# Solving the multifunctionality dilemma in biorefineries with a novel hybrid mass-energy allocation method

Sylvestre Njakou Djomo, Marie Trydeman Knudsen, Ranjan Parajuli, Mikael

Skou Andersen, Morten Ambye-Jensen, Gerfried Jungmeier, Benoit Gabrielle,

John Erik Hermansen

► To cite this version:

Sylvestre Njakou Djomo, Marie Trydeman Knudsen, Ranjan Parajuli, Mikael Skou Andersen, Morten Ambye-Jensen, et al.. Solving the multifunctionality dilemma in biorefineries with a novel hybrid mass-energy allocation method. Global Change Biology - Bioenergy, 2017, 9 (11), pp.1674-1686. 10.1111/gcbb.12461 . hal-01683171

HAL Id: hal-01683171

https://hal.science/hal-01683171

Submitted on 12 Jan 2018

**HAL** is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

GCB Bioenergy (2017) 9, 1674–1686, doi: 10.1111/gcbb.12461

# Solving the multifunctionality dilemma in biorefineries with a novel hybrid mass–energy allocation method

SYLVESTRE NJAKOU DJOMO¹ ID, MARIE TRYDEMAN KNUDSEN¹, RANJAN PARAJULI¹, MIKAEL SKOU ANDERSEN¹, MORTEN AMBYE-JENSEN², GERFRIED JUNGMEIER³, BENOÎT GABRIELLE⁴ and JOHN ERIK HERMANSEN¹

¹Department of Agroecology, Aarhus University, Blichers Allé 20, P.O. Box 50, DK-8830 Tjele, Denmark, ²Department of Engineering, Aarhus University, Hangovej 2, P.O. Box 239, DK-8200 Aarhus Denmark, ³Joanneum Research Forschungsgesellschaft mbH, Elisabethstraße 18/II, 8010 Graz, Austria, ⁴EcoSys Research Unit, AgroParisTech, INRA, F-78850 Thival-val-Grignon, France

## Abstract

Processing biomass into multifunctional products can contribute to food, feed, and energy security while also mitigating climate change. However, biorefinery products nevertheless impact the environment, and this influence needs to be properly assessed to minimize the burden. Life cycle assessment (LCA) is often used to calculate environmental footprints of products, but distributing the burdens among the different biorefinery products is a challenge. A particular complexity arises when the outputs are a combination of energy carrying no mass, and mass carrying no energy, where neither an allocation based on mass nor on energy would be appropriate. A novel hybrid mass–energy (HMEN) allocation scheme for dealing with multifunctionality problems in biorefineries was developed and applied to five biorefinery concepts. The results were compared to results of other allocation methods in LCA. The reductions in energy use and GHG emissions from using the biorefinery’s biofuels were also quantified. HMEN fairly distributed impacts among biorefinery products and did not change the order of the products in terms of the level of the pollution caused. The allocation factors for HMEN fell between mass and economic allocation factors and were comparable to energy allocation factors. Where the mass or the energy allocation failed to attribute burdens, HMEN addressed this shortcoming by assigning impacts to non-mass or to nonenergy products. Under the partitioning methods and regardless of the feedstock used, bioethanol reduced GHG by 72–98% relative to gasoline. The GHG savings were 196% under the substitution method, but no GHG savings occurred for sugar beet bioethanol under the surplus method. Bioethanol from cellulosic crops had lower energy use and GHG emissions than from sugar beet, regardless of the allocation method used. HMEN solves multifunctional problems in biorefineries and can be applied to other complex refinery systems. LCA practitioners are encouraged to further test this method in other case studies.

**Keywords:** allocation, biochemical, biofuels, biomaterial, biorefinery, GHG emissions, HMEN method, life cycle assessment

Received 23 December 2016; accepted 7 May 2017

## Introduction

To cope with population growth and the rapid depletion of fossil resources, the EU and the United States have proclaimed their interest in strengthening green growth in the bioeconomy (EC, 2012, The White House, 2012). More than 30 countries have expressed their intentions to increase their reliance on biological resources (Bosch et al., 2015), so the share of biomaterials and bioenergy is expected to increase in the coming decades. The development of biorefineries is crucial for achieving the transition to a bioeconomy. Biorefineries convert biomass into food, energy, chemicals, and

materials (Sacramento-Rivero et al., 2016). They can contribute to sustainable resources use and so conserve finite resources, while mitigating climate change and other impacts. However, environmental impacts occur during the production and conversion phases of biomass into energy and bio-based materials (Creutzig et al., 2015; Gerssen-Gondelach et al., 2016), and these impacts should be reflected in the environmental assessment of individual biorefinery products.

To support environmental claims about biorefineries, a life cycle assessment (LCA) is used (Cherubini & Jungmeier, 2010; Nuss & Gardner, 2013; Pereira et al., 2015; Silalertruksa et al., 2017), but as only one product is often of interest for the LCA, environmental loads associated with a biorefinery system are often split among all biorefinery products using appropriate

Correspondence: Sylvestre Njakou Djomo, tel. +45 87157768, e-mail: sylvestre.njakouddjomo@agro.au.dk

© 2017 The Authors. *Global Change Biology Bioenergy* Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
allocation methods. Allocation in multifunctional pro-
cesses has been extensively discussed in the literature
(Azapagic & Clift, 1999), and several methods for solv-
ing these problems have been proposed (Jungmeier
et al., 2002). The LCAs for petroleum refineries and for
bioenergy and biorefinery systems are, in some cases,
sensitive to allocation methods (Wang et al., 2004;
Börjesson, 2009; Gnansounou et al., 2009; Luo et al.,
2009; Cherubini et al., 2011). The choice of an adequate
method is still a contentious issue, and an arbitrary
choice can lead to incorrect LCA results (Reap et al.,
2008) and thus poor decision-making (Weidema, 2000).

Studies on petroleum refineries and bioenergy sys-
tems have so far used mass (Gabrielle & Gagnaire,
2008), energy (Huo et al., 2009), and exergy (Cherubini
et al., 2008) as parameters to allocate resource use and
GHG emissions to the products and coproducts of these
systems. Mass, energy, and exergy allocations are often
considered to be based on physical parameters because
they use measurement units such as weight or energy
content as their basis. But the direct application of these
methods to biorefineries is challenging because of the
complexity of biorefining processes, the large number of
end products, and the diversity of their functions. Mass
allocation is unsuitable for nonmass products like elec-
tricity, while, without further assumptions, the energy
allocation would not work for nonenergy products (e.g.,
fertilizers) that do not have a heating value (Singh et al.,
2010). The exergy allocation is complex as a result of
difficulties in establishing exergy values for some sub-
stances (Cherubini et al., 2011).

The economic allocation (Spirinckx & Ceuterick, 1996;
Guinée et al., 2004) and the linear programming method
(Azapagic & Clift, 1999; Babusiaux, 2003; Pierru, 2007;
Hirshfeld & Kolb, 2012; Elgowainy et al., 2014; Balakr-
ishnan et al., 2015) have also been used to split burdens
among petroleum refinery, bioenergy, or biorefinery
products. The economic allocation considers the finan-
cial incentives, which are the main drivers of produc-
tion and associated impacts. But the method cannot be
applied to systems where coproducts do not yet have a
market or where market prices fluctuate (Wang et al.,
2011). The linear programming method models the
physical and technical relationships between the inputs
and outputs and environmental burdens of the system.
It follows similar logics of economic allocation (Bre-
deson et al., 2010) and provides detailed information on
the operations within the biorefinery (Balakrishnan
et al., 2015). But the approach does not work if there is a
fixed ratio between products and coproducts because
the functional outputs cannot be varied independently
(Azapagic & Clift, 1999). Linear programming is also
data intensive, and the data needed may not be easily
accessible.

Other studies avoid allocation either via system sub-
division (Furuholt, 1995; Wang et al., 2004; Bredeson
et al., 2010), substitution (Kim & Dale, 2002; Eriksson
et al., 2007), or using the system expansion approach
(Njakou Djomo et al., 2015). The subdivision method
disaggregates processes into subprocesses within a
given system and splits off those that are relevant to the
functional output. It captures the differences in environ-
mental loads of producing individual products at the
next sublevel (Wang et al., 2004). But biorefinery pro-
cesses are very integrated and cannot be meaningfully
split into subprocesses. The use of the substitution
method requires the identification of the main product
and the coproducts. The main product is then allocated
the entire burden, but also credited with the impacts
that the coproducts can avoid by replacing other prod-
ucts on the market. However, in the case of the biorefin-
ery, the identification of the main product is not
obvious because the overall idea of biorefining is to uti-
lize synergies in the production with the purpose of
obtaining multiple products (Parajuli et al., 2015).

The system expansion method broadens the boundary
to the point where allocation is not needed and where
the compared systems cover the same functional unit
(Ahlgren et al., 2015). However, system expansion is dif-
ficult to apply when aggregating different functions.
Neither does it show impacts of individual products, as
its results refer to a group of functions rather than a sin-
gle product or function. Finally, a small number of stud-
ies have used the surplus method (Fu et al., 2003;
Pimentel & Patzek, 2005) to overcome allocation prob-
lems with bioenergy systems. This latest method identi-
fies and assigns all burdens to the main product. In this
method, coproducts are burden-free and thus consid-
ered as waste products. This, however, seems to be a
simplification of the reality for the reason that some
biorefinery coproducts have well-established markets
(e.g., protein and lignin) and cannot thus be regarded as
wastes (Maes et al., 2015).

As none of the methods above are without drawbacks
when applied to biorefineries, new methods for solving
multifunctionality problems in biorefineries could help
to settle the debate. In response to this situation, a num-
ber of researchers proposed a framework to deal with
allocation in biorefining (Cherubini et al., 2011; Sandin
et al., 2015). Although appealing, their approaches may
be criticized in that they fail to reduce the number of
options that LCA practitioners are faced with and thus
increase the risk of controversies. For many authors,
mass- and energy-based allocation methods are very
attractive as they are simple and based on clear and
defensible rationales (Pelletier et al., 2015). They provide
an engineering perspective to allocation within a given
refinery (Wang et al., 2004). However, they are not
directly applicable to biorefinery systems for the reasons mentioned above. Therefore, a new method based on physical parameters and which is an improvement over the current mass and energy allocation is needed. Here, we present a new hybrid mass-energy 'HMEN' allocation method and the results from its application to different biorefinery concepts. To test the robustness of this new approach, its results were compared to results of other allocation methods in LCA. Finally, gasoline and natural gas were used to illustrate the effects of replacing conventional fossil fuels with bioethanol and biogas.

## Materials and methods

### Biorefinery models

Different biorefinery concepts exist today, with different pathways for biomass conversion and different final products. Simple biorefinery concepts use current available technologies to convert, via a platform, a single biomass feedstock into two or three marketable products. Complex biorefinery concepts use novel technologies to convert, via a number of platforms, a variety of biomass feedstocks into several marketable products (IEA, 2009). Both energy (e.g., biofuels) and nonenergy products (e.g., feed, biomaterial, and/or biochemicals) from simple

and complex biorefineries were evaluated from cradle-to-gate in this study (Fig. 1).

### Allocation framework

To overcome the limitations associated with allocation based solely on energy content or mass, a new hybrid mass-energy (HMEN) allocation method was developed (Fig. 1). This new allocation method captures well the differences in energy efficiency between biorefinery systems. A dispatch factor for energy $\alpha = \frac{\sum \eta_i}{\eta_0}$ was derived from the following relationship: $\eta_0 = \sum \eta_i + \sum \eta_j$, where $\eta_0$ is the overall energy efficiency of the biorefinery, $\sum \eta_i$ is the efficiency of the energy stream, and $\sum \eta_j$ the efficiency of the material stream. As this relation equals to $\frac{\sum \eta_i}{\eta_0} + \frac{\sum \eta_j}{\eta_0} = 1$ when its two sides are divided by $\eta_0$, it thus follows that $1 - \alpha$ represents the dispatch factor for the material stream (Table 1). Based on the abovementioned dispatch factors, the classical equations for distributing burdens between energy and nonenergy products can be modified as shown in Eqns (1) and (2).

$$ \Phi_i = \alpha \frac{\theta_i}{\sum_i^n \theta_i} \qquad (1) $$

$$ \Phi_j = (1 - \alpha) \frac{\lambda_j}{\sum_j^m \lambda_j} \qquad (2) $$

<figure><img src="image_3.png" /><figcaption>Fig. 1 System boundary and schematic representation of the hybrid mass-energy (HMEN) method. At split-off point 1, a dispatch factor ($\alpha$) which divides a biorefinery into energy and material streams is computed. At the split-off point 2, the dispatch factor is combined with mass and energy allocation method to derive the allocation coefficients. The dotted line represents the system boundary.</figcaption></figure>
<table><caption>**Table 1** Key figures for the processing and conversion of the different feedstocks in the selected biorefinery plants</caption><thead><tr><th colspan="3">Biorefinery 1 (BioR<sub>1</sub>)</th><th colspan="3">Biorefinery 2 (BioR<sub>2</sub>)</th><th colspan="3">Biorefinery 3 (BioR<sub>3</sub>)</th><th colspan="3">Biorefinery 4 (BioR<sub>4</sub>)</th><th colspan="3">Biorefinery (BioR<sub>5</sub>)</th></tr><tr><th>Component</th><th>Mass (t h<sup>-1</sup>)</th><th>Energy (GJ h<sup>-1</sup>)</th><th>Component</th><th>Mass (t h<sup>-1</sup>)</th><th>Energy (GJ h<sup>-1</sup>)</th><th>Component</th><th>Mass (t h<sup>-1</sup>)</th><th>Energy (GJ h<sup>-1</sup>)</th><th>Component</th><th>Mass (t h<sup>-1</sup>)</th><th>Energy (GJ h<sup>-1</sup>)</th><th>Component</th><th>Mass (t h<sup>-1</sup>)</th><th>Energy (GJ h<sup>-1</sup>)</th></tr></thead><tbody><tr><td colspan="16"><strong>Inputs</strong></td></tr><tr><td>Straw</td><td>3.44</td><td>57.6</td><td>Straw</td><td>44.1</td><td>635.0</td><td>Inputs</td><td>2.5</td><td>92.4</td><td>Sugar beet</td><td>103.5</td><td>1760</td><td>Inputs</td><td>0.29</td><td>5.2</td></tr><tr><td>Enzymes</td><td>0.034</td><td>0</td><td>Manure</td><td>71.3</td><td>42.81</td><td>Rapeseed</td><td>1.1</td><td>0.0</td><td>Catch crop &amp; leaves</td><td>64.3</td><td>1093</td><td>Green grass</td><td>0.17</td><td>3.0</td></tr><tr><td colspan="16"><strong>Outputs</strong></td></tr><tr><td>2G Bioethanol</td><td>0.573</td><td>16.0</td><td>2G bioethanol</td><td>8.58</td><td>231.7</td><td>1G Bio-oil</td><td>1.0</td><td>37</td><td>Lime (CaO)</td><td>7.5</td><td>0</td><td>Manure</td><td>0.24</td><td>4.6</td></tr><tr><td>Lignin pellets</td><td>0.47</td><td>10.1</td><td>Biogas</td><td>1.69</td><td>30.39</td><td>Protein products*</td><td>0.61</td><td>9.6</td><td>Carbon dioxide</td><td>11.5</td><td>0</td><td>Electricity</td><td>0</td><td>0.09</td></tr><tr><td>C5 molasses*</td><td>0.965</td><td>15.0</td><td>Biomethane</td><td>1.68</td><td>72.2</td><td>Soapstock*</td><td>0.02</td><td>0.4</td><td>Steam Electricity</td><td>15.6</td><td>35.2</td><td>Heat</td><td>0.14</td><td>0.32</td></tr><tr><td colspan="16"><strong>Performance</strong></td></tr><tr><td>Overall efficiency</td><td>71.3</td><td></td><td>Overall efficiency</td><td>85.5</td><td></td><td>Performance</td><td></td><td></td><td>Overall efficiency</td><td>63.4</td><td></td><td>Outputs</td><td></td><td></td></tr><tr><td>Efficiency, energy stream</td><td>45.3</td><td></td><td>Efficiency, energy stream</td><td>77.0</td><td></td><td>Overall efficiency</td><td></td><td></td><td>Efficiency, energy stream</td><td>40.0</td><td></td><td>Biogas</td><td></td><td></td></tr><tr><td>Dispatch factor, energy (α)</td><td>64.0</td><td></td><td>Dispatch factor, energy (α)</td><td>90.0</td><td></td><td>Dispatch factor, energy (α)</td><td></td><td></td><td>Dispatch factor, energy (α)</td><td>63.0</td><td></td><td>Dispatch factor, energy (α)</td><td></td><td></td></tr></tbody></table>

The overall energy efficiency is calculated as the total energy output of biorefinery products (i.e., energy and nonenergy products) divided by the total energy input.

The efficiency of the energy stream is computed as the sum of energy output of the energy products divided by the total energy input.

The dispatch factor for energy (α) is calculated by dividing the efficiency of the energy stream by the overall energy efficiency.

Note that for the BioR<sub>1</sub>, and BioR<sub>2</sub>, part of the biomass input is used to produce the energy needed for the process, so no external energy is required.

*Represents the nonenergy products which can be materials or chemicals.

†Represents waste (i.e., products with no economic value).
In Eqns (1) and (2) above, $θ_i$ denotes the energy content of a given energy product i from a multifunctional process, and $λ_j$ is the mass of a given nonenergy product j from a multifunctional process, while $\sum_{i}^{n} \theta_i$ and $\sum_{j}^{m} \lambda_j$ represent the total energy content and the total mass of the generated energy and nonenergy products, respectively. The indices n and m represent the number of energy and nonenergy products, respectively. If $α = 0$, the biorefinery produces only material or chemical products; if $α = 1$, the biorefinery generates only an energy product, and if $0 < α < 1$, the biorefinery system yields both energy and material/chemical products. Note that Eqns (1) and (2) are reduced to a classical energy or mass allocation method, respectively, when $α = 1$ or $α = 0$.

Finally, the share of environmental burdens of a bioenergy/biofuel product from the biorefinery can be calculated as indicated in Eqn (3). Similarly, the environmental burdens assigned to a biomaterial/biochemical product from the biorefinery can be computed using Eqn (4):

$$E_i = \Phi_i E_T = \left( \frac{\theta_i}{\sum_{i}^{n} \theta_i} \right) \cdot E_T \quad (3)$$

$$E_j = \Phi_j E_T = \left( (1 - \alpha) \frac{\lambda_j}{\sum_{i}^{m} \lambda_j} \right) \cdot E_T \quad (4)$$

where $E_i$ is the share of environmental burdens for a given energy product, $E_j$ represents the share of environmental impacts for a given biomaterial/biochemical product from the biorefinery, and $E_T$ is the total environmental impact of the biorefinery system. Note that the sum of all allocated burdens should equal the total environmental impact generated by the biorefinery (i.e., 100% rule).

## Case study selection

The HMEN method was used to estimate and compare the share of energy use and GHG emissions of biorefinery products. Five biorefinery plants were selected, based on the criteria that (i) they generate both energy and nonenergy products, and (ii) there are data available on the overall efficiency of the system. The selected biorefinery plants differed in terms of feedstock used and product outputs (Table 1). The first biorefinery plant (BioR1) converts straw into bioethanol, lignin pellets, and molasses (Larsen et al., 2012). The second plant (BioR2) transforms a mixture of straw, manure, industrial waste and bio-waste to bioethanol, biogas, biomethane, heat, power, lignin, and digestate (MEC I/S, 2015). The third plant (BioR3) processes oilseed rape grains into bio-oil, meal cake, and soapstock (Schneider & Finkbeiner, 2013), while the fourth plant (BioR4) converts sugar beet and catch crop leaves into bioethanol, biogas, sugar, protein products, and foam earth (CaCO3) (ECN, 2010). The fifth plant (BioR5) uses grass, grass silage, and manure to produce biogas, fiber, protein products, and fertilizers (ECN, 2010).

Because the impacts from biomass production can be larger than those from conversion (Jungmeier et al., 2002), the study also covers all relevant agricultural operations as well as upstream production of inputs to these operations. Biorefinery inputs such as enzymes, electricity, and heat were included

where relevant in the calculation (Table 1). However, changes in soil carbon stock due to land use changes were excluded from the analysis, as were the storage and end use of biorefinery products. As manure, industrial waste, and biowaste are wastes, only the energy use and GHG emissions related to their transport to the biorefinery plants were considered. A transport distance of 50 km was assumed for all biomass feedstock. Data on energy use and GHG emissions during the production and transport of biomass were derived from the Ecoinvent database (Ecoinvent, 2014), while the data on electricity and heat used for enzyme production were derived from Dunn et al. (2012).

## Comparison with other allocation methods

The robustness of the HMEN method was assessed by comparing its results to those of the mass, energy, economic allocation, surplus method, system expansion, and the substitution method. For the substitution approach, it was assumed that biofuels represent the main product of the biorefinery in each case study. The economic allocation was based on market prices, while the energy allocation was based on the lower heating values of the different biorefinery products (Table S1).

## Comparison of biorefinery biofuels and conventional fuels

The energy use and GHG emissions of the different biofuels (i.e., bioethanol, biogas, biomethane) were compared to those of conventional fuels. To this end, it was assumed that bioethanol replaces gasoline, while biogas or biomethane replaces natural gas. It was further assumed that biofuels are carbon neutral because the CO2 emitted during the combustion of biofuels corresponds to the CO2 uptake by the feedstock during their growth. Gasoline production uses 52 GJ t⁻¹ and emits about 624 kg CO2 t⁻¹ (Ecoinvent, 2014), whereas its combustion releases an additional ~3341 kg CO2 t⁻¹ (Cherubini & Jungmeier, 2010). Natural gas production consumes 50 GJ t⁻¹ and emits 340 kg CO2 t⁻¹ (Ecoinvent, 2014), while about 2805 kg CO2 t⁻¹ is emitted during natural gas combustion. Assuming an energy content of 43 GJ t⁻¹ for gasoline and 27 GJ t⁻¹ for bioethanol, 1 t bioethanol can thus replace 0.62 t gasoline. Similarly, 1 t biogas can displace 0.35 t natural gas if assuming an energy content of 18 and 52 GJ t⁻¹ for biogas (@ 55% CH4) and natural gas (@ 92% CH4), respectively. The energy content of biomethane (@ 96% CH4) is 43 GJ t⁻¹, so 1 t biomethane can displace 0.83 t natural gas (Table S1).

## Results

The allocation coefficients, the energy use, and GHG emissions computed using the HMEN and other allocation procedures are shown in Table 2. Under the mass allocation approach, liquid biofuels (e.g., bioethanol) received a lower allocation factor because of their lower weights relative to the other biorefinery products (Table 2). However, under the economic allocation, liquid biofuels were assigned high allocation coefficients
because of the high ratio of price relative to the ratio of
differences in mass between liquid biofuels and other
cogenerated products. For example, the ratio of the
price differences between bioethanol and lignin pellets
was 10 times higher than the mass ratio (i.e., 0.33)
between these two biorefinery products. The energy
allocation and the HMEN method gave similar weights
to all energy products, but their allocation factors for
nonenergy products differed when these products rep-
resented a significant share of the coproducts (Table 2).
Importantly, where mass and energy allocation failed to
distribute burdens to certain products because of their
zero mass value or energy content, the HMEN approach
addressed this shortcoming by assigning actual burdens
to these products (Table 2). This shows how problem-
atic LCA results can be when mass or energy allocation
is applied to biorefineries. Indeed, there is a disproport-
ionate advantage for products without energy content
(e.g., foam earth) over products with energy content
when the energy allocation was used. The same was
true for products with mass over products without
mass (e.g., electricity) when the mass allocation was
adopted (Table 2). Although the allocation factors dif-
fered between the HMEN approach and the other parti-
tioning methods (i.e., mass or economic allocation), the
estimates of energy use and GHG emissions per
product were in most cases within the same order of
magnitude (Table 2). HMEN also had the same prioriti-
zation of biorefinery products as the energy allocation
in all cases. Its ranking of products was also similar to
that of the mass allocation method in nearly all cases,
but differed from the economic allocation in some cases.
The similarity observed between the HMEN method
and the energy allocation in this study reflects the
underlying conceptual linkage between the two
approaches. In fact, dispatch factors were computed
using the energy efficiency, which is, in turn, based on
the total energy outputs and energy inputs of the biore-
finery systems.

The substitution method credited the main products (i.e., biofuels) with the impacts generated by the alternative goods displaced by their coproducts. No allocation factors were computed here, but because the total avoided burdens exceeded in some cases the overall impacts of the biorefineries, negative estimates for energy use and GHG emissions were obtained (Table 2). This contrasted with the partitioning methods, which all computed a positive total physical energy use and GHG emissions. Such results suggest that when the substitution approach is used, some biorefinery products may become a net sink for energy and/or GHG emissions even before a comparison of these bio-based products with their conventional counterparts.

Figure 2 shows the comparison of the different alloca-
tion methods used in this study. Estimates of other allo-
cation schemes were compared to those of the surplus
method set as a reference. For simplicity, only biore-
fineries producing bioethanol were selected (i.e., BioR<sub>1</sub>,
BioR<sub>2</sub>, and BioR<sub>4</sub>). The comparison between the mass,
energy, economic, and HMEN method showed only
small differences among them. Indeed, all estimates of
energy use (Fig. 2b) and GHG emissions (Fig. 2a) were
within the same order of magnitude. Under the substi-
tution approach, it was clear that avoided impacts by
some of the coproducts more than compensated for the
overall environmental impacts of some biorefinery
products. Figure 2 also shows that the substitution
method is not only sensitive to the choice of the conven-
tional good displaced by the coproducts, but also to the
number and type of coproducts generated. In fact, large
energy and GHG credits were given to the main pro-
duct when the biorefinery system generated many
energy products as in the BioR<sub>2</sub> case (Fig. 2).

The variability in specific energy use and GHG emis-
sions of biorefinery products is presented in Fig. 3.
These estimates were obtained by dividing the energy
use and GHG emissions in Table 2 by the amount of
final products of each biorefinery plant in Table 1.
Given that substitution provided only results for the
main product, the choice of the main product was var-
ied in each case study to obtain estimates of specific
energy use and GHG emissions for all products. Fig-
ure 3 shows that the energy use and GHG emissions
vary widely depending on the allocation method
adopted. Under the partitioning approach (i.e., mass,
energy, economic, HMEN), the energy use of bioethanol
from straw ranged from 1.7 to 2.9 GJ t⁻¹ (Fig. 3c), while
its GHG emissions varied from 149 to 247 kg CO₂ t⁻¹
(Fig. 3a). For straw–bioethanol, the substitution approach
computed values of −15.9 GJ t⁻¹ for energy use (Fig. 3d)
and 155.6 kg CO₂ t⁻¹ for GHG emissions (Fig. 3b),
while the energy use and GHG emission values calcu-
lated using the surplus method were 4.8 GJ t⁻¹ and
412.6 kg CO₂ t⁻¹, respectively (Fig. 3b,d).

The energy use of bioethanol from mixed biomass
sources ranged from 0.4 to 2.3 GJ t⁻¹ (Fig. 3c), while the
GHG emissions varied from 32.9 to 181.8 kg CO₂ t⁻¹
(Fig. 3a) under partitioning approaches. Under the sur-
plus method, the energy use was 6.3 GJ t⁻¹ (Fig. 3d)
and GHG emissions were 503.2 kg CO₂ t⁻¹ (Fig. 3b).
Negative values were obtained for energy use
(−66.1 GJ t⁻¹) and GHG emissions (−2358.6 kg
CO₂ t⁻¹) when the substitution method was chosen
(Fig. 3d,b). Under the partitioning approach, sugar beet
bioethanol consumed 1.4–1.9 times more nonrenewable
energy and emitted two to 2.8 times more GHGs than
straw-based bioethanol (Fig. 3a,c). When the surplus
<table><caption>**Table 2** Allocation methods and implications for energy and GHG emissions for biorefinery products</caption><thead><tr><th rowspan="3">Biorefinery Products</th><th colspan="10">Partitioning</th><th colspan="3">Avoiding allocation</th></tr><tr><th colspan="2">Mass allocation</th><th colspan="2">Energy allocation</th><th colspan="2">Economic allocation</th><th colspan="2">HMEN approach</th><th colspan="2">Surplus method</th><th colspan="2">System expansion</th><th colspan="2">Substitution<sup>†</sup></th></tr><tr><th>Coef (%)</th><th>Energy (GJ h<sup>-1</sup>)</th><th>GHG (kg CO<sub>2</sub> h<sup>-1</sup>)</th><th>Coef (%)</th><th>Energy (GJ h<sup>-1</sup>)</th><th>GHG (kg CO<sub>2</sub> h<sup>-1</sup>)</th><th>Coef (%)</th><th>Energy (GJ h<sup>-1</sup>)</th><th>GHG (kg CO<sub>2</sub> h<sup>-1</sup>)</th><th>Coef (%)</th><th>Energy (GJ h<sup>-1</sup>)</th><th>GHG (kg CO<sub>2</sub> h<sup>-1</sup>)</th><th>Energy (GJ h<sup>-1</sup>)</th><th>GHG (kg CO<sub>2</sub> h<sup>-1</sup>)</th></tr></thead><tbody><tr><td>BioR<sub>1</sub> 2G bioethanol*</td><td>0.36</td><td>1.00</td><td>85.49</td><td>0.39</td><td>1.07</td><td>92.03</td><td>0.60</td><td>1.65</td><td>141.51</td><td>0.39</td><td>1.07</td><td>92.03</td><td>0.100</td><td>2.76</td><td>236.41</td><td>-</td><td>-</td><td>-9.09</td><td>89.18</td></tr><tr><td>Lignin pellets</td><td>0.03</td><td>0.08</td><td>7.01</td><td>0.25</td><td>0.68</td><td>58.10</td><td>0.04</td><td>0.11</td><td>9.59</td><td>0.25</td><td>0.68</td><td>58.10</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-</td><td>-10.83</td><td>-56.8</td></tr><tr><td>C<sub>5</sub> molasses</td><td>0.61</td><td>1.68</td><td>143.91</td><td>0.36</td><td>1.01</td><td>86.28</td><td>0.36</td><td>1.00</td><td>85.31</td><td>0.36</td><td>1.01</td><td>86.28</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-</td><td>-1.02</td><td>-90.43</td></tr><tr><td>Total</td><td>1.00</td><td>2.76</td><td>236.41</td><td>1.00</td><td>2.76</td><td>236.41</td><td>1.00</td><td>2.76</td><td>236.41</td><td>1.00</td><td>2.76</td><td>236.41</td><td>1.00</td><td>2.76</td><td>236.41</td><td>2.76</td><td>236.41</td><td>2.76</td><td>236.41</td></tr><tr><td>BioR<sub>2</sub> 2G bioethanol*</td><td>0.07</td><td>3.54</td><td>282.19</td><td>0.29</td><td>15.8</td><td>1259.64</td><td>0.36</td><td>19.57</td><td>1559.5</td><td>0.29</td><td>15.8</td><td>1259.64</td><td>1.00</td><td>54.2</td><td>4317.8</td><td>-</td><td>-</td><td>-567.31</td><td>-2023.73</td></tr><tr><td>Biogas</td><td>0.013</td><td>0.70</td><td>55.58</td><td>0.04</td><td>2.07</td><td>165.19</td><td>0.02</td><td>1.33</td><td>105.94</td><td>0.04</td><td>2.07</td><td>165.19</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-</td><td>-29.51</td><td>-199.4</td></tr><tr><td>Biomethane</td><td>0.013</td><td>0.69</td><td>55.39</td><td>0.09</td><td>4.92</td><td>392.4</td><td>0.04</td><td>2.10</td><td>164.1</td><td>0.09</td><td>4.92</td><td>392.45</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-</td><td>-70.3</td><td>-475.1</td></tr><tr><td>Lignin pellets</td><td>0.03</td><td>1.72</td><td>137.48</td><td>0.12</td><td>6.28</td><td>500.29</td><td>0.02</td><td>0.83</td><td>66.48</td><td>0.12</td><td>6.28</td><td>500.29</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-</td><td>-99.36</td><td>-520.9</td></tr><tr><td>Heat</td><td>0.85</td><td>46.01</td><td>3667.17</td><td>0.32</td><td>17.19</td><td>1369.71</td><td>0.39</td><td>21.3</td><td>1695.8</td><td>0.32</td><td>17.19</td><td>1369.71</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-302.4</td><td>-16732.1</td><td>-302.4</td></tr><tr><td>Electricity</td><td>0</td><td>0</td><td>0</td><td>0.05</td><td>2.44</td><td>194.65</td><td>0.13</td><td>7.06</td><td>562.31</td><td>0.05</td><td>2.44</td><td>194.65</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-105.25</td><td>-5083.6</td><td>-105.25</td></tr><tr><td>Digestate</td><td>0.03</td><td>1.51</td><td>120</td><td>0.10</td><td>5.47</td><td>435.93</td><td>0.04</td><td>2.05</td><td>163.75</td><td>0.10</td><td>5.47</td><td>435.94</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-</td><td>-14.66</td><td>-1544.0</td></tr><tr><td>Total</td><td>1.00</td><td>54.2</td><td>4317.8</td><td>1.00</td><td>54.2</td><td>4317.8</td><td>1.00</td><td>54.2</td><td>4317.8</td><td>1.00</td><td>54.2</td><td>4317.8</td><td>1.00</td><td>54.2</td><td>4317.8</td><td>54.2</td><td>4317.8</td><td>54.2</td><td>4317.8</td></tr><tr><td>BioR<sub>3</sub> 2G Bio-oil*</td><td>0.42</td><td>5.59</td><td>826.97</td><td>0.63</td><td>8.37</td><td>1238.42</td><td>0.81</td><td>10.85</td><td>1605.4</td><td>0.63</td><td>8.36</td><td>1236.95</td><td>1.00</td><td>13.4</td><td>1976.5</td><td>-</td><td>-</td><td>-14.66</td><td>-1544.0</td></tr><tr><td>Meal cake</td><td>0.26</td><td>3.51</td><td>519.34</td><td>0.16</td><td>2.18</td><td>321.99</td><td>0.18</td><td>2.42</td><td>358.85</td><td>0.17</td><td>2.26</td><td>334.11</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-</td><td>-20.66</td><td>-223.32</td></tr><tr><td>Fiber</td><td>0.31</td><td>4.15</td><td>613.61</td><td>0.20</td><td>2.72</td><td>402.66</td><td>0.002</td><td>0.031</td><td>4.55</td><td>0.20</td><td>2.67</td><td>394.76</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-0.100</td><td>-8.90</td><td>-0.100</td></tr><tr><td>Soapstock</td><td>0.01</td><td>0.11</td><td>16.54</td><td>0.01</td><td>0.09</td><td>13.39</td><td>0.004</td><td>0.051</td><td>7.62</td><td>0.01</td><td>0.072</td><td>10.64</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-0.084</td><td>-6.542</td><td>-0.084</td></tr><tr><td>Total</td><td>1.00</td><td>13.4</td><td>1976.5</td><td>1.00</td><td>13.4</td><td>1976.5</td><td>1.00</td><td>13.4</td><td>1976.5</td><td>1.00</td><td>13.4</td><td>1976.5</td><td>1.00</td><td>13.4</td><td>1976.5</td><td>13.4</td><td>1976.5</td><td>13.4</td><td>1976.5</td></tr><tr><td>BioR<sub>4</sub> 1G bioethanol*</td><td>0.12</td><td>35.58</td><td>4351.7</td><td>0.19</td><td>56.51</td><td>7112.28</td><td>0.27</td><td>79.34</td><td>9986.1</td><td>0.19</td><td>56.5</td><td>7112.3</td><td>1.00</td><td>295.1</td><td>37137.8</td><td>-</td><td>-</td><td>-636.81</td><td>20723.4</td></tr><tr><td>Biogas</td><td>0.36</td><td>107.08</td><td>13477.4</td><td>0.39</td><td>116.37</td><td>14646.15</td><td>0.29</td><td>84.64</td><td>10652.2</td><td>0.39</td><td>116.4</td><td>14646.2</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-</td><td>-785.67</td><td>-5310.7</td></tr><tr><td>Sugar</td><td>0.35</td><td>102.49</td><td>12899.4</td><td>0.32</td><td>94.09</td><td>11841.57</td><td>0.34</td><td>101.35</td><td>12755.2</td><td>0.28</td><td>81.6</td><td>10274.4</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-120.5</td><td>-8407.9</td><td>-120.5</td></tr><tr><td>Protein products</td><td>0.10</td><td>30.602</td><td>3851.55</td><td>0.10</td><td>28.109</td><td>3537.81</td><td>0.09</td><td>26.3</td><td>3309.8</td><td>0.08</td><td>24.4</td><td>3067.7</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-24.2</td><td>-2613.2</td><td>-2613.2</td></tr><tr><td>Foam earth</td><td>0.07</td><td>20.322</td><td>2557.72</td><td>0</td><td>0</td><td>0</td><td>0.01</td><td>3.45</td><td>434.39</td><td>0.05</td><td>16.2</td><td>2037.2</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-1.54</td><td>-82.7</td><td>-1.54</td></tr><tr><td>Total</td><td>1.00</td><td>295.1</td><td>37137.8</td><td>1.00</td><td>295.1</td><td>37137.8</td><td>1.00</td><td>295.1</td><td>37137.8</td><td>1.00</td><td>295.1</td><td>37137.8</td><td>20.5</td><td>1.00</td><td>0.97</td><td>82.2</td><td>-</td><td>-295.1</td><td>37137.8</td></tr><tr><td>BioR<sub>5</sub> Biogas*</td><td>0.27</td><td>0.26</td><td>24.47</td><td>0.25</td><td>0.241</td><td>20.5</td><td>0.60</td><td>0.58</td><td>49.14</td><td>0.25</td><td>0.24</td><td>49.14</td><td>0.25</td><td>0.97</td><td>0</td><td>-</td><td>-0.40</td><td>-63.6</td><td>-63.6</td></tr><tr><td>Coagulated protein</td><td>0.11</td><td>0.11</td><td>9.09</td><td>0.11</td><td>0.10</td><td>8.69</td><td>0.016</td><td>0.02</td><td>1.33</td><td>0.11</td><td>0.11</td><td>1.33</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-0.16</td><td>-177.8</td><td>-177.8</td></tr><tr><td>Fiber</td><td>0.14</td><td>0.14</td><td>11.69</td><td>0.12</td><td>0.113</td><td>9.61</td><td>0.002</td><td>0.002</td><td>0.153</td><td>0.15</td><td>0.14</td><td>0.15</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-0.01</td><td>-1.10</td><td>-1.10</td></tr><tr><td>Digestate</td><td>0.47</td><td>0.46</td><td>39</td><td>0.53</td><td>0.511</td><td>43.4</td><td>0.384</td><td>0.372</td><td>31.60</td><td>0.49</td><td>0.47</td><td>40.3</td><td>0</td><td>0</td><td>0</td><td>-</td><td>-1.2</td><td>-126.9</td><td>-126.9</td></tr><tr><td>Total</td><td>1.00</td><td>0.97</td><td>82.2</td><td>1.00</td><td>0.97</td><td>82.2</td><td>1.00</td><td>0.97</td><td>82.2</td><td>1.00</td><td>0.97</td><td>82.2</td><td>1.00</td><td>0.97</td><td>0.97</td><td>82.2</td><td>0.97</td><td>0.97</td><td>82.2</td></tr></tbody></table>

*Considered as the main product of the biorefinery.

†Note that in the case of substitution, the total energy use (or GHG emissions) is calculated as the difference between the energy use (or GHG emissions) of the main product and that of the coproducts.
method was selected, the energy used was 20.3 GJ t⁻¹
(Fig. 3a), while the GHG emissions were
2555.9 kg CO₂ t⁻¹ (Fig. 3b). Estimates of energy use and
GHG emissions for sugar beet bioethanol were
-43.8 GJ t⁻¹ and 1426.2 kg CO₂ t⁻¹, respectively, when
the substitution method was adopted (Fig. 3b,d).

Overall, the variability in energy use and GHG emissions across the allocation methods suggested the latter had a strong influence on the environmental performance of biorefinery products, but the degree of the influence depended on the biomass feedstock utilized. Some trends were, however, uncovered: In all cases, bioethanol from mixed biomass sources (i.e., manure + straw + food waste) consumed less nonrenewable energy and emitted less GHGs than both bioethanol from straw and sugar beet. Bioethanol from straw had better environmental performances than sugar beet bioethanol. In general, biofuels from cellulosic crops (i.e., bioethanol, biogas, biomethane) used less energy and emitted less GHGs than bioethanol from sugar beet or bio-oil from rapeseed (Fig. 3). Likewise, protein products from cellulosic crops performed better than those produced from sugar beet and rape-seed. The same observation was true for fiber from cellulosic and sugar beet crops. Finally, digestates from cellulosic biomass had a lower impact both in terms of energy use and GHG emissions than digestate from sugar beet (Fig. 3).

Relative to gasoline and under the partitioning meth-
ods, all bioethanol reduced the energy use (82–99%) and
GHG emissions (72–98%). The largest reduction in
energy used (93–99%) occurred when mixed biomass
was the feedstock, while the lowest reduction in energy
use (82–93%) was linked to sugar beet. Bioethanol from
mixed biomass also achieved the largest reduction in
GHG emissions (93–98%), while bioethanol from sugar
beet had the lowest reduction in GHG emissions (72–
88%). Small savings in energy use (37%) and a small
increase in GHG emissions (4%) relative to gasoline
were observed when the surplus method was used, but
under the substitution method, savings in energy use
and reduction in GHG emissions reached 304% and
196%, respectively. Compared to natural gas and under
the partitioning methods, all biogas reduced the energy
use by 80–98% and GHG emissions by 70–97%. As with
bioethanol, little to no reduction in either energy use or
GHG emissions was achieved when the surplus method
was adopted as allocation method. However, when the
substitution approach was chosen, the maximum saving
in energy used was 2855%, while the maximum reduc-
tion in GHG emissions was 1366%. These results
showed that allocation methods influenced the savings
of biofuel relative to conventional fuels.

Discussion

The HMEN method presented in this study overcomes the limitations of allocation methods based solely on the mass or energy content of biorefinery products, which both suffer from drawbacks. In particular, this new, hybrid method was able to assign environmental impacts to the products with no mass (such as electricity) or no energy content (e.g., earth-foam), which is a major improvement over classical allocations (Table 2). This means that LCA practitioners using these schemes run the risk of overlooking important environmental burdens for certain biorefinery products, a risk which the new method (HMEN) can handle and mitigate (Table 2). Compared to the economic allocation, another widely used method for biorefineries, HMEN is still based on the physical relationship between products and is therefore independent of price fluctuations. Because it uses only the physical flows within biorefineries to split burdens between products, it can limit the freedom of choice and thus the risks of controversy around the outcomes of the evaluation. The use of dispatch factors (i.e., energy content) as a weighting factor is not only consistent with the hypothesis that energy consumption is tied to the amount of mass transported, but also consistent with the conservation of energy during biorefining processes. However, such use of energy content as a weighting factor does not provide information on the degradation of energy or resources during a process, nor does it quantify the usefulness or quality of the various material streams flowing through a system and exiting as products and/or wastes (Wang et al., 2004). Nevertheless, the HMEN method is more robust than the mass and energy allocation currently used in LCA of biorefineries (Table 2). Like the HMEN approach, the economic allocation method distributed impacts among biorefinery products. Differences in estimates between the economic allocation and the HMEN method were in most cases insignificant; for bioethanol, for example, the differences in allocated energy use and GHG emissions were less than a factor of 1.5 (Table 2). Although the economic allocation captures some underlying motivation to produce different biorefining products, the potential market fluctuations (Malça & Freire, 2006), the lack of a physical basis for using market values as weighting factors (Wang et al., 2004), the difficulty of applying this method when no market experience exits, and the uncertainties inherent in this approach favor the use of the HMEN method for attributional LCAs of biorefining products.

The HMEN method performs at a high level of reso-
lution (i.e., at biorefinery level) with regard to process
streams in biorefineries. This means it does not allocate
<figure><img src="image_4.png" /><figcaption>Fig. 2 Comparison of the influence of allocation methods on specific energy use (b) and GHG emissions (a).</figcaption></figure>

emissions to each intermediate product as the subdivision or the LP method does. Although it has been demonstrated that differences in energy use and GHG emissions between allocation at refinery level and the refining at process level can be up to 40% (Furuholt, 1995), our opinion is that intermediate products in biorefineries are often used for internal processes within biorefineries and not sold as final products. Moreover, except for the subdivision and the linear programming, most of the allocation models in LCA perform at a high level of resolution because of a data availability issue. Finally, it has to be acknowledged that the HMEN method does not accommodate the indirect market-mediated effects, a situation which can only be dealt with through substitution. However, this approach is not exempt from shortcomings: Substitution fails to quantify the environmental impacts of a specific

product with sufficient accuracy, as the uncertainties of added system can be overriding (Pawelzik et al., 2013).

We showed that estimates of the energy use and GHG emissions of biorefinery systems varied strongly depending on the allocation method adopted (Fig. 3), which is in line with several previous findings that report effects of allocation method on LCA results and conclusions (Azapagic & Clift, 1999; Heijungs & Guinée, 2007). We found that very few allocation methods arrived at the same prioritization of biorefinery products. This contrasts with the conclusions of Curran (Curran, 2007) that different allocation methods lead to same prioritization of products (Table 2). HMEN results in the same prioritization as the energy allocation, and similar ranking as the economic or mass allocation. Overall, these results reiterate the need for additional and more stringent guidance on allocation issues for
<figure><img src="image_5.png" /><figcaption>Fig. 3 Variability in specific energy use and GHG emissions of various biorefinery products, depending on type of allocation method applied: (a,c) mass, energy, and economic allocation; and (b,d) surplus (straight) and substitution (striped) methods. Functional unit is 1 t (tonne) for fuels and materials and 1 GJ for heat and electricity.</figcaption></figure>

LCA practices. Without additional guidance, inconsistencies will not be reduced, and this will limit the usability of information from LCA studies in decision-making (Pelletier *et al.*, 2015).

Existing directives are not very helpful for practitioners as they provide different recommendations for assessments of multifunctional systems (Wardenaar *et al.*, 2012). The EU-RED (RED, 2009) suggests the use

of energy allocation, the US-EPA (EPA, 2010) and IEA (Jungmeier *et al.*, 2014) adopt the system expansion method, while the PAS2050 (BSI, 2011) suggests the use of economic allocation where system expansion is not possible. The ISO (ISO 14044, 2006) and the ILCD book (EC-JRC, 2010) recommend firstly avoiding allocation, and secondly applying physical causality-based allocation methods when allocation is unavoidable. Allocation
problems in biorefining can be solved in many ways
depending on the research questions (Reap *et al.*, 2008;
Cherubini *et al.*, 2011), but any method used for solving
this problem must reduce risks of controversy, have the
fewest unintended consequences, and generate solu-
tions that are consistent with the principles of ISO stan-
dards for LCA. Although substitution is the preferred
method in ISO, we showed that it may result in nega-
tive estimates of energy use and GHG emissions for the
main products because of the credits from the exported
energy products of the biorefinery systems. This obser-
vation suggests that substitution should not be applied
universally without examining the individual situation
(Wang *et al.*, 2011). Moreover, the application of the
substitution method requires the identification of the
main product, which in the case of the biorefinery is dif-
ficult (Parajuli *et al.*, 2015), especially if the products are
given equal importance. Guidance on identification of
the main products exists (Weidema, 2000), but its
importance in biorefineries is limited because they gen-
erate several marketable products (Bozell & Petersen,
2010). These, coupled to uncertainties about the dis-
placed products, further limit the suitability of this
method for biorefineries.

Direct comparison of results from this work with
those of other studies on allocation in biorefineries is
difficult due to differences in methods used, system
boundaries, feedstock and technologies investigated,
generated products, in addition to other assumptions.
In their analysis of bioethanol from sugar beet refinery,
Malça & Freire (2006) assigned between 28% and 37%
of energy use to bioethanol. Allocation factors for
energy use attributable to sugar beet bioethanol in this
study ranged from 12% to 27%. Pulps and sugar were
the coproducts in Malça & Freire (2006), while in our
study, biogas, protein products, foam earth, and sugar
were produced, and this may explain the difference
observed. The allocation factors (63–99%) for GHG
emissions assigned to straw–bioethanol in Cherubini
et al. (2011) are higher than those for GHG emissions
(36–60%) attributed to straw-based bioethanol in this
study (Table 2). Also, the hybrid method of Cherubini
et al. (2011) assigned 84% of GHG emissions to bioetha-
nol. This latest allocation factor is more than double that
for straw-based bioethanol estimated by the HMEN
method (39%). Differences in the conceptual framework
as well as the type and amount of coproducts explain
the difference in results of the two approaches. The
hybrid method of Cherubini et al. (2011) is a combina-
tion of the partitioning and substitution approaches,
while the HMEN in this study is a mass- and energy-
based allocation. Electricity, heat, and phenols were the
coproducts in Cherubini et al. (2011), while molasses
and lignin pellets were the coproducts in our study

(Table 2). For switchgrass bioethanol in the United
States, allocation factors for energy use (62-98%) and
GHG emissions (69-98%) (Wang et al., 2011) were also
higher than the calculated factors for energy use (36-
329%) and GHG emissions (36-60%) in this study. In
the US study, switchgrass was converted into bioetha-
nol and electricity, but in our study, straw was con-
verted into bioethanol, molasses, and lignin pellets.
Finally, the comparison of bioethanol to gasoline, like
that of biogas and biomethane to natural gas, showed
that, in general, these biofuels save GHG emissions rela-
tive to their fossil fuel counterparts. Reduction in GHG
emissions ranged from 42% to 196% for bioethanol, 58%
to 1365% for biogas, and from 98% to 616% for bio-
methane. This reinforces the generally accepted conclu-
sion that biofuels reduce GHG emissions (Jury et al.,
2010; Bühle et al., 2011; Monti et al., 2012; Muñoz et al.,
2014). However, for bioethanol, the saving in GHG
emissions would be much lower because only 5%
bioethanol is blended with gasoline in many countries.

By integrating mass and energy allocation methods,
the HMEN approach solves the dilemma of multifunc-
tional allocation in biorefineries. HMEN is based on
physical relationships between the products and fac-
tors in the amounts of biomass used to produce each
biorefinery product. It allows environmental burdens
to be distributed to all biorefinery products without
significant computational difficulties. The application
of HMEN to a set of diverse case studies showed that
HMEN surpassed existing mass and energy allocation
methods. HMEN is thus preferable for complex sys-
tems such as biorefineries. Some weaknesses of the
HMEN method were highlighted, and future work
will explore the possibility to overcome them.
Although we have limited the test to biorefineries, the
approach can be used in other systems where multi-
functionality problems occur. LCA practitioners are
thus encouraged to test the method in other case stud-
ies.

References

Ahlgren S, Björklund A, Ekman A *et al.* (2015) Review of methodological choices in LCA of biorefinery systems – key issues and recommendations. *Biofuels, Bioproducts and Biorefining*, **9**, 606–619.

Azapagic A, Clift R (1999) Allocation of environmental burdens in co-product sys-
tems: product-related burdens (Part 1). *The International Journal of Life Cycle
Assessment*, **4**, 357–369.

Babusiaux D (2003) Affectation des émissions de CO₂ et de polluants d'une raffinerie aux produits finis pétroliers. *Oil & Gas Science and Technology – Revue IFP*, **58**, 685–692.

Balakrishnan M, Sacia ER, Sreekumar S *et al.* (2015) Novel pathways for fuels and lubricants from biomass optimized using life-cycle greenhouse gas assessment. *Proceedings of the National Academy of Sciences*, **112**, 7645–7649.

Börjesson P (2009) Good or bad bioethanol from a greenhouse gas perspective – what determines this? *Applied Energy*, **86**, 589–594.

Bosch R, Van De Pol M, Philp J (2015) Policy: define biomass sustainability. *Nature*, **523**, 526–527.
Bozell JJ, Petersen GR (2010) Technology development for the production of bio-
based products from biorefinery carbohydrates-the US Department of Energy's
"Top 10" revisited. *Green Chemistry*, **12**, 539–554.

Bredeson L, Quiceno-Gonzalez R, Riera-Palou X, Harrison A (2010) Factors driving refinery CO₂ intensity, with allocation into products. *The International Journal of Life Cycle Assessment*, **15**, 817-826.

BSI P (2011) Specification for the assessment of the life cycle greenhouse gas emissions of goods and services. BSI-British Standards Institution, London, UK. Available at: http://www.bsigroup.com/upload/Standards%2&%20Publications/Energy/PAS2050.pdf (accessed 7 February 2016).

Bühle L, Stülpnagel R, Wachendorf M (2011) Comparative life cycle assessment of the integrated generation of solid fuel and biogas from biomass (IFBB) and whole crop digestion (WCD) in Germany. *Biomass and Bioenergy*, **35**, 363–373.

Cherubini F, Jungmeier G (2010) LCA of a biorefinery concept producing bioethanol, bioenergy, and chemicals from switchgrass. *The International Journal of Life Cycle Assessment*, **15**, 53–66.

Cherubini F, Bargigli S, Ulgiati S (2008) Life cycle assessment of urban waste management: energy performances and environmental impacts. The case of Rome, Italy. *Waste Management*, **28**, 2552–2564.

Cherubini F, Stromman AH, Ulgiati S (2011) Influence of allocation methods on the environmental performance of biorefinery products—a case study. *Resources, Conservation and Recycling*, **55**, 1070–1077.

Creutzig F, Ravindranath NH, Berndes G et al. (2015) Bioenergy and climate change mitigation: an assessment. *GCB Bioenergy*, **7**, 916-944.

Curran MA (2007) Studying the effect on system preference by varying coproduct allocation in creating life-cycle inventory. *Environmental Science & Technology*, **41**, 7145-7151.

Dunn JB, Mueller S, Wang M, Han J (2012) Energy consumption and greenhouse gas emissions from enzyme and yeast manufacture for corn and cellulosic ethanol production. *Biotechnology Letters*, **34**, 2259-2263.

EC (2012) Innovating for Sustainable Growth: a bioeconomy for Europe. European Commission, Brussels. Available at: http://ec.europa.eu/research/bioeconomy/pdf/bioeconomycommunicationstrategy_b5_brochure_web.pdf (accessed 15 July 2016).

EC-JRC (2010) International Reference Life Cycle Data System (ILCD) Handbook-General Guide for Life Cycle Assessment-Detailed guidance. First edition March 2010. EUR 24708 EN. Luxembourg. Publication Office of the European Union.

Ecoinvent (2014) Swiss centre for life cycle inventory, Database, Ecoinvent Centre, Dubendorf, 2013.

Elgowainy A, Han J, Cai H, Wang M, Forman GS, Divita VB (2014) Energy efficiency and greenhouse gas emission intensity of petroleum products at U.S. refineries. *Environmental Science & Technology*, **48**, 7612-7624.

Energy Center of the Netherlands (ECN) (2010) *Development of Advanced Biorefinery schemes to be Integrated into existing industrial (fuel producing) complexes*. Report of the BIOREF-INTEG, The LE Petten, Netherlands.

EPA (2010) Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis, US Environmental Protection Agency, Office of Transportation and Air Quality, Washington, DC.

Eriksson O, Finnveden G, Ekvall T, Björklund A (2007) Life cycle assessment of fuels for district heating: a comparison of waste incineration, biomass- and natural gas combustion. *Energy Policy*, **35**, 1346–1362.

Fu GZ, Chan AW, Minns DE (2003) Life cycle assessment of bio-ethanol derived from cellulose. *The International Journal of Life Cycle Assessment*, **8**, 137–141.

Furuholt E (1995) Life cycle assessment of gasoline and diesel. *Resources, Conservation and Recycling*, **14**, 251-263.

Gabrielle B, Gagnaire N (2008) Life-cycle assessment of straw use in bio-ethanol production: a case study based on biophysical modelling. *Biomass and Bioenergy*, **32**, 431-441.

Gerssen-Gondelach SJ, Wicke B, Faaij APC (2016) GHG emissions and other environmental impacts of indirect land use change mitigation. *GCB Bioenergy*, **9**, 725-742.

Gnansounou E, Dauriat A, Villegas J, Panichelli L (2009) Life cycle assessment of biofuels: energy and greenhouse gas balances. *Bioresource Technology*, **100**, 4919–4930.

Guinée JB, Heijungs R, Huppes G (2004) Economic allocation: examples and derived decision tree. *The International Journal of Life Cycle Assessment*, **9**, 23–33.

Heijungs R, Guinée JB (2007) Allocation and ‘what-if’ scenarios in life cycle assessment of waste management systems. *Waste Management*, **27**, 997–1005.

Hirshfeld DS, Kolb JA (2012) Analysis of energy use and CO₂ emissions in the U.S. refining sector, with projections for 2025. *Environmental Science & Technology*, **46**, 3697–3704.

Huo H, Wang M, Bloyd C, Putsche V (2009) Life-cycle assessment of energy use and greenhouse gas emissions of soybean-derived biodiesel and renewable fuels. *Environmental Science & Technology*, **43**, 750–756.

IEA (2009) Biorefineries: adding value to the sustainable utilisation of biomass (Task 42).

ISO 14044 (2006) ISO-14044 Environmental management-Life cycle assessment. Requirements and guidelines, Geneva, Switzerland.

Jungmeier G, Werner F, Jarnehammar A, Hohenthal C, Richter K (2002) Allocation in LCA of wood-based products experiences of cost action E9. *The International Journal of Life Cycle Assessment*, **7**, 369–375.

Jungmeier G, Van Ree R, De Jong E et al. (2014) Assessing biorefinery using wood for bioeconomy-Current statut and future perspective of IEA bioenergy Task 42. "Biorefining" 24th European Biomass Conference and Exhibition, 6-9 June 2016. Amsterdam, The Netherlands.

Jury C, Benetto E, Koster D, Schmitt B, Welfring J (2010) Life cycle assessment of bio-gas production by monofermentation of energy crops and injection into the natu-ral gas grid. *Biomass and Bioenergy*, **34**, 54–66.

Kim S, Dale BE (2002) Allocation procedure in ethanol production system from corn grain - I. System expansion. *International Journal of Life Cycle Assessment*, **7**, 237–243.

Larsen J, Haven MØ, Thirup L (2012) Inbicon makes lignocellulosic ethanol a commercial reality. *Biomass and Bioenergy*, **46**, 36-45.

Luo L, Van Der Voet E, Huppes G, Udo De Haes HA (2009) Allocation issues in LCA methodology: a case study of corn stover-based fuel ethanol. *The International Journal of Life Cycle Assessment*, **14**, 529-539.

Maes D, Van Dael M, Vanheusden B, Goovaerts L, Reumerman P, Márquez Luzardo N, Van Passel S (2015) Assessment of the sustainability guidelines of EU Renewable Energy Directive: the case of biorefineries. *Journal of Cleaner Production*, **88**, 61-70.

Malça J, Freire F (2006) Renewability and life-cycle energy efficiency of bioethanol and bio-ethyl tertiary butyl ether (bioETBE): assessing the implications of allocation. *Energy*, **31**, 3362-3380.

MEC I/S MEC (2015) *The Dawning of Danish Bio-Economy*. Statut Report, Holstebro, Denmark.

Monti A, Barbanti L, Zatta A, Zegada-Lizarazu W (2012) The contribution of switch-grass in reducing GHG emissions. *GCB Bioenergy*, **4**, 420–434.

Múnoz I, Flury K, Jungbluth N, Rigarslford G, I Canals LM, King H (2014) Life cycle assessment of bio-based ethanol produced from different agricultural feedstocks. *The International Journal of Life Cycle Assessment*, **19**, 109–119.

Njakou Djomo S, Witters N, Van Dael M, Gabrielle B, Ceulemans R (2015) Impact of feedstock, land use change, and soil organic carbon on energy and greenhouse gas performance of biomass cogeneration technologies. *Applied Energy*, **154**, 122–130.

Nuss P, Gardner KH (2013) Attributional life cycle assessment (ALCA) of polyitaconic acid production from northeast US softwood biomass. *The International Journal of Life Cycle Assessment*, **18**, 603-612.

Parajuli R, Dalgard T, Jørgensen U et al. (2015) Biorefining in the prevailing energy and materials crisis: a review of sustainable pathways for biorefinery value chains and sustainability assessment methodologies. *Renewable and Sustainable Energy Reviews*, **43**, 244–263.

Pawelzik P, Carus M, Hotchkiss J et al. (2013) Critical aspects in the life cycle assessment (LCA) of bio-based materials – reviewing methodologies and deriving recommendations. *Resources, Conservation and Recycling*, **73**, 211–228.

Pelletier N, Ardente F, Brandão M, De Camillis C, Pennington D (2015) Rationales for and limitations of preferred solutions for multi-functionality problems in LCA: is increased consistency possible? *The International Journal of Life Cycle Assessment*, **20**, 74-86.

Pereira LG, Chagas MF, Dias MOS, Cavalett O, Bonomi A (2015) Life cycle assessment of butanol production in sugarcane biorefineries in Brazil. *Journal of Cleaner Production*, **96**, 557-568.

Pierru A (2007) Allocating the CO₂ emissions of an oil refinery with Aumann-Shapley prices. *Energy Economics*, **29**, 563-577.

Pimentel D, Patzek TW (2005) Ethanol production using corn, switchgrass, and wood; biodiesel production using soybean and sunflower. *Natural Resources Research*, **14**, 65-76.

Reap J, Roman F, Duncan S, Bras B (2008) A survey of unresolved problems in life cycle assessment. *The International Journal of Life Cycle Assessment*, **13**, 290–300.

RED (2009) Directives 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC vol 2013.
Sacramento-Rivero JC, Navarro-Pineda F, Vilchiz-Bravo LE (2016) Evaluating the sustainability of biorefineries at the conceptual design stage. *Chemical Engineering Research and Design*, **107**, 167–180.

Sandin G, Røyne F, Berlin J, Peters GM, Svanström M (2015) Allocation in LCAs of biorefinery products: implications for results and decision-making. *Journal of Clea-ner Production*, **93**, 213–221.

Schneider L, Finkbeiner M (2013) *Life Cycle Assessment of EU Oilseed Crushing and Vegetable Oil Refining*. Report, Technische Universitat Berlin, Berlin, Germany.

Silalertruksa T, Pongpat P, Gheewala SH (2017) Life cycle assessment for enhancing environmental sustainability of sugarcane biorefinery in Thailand. *Journal of Clea-ner Production, 140*(Part 2), 906–913.

Singh A, Pant D, Korres NE, Nizami A-S, Prasad S, Murphy JD (2010) Key issues in life cycle assessment of ethanol production from lignocellulosic biomass: chal-lenges and perspectives. *Bioresource Technology*, **101**, 5003–5012.

Spirinckx C, Ceuterick D (1996) Biodiesel and fossil diesel fuel: comparative life cycle assessment. *The International Journal of Life Cycle Assessment*, **1**, 127–132.

The White House (2012) National bioeconomy blueprint. The White House Office of Science and Technology, Washington, DC. Available at: https://www.whitehou.se.gov/sites/default/files/microsites/ostp/national_bioeconomyenden blueprint a-pril_2012.pdf (accessed 5 October 2016).

Wang M, Lee H, Molburg J (2004) Allocation of energy use in petroleum refineries to petroleum products. *The International Journal of Life Cycle Assessment*, **9**, 34–44.

Wang M, Huo H, Arora S (2011) Methods of dealing with co-products of biofuels in life-cycle analysis and consequent results within the U.S. context. *Energy Policy*, **39**, 5726–5736.

Wardenaar T, Van Ruijven T, Beltran AM, Vad K, Guinée J, Heijungs R (2012) Dif-ferences between LCA for analysis and LCA for policy: a case study on the conse-quences of allocation choices in bio-energy policies. *The International Journal of Life Cycle Assessment*, **17**, 1059–1067.

Weidema B (2000) Avoiding co-product allocation in life-cycle assessment. *Journal of Industrial Ecology*, **4**, 11–33.

## Supporting Information

Additional Supporting Information may be found online in the supporting information tab for this article:

**Table S1.** Lower heating value and market prices of biorefinery products and coproducts.

**Table S2.** Avoided (i.e. credits) energy use and GHG emissions due to replacement of conventional products by biorefinery coproducts.