha
1. Nitrogen content in branches and tops is around 4.5 g N kg
1 DM (Hellsten et al., 2008), equivalent to 118 kg N ha
1 in one rotation period. GHG emissions from nitrogen fertilizer production were estimated to be 5.1 kg CO2eq kg
1 based on data from Ahlgren et al. (2012) representing the weighted average for production in Norway, Russia and other suppliers in the EU. For primary energy use, 54 MJ kg
1 N was used (IEE, 2012).

4.1. Greenhouse gas performance

3. Inventory analysis 3.1. Harvesting of straw and forest residues

3.3. Soil carbon changes In the straw scenario, an annual SOC loss of 150 kg C ha
1 per € rjesson et al., 2010), giving year for 30e50 years was assumed (Bo 75 g C kg
1 DM straw. The corresponding figure in the forest residue scenario was 90 g C kg
1 DM forest residues in a 120-year perspective (Lindholm et al., 2011). Consequently, a longer time perspective was used in the forest residue scenario, representing one complete rotation, than in the straw scenario.

The GHG performance with Method I (ISO) is shown in Fig. 2. The forest residue scenario gave a lower impact per MJ ethanol, but with the emissions (positive values) and avoided emissions (negative values) both being significantly higher than in the straw scenario. There were two reasons for this. Firstly, the reference flow of feedstock to produce 1 MJ ethanol was higher in the forest residue scenario (0.19 kg DM) than in the straw scenario (0.13 kg DM), causing higher emissions. Secondly, the forest residue scenario produced more biogas and electricity in relation to ethanol than the straw scenario, resulting in more co-product substitution effects. The most important stages in the life cycle, apart from the substitution effects, were SOC changes, representing 59% and 66% of the emissions for the straw and forest residue scenarios, respectively, and enzymes, representing 21% and 13%, respectively. When biogas was considered the main product in the forest residue scenario,

Table 3 Transport distance, bulk density and DM content of straw and forest residues. Straw Transportation distance (km) Bulk density (kg m
3) DM content (%)

45.0 175 82.0

Forest residues

Reference

136

Ekman et al. (2012)/ Lindholm et al. (2010) Agriwise (2012)/ Lindholm et al. (2010) €rjesson (2006)/ Berglund and Bo Lindholm et al. (2010)

797 54.0

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H. Karlsson et al. / Journal of Cleaner Production 83 (2014) 420e427

Table 4 Input of nutrients and enzymes to the production process. Input (g kg
1 DM)a

Straw

Forest residues

kg CO2eq kg
1

MJ kg
1

Reference to GHG values and primary energy use

Molasses Enzyme product Ammonia (N) Phosphorus (P) Sulphur (S)

31.5 12.4 2.30 0.62 0.08

35.4 8.23 7.64 1.16 12.9

0.14 8.00 3.23 2.32 0.84

0.57b 100 54.0 34.9 15.6

€ et al. (2008) Flysjo Kløverpris (2012) IEE (2012) IEE (2012) ecoinventCenter (2010)

a b

Inputs of nutrients and molasses based on (Barta et al., 2010; Ekman et al., 2012). € et al., 2008). Calculated based on diesel and fertilizer use in (Flysjo

with Method I (ISO) the GHG balance was
13.7 g CO2eq MJ
1 biogas, due to the large substitution effect when ethanol replaces petrol. In Method II (RED), emissions were allocated based on energy content, so the impact of 1 MJ ethanol was the same as for 1 MJ biogas or electricity (Fig. 3). The straw scenario still had the highest GHG emissions with Method II, although the difference between the scenarios declined significantly. The impact from enzymes was significant, 70% and 45% of the total for the straw and forest residue scenarios, respectively. 4.2. Energy balance The energy balance shows the primary fossil energy required to produce 1 MJ ethanol. The energy content of the biomass was not included. Again, enzymes had a significant impact in both calculation methods and proved to be the most significant contributor to both scenarios when Method II (RED) was used (Table 5). Using Method I (ISO), the importance of avoided energy use was large, especially in the forest residue scenario where biogas replacing petrol gave a large reduction in energy use (Table 5). In the forest residue scenario more energy was avoided than was used, as indicated by the negative value.

determining the optimal dose (Novozymes A/S, 2012). The altered doses resulted in the same absolute change for both scenarios with Method I (ISO), where all impacts were distributed over amount of ethanol produced. Method II (RED) used energy allocation and therefore the increased impact was divided equally over all energy carriers. The lower ethanol production in the forest residue scenario resulted in a lower dose increase with respect to total energy generation. Therefore, the forest residue scenario showed a lower increase using Method II (RED). Using straw and forest residues for bioenergy removes biomass from the soil, altering the balance between inputs and outputs of soil carbon. In the long-term, a new equilibrium of soil carbon is established. The total amount of soil carbon depends on factors such as soil type, climate, moisture and agriculture or forest management (Cowie et al., 2006; SEPA, 2006). Therefore, the actual impact of residue removal in this study was uncertain. In the sensitivity analysis, the SOC losses were varied by ±50%. For the forest residue scenario, the analysis also tested values from Lindholm et al. (2011), who modelled soil carbon changes when

5. Sensitivity analysis The impact of heat utilization and the life cycle stages with the largest impact on GHG performance (Figs. 2 and 3), i.e. enzyme dose, SOC changes and choice of avoided technology, were further analyzed in sensitivity analyses (Table 6). Enzyme dose varies significantly depending on enzyme activity, substrate and process design. In the sensitivity analysis, enzyme dose was varied ±50%. These doses were within the initial trial doses (1e6 g enzyme product/100 g cellulose) recommended for

Fig. 3. GHG performance results obtained using Method II (RED).

Table 5 Energy balance (MJ MJ
1 ethanol) obtained using Methods I and II.

Fig. 2. GHG performance results obtained using Method I (ISO).

Harvesting N compensation Transport Inputs Enzymes Molasses, ammonia, phosphorus and sulfur Avoided energy use Biogas replaces petrol Electricity replaces natural gas-based electricity Total

Method I (ISO)

Method II (RED)

Straw

Straw

Forest residues

Forest residues

0.041 0.057 0.010

0.049 0.046 0.047

0.034

0.025

0.008

0.024

0.158 0.021

0.157 0.129

0.136 0.018

0.084 0.068


0.068
0.195


0.859
0.278

0.024


0.710

0.196

0.202

H. Karlsson et al. / Journal of Cleaner Production 83 (2014) 420e427

425

Table 6 Results from the sensitivity analysis presented as percentage change in total GHG performance and energy balance. GHG performance

Base case (g CO2eq MJ
1 ethanol)/(MJ MJ
1 ethanol) Data input parameters Enzyme dosage ±50% SOC ±50% SOC changes short-term SOC changes long-term SOC included in RED Nutrient replacement in RED Substitution Electricity replaces coal powera Electricity replaces wind powerb Biogas replaces ethanolc 50% of the heat is utilized and replaces coal-based heatd 50% of the heat is utilized and replaces fuel oil-based heatd 50% of the heat is utilized and replaces pellet-based heatd a b c d

Energy balance

Method I (ISO)

Method II (RED)

Method I (ISO)

Method II (RED)

Straw

Forest residues

Straw

Forest residues

Straw

Forest residues

Straw

Forest residues

41.4

13.2

15.5

14.9

0.024


0.710

0.197

0.202

±15% ±42% e e e e

±48% ±236% þ8410%
214% e e

±35% e e e þ194% þ32%

±23% e e e þ224% þ16%

±330% e e e e e

±11% e e e e e

±35% e e e e þ25%

±21% e e e e þ12%


27% þ29% þ10%
50%
37%
2.0%


122% þ129% þ377%
388%
286%
16%

e e e e e e

e e e e e e


246% þ793% þ194%
983%
885%
75%


12% þ38% þ82%
78%
74%
6.3%

e e e e e e

e e e e e e

GWP and energy data (Gode et al., 2011), power plant efficiency 47% (Axelsson et al., 2009). GWP and energy data (ecoinventCenter, 2010). Fuel requirements (JRC, 2011a), average of sugar beet and wheat ethanol (Gode et al., 2011). Boiler efficiencies: 106% fuel pellets (condensing boiler assumed), 91% fuel oil 89% coal (Uppenberg et al., 2001) GWP and energy data (Gode et al., 2011).

harvesting/not harvesting forest residues in northern Sweden in three periods: short-term (20 years), medium-term (one rotation including a fallow, 120 years) and long-term (two rotations, 240 years). The resulting SOC losses were significantly higher in the short-term period than in the other periods. The reason was that the easily decomposable biomass had not decomposed within 20 years, creating a large difference between the reference case (no residue harvesting) and the case with forest residue removal (Lindholm et al., 2011). Changes in the SOC losses (±50%) resulted in a higher percentage change in the forest residue scenario than in the straw scenario, due to the lower initial total GHG emissions. The alternative product and production system avoided via coproduct generation is often difficult to identify and the choice can have a large impact on the results. Therefore, assumptions of avoided processes in the substitution method were changed in the sensitivity analysis to include alternative fossil energy sources and renewable energy sources, such as wind power. Heat utilization of 50%, e.g. in district heating systems, where the heat replaces other energy sources, was also analyzed. The forest residue scenario generated more co-products, so it was more affected by the changed assumptions than the straw scenario. Similarly, more heat was produced in the forest residue scenario, leading to larger benefits when the heat replaced other heat production systems. 6. Discussion This study assessed the GHG performance and energy balance of ethanol co-production with biogas and electricity using two different calculation methods. Calculation method used greatly influenced the results, with most of the difference explained by handling of the co-products and the inclusion of upstream impacts from residue harvest. Process inputs, mainly enzymes, and changes in the SOC content were the most important life cycle steps, while harvesting operations and transport of the feedstock were of minor importance. Similarly to this study, previous studies have identified enzymes as a potentially significant contributor to GHG emissions (MacLean and Spatari, 2009; Slade et al., 2009). However, enzyme production is sometimes excluded in LCA studies on lignocellulosic ethanol (Wiloso et al., 2012), making it difficult to compare the results of different studies. To lower the impact from enzymes, the dose of

enzymes needs to be lowered. This can be done for example by finding suitable pre-treatment methods or by increasing the efficiency of the enzymes. Appropriate pre-treatment methods can enhance the hydrolysis and lower the need for enzymes. Yu et al. (2011) observed improved enzymatic hydrolysis after pretreatment with green liquor and delignification especially for soft wood. Linde et al. (2008) showed that enzymatic hydrolysis of steam-treated wheat straw gives high glucose and xylose yields with low doses of enzymes when optimizing pre-treatment temperature and time. Moreover, considerable efficiency improvements in enzymes have been achieved (Novozymes A/S, 2012), and efforts are being made to further enhance enzyme efficiency. For example, significant lowering of enzyme dosages can be achieved by developing appropriate enzyme mixtures to limit the effect of inhibiting substances (Berlin et al., 2007). For large-scale plants, onsite production of enzymes to enable the use of biomass-based energy in manufacturing could be another option to lower the impact of enzymes on the overall results. In our study the SOC changes greatly affected the GHG performance when using the ISO method. Similarly, in a study by Cherubini and Ulgiati (2010) the SOC changes due to crop residue harvesting amounted to around 50% of the total impact of a biorefinery. The RED categorizes straw as a residue, so no upstream impact from the cultivation is allocated to the straw. According to Whittaker et al. (2011), this basically suggests that straw is a waste and that the sustainability of straw recovery is not taken into consideration. Although the degree and rate of SOC changes depend on multiple factors such as tillage practices, climate etc., there is a strong relationship between carbon input and SOC content (Kong et al., 2005). SOC not only stores carbon, thereby mitigating GHG emissions, but also ensures the long-term sustainability and productivity of agrosystems. For this reason, use of crop residues for bioenergy production has been questioned (Lal, 2008; Wilhelm et al., 2004). In the present study the digestate was assumed to be combusted to obtain process electricity and heat. To decrease the impact of SOC change, the digestate remaining after fermentation and anaerobic digestion could be returned to the field or forest site. The digestate is rich in lignin, which is more resistant to decomposition than cellulose and hemicellulose. The carbon in the digestate can therefore be expected to be more stable in the soil and replace some

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of the carbon removed with the residues (Kumar and Goh, 1999; Wilhelm et al., 2004). However, process energy would have to be obtained from other sources. One option could be to combust the biogas in a CHP plant to generate process electricity and heat, although this would lower the energy conversion ratio and the potential of replacing fossil transportation fuels. Returning digestate could also be a way of maintaining a good nutrient balance. Nitrogen deficiency due to removal of forest residues could result in a lower growth rate, equivalent to 2e4 years of normal growth in one rotation (SEPA, 2006), especially in northern Sweden, where nitrogen deposition is low (Egnell et al., 1998). However, removing residues may not always be negative from a nutrient point of view. In southern Sweden, nitrogen deposition is relatively high and removal of forest residues will most likely not affect the growth rate of the forest, but will decrease the risk of nitrate leaching into waterways and wetlands (SEPA, 2006). The effect of straw removal on the nitrogen cycle of agricultural soil is not clear. Compensation would probably not be necessary in a short-term perspective, but more relevant in the long-term perspective. Compensation for potassium and phosphorus removal would most likely be necessary (Powlson et al., 2011). The choice of allocation method can have a major influence on the results, as shown by Cherubini et al. (2011). The present study used two different methods to allocate emissions between coproducts, partitioning and substitution. The RED method recommends energy partitioning based on LHV, which means that energy becomes the determining characteristic of all products. For biorefineries this might be problematic when not all co-products are produced for energy purposes (Gnansounou et al., 2009; Cherubini et al., 2011). For example, excess heat in the form of warm water has no heating value in the method and is therefore not attributed any environmental burden, even though it may have an energy use in e.g. district heating. Furthermore, all energy carriers are equated, irrespective of their application in society and past production history. Another basis for partitioning instead of energy content may be used, such as mass or economic outcome. Cherubini et al. (2011) suggest that a suitable basis for partitioning when dealing with a diverse set of co-products (energy and materials) is economic, exergy or a hybrid approach of system expansion and allocation. The present study showed that when substitution was used, the results were clearly dependent on choice of main product and replaced alternative production systems. For biorefineries, this has also been shown by Cherubini et al. (2011). The production mix of the biorefinery was also very important, e.g. relatively large production of co-products resulted in significant GHG credits in the forest residue scenario in the present study. Previous studies on lignocellulosic ethanol production have largely focused on stand-alone processes in isolation from other system processes (Wiloso et al., 2012). However, it is often assumed that excess electricity is generated from burning the lignin-rich residue (Searcy and Flynn, 2008) or the lignin and co-produced biogas (Spatari et al., 2010). Spatari et al. (2010) showed that when substitution is used, the credits from exported electricity may result in a negative value for the energy balance and GHG performance of the ethanol. One advantage with the substitution method is that it can deal with products with different functions and characteristics for which it may be difficult to find a common characteristic as a base for partitioning. Owing to uncertainties in the data, the changes made in the sensitivity analysis had a great impact on the final results. However, the total GHG emissions per MJ ethanol were higher than in a fossil fuel reference (83.8 g CO2eq MJ
1) in only one case, namely the forest residue scenario with SOC changes accounted for in the short-term perspective (Method I (ISO)). The sensitivity analysis

also showed that when SOC changes of 75 g and 90 g C kg
1 DM straw and forest residues, respectively, were included in Method II (RED), the ethanol in both scenarios would only meet the current RED target of 35% reduction relative to fossil fuel and not the forthcoming up to 60% reduction target. The real enzyme dose for ethanol production from straw and forest residues in large-scale biorefineries is not fully known. Doubling the dose increased the GHG emissions from 15% to 48%. 7. Conclusions Ethanol produced from forest residues generally showed lower GHG emissions than ethanol produced from straw, although the difference was small when Method II (RED) was used. Two factors highly influenced the GHG performance, enzymes used in the process and the reduction in SOC due to residue removal. Consequently, to lower the GHG emissions from lignocellulosic ethanol, it is crucial that the enzyme dose is lowered and/or that the impact from enzyme production is decreased. In addition, the residues should be managed in such a way that the effect on SOC is minimized. The resulting GHG performance and energy balance varied significantly depending on calculation method used. The most influential factors were choice of allocation method and the inclusion of upstream impacts in the form of SOC losses. The ISO calculation method showed significant advantages for forest residue-based ethanol compared with straw-based, whereas the RED calculation method showed almost similar results. As an increasing share of biofuel can be expected to be produced in biorefineries and from lignocellulosic biomass, policy instruments, such as the RED calculation method, have to be compatible with these systems. This will involve careful consideration of allocation methods and the handling of agricultural and forestry residues in calculations. We believe that including upstream impacts from residue harvesting in the RED is recommended and justified and that inclusion of SOC changes due to residue recovery should be carefully considered. Acknowledgements The authors acknowledge The Swedish Knowledge Center for Renewable Transport Fuels (f3:R19) (the f3 center) and the Swedish Energy Agency for their financial support, the anonymous reviewers for their valuable comments, Jesper Kløverpris and Mats Sandgren for inputs regarding enzymes and Barta Zsolti and Anna Ekman for inputs on the biorefinery processes. References Agriwise, October 2012. Data Base for Management Planning. http://www.agriwise. org/demo/databok2010htm/kap27b/01_Densitet.htm. In Swedish. Ahlgren, S., 2012. Sustainability Criteria for Biofuels in the European Union-a Swedish Perspective. f3, p. 2. €o €s, E., Di Lucia, L., Sundberg, C., Hansson, P.-A., 2012. EU sustainability Ahlgren, S., Ro criteria for biofuels: uncertainties in GHG emissions from cultivation. Biofuels 3, 399e411. Balat, M., 2011. Production of bioethanol from lignocellulosic materials via the biochemical pathway: a review. Energy Convers. Manag. 52, 858e875. Barta, Z., Reczey, K., Zacchi, G., 2010. Techno-economic evaluation of stillage treatment with anaerobic digestion in a softwood-to-ethanol process. Biotechnol. Biofuels 3, 21. Berlin, A., Maximenko, V., Gilkes, N., Saddler, J., 2007. Optimization of enzyme complexes for lignocellulose hydrolysis. Biotechnol. Bioeng. 97, 287e296. Binod, P., Janu, K.U., Sindhu, R., Pandey, A., 2011. Hydrolysis of lignocellulosic biomass for bioethanol production. In: Pandey, A., Larroche, C., Ricke, S.C., Dussap, C.-G., Gnansounou, E. (Eds.), Biofuels: Alternative Feedstocks and Conversion Processes. Elsevier Inc., USA, pp. 229e250. €rjesson, P., 2009. Good or bad bioethanol from a greenhouse gas perspectiveeBo what determines this? Appl. Energy 86, 589e594.

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