# Life cycle assessment of liquid biofuels: What does the scientific literature tell us? A statistical environmental review on climate change

Anne Bouter, Sibylle Duval-Dachary, Romain Besseau

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Anne Bouter, Sibylle Duval-Dachary, Romain Besseau. Life cycle assessment of liquid biofuels: What does the scientific literature tell us? A statistical environmental review on climate change. Biomass and Bioenergy, 2024, 190, pp.107418. 10.1016/j.biombioe.2024.107418. hal-04808959

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# Life cycle assessment of liquid biofuels: What does the scientific literature tell us? A statistical environmental review on climate change☆

Anne Bouter<sup>a,b,*</sup>, Sibylle Duval-Dachary<sup>b</sup>, Romain Besseau<sup>a</sup>

## ARTICLE INFO

**Keywords:**

Biofuels

LCA

GHG emissions

Climate change

Literature review

Statistical analysis

Biomass

## ABSTRACT

More than 580 peer-reviewed articles dealing with life cycle assessment (LCA) of biofuels, published from 2012 to 2020, were identified in the scientific literature. Articles without a clear and complete LCA hypothesis were discarded. This resulted into 172 useable articles, providing 566 carbon footprints. LCA of biofuels is a topic that has attracted increasing interest in recent times, with an average production of 20 papers per year on the subject. The objectives addressed range from installation optimisation and the assessment of new types of biomass to comparison of pathways and the impact of methodological choices in LCA. The analysis of published carbon footprints reveals a hierarchy in terms of the carbon footprint of diesel, gasoline, and jet fuel substitutes. The type of feedstock appears to be a determinant. Data collected from the literature highlight that biofuels' carbon footprint varies greatly. However, the climate change mitigation potential of some pathways can be clearly confirmed. Methodological choices adopted by LCA practitioners remain an important source of differences in LCA results, as the allocation choices for co-products or the inclusion of land use change can lead to a wide range of results. The carbon neutrality principle also has the potential to influence LCA results, as described in the statistical analysis. Through an extensive literature review and a statistical analysis using econometric methods, this work provides an overview and analysis of the variability of the environmental impacts of biofuels in terms of greenhouse gas (GHG) to help shape stakeholder decision-making.

## 1. Introduction

Since the Kyoto Protocol in 1997 [1], the Paris Agreements in 2015 [2], and more recently the 28th Conference of the Parties in 2023 [3], countries have been trying to reduce their greenhouse gas (GHG) emissions to limit the increase in atmospheric temperature and its direct and indirect consequences. The transport sector is a significant contributor to GHG emissions throughout the combustion of fossil fuels used as energetic carriers to propel vehicles. In 2018, the global energy consumption of the road transport sector amounted to 2.1 GTOe [4] and accounted for around 17 % of global GHG emissions [5]. Several solutions to reduce GHG emissions in the transport sector exist, and biofuels are one of them. In 2018, bio-based substitutes for gasoline and diesel accounted for 7.7 % of the total fuels consumed worldwide [4]. Following the established assumption in the Kyoto Protocol of their biogenic carbon neutrality, biofuels have the potential to reduce GHG emissions through CO₂ uptake during photosynthesis as the plants' grow

[6,7]. Moreover, use of biofuels in the road transport sector can reduce countries' energy dependence on fossil fuels [8], and they also contribute to diversifying the sources of energy supply [9]. Unlike fossil fuel production, which is linked to the subsoil wealth of a country, a wide variety of biomass can be cultivated, even on marginal land and then used to produced biofuel and contribute to diversifying the source of energy supply [10]. However, competition between the food and feed sectors and the fuel sector still represents a potential risk [11–13], even if described as aa modest one [14].

This is the reason why the use of biofuels is supervised in several countries such as in the United States with the US renewable fuel standard programme [15]. California launched a specific regulatory framework in parallel in the form of the low-carbon fuel standards [16]. In Europe, the directive of the European Parliament and of the Council on the promotion of the use of energy from renewable sources (RED II) [17] aims to ensure a sustainable playing field for biofuels and alternative fuels. The directive sets out objectives on incorporation rates for

<footnote><sup>a</sup> European Commission, Joint Research Centre (JRC), Ispra, 21027, Italy</footnote>

<footnote><sup>b</sup> IFP Energies Nouvelles, Rueil-Malmaison, France</footnote>

<footnote>* The views expressed in the paper are purely those of the authors and should not be interpreted as an official position of the European Commission.</footnote>

<footnote>* Corresponding author. European Commission, Joint Research Centre (JRC), Ispra, 21027, Italy.</footnote>

<footnote>E-mail address: anne.bouter@ec.europa.eu (A. Bouter).</footnote>
renewable energies used in the transport sector. To be eligible as renewable energy, biofuels must meet the sustainability criteria (quantitative and qualitative) defined in the RED II, complying with the reduction of GHG emissions compared to fossil fuels. However, biofuels generate other environmental impacts beside climate change. For example, the production and use of chemicals have impacts on terrestrial acidification, eutrophication or even stratospheric ozone depletion [18,19]. The production and use of energy generate impacts on terrestrial acidification, particulate matter formation, or depletion of resources [20,21]. Water use is also a critical impact category, not only for algae [18] but also for more conventional biomass as sugar beet in some regions [9]. Nonetheless, climate change is still the most widely studied impact category and often the only one. Thus, this literature review focuses on life cycle assessment (LCA) of biofuels for the sole environmental impact category of climate change. The primary purpose of this study is to identify parameters having an impact on the environmental results for climate change and quantify it.

Numerous LCA studies are dealing with environmental impacts of biofuels. However, they are often restricted to a certain geographical area Cliquez ici pour taper du texte. [22], a specific type of biomass [23, 24], an/or a specific energetic conversion pathway [25]. Having noted these limited perimeters, the definition of an average greenhouse gas emissions range for biofuels is an arduous task. LCA studies can have a very wide range of results even when assessing the same product [26,27] given the high range of uncertainties and despite the ISO norms [28,29] and detailed guidelines to conduct LCA [30]. This work aims at tackling the previous mentioned issues by:

i) Providing an extensive literature review from 2012 to 2020 focusing on the GHG emissions of liquid transportation biofuels, both for road transport and aviation, determined with LCA methodology following the ISO standards [28,29]. Maritime fuels are excluded as their production pathways are too different compared to road and aviation fuels production pathways.

ii) Characterizing and quantifying the parameters influencing the life cycle impact assessment results of biofuels on climate change thanks to statistical methods, using some basics of meta-analysis methodology [31,32]. The parameters studied are as broad as the type of biomass related to the type of biofuel, or the choices regarding the LCA methodology [28,29].

This study ensures inputs to the existing research in this topic in several ways. The first one being that, although biofuels have been a widely treated LCA topic in scientific literature, only a few outdated studies examine them all together: gasoline, diesel and jet pool; as a statistical dataset [27,33]. Second, the study covers a wide and quite exhaustive range of liquid biofuels, both for road transport and aviation sector. Third, a comprehensive statistical analysis among the reviewed publications is performed and provide an overview and analysis of the variability of the environmental impacts of biofuels in terms of GHG emission to help shaping decision making for stakeholders.

This article is structured into three parts. Firstly, section 2 presents the methodology followed to build the database. Secondly, section 3 describes the pieces of information extracted from the database and their influence on the climate change impact are analysed. Lastly, section 3.4 focuses on the statistical analysis and examines the sensitivity of the results to the several parameters highlighted from the previous sections.

## 2. Methodology

### 2.1. Literature search and selection of the studies

A systematic literature screening was carried out using the Scopus database, as it indexes a greater number of sources not covered in other bibliographic databases, as stated by Pranckutė (2021) [34]. The search

<table><caption>**Table 1**

Research performed.</caption><thead><tr><td>Database</td><td>Search strategy</td><td>Search results</td></tr></thead><tbody><tr><td>Scopus</td><td>(TITLE-ABS-KEY ("biofuel" OR "bioethanol" OR "biodiesel" OR "biojet" OR "jet") AND TITLE ("lca" OR "life cycle assessment" OR "environmental assessment" OR "environmental comparison")) AND PUBYEAR &gt;2011 AND PUBYEAR &lt;2021 AND (LIMIT-TO (DOCTYPE, "ar"))</td><td>585 results</td></tr></tbody></table>

<table><caption>**Table 2**

Detailed characteristics used to build the database.</caption><thead><tr><td>Typology of the study</td><td>Technical data</td><td>Methodological choices</td></tr></thead><tbody><tr><td>Year of publication</td><td>Geographical location of the case study<br>Substituted fossil fuel energy pathway<br>Type of biomass feedstock<br>Type of co-products</td><td>Method to consider co-products<br>Carbon neutrality principle<br>Consideration of land use change</td></tr></tbody></table>

was conducted in a systematic way to identify available studies in the English-language literature devoted to LCA methodology following ISO standards [28,29]. The second criterion to be met by the selected studies was a focus on biofuels intended to serve as a substitute for either road transport or aviation fuels – that is, gasoline, diesel or jet fuel. The time frame ranged from 2012 to 2020, as older studies were previously reviewed by Menten et al. (2013) [27]. As this study aimed to explain the differences between numerous LCA results on the impact of biofuels on climate change, the common denominator among the bibliographic corpus refers to the climate change impact of the studied biofuels, expressed in terms of GHG emissions, only studies assessing this indicator were selected for inclusion. As stated in Bouter and Guichet (2022) [35], the LCA characterisation methodology assessing global warming potential over a 100 years' time horizon has a very low impact on the results and is not included in this study. All results are thus reported for climate change impacts calculated over 100 years, expressed in grams of carbon dioxide equivalent per megajoule of fuel (g CO₂eq/MJ).

This review focuses solely on peer-reviewed articles. The search strategy was based on a combination of keywords and logical operators, as described in Table 1.

The homogeneity of the literature review was ensured by screening all 585 results to produce a more precise selection. The articles focusing on electricity generation [36,37], biochemical production [38,39] or marine biofuel [40,41] were disregarded as we focused on only biofuels for road transport or aviation for this specific study. Articles focusing on LCA methodological questions or broader environmental integration [42,43] were also disregarded as they were out of our research goal, as were articles using consequential LCA methodology [44,45]. This lead to a set of 172 studies used as the material for the analysis and entered into the study database. This is not unusual to have such ratio of around 30 % of useable materials after the literature search. In Bouter et al. [35], only 8 % of the corpus was used for the statistical analysis. Also in Garcia et al. [36], 41 % of the studies were analysed further.

### 2.2. Database building and characterisation of the studies

The building of the database is an important stage as it enables the comparison of the selected studies through a systematic characterisation. As stated in Refs. [27,35], three categories are chosen to describe the studies: the typology of the study, the technical data, and the methodological choices are considered. The previous mentioned three categories are then divided into sub-categories helping to meet the objective of the review by extracting the right information from each
<figure><img src="image_3.png" /><figcaption>Fig. 1. Cumulated number of articles across the studied timeframe.</figcaption></figure>

article. In addition to the usual metadata such as the authors, the title of the publication, and the journal, 8 characteristics are defined as described in Table 2. These eight characteristics are divided into sub-categories used for the statistical analysis.

The technical data category comprised four subcategories. The ‘substituted fossil fuel energy pathway’ subcategory was divided into three variables: the diesel pool, the gasoline pool and the jet fuel pool. For ‘type of biomass feedstock’, all the feedstock types listed in the studies were reported initially before being classified into broader groups, as reported in the renewable energy directive (RED) [17] Annex IX. They were split into ‘conventional’ biomass to produce conventional biofuel, being based on either food or feed feedstock, and ‘advanced’ biomass to produce advanced biofuel, as listed in Annex IX, Part A, of the RED [17]. The third group comprised feedstock listed in Annex IX, Part B, and the last group included other feedstock not listed at all. The ‘type of co-products’ subcategory was divided into three variables after identifying the most frequently produced co-products, which were feedstock residues, electricity and glycerol.

In the methodological choices category, three subcategories were identified. The first one was ‘method to consider co-products’, resulting in seven variables: economic allocation, energy allocation, mass allocation, exergy allocation, hybrid allocation, system expansion and no allocation. The subcategories from Table 2 not detailed here were either binaries – yes or no – or a discrete variable, such as the year or the geographical location.

The variable of interest in this literature review was GHG emissions per megajoule of biofuel. The articles that published results in LCA endpoints [46] or expressed in a unit that did not allow a direct conversion to g CO₂eq/MJ were removed. Some articles expressed their results per mass or per volume of biofuel. In this case, either the lower heating value (LHV) indicated in the article or the default LHV from the JRC-Eucar-Concawe study [9] were used to convert the value. The results expressed per hectare or per kilogram of feedstock were converted using the yield of the transformation process. If it was not indicated, the article was disregarded. Articles studying biofuel and fossil fuel blends were kept only if they included intermediate results that enabled the impact of the production of the biofuel to be separated from the impact of the production of the fossil fuel. A total of 172 articles were kept, producing 566 observations.

## 3. Results and discussion

Section 3 presents the main observations and results based on the 566 observations of the database coupled to the different selected

<figure><img src="image_4.png" /><figcaption>Fig. 2. Origin of the biomass (all types).</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 3. Origin of the biomass (divided per type of biomass as described in section 3.1.3).</figcaption></figure>

variables presented in section 2.

### 3.1. Overview of the studies

#### 3.1.1. Observation per year of publication

Fig. 1 reveals that the publication rate of articles on the topic is around 20 per year and appears to be relatively constant.

#### 3.1.2. Observation per origin of biomass

The United States and Brazil produced 84 % of the world's ethanol market in 2019, and Europe alone accounted for 38 % of the world's biodiesel market [4]. This is reflected in the geographical location of the biomass studied in the articles, as shown in Fig. 2. US origin leads the scene, with 51 articles studying biomass from the United States, followed by Europe (43 articles) and Brazil (21 articles). The types of biomass studied in the United States are very diverse, as depicted in Fig. 3, where all four categories of biomass are represented. Neither Europe nor Brazil is represented in the category of ‘other’ biomass. India is highly represented in this category, with 4 out of 12 publications from this country assessing oleaginous yeast, biogas and jatropha.

Some types of biomass are more studied than others. Brazilian studies focus more on soy biomass (5/21 publications) and sugarcane (10/21 publications). In the United States, algae biomass is very well represented (16/51 publications), as well as lignocellulosic residues (21/51 publications). Short rotation coppices are also notable, accounting for eight publications. In Europe, the focus is more on lignocellulosic biomass (13/43 publications), rapeseed (8/43 publications) and algae (7/43 publications).
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Cumulative number of articles per type of biomass and per year of publication.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Cumulative number of articles per type of fuel and per year of publication.</figcaption></figure>

### 3.1.3. Observation per type of biomass

Different types of biomass can be used to produce biofuel. We analysed approximately 70 types of biomass across the different articles. The ‘conventional’ category of biomass is capped under RED II at 7 % of final energy consumption in the road and rail transport sectors. The ‘advanced’ category of biomass is produced mostly using advanced technologies. Unlike the ‘conventional’ category of biomass, this one is not capped under RED II, but rather encouraged, having a lower-bound objective of at least 3.5 % of final energy consumption by 2030. The third category of biomass is defined as the feedstock registered in Part B of Annex IX of RED II, being feedstock processed using mature technologies, but not competing with the food and feed sector. This category is limited to 1.7 % of the energy content of transport fuels supplied for consumption or use on the market. The fourth and last category, named ‘other’, represents unclassified types of biomass that do not fit into the previous categories, such as jatropha.

Categorising biomass in this way allows for a clear and standardised approach to analysing and comparing different types of biomass. Fig. 4 depicts the results according to the four groups of biomass. The category of ‘advanced biomass’ accounts for the most articles, with 109, followed by ‘conventional biomass’, with 63 articles. The last two categories, dealing with feedstock listed in Part B of Annex IX of RED II and biomass unclassified under RED II, accounted for 12 and 8 articles, respectively.

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Dispersion of LCA GHG emissions results per type of energy pathway substitute.</figcaption></figure>

From 2014 onwards, the number of publications describing biofuels pathways using advanced biomass increased significantly. This corresponds with the 2014–2020 programming period when the EU financially supported research on biofuels produced from wastes and residues.

### 3.1.4. Observation per type of substituted fossil fuel

Looking at the fossil fuel substitutes, two studies [47,48] were removed from the sample as it was not possible to differentiate between the main outputs studied, being both gasoline and diesel. This lead to 5 observations being removed, leaving a bibliographic corpus of 170 studies and 561 observations. As shown in Fig. 5, substitutes for diesel (91 publications) and gasoline (77 publications) are more or less equally studied. However, the environmental impact of substitutes for fossil jet fuel is still an emerging topic, with only 18 publications.

## 3.2. Analysis of the environmental impact on climate change

Fig. 6 presents the climate change impact of the biofuels grouped per type of energy pathway substitute. The box plots depict the median for the three types of fuel in purple. The lower bound of the box represents the first quartile, while the upper bound represents the third quartile. The whiskers are calculated as 1.5 times the range between the third quartile and the first quartile. Black circles are observations outside of the bounds of this box plot representation. Jet fuel substitutes have a statistically lower impact on climate change than substitutes for diesel and gasoline. However, this result is supported by fewer observations: 60 for jet fuel substitutes compared with more than 235 for diesel and gasoline substitutes. The impact on climate change of diesel substitutes appears to be within a similar range as the impact on climate change of gasoline substitutes. Processes and feedstock used to produce substitutes for diesel, gasoline and jet fuel are different. Therefore, in the following sections, we present the results per type of fuel for more clarity.

### 3.2.1. Diesel pool

Fig. 7(a) presents the main resources used to produce diesel substitutes. Diesel substitutes can be produced using several processes, mainly using oily feedstock from different sources as an input [49,50] to produce hydrotreated vegetable oils or fatty acid methyl ester. Thus, the types of biomass in the ‘conventional biomass’ category in Fig. 7(a) are oil-rich feedstock. Algae accounts for half of the articles studying biomass in the ‘advanced biomass’ category. Lipids can be extracted from algae and then used as a feedstock in the esterification process. Biodiesel can also be made from lignocellulosic feedstock processed using gasification and Fischer–Tropsch upgrading or catalytic processes [51]. Thus, lignocellulosic biomass accounts for the other half of the articles studying biomass in the ‘advanced biomass’ category. Feedstock
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** (a) Cumulated number of article per type of biomass studied in substitution to the diesel pool. The filling of the bars indicates the category of the biomass as defined in section 3.3., and (b) the associated statistical results.</figcaption></figure>

listed in Part B of Annex IX of RED II is also well represented, with a total of 14 articles dealing with used cooking oils [21] and animal fats [52], which are, indeed, considered very promising. They are processed using mature technologies similar to those used to process conventional feedstock but this type of biomass is not in competition with the food and feed sector. Jatropha and one other feedstock are in the ‘other’ category, as they have not been categorised in RED II so far. Advanced biomass, as listed in Annex IX, Part A, of RED II, accounts for the largest number of observations (127/266). Algae accounts for a significant share of the observations in the advanced biomass category (74). These types of biomass are not in competition with the food and feed sector and are more promising than conventional biomass (91/266 observations). Conventional and advanced biomass have been well covered in scientific studies compared with the other two categories of biomass – that is, feedstock listed in Annex IX, Part B (27/266 observations), and

unclassified biomass (21/266 observations).

Fig. 7(b) presents the climate change impact of biofuels in the diesel pool grouped by category of biomass. The impact of advanced biodiesel (median of 54.7 g CO₂eq/MJ) is comparable to the impact of conventional biodiesel (median of 51.2 g CO₂eq/MJ). It is notable that without algae in the sample the results for biodiesel produced from advanced biomass, essentially lignocellulosic biomass, would be much lower, with a median of 39.7 g CO₂eq/MJ. Algae have several advantages, namely being able to treat wastewater [53], having a rapid growth rate and having a higher CO₂ absorption rate per unit of cultivated area than terrestrial crops. However, growing algae for biofuel production requires a lot of energy for cultivation: water pumping, CO₂ injection, nutrient mixing, etc. [54]. The harvest and drying stages also consume a lot of energy, as algae are diluted into water, centrifuged, pressed, etc. [18,55,56]. The impact on climate change of biodiesel produced with

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** (a) Cumulated number of article per type of biomass studied in substitution to the gasoline pool. The filling of the bars indicates the category of the biomass as defined in section 3.3. and (b) the associated statistical results.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** (a) Cumulated number of article per type of biomass studied in substitution to the jet pool. The filling of the bars indicates the category of the biomass as defined in section 3.3., and (b) the associated statistical results.</figcaption></figure>

algae is thus affected by the substantial energy consumption, with a median of 72.1 g CO₂eq/MJ. Lignocellulosic biomass has been studied mainly as a feedstock for fast pyrolysis or hydrothermal liquefaction. It is still an emerging technology [9]. These processes currently consume a significant amount of hydrogen to upgrade algae to bio-oil or bio-crude. The impact of the production of this hydrogen increases the environmental impact of the biodiesel produced. Biodiesel produced using mature technologies from Annex IX, Part B, feedstock has the lowest impact on climate change, at 10.8 g CO₂eq/MJ. This might be related to the system boundary for such feedstock, as no impacts are usually considered up to the collection point as the feedstock is waste.

### 3.2.2. Gasoline pool

Fig. 8(a) presents the different types of biomass useable as a source for gasoline substitutes studied in the articles. The main substitute for gasoline has been ethanol since the emergence of the biofuel market. One common method of producing ethanol is the fermentation of sugars, such as those found in corn or sugarcane ('conventional biomass'), using yeast or bacteria [57,58]. This process produces ethanol as a by-product, which can then be distilled to increase its concentration. Thus, biomass in the 'conventional' category is either sacchariferous plants or starchy plants. Another approach is the gasification of biomass, such as wood chips or agricultural residues (lignocellulosic feedstock), to produce a synthesis gas that can be converted into ethanol through a series of chemical reactions [59–61]. Ethanol can also be produced from the hydrolysis and fermentation of cellulosic materials, such as switchgrass or agricultural waste. Thus, biomass in the 'advanced' category is both lignocellulosic and oily. The various methods of ethanol production have been extensively studied and reviewed in the scientific literature. For example, a study by Himmel et al. (2007) [62] provides an overview of the different methods for converting cellulosic biomass into ethanol. Overall, these peer-reviewed sources demonstrate the diverse approaches to ethanol production [63] and the ongoing research into making it more sustainable and cost-effective.

After fermentation, the ethanol is purified by distillation and, if necessary, dehydration. Depending on the feedstock used, different co-products are produced during the process; for example, dried distillers' grains with soluble lignin can be used as animal feed or fertiliser, or can be burned for energy recovery. Pre-treatment, a step that consumes a lot of energy and/or chemicals, is necessary to produce ethanol

from lignocellulosic feedstock, but this feedstock is often agricultural or forestry residues, with no environmental burden. All in all, producing biogasoline using advanced biomass results in a lower impact on average on climate change than using conventional biomass, at 35.9 g CO₂eq/MJ and 48.5 g CO₂eq/MJ, respectively, as presented in Fig. 8(b).

### 3.2.3. Jet pool

Fig. 9(a) presents the main resources used to produce substitutes for jet fuel. There are several possible methods of producing bio-jet fuel, a renewable alternative to traditional jet fuel derived from biomass. One common method is the hydroprocessing of vegetable oils or animal fats to produce hydroprocessed esters and fatty acids that can be further refined into bio-jet fuel. Another approach is the conversion of biomass, such as algae or agricultural residues, into bio-oil using pyrolysis or hydrothermal liquefaction, which can then be upgraded into bio-jet fuel. Thus, both oleaginous biomass and sacchariferous and starchy biomass are studied as raw materials to produce bio-jet fuel, as illustrated in Fig. 9. Biomass can also be converted into synthesis gas through gasification, followed by Fischer-Tropsch synthesis to produce liquid hydrocarbons that can be used as bio-jet fuel. This explains the articles on forest residues and other cellulosic materials. These different methods of bio-jet production have been extensively studied and reviewed in the scientific literature. For example, a study by Mawhood et al. (2016) [64] provides an overview of the different pathways for producing bio-jet fuel from different biomass sources, as does Gutiérrez-Antonio et al. (2017) [65]. The small number of observations related to bio-jet fuel makes it more difficult to compare conventional and advanced bio-jet fuel; see Fig. 9(b).

### 3.3. Influence of methodological choices

LCA is a widely used methodology for evaluating the environmental impact of products and processes. Despite the framework of ISO standards [28,29], studies have shown that the results of LCAs can vary depending on the practitioner conducting the assessment. This variability can be attributed to differences in assumptions, data sources and methodological choices. For example, a study from 1998 [66] examined the variability in LCA results for the same product conducted by different practitioners and found that the choice of impact assessment method and interpretation of results led to significant differences in the
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Cumulative number of articles per co-product allocation method per year of publication.</figcaption></figure>

outcomes. Similarly, a study by Scrucca et al. (2020) [67] investigated the influence of practitioner choices on the results of LCAs and found that variations in modelling assumptions and data sources led to divergent environmental impact assessments and that these should be considered sources of uncertainty.

The methodological choices selected for this review, and previously identified as having an impact on the results by Menten et al. (2013) [27], are the following: the approach to solving multifunctionality, the counting of biogenic carbon, and the consideration of land use change (LUC). These are each discussed in the following subsections.

### 3.3.1. Allocation among the co-products

One of the key challenges in LCA is addressing multifunctionality, where a product or process provides multiple functions or services. ISO has provided guidelines for addressing multifunctionality in LCA, which are outlined in ISO 14040:2006 and ISO 14044:2006 [28,29]. These standards recommend using allocation procedures to distribute the environmental burdens across the different functions of a product or process. ISO provides a framework that enables practitioners to select the most appropriate allocation method based on the specific circumstances of the LCA study, ensuring a comprehensive and accurate assessment of the environmental impacts of products and processes. First, ISO recommends trying to avoid allocation. Thus, 45 articles producing 104 observations chose to manage the co-products by consuming them, all or a part, into the system boundaries [68–70]. For instance, the produced lignin can be burned in a co-generation

installation in the system boundaries to self-produce part of the steam and electricity needed for the process. If multifunctionality is inevitable, ISO suggests that practitioners should preferably consider using system expansion. Otherwise, they should consider another allocation method based on physical relationships, such as mass or energy content, or based on the economic value of the different functions, or use another method to account for multifunctionality in LCA. Legislation may require the use of a specific allocation method, such as with RED II [17], which requires the use of an energy allocation. As a result, there is no consensus on the approach to use to deal with co-products from biofuel production processes; this is depicted in Fig. 10. In some articles, hybrid allocation was even performed [71,72]; mass allocation was used for agricultural co-products and energy allocation was used for co-products from the production process. It was often due to the fact that one of the co-products was electricity [73,74], in which case mass allocation was not possible.

As highlighted in Fig. 10, the main approach chosen is substitution, described as a variant of system expansion in the International Life Cycle Data System handbook [30]. However, substitution is not explicitly cited in the ISO standards. It is a mathematical extrapolation of system expansion that enables the comparison of systems that do not provide exactly the same function [75]. Substitution allows partitioning among the resource use/emissions to a specific product to reflect its current share of burdens [76] if reporting impacts at the individual co-product level is desired. This method of solving multifunctionality is particularly relevant in cases where the co-product enables to avoid the production of a product providing the same function by an alternative process. However, for non-experts, interpretation can be all the more complex as it can be difficult to judge the relevance of the replaced product and its modelling.

Fig. 11 describes the climate change results per MJ of fuel based on the various choices found in the studies to handle the co-products, alongside with their interquartile range (IQR). IQR is a dispersion measure using the difference between the third quartile: Q3 on Fig. 11, and Q1, also on Fig. 11. As a result, the ‘no allocation’ choice displays the highest results’ dispersion, followed by the substitution methodology. As stated above, substitution allocation is very context specific and complicated to be reproduced as the substitutions’ hypothesis will vary a lot depending on the scope and the objectives of the study. Overall, the allocation among the co-products as a key parameter varies a lot between studies, even though the allocation procedure is clearly explained in the ISO standards [29].

### 3.3.2. Consideration of carbon neutrality

Since the Kyoto Protocol [1], the consensus in LCA is not to account for biogenic CO₂ [77]. The CO₂ emitted during the combustion of bio-products is compensated for by the sequestration of CO₂ during the growth of the biomass. Thus, the climate change advantage of biofuel

<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Climate change results depending on the method to handle the co-products.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Fig. 12. Ordinary Least Squares (OLS) and adjusted OLS for the filtered sample (527 observations).</figcaption></figure>

over fossil fuel is only visible if the combustion stage is taken into ac-
count. However, this assumption of carbon neutrality is valid only for
carbon emitted as CO2. None of the articles studied in this review took
into account quantitatively the possibility that CH4 or N2O would be
emitted during the combustion of biofuel. This amounts to 0.97 g
CO2eq/MJ for fossil-based diesel and 0.39 g CO2eq/MJ for fossil-based
gasoline [10].

In 33 articles, biogenic CO₂ and fossil CO₂ were both accounted for [19,78,79]. It can change the results of a comparison between bio-based and fossil-based fuel in the case of an analysis of well-to-tank emissions. In fact, excluding the combustion step in the comparison allows bio-based fuel to benefit from the capture of atmospheric CO₂ without the burden of the emissions released during the combustion of the fuel. It can bias the comparison, as the purpose of fuel is to be burned and not stored. Fortunately this appears in only very few articles. Apart from this issue, counting biogenic CO₂ separately can be beneficial to go further in LCA. Burning biomass is quicker than growing it. Coupled with global phenomenon that regulate the quantity of carbon in the atmosphere or taking into account the time of emissions and capture can lead to different conclusions [80-82]. However, the current software broadly used to compute LCA does not yet enable the creation of a time-dependent life cycle inventory, nor does it provide corresponding time-dependent characterisation factors. Thus, counting biogenic CO₂ separately in a classical attributional LCA rather than using the ‘carbon neutrality’ assumption does not substantially change the results.

3.3.3. *Consideration of land use change*

LUC is a significant driver of environmental impacts, with potential
effects on biodiversity, carbon sequestration and ecosystem services. In
the context of LCA, LUC is an important consideration when evaluating
the environmental impacts of agricultural products, bioenergy and other
land-based commodities. Studies have demonstrated that LUC, partic-
ularly deforestation and conversion of natural habitats to agricultural
land, can lead to substantial GHG emissions and loss of biodiversity [83,
84]. Furthermore, indirect LUC impacts associated with the expansion of
agricultural production can have far-reaching environmental conse-
quences, highlighting the need for robust methodologies to account for
these impacts in LCA [85]. By integrating the latest research on LUC into
LCA frameworks, it is possible to improve the accuracy and compre-
hensiveness of assessments related to land-based commodities and their
associated environmental impacts.

Direct LUC (dLUC) refers to the immediate conversion of land for
specific purposes, such as agriculture or urban development [86]. In
contrast, indirect LUC (iLUC) occurs when the expansion of agricultural
production leads to the displacement of natural ecosystems, resulting in
additional land conversion and associated environmental impacts [87].
Both dLUC and iLUC can have significant implications for GHG emis-
sions and land degradation, emphasising the importance of considering
these factors in LCA studies to provide a comprehensive assessment of
the environmental impacts associated with land-based commodities.
The number of articles that take into account dLUC is steadily growing.

There were 34 in total for the whole time frame studied and a significant number of them were published in 2015 (7 publications) [88–94], 2017 (also 7 publications) [95–100] and 2018 (6 publications). The Inter-governmental Panel on Climate Change methodology [101] is used in half of the articles to calculate dLUC. iLUC is calculated only in two articles [52,99], which were removed from the bibliographic sample as part of consequential LCA.

3.4. *Statistical analysis*

Assessing the climate change impacts of biofuels involves inherent variability due to factors such as feedstock type, LUC and production processes, as discussed in Section 3. This variability can lead to diverse outcomes in terms of GHG emissions, depending on specific circumstances and methodological choices. In this section, we aim to differentiate between the effects of uncertainty resulting from the technical and methodological choices. To this aim, we decided to test a model where the common variable of interest, being the climate change impact of a biofuel, could be explained by the different variables studied in this paper in Section 3. The tested model reflects the statistical results of different simulations and is not based on observations but predictions, only compared to those. The final aim was to estimate the influence of each independent variable on the climate change indicator: dependent variable, and to determine a relationship between the variables to predict the dependent one.

The regression analysis was performed using the Python statsmodels package [102]. As highlighted in Section 3, the spectrum among biofuels is very wide depending on the substitution pool and the category of biomass studied. We removed the extreme outliers, the lower bound being – 250 g CO2eq/MJ and the upper one being 350 g CO2eq/MJ. The median value being 40 g CO2eq/MJ and the standard deviation being 65 g CO2eq/MJ, such a range is wide enough to keep sufficient variability in the sample by keeping 527 observations out of 566.

We ran the statistical analysis starting with all independent vari-
ables, before testing to split first by biomass category, as described in
Section 3.1.3, then by substitution pool, as described in Section 3.1.4,
and then by both. This resulted in the following variables to be tested:
the allocation choice, the inclusion of land use change, the carbon
neutrality principle, some types of co-products (such as electricity co-
production), the geographical origin of the biomass and the year of
publication. The minimum threshold to run the statistical analysis was
set at 25 observations. This resulted in assessing substitutes for diesel
and gasoline pools, using both advanced and conventional biomass, and
substitutes for jet fuel using conventional biomass. When establishing
the model, we first calculated the regression, including all possible pa-
rameters, before recalculating the model using only the parameters
showing a significant correlation. To determine if the correlation was
significant or not, we used a Student's t-test [103]. The threshold value
was set to 0.05 for the p-value to test the null-hypothesis, meaning that
independent variables obtaining a result for p > [t] > 0.05 could be
discarded from the model as having no real effect on the dependent
<figure><img src="image_15.png" /><figcaption>Fig. 13. Observed versus predicted values for the GHG emissions by energy pathways substitutes and biomass categories.</figcaption></figure>

variable. Fig. 12 presents the results. It is notable that the adjusted $R^2$ value is lower than the $R^2$ for all simulations. $R^2$ values are the statistical measure in the regression models assessing the proportion of variance in the dependent variable: the climate change impact of the several types of biofuels studied that could be explained by the independent variables: the ones mentioned before. The adjusted $R^2$ value is similar to the $R^2$ but takes into account the number of independent variables and the sample size. It may decrease if additional independent variables are added to the model without a significant increase in explanatory power. This can be explained by the high number of independent variables tested here in the first step of the statistical analysis.

The climate change LCA result is the dependent variable to be explained by the other independent variables. The results in Fig. 12

highlight that for many energy pathway substitutes and biomass categories, the $R^2$ value is relatively low. Such a result should not be discarded; on the contrary, it indicates that it is simply not possible to explain the result variability using only the input parameters considered, and that other parameters must intervene. We also see that feedstock listed in Annex IX, Part B, of the RED, corresponding to feedstock processed using mature technologies, can be largely explained by the input parameters considered. Use of these mature technologies results in an adjusted $R^2$ of 0.95, meaning that 95 % of the variability can be explained by the input parameters considered. Diesel substitute observations dominated this biomass category, with only 32 jet fuel observations.

Fig. 13 presents the ‘observed carbon footprint’ corresponding to the
assessed footprint in the literature versus the carbon footprint predicted by the statistical model. Each row corresponds to a biomass category: all feedstock (first row), Annex IX Part B (second row), conventional feedstock (third row), and advanced feedstock (last and fourth row). Each column corresponds to an energy pathway substitute: first column does not differentiate among energy pathways substitutes (all energies), the second column focuses on diesel substitutes, the third one on gasoline, and the last one is dedicated to jet.

Results for feedstock from Annex IX, Part B (mature technologies in the second row) show good correlation between the observations and the model, as well as between the model and the jet pool: last column. However, results should be taken with caution as there are fewer observations in these categories advanced feedstock and jet substitutes.

Regarding the other feedstock and substitutes' types, a significant share of the variability cannot be explained as previously highlighted. We believe that other parameters, not reported in the studied publications, and also potential not quantifiable parameters, such as the LCA practitioner or others, could be the source of the variability. At least, we were not able to explain the variability among the main corpus of the selected biofuels' studies.

We performed numerous statistical tests (all available upon request), starting to consider all parameters described in the previous sections, and keeping step by step only the ones resulting as significant in the statistical outcomes.

Variables appearing as statistically correlated with the carbon footprint are the allocation method and the co-product considered, the type of biomass, especially if those are residues, the consideration of carbon neutrality, the land use change and the location. Detailed statistical analysis can be found in the supplementary information for models including all parameters and only parameters showing a significant p-value: $P > [t] < 0.05$.

As displayed in Fig. 13, the $r^2$ are not that high despite all the efforts we put in the literature review, the identification of the parameters, and the statistical analysis. It is a possibility that the LCA practitioner is a key parameter to explain the variability among the results. For example, in Brandão et al. [95], they also performed the same type of work but achieving much higher $r^2$. However, the carbon footprints' results are based on one only source being [96], that reinforce the hypothesis of variability among LCA practitioners. This could be further investigated in future works.

## 4. Conclusion

This comprehensive statistical environmental review of 585 peer-reviewed articles from 2012 to 2020 highlights the significant variability in LCA environmental impact results for biofuels, particularly in terms of GHG emissions. The study focuses on liquid biofuels for the road and aviation sectors and emphasises the importance of methodological choices in LCA, which can cause substantial differences in LCA results.

The analysis of published results confirms the large disparity in climate change impact results across fuel substitution pools and among studied feedstock. Among the most influential parameters are the allocation method and the existence of relevant co-products, the type of biomass used and its geographical origin, and the inclusion or not of LUC. Except for feedstock from Annex IX, Part B of the RED II, a significant share of the variability cannot be explained by these parameters. This result suggests that other important factors are responsible for this variability. Indeed, our study highlights the limitations of linear regression models in assessing fully the complex and multifaceted relationship between biofuels and climate change. Incorporating time series analysis or complex systems modelling can provide additional insights into this critical issue.

We have provided the input data and statistical analysis in supplementary information to enable further research on this topic of sustained interest. The development of robust and transparent methodologies,

coupled with the establishment of comprehensive databases and guidelines, could help improve the consistency and comparability of LCA results for biofuels. This would ultimately provide stakeholders and decision-makers with more reliable and actionable information to support the sustainable development and deployment of biofuels as a climate change mitigation strategy.

Finally, this study considered only attributional LCA to assess biofuel carbon footprints, meaning that iLUC and other consequential parameters were disregarded as being beyond the scope of this research. It might be interesting to perform similar assessments using a consequential approach.

## CRediT authorship contribution statement

**Anne Bouter:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. **Sibylle Duval-Dachary:** Writing – original draft, Investigation, Data curation. **Romain Besseau:** Visualization, Software, Methodology, Investigation.

## Data availability

Data will be made available on request.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.biombioe.2024.107418.

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