1 Introduction

The automotive industry is confronted with increasing environmental and social requirements. Those challenges are posed by growing social and environmental awareness in the society and the recognition that sustainability is one driver for innovation (Ribeiro et al. 2007). Weight reduction through lightweight design has become an important lever in the automotive industry to decrease use-phase emissions (Delogu et al. 2017). Hence, the choice of materials used for a vehicle can contribute to reduce negative environmental and social impacts. Bio-based materials are considered a promising solution, because depending on the component and the system under study, using wood as a substitute for other materials has the potential to reduce the environmental impacts (e.g., less energy intensive than metals and plastics or less embodied impacts for acidification, climate change, and eutrophication) (Cordella and Hidalgo 2016; Petersen and Solberg 2005; Spitzley et al. 2006). Additionally, wood-based product systems are expected to contribute to social benefits, like an increase in local/rural employment and an improvement of infrastructure (Siebert et al. 2018b). Additionally, Touceda et al. (2018) mention that a substitution with wood can lead to improvements in supply chains like reducing the risk of injuries and fatalities.

The first vehicles were entirely made out of wood like the 1909 Ford Model T (Brooke 2008). Today, a typical passenger vehicle is composed of many materials, but wood is usually not part of a vehicle anymore (Mayyas et al. 2012a; Omar 2011). In recent years, the idea of bringing wood-based materials back into automotive applications has gained some attention again and was studied by several research projects (e.g., HAMMER or WoodC.A.R.Footnote 1) focusing mostly on the technical feasibility but also on potential environmental and social impacts of material substitution or tradeoffs such as the opportunity of regional job creation at the cost of increasing risk of forced labor or increasing GHG emissions (Asada et al. 2020). Accepted methods to assess the potential environmental and social impacts are Environmental Life Cycle Assessment (E-LCA) or Social Life Cycle Assessment (S-LCA) (Benoît et al. 2010; Finnveden et al. 2009; Kloepffer 2008).

S-LCA aims to assess the potential positive and negative social and socioeconomic impacts of products along their life cycle (Dreyer et al. 2010a; Garrido 2017; UNEP/SETAC 2009). One of the most crucial steps in S-LCA is the selection of relevant indicators and impact categories. Impact categories and subcategories are different topics, which are of social relevance and “are the basis of a S-LCA assessment because they are the items on which justification of inclusion or exclusion needs to be provided” (UNEP/SETAC 2009, p. 44). When deciding for impact categories, a prioritization of the most relevant categories is challenging due to a lack of previous experience and knowledge (UNEP/SETAC 2009). Although there is a growing interest in S-LCAs in general and in S-LCAs for bio-based product systems, there are few recommendations available on how to decide for impact categories and relevant indicators for assessing bio-based product systems (Hasenheit et al. 2016; Rafiaani et al. 2018; Siebert et al. 2018a; Touceda et al. 2018). The lack of S-LCAs of the respective areas as well as the fact that those are still in its infancy (Petti et al. 2018) makes the identification of relevant social aspects challenging. Some guidance on S-LCA is provided by the UNEP/SETAC guidelines, where a general framework for conducting a study is proposed (Chen and Holden 2017; UNEP/SETAC 2009).

E-LCA aims to assess the environmental impacts of products and services along their life cycle from cradle to grave (ISO 2006a). As it is the case in S-LCA, E-LCA also requires as a first step the definition of the system under study. One aspect hereby is to identify which environmental issues (EC 2010c; ISO 2006a) and respectively which social topics and indicators (UNEP/SETAC 2009) are relevant for the system and should be covered in the analysis. In E-LCA the number and variety of indicators and LCIA methods available makes it difficult to choose the appropriate ones for a certain system under study (Finnveden et al. 2009; Rosenbaum et al. 2018). Understanding the main characteristics of a method and indicator as well as staying up-to-date with the developments in LCIA can be complex and time-consuming (Rosenbaum et al. 2018). Nevertheless, the choice of a LCIA method can be decisive for the outcome of the study, as well as calculating one impact category with different LCIA methods can lead to different results (Dreyer et al. 2003; Höglmeier et al. 2016; Owsianiak et al. 2014; Pizzol et al. 2011). The ISO standards 14040/14044 (2006a, 2006b) recommend to apply internationally accepted models and factors; however, Hauschild et al. (2013) state that none of the existing LCIA methods are accepted like the ISO standards call for. Because every system under study is unique, the recommendations from the ILCD handbook (EC 2010c) or the ISO standard 14040/14044 (ISO 2006a; 2006b) regarding indicators or methods are vague, which leads to rather loose guidance for LCA practitioners. One common recommendation is to include all main relevant environmental issues related to the system under study (EC 2010c; ISO 2006a) without giving criteria for how to rate the relevancy. In the ISO 14044 (2006b), it is stated that the choice of impact categories, category indicators, and characterization models should be consistent with the goal and scope of the study. Additionally, the choice depends on the environmental issues to be covered, the geographical context of the study (including mid- or endpoint assessment), and the interpretability and documentation as well as the up-to-dateness of the method (Rosenbaum et al. 2018).

Besides the recommendations provided by the UNEP/SETAC guidelines ( 2009) for S-LCA and the ISO standards ( 2006a, 2006b) or the ILCD handbook (EC 2010b) for E-LCA, inputs on relevant social as well as environmental issues can be identified by referring to previous literature with similar research focus (Höglmeier et al. 2016). Unfortunately, just a limited number of E-LCA and S-LCA case studies for wood in automotive applications are available. Just a single study was found assessing the environmental impacts of an engineered wood product in an automotive application (Kohl et al. 2016). However, the authors did not consider the whole life cycle of the component compared with a functional equivalent nor the social effects of the component. No S-LCA case study of wood-based components in the automotive industry is currently available.

Therefore, the aim of this study is to shed light on the choice of impact categories, methods, and indicators for E-LCA and S-LCA when assessing wood as substitute for conventional materials in automotive applications. More precisely the aim is to answer the following research questions. RQ 1: Which environmental and social impact categories or subcategories are considered as relevant when assessing wood as a substitute for steel, aluminum, or composites in automotive applications? RQ 2: Which factors shape methodical choices (e.g., methods or indicators) in environmental and social LCA?

2 Method and material

In order to identify potential social topics and environmental issues as well as influencing factors for the choice of indicators, impact categories, and methods, an analytical approach is proposed by using a literature review. A research literature review can be defined as “a systematic, explicit, and reproducible method for identifying, evaluating, synthesizing the existing body of completed and recorded work produced by researchers, scholars, and practitioners” (Fink 2020, p. 6). Literature reviews usually reprocess the current state of research by aiming to provide a summary of themes and issues in a specific field or to identify the theoretical content within the analyzed research field (Engert et al. 2016; Meredith 1993). The approach applied for the present thesis is an interpretative content analysis, which, according to Neuendorf (2017), involves the theoretical sampling, the definition of analytical categories, the cumulative and comparative analysis of literature, as well as the formulation of types or conceptual categories.

To collect relevant E-LCA and S-LCA case studies for answering the research questions , a literature search was performed using the Scopus database, which comprises an adequate amount of scientific papers and journals (Klein et al. 2015). The examined study period was between 1990 and July 2018. The starting year of 1990 for the present study is adequate since the E-LCA literature started to emerge at this time (Zimek et al. 2019) and S-LCA later than the E-LCA literature. To identify the most relevant case studies, the literature research was limited to peer-reviewed articles and primary studies in English. Gray literature was excluded, as mentioned by Petti et al. (2018) within gray literature, a quality control is difficult and a higher quality is given in peer-reviewed studies. In the case of S-LCA, only a limited number of case studies focusing on bio-based product systems, wood-based products, or on components in automotive applications are available. However, the sector or industry specific as well as the geographical context is very important in S-LCA, as — dependent on the product system — there can be important differences within one sector in a country (Dreyer et al. 2006; Jørgensen 2013; Siebert et al. 2018a; Siebert et al. 2018b). Consequently, other case studies besides studies focusing on wood-based and automotive product systems can provide important insights on sector as well as geographic dependent indicators. Therefore, the scope of literature was broadened to include all S-LCA case studies available on Scopus. To collect all those case studies, essential descriptors (keywords) of the examined subjects were chosen: “S-LCA” or “SLCA” or “Social Life Cycle Assessment” or “Social Life Cycle Analysis” and “case study” (present in Article title, Abstract, Keywords). In total 104 S-LCA case studies were identified. However, in this study, publications were analyzed specifically in which a geographical context and sector or product system was indicated since this is seen as essential in S-LCA (Dreyer et al. 2006; Jørgensen 2013; Siebert et al. 2018a; Siebert et al. 2018b). The UNEP/SETAC (2009) provide the general framework for conducting a S-LCA; wherefore, only those case studies are included, in which one or more of the 31 subcategories of the UNEP/SETAC guideline are included, or the chosen impact categories were assignable to the 31 subcategories of the UNEP/SETAC guideline (e.g., “job creation” is assignable to “local employment”). This resulted in 50 case studies for further analysis (see Fig. 1).

Again, just a limited number of E-LCAs with the particular focus of wood for automotive applications are available. However, studies focusing either on assessing wood-based products or on components in automotive applications can provide important insights on the considered environmental issues and impact categories, the chosen LCIA approach, as well as to identify possible methodological differences when assessing the environmental performance of different materials (bio-based and non-bio-based). Therefore, two different sets of literature were analyzed: E-LCAs of materials and components in automotive applications and E-LCAs of wood-based products. To identify relevant case studies focusing on assessing wood products, the keywords “Life Cycle Assessment” or “LCA” and “wood” and “product” or “component” or “part” (358) were used for the search. In the case of E-LCAs focusing on automotive components, the search string “Life Cycle Assessment” or “LCA” and “automotive” or “car” or “automobile” was applied which resulted in over 1000 publications. Many of those publications were out of scope and focused on, e.g., unmanned aerial vehicles, spacecrafts, underwater vehicles, planetary robotic rovers or rail, or air vehicles. After trying several additional keywords to limit the results to the most relevant for the present study, the following were found to be the most sufficient: “component” or “material” or “part” or “light*weight” (312). Hereby, those studies that actually focused on components in automotive vehicles and not on the complete vehicle as well as on material substitution effects were identified. The keyword “light*weight” was included because after screening through the titles after the first search, it became clear that one motivation for carrying out LCAs focusing on effects of material substitution was to identify sustainable lightweight solutions for the automotive industry.

In total this search resulted in 670 studies (Fig. 1) were several studies are still out of the scope for the present literature review. Therefore, the sample was limited according to the following criteria:

  • Where a comparative E-LCA case study was performed

  • Where the LCIA methods and/or impact categories were explained

  • Where the focus of the case study was on assessing the impacts of solid wood or engineered wood products or wood-based products/materials and not on biofuel or construction

  • Where the focus of the case study was on assessing the impacts of components or materials for automotive applications

  • Where the focus was on assessing scenarios in order to identify potential benefits of lightweighting

After limiting the number of publications according to the previous listed criteria, a snowballing approach was used to identify additional relevant case studies. Using a snowballing approach means to screen the reference list of a paper or citations to the paper to gather additional relevant literature (Wohlin 2014). In the present study, the references of the papers included in the intermediate literature sample were screened in order to identify additional relevant E-LCA case studies (eight automotive and nine wood LCAs were added to the sample) (Fig. 1)

Fig. 1
figure 1

Identified number of relevant case studies

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A qualitative content analysis as described by Neuendorf (2017) of the identified sample was performed by using the software MAXQDA (VERBI GmbH 2019). This method has been chosen as a huge amount of texts can be analyzed in a systematic way. Also, the method is not limited to any specific discipline and supports the researcher in interpreting the content of texts through a coding process (Hsieh and Shannon 2005; Mayring 2000). This was done to identify the factors influencing the choice of indicators as well as subcategories in S-LCA and respectively LCIA methods and impact categories in E-LCA (RQ2). The software was used to code each publication according to the following criteria: considered stakeholder groups and chosen subcategories (S-LCA sample), sector or product system (S-LCA sample), year of publication and geographical scope (S-LCA and E-LCA sample), chosen LCIA method(s) and impact categories, objective(s) of the studies, analyzed materials and used software support (E-LCA samples). The coded text was then analyzed step by step for each criterion to gain relevant information to answer the research questions. In the following chapters, the results of the literature and content analysis are presented. The results are spilt into two sections, one for S-LCA and the second for E-LCA. In each section, a brief insight into the respective method is provided, followed by the results on the relevant environmental and social impact categories or subcategories (RQ1). The last subchapter in the two sections presents the results regarding the influencing factors shaping the choice of methods and indicators in S-LCA and E-LCA (RQ2).

3 Results

3.1 Social life cycle assessment

The UNEP/SETAC guidelines provide a general framework for conducting a S-LCA, where 31 subcategories are being suggested as the basis of a S-LCA (Chen and Holden 2017; UNEP/SETAC 2009). These subcategories are classified into five stakeholder groups (workers, local community, society, consumers, and value chain actors) and impact categories (UNEP/SETAC 2009) (Fig. 2). Although the UNEP/SETAC provide a general framework for conduction an S-LCA, several authors point out that S-LCA is still under development, faces several (methodological) challenges, and lacks empirical studies (Arcese et al. 2018; Baumann et al. 2013; Benoît et al. 2010; Kühnen and Hahn 2017; Salazar et al. 2012; Sureau et al. 2018).

Fig. 2
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Considered stakeholder groups and subcategories in S-LCA recommended by the UNEP/SETAC ( 2009) guidelines for S-LCA

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The product or sector/industry and company-specific social impacts highly influence different stakeholders and stakeholder groups (Dreyer et al. 2006). Especially workers and labor conditions gained increasing interest in the S-LCA literature. Labor rights was the focus of Dreyer et al. (2010a), and within their so-called contextual risk classes (CRC), they assessed child labor, forced labor, discrimination, and restrictions of freedom of association (Dreyer et al. 2010b). Additionally, the geographical context and the industry of the companies along the whole supply chain are important since, for instance, the social impacts of an ore mine in Brazil might be completely different compared with the impacts of a sawmill in Austria (Benoît et al. 2010; Dreyer et al. 2010a; Jørgensen 2013).

3.1.1 Social issues for automotive and bio-based product systems

While there is currently no S-LCA case study of wood components in the automotive industry available, there are some studies available that deal with social impacts in the automotive industry (Traverso et al. 2018; Zimmer et al. 2017) or of wood-based products (Siebert et al. 2018a; Siebert et al. 2018b; Touceda et al. 2018) or the social impacts of the bioeconomy (Hasenheit et al. 2016; Rafiaani et al. 2018). A bioeconomy is seen as an economy where the basic components of materials, chemicals, and energy are made out of bio-based resources (McCormick and Kautto 2013). This means a bioeconomy focuses on bio-based products and bio-based product systems; wherefore, the studies of Hasenheit et al. (2016) and Rafiaani et al. (2018) are considered as relevant in identifying social issues of bio-based product systems.

Regarding the social issues of automotive product systems, Zimmer et al. (2017) assessed the social risks of global supply chains and demonstrated different options of analysis for a case of a premium car manufacturer. They point out that not only the choice of the right indicators but also the weighting of indicators (e.g., together with different stakeholders) is of great importance for assessing social risks in supply chains (Zimmer et al. 2017). Traverso et al. (2018) identified 26 relevant indicators to assess the social impacts of a run on flat tire mounted on a BMW3 series. These indicators (e.g., health and safety, wages and social benefits, working hours, child labor, forced labor for workers; no details on the stakeholder groups other value chain actors and society) are split into three stakeholder groups, namely, worker, customers, and local communities.

The social issues of bio-based product systems are analyzed; for instance by Hasenheit et al. (2016) who summarized possible social indicators to assess a bio-based economy, e.g., the use of agrochemicals, malnutrition, or job quality (see Table 1). The review of Rafiaani et al. (2018) shows common social indicators within the bio-based economy, namely, health and safety, food security, income, employment, land- and worker-related concerns, energy security, profitability, and gender issues. Siebert et al. (2018a, 2018b) define appropriate and relevant indicators for a social assessment of wood-based products within a bioeconomy region in Germany (see Table 1). Another case study of Touceda et al. (2018) assesses the sustainability of a tailored development for housing retrofit. This study does not address a specific wood example. However, they propose wood as a substitute for metallic windows frames. An overview and summary of the social impact topics and possible indicators of these studies is given in Table 1.

Table 1 Overview of identified social impacts and indicators for assessing bio-based product systems, proposed by Hasenheit et al. (2016) [1], Rafiaani et al. (2018) [2], Siebert et al. (2018a) [3], and Touceda et al. (2018) [4]
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The identified social impacts as shown in Table 1 provide an overview of possible impacts of wood-based products and of a bioeconomy and, respectively, bio-based product systems. It has to be noted that each factor is somehow related to other factors, e.g., a change in prices possibly directly or indirectly affects food security in a region (Hasenheit et al. 2016).

3.1.2 Factors influencing methodical choices in S-LCA

The products or sectors assessed within the 50 identified S-LCA case studies range from textiles, sugarcane, car tires to recycling systems or manufacturing companies (Table 3). Out of the 50 S-LCA case studies, seven case studies have been identified, which analyze bio-based product systems (e.g., Pizzirani et al. 2018; Siebert et al. 2018a or Agyekum et al. 2017), and one case study has been identified directly related to the automotive industry (Zimmer et al. 2017) (Table 3). These are the basis to identify possible relevant stakeholder groups, subcategories, and/or indicators.

The importance of a sector-specific and geographical context has already been mentioned in several papers in the past (e.g., Benoît et al. (2010) highlight the need of site-specific data; Dreyer et al. (2010a) conclude that the regional context and the industry itself highly influence the external risk environment). The geographical context might also influence the choice of indicators, e.g., in the USA the risk of child labor is considered to be lower compared with, e.g., India or countries with high poverty rates, whereas for other indicators, the opposite is the case (e.g., the risk of not having collective bargaining rights) (Arvidsson et al. 2015; Benoit-Norris et al. 2012). Because of the importance of the regional context in S-LCA (Dreyer et al. 2010a; Jørgensen 2013), it is reasonable to assume that stakeholder groups, subcategories, and/or indicators are chosen on the basis of the geographical context and country. Therefore, investigated countries in the case studies analyzed are split into developing and developed countries based on the United Nations (2014) (see also in Petti et al. 2018). The countries chosen in S-LCAs of bio-based product systems are highlighted with an asterisk in Table 2. The case study within the automotive industry has been conducted for Germany.

Table 2 Overview of the chosen countries of the selected case studies split in developing and developed countries
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An overview of all considered S-LCAs is provided in Table 3, whereby the case studies are categorized by the topic (product system under study) and split up concerning the geographical context (developing and developed countries) (see Table 2).

Table 3 S-LCA case studies (product, sector, company) categorized by topic and geographical context (developing and developed countries); some studies are in both categories as some case studies relate to more countries
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Slightly more case studies are found for developing countries (29 vs 32), whereby several case studies are conducted for more than one country, including both developing and developed countries. The chosen product system, company, or sector is very heterogeneous in both groups. All 50 case studies were analyzed concerning the chosen subcategories based on UNEP/SETAC ( 2009) in relation to the geographical context, each split in developed and developing countries (see Fig. 3).

Fig. 3
figure 3

Chosen subcategories based on UNEP/SETAC ( 2009) split in developing (n = 32) and developed (n = 29) countries (relative to total amount of the selected subcategories by the reviewed case studies)

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The results in Fig. 3 show that 57% of the 50 S-LCA case studies investigated subcategories concerning the stakeholder group worker and 25% investigated subcategories within the stakeholder group local community. Subcategories from the other three stakeholder groups (value chain actors, society and consumers) were examined in 18% of the considered studies.

Regarding the assumption that subcategories might be chosen depending on the geographical context or the economic status of countries (developed and developing countries), no big differences could be identified. The stakeholder groups value chain actors, society, local community, and worker are chosen more often when S-LCAs were conducted in developing countries (biggest differences for the stakeholder group worker). Only for the stakeholder group consumer, more S-LCA case studies are conducted in developed countries. As illustrated within Fig. 3 only six subcategories show slightly bigger differences between developing and developed countries, namely, child labor, fair salary, working hours and access to material resources are chosen more often in developing countries and forced labor, health and safety (consumer) and local employment are more often chosen in developed countries. Of the case studies which analyzed bio-based product systems (see Table 3), six refer to developing and four to developed countries. Within all studies, the subcategory “local employment” is chosen, followed by “health and safety” (9 studies) and ‘fair salary’ (8 studies). Additionally, more than half of the studies include “social benefits/social security,” “equal opportunities/discrimination,” “working hours,” and “child labor” as subcategories.

3.2 Environmental life cycle assessment

E-LCA has developed to a widely acknowledged tool — used across various areas — for assessing the potential environmental impacts of a product or service system (Finnveden et al. 2009; Guinée et al. 2011; Zimek et al. 2019). It has been standardized within ISO14040/14044 (ISO 2006a, b) and defines four phases of an LCA study: goal and scope definition, life cycle inventory (LCI), life cycle impact assessment (LCIA), and the interpretation phase. In the LCI phase, the input/output data of the studied system is collected and analyzed (ISO 2006a). The LCIA phase includes three mandatory elements: first, the selection of impact categories, category indicators, and characterization models; second, assigning inventory results to impact categories; and third, calculating the category indicator results (ISO 2006b). The calculation of LCIA can be done by using an LCIA method (Table 4), which combines a number of category indicators as well as calculates the results based on specific characterization models (Hauschild et al. 2013) and are partly implemented in LCA software (Rosenbaum et al. 2018). LCIA methods exist for midpoint level (CML 2002, EDIP, TRACI) and endpoint level (EPS, Eco-Indicator 99) as well as methods that try to combine the mid- and endpoint levels (LIME, ReCiPe, IMPACT 2002+) (EC 2010c; Hauschild et al. 2013; Pennington et al. 2004) (Table 4). On midpoint level, a higher number of impact categories are differentiated, and at endpoint level, the impacts are shown as effects on the areas of protection (human health, natural environment, natural resources) (EC 2010c; Pennington et al. 2004; Udo de Haes et al. 1999; Udo de Haes et al. 2002) or aggregated as a single score (e.g., eco-indicator).

Table 4 Overview of existing LCIA methods including the respective year and geographical origin of each method (Rosenbaum et al. 2018)
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3.2.1 Environmental issues of wood in automotive applications

To identify the environmental issues of wood in automotive applications, literatures dealing with bio-based and automotive product systems have been analyzed in regard to relevant environmental issues connected with specific materials as well as in regard to methodical aspects connected with either product system. Starting with LCAs of bio-based product systems, the first tangible LCAs for the forestry and wood products sector in Europe were published in in the 1990s (e.g., Frühwald and Wegener 1993 or Karjalainen and Asikainen 1996). Since then LCA has been frequently applied to assess the impacts of wood and wood-based products (see, e.g., Petersen and Solberg 2004, González-García et al. 2011b, Hesser et al. 2016 or Lettner et al. 2018). However, as, for example, stated by Klein et al. (2015), there are still no consistent and comprehensive LCA studies of wood-based production systems. Due to the multitude of different methods and approaches (e.g., see Hesser 2015 in terms of carbon accounting or Sutterlüty et al. 2017 in terms of water footprint), it is difficult to make a comparative statement between different studies.

When performing comparative LCAs of bio-based materials in comparison with petrochemical materials, Pawelzik et al. (2013) argue that the treatment of biogenic carbon storage is critical for quantifying the greenhouse gas emissions. However, a current shortcoming frequently discussed in the LCA community is the difficulty to fully capture the dynamic nature of carbon flows (see, e.g., Brandão et al. 2013; Lippke et al. 2011; McKechnie et al. 2011). Also, the question of whether to account for carbon storage or not is subject to ongoing debates (Levasseur et al. 2012; Pawelzik et al. 2013). The questions concerning carbon accounting are closely connected to the questions concerning land use. IPCC (2006) and EC (2010a) have published specific guidelines for calculating the carbon stock for agriculture and forestry land use. Besides following a more simplified approach, the guideline published by EC provides the possibility to calculate changes in total of carbon stocks for different soil types or land cover (Pawelzik et al. 2013).

Klein et al. (2015) reviewed LCAs in the forestry sector and found that all reviewed studies considered the global warming potential as impact category, yet, solely focusing on climate change as impact category may not be sufficient. In general bio-based products and an increased cultivation of biomass are connected to the risk of biodiversity loss (Koh 2007; Koh and Ghazoul 2008; Pawelzik et al. 2013). Assessing the loss of biodiversity as well as water use or soil degradation are often excluded in LCAs due to persisting methodological problems and limited data availability (dos Santos et al. 2014; Pawelzik et al. 2013). In order to overcome this limitation, the impact categories global warming, acidification, eutrophication, and ecotoxicity currently cover the main drivers of biodiversity losses (dos Santos et al. 2014; Pawelzik et al. 2013). With the impact categories fresh water aquatic ecotoxicity and terrestrial ecotoxicity, the issues of water and soil protection can be evaluated (dos Santos et al. 2014). Nevertheless, it is worth to note that methodological differences within each proxy should be considered individually. The cumulative energy demand is often used as a proxy indicator in LCA studies (Huijbregts et al. 2006).

Klein et al. (2015) focused on the methodological aspects of forest production and highlighted the large ranges of results in dependence on methodological choices, such as system boundaries and functional unit. In terms of system boundaries, most of the reviewed studies followed a cradle to gate approach. While the endpoint in the individual studies was very similar, the starting point for the assessment at the forest site varied considerably. Most of the studies actually started at the harvesting thinning/operations and thus excluded other processes such as seedling/seed production or planting (Klein et al. 2015). At the end, most of reviewed studies investigated wood for energy purposes or for pulp wood. Another aspect is the determination of the functional unit. In total Klein et al. (2015) identified 12 different functional units applied in the 24 studies. Furthermore, around 50% of the studies did not mention any characteristics of the wood or the wood-based product.

In E-LCAs focusing on automotive product systems, the potential benefits of lightweighting are often the objective of the studies since weight reduction of vehicles can lead to major environmental benefits (Hottle et al. 2017). Hottle et al. (2017) found in their review on critical factors affecting LCAs that the use phase for vehicles with combustion engines accounts for 84–88% of the life cycle emissions and energy demand, whereas the production just accounts for about 4–7% of the energy consumption. However, the production and processing of materials can have dramatic impacts on the environment like undesirable emissions to air, water, and land or land use pattern and water use (Allwood et al. 2011).

The most commonly used material in the automotive industry is steel (Poulikidou et al. 2015), which makes up about 65–70% of the body mass (Dalmijn and de Jong 2007). Typical lightweight alternatives are high-strength steels, aluminum, magnesium, and glass or carbon fiber composites (Hottle et al. 2017; Mayyas et al. 2012b; Poulikidou et al. 2015; Raugei et al. 2015; Witik et al. 2011). All those materials are non-renewable and will eventually be exhausted up to the point where it will be too expensive to extract them; wherefore, the depletion potential and in some cases the scarcity needs to be assessed (Allwood et al. 2011; Cordella and Hidalgo 2016; Klinglmair et al. 2014).

Depending on the material to be assessed, different environmental issues are of concern, and the results vary considering different impact categories. For example, current used lightweight materials such as composites do indeed reduce weight, but the recycling is difficult, toxic, and energy intensive (Allwood et al. 2011; Diener and Tillman 2016). Consequently, using composites can lead to problems in reaching the ELV targets (European Commission 2000). Besides that, glass fibers need approximately four times more energy in the production compared to kenaf fibers (Mohanty et al. 2001) or jute fibers (Alves et al. 2010). Although natural fiber-reinforced composites may be seen as sustainable in terms of integrating renewable materials, they are not sufficiently eco-friendly because of their petroleum-based matrix (Mohanty et al. 2002).

Hottle et al. (2017) reviewed LCAs of materials for vehicle mass reduction and found that most studies assessed GHG emissions and life cycle energy use. This makes sense since those impacts are the main concern for the aluminum industry (Liu and Müller 2012) as well as for the production of magnesium (Cherubini et al. 2008). Liu and Müller (2012) mention other impact categories which should be assessed when analyzing aluminum: the toxicity of the aluminum production through emissions to air, water, and soil as well as the land use of bauxite mining and red mud generation (Liu and Müller 2012). Aluminum is ranked under the top 10 for its land use change, fresh water ecotoxicity, and final solid waste production in a top twenty priority list for the environmental profiles of materials consumed in the Netherlands (Liu and Müller 2012; van der Voet et al. 2003). van der Voet et al. (2003) published a report named “dematerialization: not just a matter of weight” where a contribution of materials to 13 environmental impact categories consumed in the Netherlands is provided. They screened and summarized for each impact category the top 20 contributions of materials to environmental problems. Their results show that steel and iron contribute to depletion of abiotic resources, land use competition, as well as fresh water ecotoxicity (van der Voet et al. 2003). The materials such as high-alloyed steel, aluminum, and plastics contribute at least to ten out of 13 impact categories (Table 5).

Table 5 Environmental problems of selected materials (van der Voet et al. 2003)
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3.2.2 Factors influencing methodical choices in E-LCA

In total 33 E-LCA case studies of wood-based products and 32 E-LCA case studies of automotive components were identified. An overview of the identified E-LCA case studies is provided in Table 6, where the 65 case studies are categorized according to the focus of the study (E-LCA of wood or automotive components) and the objective of the reviewed studies. The objectives of E-LCA literature were grouped into six categories: method development, impacts of lightweighting, material substitution, re-/eco-design, material development, and life cycle inventory. A description of each category is provided in Table 7.

Table 6 Objectives of the reviewed E-LCA studies
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Table 7 E-LCA literature sample (n = 65) categorized by focus and aim of the study
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Table 8 Overview of the identified impact categories in the reviewed literature grouped into 14 category groups for further analysis. A more detailed overview is available in the appendix
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Besides studying the E-LCA literature for the objective, it was further analyzed for the chosen impact categories (Table 8) and LCIA methods (Table 4). For further analysis, the LCIA methods rarely used in the studies (EPS, EDIP, ILCD, BEES) as well as the characterization model USEtox are grouped into “Others.” Another possibility to do the LCIA is by calculating the cumulative energy demand (CED) (Huijbregts et al. 2006; Huijbregts et al. 2010). Several studies calculated the global warming potential (GWP) by referring to the latest IPCC characterization factors (IPCC 2013) which is also included in several LCIA methods like in CML. Some studies did not mention a particular LCIA approach but performed own calculation with, e.g., energy-related indicators — those studies were categorized into the LCIA group own calculations.

A common practice is to explain the choice of certain impact categories and indicators by referring to previous studies with a similar research focus, which is also suggested by Höglmeier et al. (2016). However, the use of categories that have been selected in comparable studies does not mean that all relevant aspects are automatically taken into account. Therefore, the literature sample was screened to identify the impact categories chosen to assess wood as well as automotive product systems. In total over 50 differently named impact categories were identified in the E-LCA sample. An overview of all identified impact categories is provided in the appendix. In order to identify potential tendencies between impact category choice and, e.g., the analyzed material as well as to reduce the complexity of the identified impact categories, those over 50 identified impact categories were grouped into 14 impact category groups (Table 8) according to the overview provided by Rosenbaum (2018). The grouping was done (a) on the basis of the unit used to express an impact category, (b) based on the theme addressed, e.g., the CA group contains carcinogenic and non-carcinogenic impact categories (c) if similar environmental issues were addressed as in the groups BD and RRU, and (d) when the impact category was used just once or twice they were included into a group others. A more detailed overview of the grouping including the units and their respective LCIA methods is provided in the appendix.

The results of the content analysis show that more than half of the studies used software support to perform the E-LCA. Most of them used SimaPro followed by GaBi, Umberto, OpenLCA, or ATHENA. Using software support can influence the choice of an LCIA method since those software solutions have various LCIA methods implemented (Rosenbaum et al. 2018; SimaPro 2019). Incidentally, 78% of the studies which performed own calculation had no LCA software support. The availability of the characterization sheet of an LCIA method could be another argument for choosing one LCIA method over the other. The results of the present study show that 56% of the studies where TRACI was chosen for LCIA used no software support. The environmental impact assessment tool TRACI includes characterization factors for the LCIA and is available for free at the EPA homepage (EPA 2016). The same is possible with CML-IA, where the characterization factors can be downloaded at the homepage of Leiden University.Footnote 2

Other influencing factors for the choice of an LCIA approach might be the availability and up-to-dateness of a method as well as the geographical scope of the study. In the reviewed E-LCA literature CML was used only after the year 2006, ReCiPe after 2013, TRACI and IMPACT2002+ only after 2009, and Ecoindicator95/99 only before 2010. Looking at the year when a certain LCIA method was first introduced (Table 4) plus a few years until a study first uses a new method, the results are not surprising. The geographical focus of a study can also influence the choice of an LCIA method, e.g., all studies which defined the geographical scope to be North America used TRACI as LCIA method.

From the 65 studies, 95% considered climate change (CC) impact categories. Over 60% assessed acidification and eutrophication whereas ozone depletion, abiotic resource use, respiratory effects, or impacts measured by energy demand have been analyzed by over 50%. Just little difference could be found between impact category choice and focus of the study, but, e.g., bio-based materials were more often analyzed with impact categories like acidification, abiotic resource use, eutrophication, or ecotoxicity (Fig. 5). The latter can at least be partly explained by the fact that E-LCAs of wood products chose more often CML as an LCIA method which already includes several different impact categories (Fig. 4).

Fig. 4
figure 4

LCIA approaches chosen by the reviewed E-LCA studies either focusing on wood or on automotive (absolute numbers). Some case studies chose more than one LCIA approach

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Numerous different materials were analyzed in the E-LCA literature. In order to analyze relations between a material and the LCIA method or impact category for bio-based materials in the automotive industry, the materials were categorized into eight material groups (glass or carbon fiber composites (G/CFRC); steel (including boron, stainless, or high strength steel); aluminum, wood and engineered wood products (e.g., wood, glulam, MDF, OSB, or particleboards); plastics (e.g., polypropylene); natural fiber reinforced composites (NFRC, e.g., with kenaf, jute or wood); magnesium; and other materials (e.g., mineral reinforced composites or concrete)). The latter two were not considered for further analysis.

The impact category choice per material analyzed is illustrated in Fig. 5. Figure 6 illustrates the LCIA method chosen for assessing different materials. When just comparing the choice of impact categories between two materials, especially when comparing bio-based (natural fiber reinforced composites and engineered wood products) versus steel, aluminum, and composites, the results show that the latter materials were more often analyzed with energy-related impact categories. The studies analyzing bio-based materials more often chose various impact categories. However, this can also be explained by the fact that studies focusing on wood more often used CML for LCIA (see Fig. 4) which already includes different impact categories, whereof automotive studies more often did the LCIA with own calculations. Most studies, which did not mention any LCIA approach, performed their own calculations or added impact categories not covered in their chosen LCIA method (mostly climate change and/or energy demand impacts). The materials assessed in the “own calculation” studies are steel (48%) and aluminum (58%) (Fig. 6).

Fig. 5
figure 5

Impact categories chosen for assessing different materials relative to the total number of studies that examined this material (NFRC - natural fiber-reinforced composites; G/CFRC - glass or carbon fiber-reinforced composites; EWPs - engineered wood products). The description of impact category groups including their abbreviations is provided in Table 8

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Fig. 6
figure 6

LCIA methods chosen for assessing different materials relative to the total number of studies that examined this material (NFRC - natural fiber-reinforced composites; G/CFRC - glass or carbon fiber-reinforced composites; EWPs - engineered wood products)

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Analyzing the LCIA approach chosen per objective of a study, the results show that studies focusing on material development and life cycle inventory more often chose CML, whereas studies focusing on impacts of lightweighting and method development more often performed own calculations. The latter might be explained by the fact that automotive studies mostly aimed at assessing material substitution effects, identifying potential benefits of lightweighting, or developing a method for sustainable material selection (Fig. 7). On the contrary, E-LCAs of wood products focused more often on life cycle inventories, material development, or re-/eco-design. The results of Fig. 7 are in line with the results presented in Fig. 4: here E-LCAs of wood products more often chose CML whereas automotive E-LCAs more often performed own calculations. All of the automotive studies analyzed the whole life cycle of the product under study. On the contrary, only 45% of the wood E-LCAs considered the whole life cycle in their analysis whereof the other 55% consider just parts of the life cycle, mostly from cradle to grave of which 70% aimed at analyzing the life cycle inventory of several processes.

Fig. 7
figure 7

Aim of the reviewed E-LCA studies either focusing on wood or automotive (absolute numbers)

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4 Discussion and conclusion

The aim of the conducted literature review was to identify the relevant social topics and environmental issues when assessing wood in automotive applications as well as to shed light on the factors influencing the choice of the respective methods, impact categories, and indicators. The first step was to gain knowledge about (RQ1) which environmental and social impact categories or subcategories are considered in the literature and secondly to systematically analyze the factors shaping the choice of methods, impact categories, and indicators in environmental and social LCA (RQ2).

4.1 Relevant social and environmental issues

In order to identify relevant social and environmental issues, literature dealing with these topics was reviewed. The results of this review show that most of the S-LCA case studies performed so far, referred to the subcategories proposed by the UNEP/SETAC (2009) guidelines. Additional relevant social topics for bio-based product systems could be identified (see Table 1), such as food security or land- and worker-related concerns. Although the identified social impacts listed in Table 1 can be a good starting point to decide for impact categories and indicators for bio-based product systems, it might not be complete yet. S-LCAs specific for bio-based product systems are still very limited in order to analyze and compare the different methodologies and indicators chosen in the case studies. Furthermore, there is a risk that potentially relevant indicators will be neglected, if only the indicators of the UNEP/SETAC (2009) guideline are taken into account. Nevertheless, the literature suggests that the geographical and sectorial context determine methodical choices in the case of S-LCA (see in Benoît et al. 2010; Dreyer et al. 2010a; Garrido et al. 2018; Hunkeler 2006; Sanchez Ramirez et al. 2014; Siebert et al. 2018a; Siebert et al. 2018b).

Regarding E-LCA the literature suggests that the objective and material type determine calculation approaches and impact category choices (EC 2010c; ISO 2006a; Rosenbaum et al. 2018); for instance, GWP is described as a major concern in the automotive industry as well as in the production of certain materials like aluminum or magnesium (Cherubini et al. 2008; Hottle et al. 2017; Liu and Müller 2012). However, some authors recommend to consider additional impact categories, e.g., biodiversity for bio-based systems, where the main drivers of biodiversity loss can be covered with the impact categories global warming potential, acidification, eutrophication, and ecotoxicity (dos Santos et al. 2014; Klein et al. 2015; Pawelzik et al. 2013); the depletion potential or scarcity indicators for non-renewable materials (Allwood et al. 2011; Cordella and Hidalgo 2016; Klinglmair et al. 2014); or ecotoxicity for steel and toxicity in the case of aluminum (see Table 5) (Liu and Müller 2012; van der Voet et al. 2003).

4.2 Factors influencing methodical choices

To gain insights on the factors that shape the choice of methods and indicators in environmental and social LCA, a content analysis approach of three literature samples (all published S-LCA case studies; E-LCA case studies focusing on wood products or on components in automotive applications) was performed. The sample was analyzed covering the following criteria: considered stakeholder groups and chosen subcategories (S-LCA), sector or product system (S-LCA), year of publication and geographical scope (S-LCA and E-LCA), chosen LCIA method(s) and impact categories, objective(s) of the studies, analyzed materials and used software support (E-LCA).

In S-LCAs the identification of relevant countries and sectors as well as the targeted stakeholder groups is central in order to identify relevant social aspects (subcategories, indicators) (Dreyer et al. 2010a; Jørgensen 2013; UNEP/SETAC 2009). The results of the present study show that the stakeholder groups value chain actors, society, local community, and worker who were chosen slightly more often when S-LCAs were conducted focusing on developing countries. Hereby, the biggest differences can be observed for the stakeholder group worker (see Fig. 3). Only the stakeholder group consumer has been chosen more often for developed countries. In globalized value chains, the steps in the production phase, such as resource extraction, are often located in developing countries; hence, the focus on the stakeholder group workers can be explained. Following, the consumers as stakeholder groups affected during the use-phase are considered in studies conducted in developed countries. This is also reflected in the choice of indicators. The indicators child labor, fair salary, working hours, and access to material resources have been slightly more often chosen in developing countries, and two indicators have been slightly more often chosen in developed countries (forced labor and local employment). For example, in the case of child labor, studies showed that poverty drives this social issue in developing countries because the income of child labor is highly needed (Arvidsson et al. 2015). Following it can be assumed that the socio-political context is reflected in the selection of indicators. However, within the 50 case studies under investigation, the results showed not as many differences as might be expected. It has to be noted that this study included only a small sample, yet it included all available case studies at the date of analysis. No clear connection between the geographical area and the choice of subcategories/indicators could be found within the present study. Petti et al. (2018) did a review on S-LCA and came to a similar conclusion that the geographical area has just a minor influence on the choice of social indicators. They also found that over 40% of the S-LCAs conducted have been performed in European countries where the highest concentration of researchers can be observed (Mattioda et al. 2015; Petti et al. 2018). A general problem in S-LCAs focusing on developing countries might be data availability (Petti et al. 2018). The difficulty in finding data (e.g., socially sensitive ones) could be one reason why fewer studies than expected were conducted in developing countries (Petti et al. 2018). Especially in developing countries, it could be important to implement S-LCAs in order to identify the hotspots to improve the social conditions on site (Petti et al. 2018).

The environmental issues related to the production of materials are diverse, yet most studies focus on assessing the impacts on climate change (95%). Similar results are shown by Hottle et al. (2017) in context of automotive LCAs, which is probably connected to the emission performance standards of the European Commission (2014). The results of the present study show that climate change is also the most chosen indicator in LCAs of wood-based products and Klein et al. (2015) found the same result on LCAs of forest productions. In the analyzed case studies, over 60% assessed acidification and eutrophication, whereas ozone depletion, abiotic resource use, respiratory effects, or impacts measured by energy demand have been analyzed by over 50%. Over 30% of the analyzed case studies focused just on one or two indicators, which is relatively modest considering the environmental issues connected with materials. However, just because a category is chosen by a lot of different studies, it does not imply that this is an environmental issue to be included as recommended by EC (2010b) and ISO (2006a). One way to identify relevant impact categories for a specific product system is to screen results of different impact categories after an LCA study has been performed. Using software support facilitates this since various LCIA approaches covering various impact categories are partly already implemented in LCA software (Rosenbaum et al. 2018; SimaPro 2019). This means the environmental issues connected with a system can at least be identified after the LCA has been performed by analyzing the results of the LCA. If no software support is available, the identification of the relevant environmental issues is more complex and time-consuming than with software support. For that the characterization tables of CML or TRACI can be used, although here data must be available and in the case of ex-ante assessment data availability is an issue. If no resources for software support or generic databases such as Ecoinvent are available, researchers and practitioners have to rely on preliminary studies with a similar research focus. In that way, insights into the relevant environmental issues for specific product systems are gathered, which might help in the selection of impact categories. Another possibility on how to deal with the issue of no software support or no generic database available is to use CED as a proxy for the environmental burden of impacts, as recommended by Huijbregts et al. (2006). However, CED looks only into the primary energy demand, and other environmental impacts are not covered.

In the present study it could be shown that the identification of relevant environmental issues of wood in automotive applications is difficult, also when referring to previous studies with similar research focus. In the studies analyzed, the choice for a certain impact category or LCIA approach was often not argued or was influenced by more than just the environmental issues connected with the system under study. In the conducted literature review influencing factors such as the objective of the study, the up-to-dateness of the LCIA approach, the geographical scope of the study, the materials analyzed, and the software support have been identified by reviewing and analyzing relevant literature. The two investigated samples on E-LCA of wood products and of automotive components had quite different objectives (Fig. 7), which might be one explanation for the different choice of LCIA approaches (Fig. 4). Other influencing factors for methodical choices might be data availability or the usage of software support. The former was rarely mentioned within the studies analyzed. The latter is not always available, e.g., due to limited budgets or tradition (e.g., Excel as the usual tool). The results show that 40% did not mention any software support, of which the majority (80%) did not mention the LCIA approach or calculated the LCIA on their own. However, analyzing the potential environmental impact with a whole set of impact categories is easier when using software support. Other influencing factors might be the up-to-dateness of the LCIA approach or the geographical scope of the study. In case of the geographical scope of the study, it could be shown that it can influence the choice for an LCIA approach, e.g., all studies which defined the geographical scope to be North America used TRACI as LCIA method. Regarding the influence of the material analyzed on the choice of impact category, it was found that studies which analyzed product systems involving wood products or natural fiber composites more often chose impact categories such as acidification, eutrophication, abiotic resource use, or ozone depletion (see Fig. 5). However, this might be connected to the fact that LCAs of wood products more often performed the LCIA with CML (Fig. 4) and therefore with various impact categories.

4.3 Limitations and outlook

The aim of this study was to shed some light on the choice of impact categories and LCIA approach for E-LCA and subcategories and indicators for S-LCA when assessing the environmental and social performance of wood-based components in automotive applications. The applied methods, literature review, and content analysis are subject to some typical limitations, i.e., selection of literature and criteria for coding might be biased by the researcher conducting the study, the aspects discussed depend on other authors work in this field, setting the boundaries according to the research question for the literature search as well as discriminating between relevant and irrelevant literature (e.g., Engert et al. 2016).

Some relevant environmental and social impact categories as well as influencing factors in methodical choices could be identified from existing literature. However, a clear picture on these issues could not be drawn, e.g., concrete recommendations on material-related environmental issues or geographical-related social topics could not be found when reviewing the literature focusing on automotive or wood-based product systems. Expanding the literature sample to include LCAs of other product systems most likely enables to generate additional knowledge on relevant social and environmental issues for specific product systems.

Identifying the relevant impact categories before conducting a study is a general problem (not just for assessing wood in vehicles), and the literature review carried out shows that this problem can be confirmed for the specific case of wood in automotive applications. The results of the present study are based on a small sample, namely, E-LCAs of wood-based or automotive product systems and S-LCA case studies. Further research might analyze if LCAs of other product systems face similar challenges in identifying the relevant environmental and social issues as well as in selecting the appropriate impact categories and assessment methods for the system under study.

Further knowledge on methodical choices might be gained by social science research on, e.g., the motivation of researchers to choose a certain LCIA approach or impact category in E-LCA or subcategory and indicator in S-LCA. This may provide an understanding of what information and guidance researchers and practitioners may consider regarding methodological decisions for specific product systems. A starting point for providing guidance on the identification of environmental and social issues for specific product systems could be based on environmental issues connected with materials or geographical-related social topics. Furthermore, there is currently neither a full E-LCA nor an S-LCA case study of wood-based products for automotive applications available. Such a study would be needed to address the differences in assessing bio-based and non-bio-based products and in particular the extent to which those products are comparable in terms of the different environmental issues that should be considered for each system.