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Analysis of the main elements affecting social LCA applications: challenges for the automotive sector

  • Laura Zanchi
  • Massimo Delogu
  • Alessandra Zamagni
  • Marco Pierini
SOCIAL LCA IN PROGRESS

Abstract

Purpose

Social life cycle assessment (S-LCA) applications have been growing during the last years. Most of the scientific articles published so far have addressed the applicability of S-LCA, focusing on selecting suitable indicators, and only recently, the developments in the area of impact pathway are increasing. However, a critical analysis of how to set an S-LCA study, in particular the goal and scope and inventory phase, is missing. This article critically analyses the most important elements affecting the goal and scope and inventory phase of S-LCA, with a focus on the automotive sector, with the ultimate goal of developing a structured approach to guide practitioners in the critical application of S-LCA.

Methods

The literature review covers 67 publications from 2006 to 2015, including all the case studies published so far, to the best knowledge of the authors, in several sectors and the automotive one. The reviewed works have been structured along the key elements affecting the goal and scope and inventory phases of the S-LCA.

Results and discussion

The methodological and practical issues affecting S-LCA have been organized into a conceptual map, in which all the elements are sequentially placed. This sequence is an orderly procedure consisting of several nodes representing crucial points where a decision needs to be taken or a further reflection is necessary. The case studies of the automotive sector and the corporate-related documents have been used also for the discussion of the conceptual map nodes to identify which aspects are already covered by the literature and which ones need further research.

Conclusions

Facing the inventory phase of S-LCA needs also to set specific elements of the goal and scope phase which are fundamental for approaching coherently the product system at hand and for supporting the selection of stakeholders, indicators, and data. Moreover, in order to foster S-LCA applications and make it a robust decision-support tool, the authors suggest to re-define its framework and approach according to the organizational perspective, as laid down in the recent Organisation Environmental Footprint and Organizational LCA. This implies that social aspects will be evaluated both in relation to the organization behavior and to the basket of products, thus reconciling the need to keep together the conduct-of-a-company perspective, typical of social evaluations, and the product-oriented approach, inherent to the life cycle and in particular to the functional unit concept.

Keywords

Automotive sector Case studies Conceptual map Organizational LCA Product LCA Social LCA 

1 Introduction

Social life cycle assessment (S-LCA) developments and applications have been growing during the last years, both as a stand-alone method and within a more comprehensive life cycle-based sustainability assessment (hereinafter life cycle sustainability assessment—LCSA). However, from the methodology point of view, despite the initiatives at international and national level, S-LCA still presents many open issues which need further progress for the full operationalization of the method. Indeed, it is generally recognized that selection of indicators (and inventory analysis), data availability, and impact assessment methods are the most crucial issues for S-LCA, as well as for LCSA. Most of the scientific articles published so far have addressed the applicability of S-LCA, focusing on selecting and quantifying suitable and relevant indicators for the case study at hand, and only recently, the developments in the area of impact pathway are increasing in number and relevance (Dreyer et al. 2005; Weidema 2006; Jørgensen et al. 2009; Parent et al. 2010; Reitinger et al. 2011; Feschet et al. 2012; Macombe 2014; Neugebauer et al. 2014; Bocoum et al. 2015). However, a critical analysis of how to set a S-LCA study, in particular the goal and scope and inventory phase, is missing: practitioners rely on the procedure described within the UNEP/SETAC guidelines, but do not question some key aspects that make the analysis a challenge, such as functional unit and system boundary definition, the scope of the assessment (company vs. product), just to mention some. Moreover, the lack of comprehensive and robust databases and the different type of social indicators (i.e., quantitative and qualitative), contribute to make the inventory phase a critical step.

The recent methodological developments in life cycle assessment (LCA) go in the direction of sector-specific and context-specific approaches (Masoni and Zamagni 2011; Del Duce et al. 2013) as opportunity to foster the applicability of this method among organizations and strengths its decision-support role in the day-by-day management. Examples are provided by the Product/Organization Environmental Footprint (EC—European Commission 2013) and the Environmental Product Declaration initiatives (EPD, 2015), which are built upon the product category rule (PCR) concept, i.e., the definition of technical criteria and data, tailored to the product-group under study, which can increase consistency in LCA applications and support comparability, by means of reducing the subjectivity of the analysis. A similar trend is expected and foreseen also in the S-LCA, as testified by several applications in which attempts to develop sector- or product-specific indicators can be identified. Among the sectors, the automotive one is analyzed in this paper and stands out for the already several initiatives undertaken in the field of environmental and socio-economic assessment.

1.1 Initiatives in the automotive sector

The automotive is considered a sector on the rise. In 2005, an amount of 600 million cars were estimated around the world and the number of vehicles is expected to grow up to 2.9 billion by 2050. Much of this growth is foreseen to occur in emerging markets such as China and India (Chamon et al. 2008), and it will result in significant increases in air emissions, global fuel demand, and material requirements, and a corresponding increase of waste produced during the end of life is expected (Berzi et al. 2013, 2016). Thus, decisive actions and initiatives are necessary to foster an industrial renaissance rooted into the sustainability concept, as promoted by the European Commission (EC—European Commission 2014a).

The automotive sector is a complex network of companies which work at different levels of the production phase of a vehicle, vans, trucks, and caravans from the materials production to the final product sale, material recovery, and disposal.

The sector consists primarily of vehicle makers that own and manage large manufacturing plants where the production of some parts and the assembly lines are carried out; meanwhile, material suppliers, the technology developers, and components producers make up another relevant part. The automotive industry is considered highly capital and labor intensive. The European automotive industry is central to Europe’s prosperity since it is among the world’s largest producers of vehicles: it is a huge employer of skilled workforce and a key driver of knowledge and innovation and represents the largest private investor in research and development (ACEA 2015a).

In the last years, the R&D investments have been related mainly to environmental sustainability of new products and social sustainability of companies and related supply chains, which are perceived as the key factor for company’s public reputation and attractiveness on the market (Koplin et al. 2007) as they are generally considered responsible for the environmental and social impacts caused by their supplier.

In Europe, the environmental regulation (e.g., 2009/125/EC, energy-related products, ERP; 2009/443/EC, CO2 emissions from light-duty vehicles; 2000/53/EC, end-of-life vehicle, ELV) is a key driver for promoting the eco-innovation in the sector, leading to the development of new materials, and related technologies, able to reduce the environmental impact of vehicles and their components. Lightweight design, alternative fuels, and power train efficiency increase are examples of the challenges the sector is facing (Bein et al. 2016). At the same time, they represent an opportunity for innovating and competing in a globalized market, using the leverage of environmental qualification. The environmental challenges require the availability of methods and tools able to identify environmental hot spots along the whole value chain and to measure the improvements achieved when new or alternative solutions are designed and implemented. Life cycle-based assessment methods and tools such as life cycle assessment (LCA) and life cycle costing (LCC) are already used by several companies in the transport sector concerning traditional and alternative fuels (Larson 2006), and specific means of transports (Spielmann and Scholz 2004; Chatzinikolaou and Ventikos 2015; Cichowicz et al. 2015; Del Pero et al. 2015; Dattilo et al. 2016; Zanchi et al. 2016), as demonstrated by the high numbers of technical reports of car manufacturers (Renault 2011; Mercedes-Benz 2013) and scientific publications (Finkbeiner and Hoffmann 2006; Schau et al. 2012; Mayyas et al. 2012; Kim and Wallington 2013; Raugei et al. 2015; Delogu et al. 2015; Delogu et al. 2016).

Furthermore, the reporting of social issues within the automotive sector is a well-rooted activity at corporate level. The corporate social responsibility (CSR) is a major approach in the assessment of social performance of companies, widely used for four main reasons: (i) it is supported by many emerging normative measures (standards, certification, codes of conduct, rankings) (EC—European Commission 2011; EC—European Commission 2014b); (ii) it has a tripartite dimension that embraces all the stakeholders, organizational (business practices), academic (theoretical formalization), and political (governance); (iii) it allows for a clear and effective communication to different target audience; and (iv) it is the place where social, environmental, ethical, human rights, and consumer concerns can be integrated in the business operations and strategy by collaborating with stakeholders, thus providing a broad overview of the organization behavior and attitude beyond the social aspects ( EC—European Commission 2011).

CSR in the sector includes a variety of issues along the whole life cycle, ranging from alternative technologies and fuels to the supply chain and end of life management. The most frequent CSR activities are based on the International Labour Organization (ILO) code, with focus on essential working conditions, companies’ individual codes of conduct, Global Reporting Initiative (GRI) standards, supply chain responsibility, and environmental management systems (Martinuzzi et al. 2011).

The GRI is a voluntary standard that provides a reporting framework for companies that want to communicate about their supply chains sustainability by means of a set of economic, social, and environmental indicators (Global Reporting Initiative 2015). Since different sectors have been recognized to face specific sustainability issues, a GRI’s Sector Guidance has been developed: for the automotive one, a sector guidance in pilot version has been proposed but not finalized yet (Global Reporting Initiative 2015).

While many initiatives have been undertaken by single companies, a collaborative effort within the automotive sector has been launched with the “Self-Assessment Questionnaire on CSR/Sustainability for Automotive Sector Suppliers.” Subscribed by a number of car manufacturers, the questionnaire is designed to be a first step for suppliers’ performance assessment on CSR that all the members of the European Automotive Working Group on Supply Chain Sustainability will apply. It accounts for expectations towards business ethics, working conditions, human rights, and environmental leadership, for tier 1 suppliers1 as well as their subcontractors and tier n suppliers. As for the social aspects, the questionnaire requires answers about the following : policy and management system to manage social issues (e.g., respect for human rights, forced or compulsory labor, child labor, working conditions, freedom of association) according to international standards (e.g., ISO26000, SA8000); social audits; health and safety policy and management system; and policy regarding business conduct and compliance (corruption, extortion, bribery). This initiative is quite relevant as it sets common and standardized requirements in reporting social issues of concern within the organization and beyond, and thus, it is an important step towards the definition of shared, harmonized, and robust indicators.

This questionnaire is expected to be used within a supplier sustainability assessment procedure and to activate a process of evaluation down to the supply chain of a company (ACEA 2015b). It represents the first concrete example of a common, and shared and sector-specific action, to enlarge the sustainability concept boundaries from the single organization level (both corporate and site/plant level) to the whole supply chain, embracing the life cycle approach.

Moreover, the sector is at the forefront in applying new approaches and methods such as S-LCA (UNEP/SETAC 2009) and LCSA (Braithwaite 2001; UNEP/SETAC 2011; Traverso et al. 2013; Salvado et al. 2015). The main reasons of that can be ascribed to some peculiarities of the sector:
  • Complexity of product (Mathieux et al. 2008; Golinska and Kosacka 2014)

  • High raw material exploitation (i.e., metals, biomaterials/ biopolymers) (Edwards 2004; Sullivan et al. 2013)

  • Globalized and high number of value chain actors (Peiró-Signes et al. 2014)

  • Complexity of the supply and value chains, which involves both big companies (car manufacturers and OEMs) as well as SMEs (supply chain) (Blume and Walther 2013; Simboli et al. 2014)

As far as the S-LCA is concerned, the automotive sector was among the founders of the recent Roundtable for Product Social Metrics initiative, coordinated by PRé Consultant, aimed at developing a practical and consensus-based methodology for organizations to assess the social sustainability of products2 (PRé Sustainability 2014).

This article is aimed at critically discussing and structuring the goal and scope and inventory phases of S-LCA, and in deriving insights that can further support the tailoring of the method to the automotive sector.

The paper is structured as follows. The method adopted for the analysis is presented in Section 2. The starting point of the analysis is a critical review, whose results are shown and analyzed in detail in Section 3.1. They are then structured into a conceptual map (Section 3.2) which is furthermore discussed in Section 3.3 in order to be targeted to the automotive sector. The map covers the goal and scope and inventory phase, while the impact assessment phase of S-LCA is out of the scope of the paper: the discussion about the types I and II of S-LCA is introduced only to the extent of further clarifying the conceptual map borders. Finally, conclusions are drawn in Section 4.

2 Methods

The method adopted is described in Fig. 1. First, a critical review has been carried out, which covers 67 publications, both scientific articles and gray literature in the field of S-LCA, over a time span of 9 years, from 2006 to 2015. To the best knowledge of the authors, the review has involved all the case studies published so far, in several sectors including the automotive one. Both stand-alone S-LCAs and those carried out within a more comprehensive LCSA have been included since, in the authors opinion, different but interrelated and mutual helpful perspectives could be observed.
Fig. 1

Steps of the workflow followed in this study

Scopus, sciencedirect, and googlescholar were used as search engines, with the following keywords: social LCA, social life cycle assessment, life cycle sustainability assessment, life cycle sustainability, social sustainability, social LCA AND automotive, social sustainability AND automotive, sustainability AND automotive. For the automotive sector the review has included also the corporate-related documents, selected among the best-selling car manufacturers in Europe in the last years (ACEA 2015a).

The reviewed papers have been analyzed and discussed in terms of key elements affecting the goal and scope and inventory phases of the S-LCA case studies (Section 3.1) according to the best knowledge of the author and to literature findings (Petti et al. 2014). Those elements have then been organized into a conceptual map where all the methodological and practical issues have been sequentially placed by taking into account how they could affect the goal and scope and inventory phase of the S-LCA methodology. This sequence is intended to support S-LCA applications by means of highlighting and structuring key decision points the practitioner has to cope with. The aim of the conceptual map is not to solve open methodological issues but to push practitioners in critically facing all of them and therefore contribute to the enhancement of the S-LCA.

The conceptual map is then discussed by means of the case studies of the automotive sector, both S-LCA applications and sustainability reports. The S-LCA studies provided support to better understand the key methodological options and possible solutions for each node of the conceptual map (i.e., functional unit, system boundaries), whereas the corporate-related documents supported the identification, selection, and measurement of the social issues taken into account by the companies of the sector; the stakeholders involved and the engagement practices generally applied have also been considered.

3 Results and discussion

3.1 Main elements affecting S-LCA applications

The critical review results are described in the following paragraphs and are discussed in relation to the elements affecting the S-LCA applications, in particular the goal and scope and inventory phases (Table 1).
Table 1

Sum up of the elements affecting the goal and scope and inventory phases of S-LCA applications stemmed from the review

Elements affecting S-LCA applications (application-independent)

• Perspective

• S-LCA as stand-alone method or within LCSA

• Selection and prioritization of indicators

• Functional unit

• System boundaries

• Background, foreground unit processes

• Data sources, quality, and geographic level

3.1.1 Perspective

The term “perspective” is used to indicate the angle from which the analysis is carried out. As such, it includes also the concept of “level of concern,” i.e., who should care about the consequences of a decision/action (Macombe et al. 2013). This concept stands out as an important aspect for better defining the scope of the analysis and the identification of the affected stakeholders. Three levels of concern are identified—company, regional, state—and they represent three different levels of decision-making whose different and potentially competing concerns may be regarded as aspects of assessing sustainable development of a project (Elghali et al. 2007). Moreover, within the same application, different levels of concerns can be identified according to the scope of the analysis. For example, in the case of a waste management system, if a company level of concern is considered, the evaluation of the social consequences of placing a new plant in a given area are at the core of the analysis. On the other hand, if a regional or state level of concern is relevant, the analysis should investigate the social consequences of the waste management system on the population and other stakeholders. However, only one of the reviewed papers deals with this aspect in a clear way, by relating the level of concern with the stakeholder group identification (Macombe et al. 2013). In the other cases, it seems that a super partes perspective is adopted (Umair et al. 2015). As guidance, the UNEP/SETAC guidelines provide a list of questions that need to be answered in the goal and scope phase of the study: Why is an S-LCA being conducted? What is the intended use? Who will use the results? What do we want to assess? (UNEP/SETAC 2009). However, it seems that these aspects are usually not dealt with in details, or at least evidence is not given in the published literature.

3.1.2 Social LCA: a stand-alone analysis or within a life cycle sustainability assessment

In most of the papers analyzed, the S-LCA is conducted as a stand-alone method and according to the UNEP/SETAC guidelines (UNEP/SETAC, 2009).

Overall, the review points out that those works in which S-LCA was applied as a stand-alone analysis or within LCSA differ in three main aspects: definition of functional unit (Section 3.1.3) and system boundaries, due to the need of ensuring consistency among the life cycle-based methods applied in the framework and number of indicators (Schau et al. 2012; Traverso et al. 2012a; Martínez-Blanco et al. 2014).

As far as the indicators are concerned, the management and integration of a large amount of indicators characterized by a high heterogeneity is often mentioned as a key issue (Busset et al. 2014). The need of reducing and simplifying the number of indicators is particularly discussed in the LCSA context (Neugebauer et al. 2015), and more efforts are applied for selecting them, so as to ease the interpretation phase of the LCSA assessment and the communication of the results (Finkbeiner et al. 2010; Traverso et al. 2012b). This is particularly true for the social part since the number and the heterogeneity of the indicators still need further selection or aggregation procedures.

3.1.3 Functional unit

One of the most discussed aspects in the analyzed papers is the use of functional unit, in particular in relation to how to link social indicators to the functional unit (Parent et al. 2010; Zamagni et al. 2011; Norris 2013; Wu et al. 2014).

Overall, most of the S-LCA studies define the functional unit (Petti et al. 2014), especially when S-LCA is carried out in the framework of a broader sustainability analysis, where the need of consistency with environmental LCA and life cycle costing affects such choice. Nevertheless, besides the inherent quantitative nature of the functional unit, only few works specify the reference flow of their analysis, and many claim that the functional unit is identified only with the aim to better define the scope of the analysis (Foolmaun and Ramjeeawon 2012; Manik et al. 2013; Hosseinijou et al. 2013; Umair et al. 2015; Veldhuizen et al. 2015).

A few works, mainly those carried out within the LCSA framework, link social indicators to the functional unit (Busset et al. 2014; Martínez-Blanco et al. 2014) by means of applying different approaches. Indicator results can be scored according to their relative relevance based on international agreements and then aggregated using a weighting system (Martínez-Blanco et al. 2014). An additional data collection should be made in order to get information about activity variable values for each unit process analyzed, since the current databases do not provide such information; moreover, such values could be very different depending on the country (Martínez-Blanco et al. 2014). In other cases, results are translated into a midpoint or endpoint indicator, as in the characterization models of LCA, and then related to a functional unit (Martínez-Blanco et al. 2014).

Another issue related to the use of the functional unit is the linkages of social inventory information at organizational level (company behavior information) to the product system (Zamagni et al. 2011): this could be avoided by means of using the life cycle attribute assessment (Norris 2006), which carries information about the scope (“what percentage of my supply chain has attribute X?”). This is an alternative to the use of the functional unit, which does not imply, as pointed out by Norris (2013), that a functional unit is not necessary but simply that the functional unit “might not be used as a way to report about” (Norris 2013, pp. 3).

However in most of the case studies, this step is not performed and results are presented without a direct mathematical link to the functional unit (Franze and Ciroth 2011; Ekener-Petersen and Finnveden 2013; Macombe et al. 2013) or even kept at company level (Dreyer et al. 2010).

3.1.4 System boundaries

Including or not a specific unit process or flow in the analysis could depend on several factors such as the scope of the analysis, the relevance of the process and also the product system scheme (Dreyer et al. 2005). According to the UNEP/SETAC guidelines, system boundaries should not be crossed by “product flows” (economic flow) but only by elementary flows, similarly to LCA. In addition to that, when S-LCA is performed within the LCSA framework, system boundaries need to show congruence among the different methods, i.e., they should include all unit processes with a meaningful impact on one of the three sustainability dimensions.

The concepts and set of rules used to describe the product system and the boundaries are not clearly explained in S-LCA applications (Lagarde and Macombe 2013).

According to (Foolmaun and Ramjeeawon 2012) and to the reviewed works, two different approaches to system boundaries definition can be seen: on the one hand, the inclusion of only those parts of the life cycle which are directly influenced by the company performing the assessment, and on the other hand, the inclusion of the entire life cycle, excluding the processes which can be considered non influential for the overall conclusions of the study. Most of the studies focus on those phases which are perceived more relevant and for which more specific data can be collected. For example, those works concerning fuel and biofuel production include only feedstock production, processing steps, and transport to pump and exclude the use phase (Blom and Solmar 2009; Manik et al. 2013; Ekener-Petersen et al. 2014); the study about automotive shredder residue includes only processes related to the treatment and management system, excluding production and use phases of the vehicle (Vermeulen et al. 2012).

Overall, it is not clear how to measure the relevance of a given process and only few works deal with this aspect, using for example material flow analysis and assuming that the more important material flows are also those more responsible for socio-economic impacts, since more stakeholders are expected to be involved (Hosseinijou et al. 2013).

Moreover, it is claimed that when the product under evaluation interacts and/or as linkages (i.e., economic transactions and relationships) with other production chains, then alternative approach to represent product system and system boundaries could be needed, such as the systematic competitive model, supported by cutoff criterion guiding the system boundaries definition (Lagarde and Macombe 2013). In this case, including or not a specific process, and the related organization, mostly depends on the socio-economic effect that a change in the product life cycle would produce.

3.1.5 Indicators selection and development

As far as indicators are concerned, they emerge as a challenging issue for two main reasons: (i) there is not a clear distinction between impact indicators and inventory indicators (Neugebauer et al. 2014), and (ii) a robust approach for indicators selection is seldom discussed and reported in a transparent way.

The first aspect refers to the positioning of a given indicator along the impact pathway. For the time being a different approach in the indicators handling can be seen in type I and type II S-LCA. According to the S-LCA guidelines, type I and type II differ in the different position of the collected data and results along the impact pathway (performance vs. impact) (Parent et al. 2010; Garrido et al. 2016).

Practitioners who rely on the first method (type I) to develop the life cycle inventory phase adopt the stakeholder-subcategories-indicators structure, starting from the inventory indicators proposed by the methodological sheet and until the evaluation of their performance (Garrido 2016). Whereas for the type II, a more heterogeneous scene can be seen; the main focus is on the identification of pathways (Norris 2006; Weidema 2006; Jørgensen et al. 2009; Feschet et al. 2012; Macombe 2014; Bocoum et al. 2015), while inventory data, which indeed are the variables computed by pathway calculations, are rarely highlighted or the data collection is seldom discussed and illustrated through case studies. In addition, Neugebauer (2014) proposes two pathways from inventory indicators to impact indicators, according to the cause-effect-chain generally used in the LCA framework. Finally, other researches have proposed to include specific indicators (midpoint level) regarding socio-economic consequences into the LCA framework (Weidema 2006; Vermeulen et al. 2012; Blok et al. 2013). In fact, even if S-LCA is proposed for the assessment of socio-economic impacts of products, some authors suggested that the environmental LCA framework would better allow to gather some social aspects (Mancini et al. 2016).

The indicators selection is the second challenging issue. The relevance is often mentioned as the criterion for indicators selection but further insights on how it is evaluated are not provided. It is generally claimed that some indicators are considered relevant for the sector at hand according to literature review outcomes (Schau et al. 2012; Ekener-Petersen et al. 2014) or to the Social Hotspot Database (SHDB) results (Ekener-Petersen et al. 2014). In some cases, indicators have been selected according to their capabilities to reflect both positive and negative social effects of the given case study (Baumann et al. 2013), but the rationale behind the choice of the single indicators is not provided.

Most of the studies from type I rely on the indicators proposed in the UNEP/SETAC methodological sheets (UNEP/SETAC 2013), and approach them on the basis of data availability, while a few stress the need of introducing additional indicators or stakeholder groups specific for their case studies (Vinyes et al. 2012; Martínez-Blanco et al. 2014). However, in most of the cases, the addition of other stakeholder groups (and related indicators) relies upon the author’s perception of what matters, while a sound and reproducible approach is neither presented nor its relevance is discussed. On the other hand, those works which develop S-LCA by means of SHDB demonstrate to give less importance to indicator selection, despite the high number of proposed indicators (22 social themes) (Rugani et al. 2014). It can be argued that the availability of database leads to consider all proposed indicators without the need of selecting those appropriate for a given application.

Prioritization among indicators is often mentioned but it is not meant in contraposition with relevance: the relevance can be considered a selecting criterion for indicators which afterwards are ranked according to a priority scale (Neugebauer et al., 2015).

Prioritization is considered a necessary step to select clear social targets and to obtain manageable results (Beaulieu et al. 2014). Moreover, an indicator hierarchy is considered fundamental for reducing the level of knowledge and deepening necessary to develop a sustainability analysis, especially in view of LCSA (Neugebauer et al. 2015). Different approaches are proposed to create scales of prioritization such as relevance, practicality, and method robustness (Neugebauer et al. 2015); in a few cases also, social issues severity and country-level socio-economic relevance are used (Beaulieu et al. 2014).

In the debate on the relevance and selection of indicators, the concept of bottom-up and top-down approaches stands up.

The first one refers to an approach to the analysis in which indicators are identified based on industry or stakeholder interests and/or data availability (Kruse et al. 2008) and mainly in the business context of the product manufacturers (Dreyer et al. 2005). This approach is usually adopted when S-LCA is conducted as a stand-alone method, according to the UNEP/SETAC guidelines; in many cases, the starting point of the analysis is the identification of the stakeholder groups; therefore, the life cycle inventory phase is developed according to the stakeholder-subcategories-indicators structure (Foolmaun and Ramjeeawon 2012; Umair et al. 2015).

Stakeholder involvement is considered fundamental for the identification of the most significant social aspects in the case of product and context-specific analysis (Mathe 2014) and the use of participatory approaches is considered useful to implement this process (Mathe 2014; De Luca et al. 2015).

Different stakeholder involvement techniques are applied in literature (Foolmaun and Ramjeeawon 2012; Manik et al. 2013; Hosseinijou et al. 2013; Umair et al. 2015; Veldhuizen et al. 2015), among which multi-step surveys and questionnaire are those more frequently presented. Surveys could involve an initial selection of stakeholders followed by a selection of indicators (Veldhuizen et al. 2015), or could involve an initial selection of relevant life cycle phases followed by a specific questionnaire for indicators (Hosseinijou et al. 2013). Stakeholders are usually asked to select social issues stemmed from different sources and not specifically classified according to stakeholder groups (Veldhuizen et al. 2015) or are asked to answer concerning stakeholder categories and indicators of UNEP/SETAC (Foolmaun and Ramjeeawon 2012; Umair et al. 2015). Whereas the application of the materiality principle is particularly used in the industrial context where the significance for the organization is related to stakeholder assessments to identify the material aspects3 (Ford Motor Company 2013; Benoît Norris and Norris 2014; BMW Group 2014; Volkswagen 2014).

Nevertheless, a common and structured approach cannot be found in S-LCA applications (Mathe 2014), and a proper evaluation of the different techniques applied is difficult since only a few cases describe questionnaires, groups, and number of people involved in a detailed way (Foolmaun and Ramjeeawon 2012).

As far as the top-down approach is concerned, what is valuable to society is the starting point of the analysis, and thus, statements and values stemmed from international conventions and guidelines are considered, together with endpoint impact categories when available (Macombe et al. 2013; Baumann et al. 2013). Thus, the use of a scoring scale based on a number of criteria (i.e., severity in term of relation to fundamental agreements) is the process used for indicators selection (Beaulieu et al. 2014; Neugebauer et al. 2015).

It is generally recognized that an analysis which would involve relevant impacts and indicators should be based on the integration of top-down (normative) approach and bottom-up approach (Kruse et al. 2008; Capitano et al. 2010; Mathe 2014). Identifying robust and reliable way to integrate both of them is an important task for further structuring S-LCA applications.

Overall, the principles which guide both indicators and stakeholders’ adoption are not always expressed since data availability and resource constraints are the main drivers of the analysis.

3.1.6 Data source, data quality, background, and foreground processes

Data source and quality is an important theme in S-LCA as a great number of information, both quantitative and qualitative, are needed, and their availability and robustness is critical to the study results. The UNEP/SETAC methodological sheets propose examples of sources (i.e., report of international agencies, NGOs, web sites) where some information can be collected; nevertheless, they do not expect to be exhaustive and often direct data collection is needed to get more representative and suitable data. Moreover, the use of generic data seems to be a more thorny aspect in S-LCA than in LCA, because performances are more locally variable and dependent on companies’ behaviors instead than on the technology system.

The quality of data can be evaluated, among others, according to a geographic scale (company, sector, country) (Martínez-Blanco et al. 2014). The company level represents the site-specific data that is considered more valuable but more difficult to collect, while the country level is the average information of a given country that is expected to be less valuable but easier to collect. Within a study, it is possible to use different types of a data depending on the product system, and on the data quality requirements set for background or foreground processes: foreground would reflect those processes where site- and product-specific data are necessary, whereas background are those processes which can be depicted by means of more general data (van Haaster et al. 2013) (Table 2). In the S-LCA context, the discerning factors between foreground and background processes, and related data requirements, are the relevance of the process(es) and the level of interest and influence (Martínez-Blanco et al. 2014).
Table 2

Geographic scale of data, unit processes classification, and product type (well-defined/undefined value chain)

 

Data type

 

Well-defined value chain

Undefined value chain

Background

Sector, country level

Sector, country level

Foreground

Company level

Another element which seems to affect the quality of data is the nature of the product analyzed, i.e., whether a specific product of a company/value chain is analyzed or a generic one (Benoît Norris et al. 2012; Ekener-Petersen and Finnveden 2013). In the latter case, country-scale data are used exclusively (Table 2). In addition to that, when the supply chain is characterized by a high complexity in term of high number of suppliers and market rules, the identification of a “country significance” for each life cycle phase is suggested (Ekener-Petersen and Finnveden 2013). For example, in the case of raw material extraction phase, the “country significance” could help in ranking the countries according to their total activity in the given phase and then identify the most active groups of countries which could be taken into account for data collection (Ekener-Petersen and Finnveden 2013).

Regarding data source and quality, it can be observed that in those works where a no case-specific study is developed, the Social Hotspot Database is used (Schau et al. 2012; Ekener-Petersen et al. 2014). The database provides country and sector-country-specific social data based on the GTAP multi-regional IO table. The database is comprehensive in terms of coverage of geographic contexts and sectors (113 countries and 57 sectors); however, it has a low granularity which does not allow to cover process-level or company-level data (Norris 2013). When case-specific studies are dealt with, a detailed data collection is carried out (Blom and Solmar 2009), even supported by surveys (Manik et al. 2013) to allow testing people perception and expectation, and even validate information from official reporting (Blom and Solmar 2009; Manik et al. 2013). In these cases also, statistic data from national or regional agencies are used (e.g., UNICEF publication concerning child labor, Work Environment Authority report of a given country concerning number of accidents). These data, which have different representativeness level for a given time period, could stem from the same sources used by the database but in this case the practitioner manages and select raw data directly.

3.2 A conceptual map to guide practitioners

The main findings of the review, listed in Table 1 and described in the previous paragraphs, have been organized into a conceptual map (Fig. 2) for guiding practitioners in setting goal and scope and inventory phase of S-LCA studies.
Fig. 2

Conceptual map. It is organized along four steps, each including different nodes that can be faced also simultaneously. The single and double rows represent the influence among the nodes

Therefore, all the methodological and practical issues have been sequentially placed by taking into account how they could influence the goal and scope and inventory phase of the S-LCA methodology. This sequence is not to be intended as a strict temporal series but as a suggestion for an orderly procedure consisting of several nodes. Each node represents a crucial point where a decision needs to be taken in order to carry out the analysis. The nodes are organized in four steps representing the procedure sequence; within each step, the nodes can be faced also simultaneously, depending on the application at hand. The single or double rows represent the relations between the nodes, i.e., the extent to which the nodes are affected each other’s, and represent one-way relationship or mutual relationship, respectively.

The conceptual map is not meant to specifically target one of the S-LCA approaches (type I and type II) as it covers aspects which are common to both. Indeed, according to the UNEP/SETAC guidelines and the scientific literature published so far, type I and type II can be considered as two different classes of impact assessment methods, which differ in two main aspects: (i) the position of the collected data and results along the impact pathway (performance vs. impact) and (ii) the connection between indicators results and product system (Parent et al. 2010; Garrido et al. 2016). In this respect, the conceptual map applies to both, as the procedure to set for the S-LCA study requires to question how to properly set the goal and scope and how to organize the inventory phase. The latter is strongly affected by the type of impact assessment adopted: however, the issues of indicator selection, relevance, and robustness are equally applicable.

It is generally recognized that S-LCA has been driven by a company’s perspective mostly (Dreyer et al. 2005; Benoît Norris and Norris 2014) but this review has pointed out that arguments exist for applying it to different levels of analysis (Macombe et al. 2013). Thus, the first step of the conceptual map includes the node of perspective setting. The question that the practitioner should answer is: where, how and to whom the product system at hand is expected to produce effects? There are two main argumentations supporting this. The first is that LCA and, consequently, S-LCA are recognized as tool for decision support and any application of the methodology needs to be developed by considering who is the user of the study, which kind of information she/he is interested in and how the system analyzed is intended to create an effect (Jørgensen et al. 2012). The second is related to the link between the perspective adopted and the stakeholders categories (step 3): there could be contradicting interests between different stakeholder groups the S-LCA intends to consider, aspect that creates an unavoidable and challenging trade-off (Kruse et al. 2008). As a consequence, the limitation of the analysis to those stakeholders where effects are expected to be achieved could lead to more clear results.

The second step involves the product system description. This node regards both the approach used to define it and the product type (well-defined/undefined value chain). For the time being, there is no consensus on how to properly define the product system in the S-LCA context. However, a suggested procedure is to combine the technology-oriented approach, typical of LCA where the product system is made of several separated technological units positioned throughout the product life cycle, with the organization-oriented approach, where the product system consists of a number of individual companies dealing with industrial processes taking place throughout the product life cycle (Dreyer et al. 2005).

The product system definition directly influences the type of data (company level, sector level, country level), in the way as described in Table 2, and the system boundaries identification which should reflect the double natures (technology- and organization-oriented) of the system under evaluation.

The step three involves four nodes. The system boundaries mostly regulate the extent of the analysis and the amount of required information; therefore, this node concerns many relevant decisions. According to the double natures of the product system, we identified two approaches for defining the boundaries of the analysis:
  • Effect-oriented approach, which is related to the level of interest and influence;

  • Technology-oriented approach, which is linked to the several physical units present in the product system.

However, the practitioner should be aware that the latter approach might not allow considering the effects on different stakeholders not directly connected to the product system. Moreover, it is not agreed how to evaluate the level of relevance, whether by means of a physical principle (physical flows) or economic one (added value) or even others, like working time contributions to the whole life cycle (Martínez-Blanco et al. 2014). On the other side, the adoption of the effect-oriented approach could not be in line with the life-cycle approach; thus, a low level of effect and influence characterizing some life cycle phases (i.e., use phase or end of life) should not hinder the development of a cradle-to-grave analysis.

Thus, the adoption of a double-layer system boundary is suggested to be adopted in S-LCA studies: the physic layer (technology-oriented approach) could allow to better define the production cycle and the entire life cycle phases; then, the effect layer (effect-oriented approach) could ease the identification of the affected stakeholders and the related effects. An example of double-layer representation is given in Fig. 3.
Fig. 3

Double-layer graphic representation of system boundaries. The example is provided for the cradle-to-gate analysis, but the same concept applies also to cradle-to-grave

The system boundary node has a mutual relationship with the stakeholders’ node; this means that the identification of affected stakeholders, which in turn depends on the perspective, depends on the life cycle phases included in the analysis.

The step four of the conceptual map includes the node of indicators selection, in terms of evaluation of their relevance and prioritization. The evaluation of the relevance can be dealt with according to the two above mentioned top-down and bottom-up approaches, and a further prioritization could be necessary among the selected indicators, to obtain robust and manageable results.

3.3 Towards tailoring the conceptual map: building upon the automotive sector initiatives

The automotive sector was found to be more experienced in organization-oriented analysis (CSR and GRI) than product-based approach by means of S-LCA. Indeed, it was found out that only a few (13) applications of S-LCA, LCSA, and more generally of social life cycle-based approaches exist.

However, the number of environmental LCA studies (product LCA), the interest in supply chain analysis and the recent initiatives about social assessment carried out by some companies of the sector are signals of an increasing interest about S-LCA applications in the sector.

The analysis of the sector-specific publications—organization-oriented analysis and product LCA—currently does not allow to fully tailor the conceptual map to the sector, due to the limited sample, but it provides directions about some of the nodes of the conceptual map, in particular regarding system boundaries, indicators, and stakeholders.

A small number of product LCA studies targeted to the automotive sector were found. They cover applications related to vehicle components/parts (Braithwaite 2001; Schau et al. 2012; Baumann et al. 2013), alternative fuels (Blom and Solmar 2009; Manik et al. 2013; Macombe et al. 2013; Ekener-Petersen et al. 2014), materials for automotive parts (Zah et al. 2007; Alves et al. 2010; Reuter et al. 2014; Singh 2014), automotive shredder residue treatment (Vermeulen et al. 2012), and manufacturing technology (Chang et al. 2015).

The small number and the high heterogeneity of these studies do not provide clear trends in facing the conceptual map nodes (Table 3); indeed, they confirm many of the criticalities already found in the other applications. For example, the FU is generally defined (addressed) but results are presented without a direct link with it, whereas in some cases, it is not even mentioned (not addressed); overall reasoning for that is not explicitly claimed (Table 3).
Table 3

Summary of conceptual map discussion according to the S-LCA studies in the automotive sector. The way each study has dealt with the conceptual map nodes is analyzed and reported

Reference

Object

Perspective

Analysis framework

FU

Product system and System boundaries

Source of indicators

Selection of indicator

Stakeholders

Selection of stakeholders

Data source

Data quality

Analysis dimension

(Reuter et al. 2014)

Materials

Designer

not expressed

Not addressed

Not addressed

SHDB

No selection

Not expressed

SHDB

sector and state levels

S-LCA

(Singh 2014)

Materials

Company

UNEP/SETAC

Not addressed

Not addressed

UNEP/SETAC methodological sheet

No selection

Not expressed

S-LCA

(Alves et al. 2010)

Materials

Company

not expressed

Addressed

Simplified system boundaries scheme based on technology-oriented product system

Not expressed

Not expressed

Local community

According to a subjective perception of the most affected

Sustainability

(Zah et al. 2007)

Materials

Designer

other (*Social Compatibility Analysis)

Addressed

System boundaries scheme based on technology-oriented product system

Not expressed

Not expressed

Local community

According to a subjective perception of the most affected

Sustainability

(Schau et al. 2012)

Automotive part

Remanufacturer and user

Perspectives

UNEP/SETAC

Addressed

Simplified system boundaries scheme based on technology-oriented product system

SHDB

According to a subjective perception of which social risk, among SHDB ones, could be affected by the specific case study

Workers

According to a subjective perception of the most affected

SHDB

Sector and state levels

LCSA

(Baumann et al. 2013)

Automotive part

Not expressed

not expressed

Addressed

Simplified system boundaries scheme based on technology-oriented product system

Eco-indicator 99 (DALY)

According to capability of measuring both negative and positive social effects of airbag system, also taking into account use phase

Not expressed

Literature

Technology level

S-LCA

(Braithwaite 2001)

Automotive part

Designer

other

Not addressed

Not addressed

GRI

Sustainability

(Chang et al. 2015)

Welding processes

Company (industry)

UNEP/SETAC

Addressed

Not addressed

UNEP/SETAC methodological sheet

Workers

Literature

Technology and state level

ELCA + S-LCA

(Blom and Solmar 2009)

Biofuel

State

UNEP/SETAC

Addressed

System boundaries scheme based on technology-oriented product system

UNEP/SETAC methodological sheet

According to their general nature (those indicators which are considered too company-specific are disregarded)

Employee, local community, society, company (consumers are excluded)

According to life cycle phases correlation

Internet, literature, journals, interviews

S-LCA

(Macombe et al. 2013)

Biofuel

Company, region and state

not expressed

Addressed

Three product systems for the different levels of concern, mostly organization-oriented approach, and three system boundaries

Eco-indicator 99, (Kim and Hur 2009), (Hofstetter and Norris, 2003), (Norris, 2006)

According to capability of measuring social impacts

Workers, population (regional and state levels)

According to level of concern

Regional, sector and state level (supposed)

S-LCA

(Ekener-Petersen et al. 2014)

Fuels and biofuel

Policy decision-maker (region, state)

UNEP/SETAC

Not addressed

Not addressed

SHDB

According to level of risk (SHDB terminology)

Not expressed

SHDB

Sector and state levels

S-LCA

(Manik et al. 2013)

Biofuel

Policy decision-maker (region, state)

UNEP/SETAC

Not used

Simplified system boundaries scheme based on technology-oriented product system

Not expressed

Not expressed

Workers, local community, actors value chain, society

Not expressed

Survey

Company, sector, country levels

S-LCA

(Vermeulen et al. 2012)

Automotive shredder residue

Not expressed

Other

Addressed

System boundaries scheme based on technology-oriented product system

Eco-indicator 99

Not expressed

Literature

Technology level

LCSA

Regarding the product system and system boundary definitions, the reviewed articles are found to mostly apply the technology-oriented approach (Table 3). However, in the automotive sector applications, adopting a double layer for the system boundaries appears preferable to face the important social issues which often are more in the background (supply chain) than in the foreground processes.

On the other hand, the organization-oriented analysis points out the social issues of relevance for the sector and generally taken into account by the companies; this could guide the selection of indicators and stakeholders to be included.

As pointed out by the review of the sustainability reports (SRs) of the ten main car manufacturers in terms of European sales in the last years (ACEA 2015a), relevant stakeholders to be included—depending on the questions at hand—are as follows: customers, dealers, employees, investors, suppliers (Tier I and beyond), local communities, governments at the national, state/provincial and local levels, nongovernmental organizations (NGOs), and academia. In most of the articles, only one stakeholder group, namely workers (Schau et al. 2012; Chang et al. 2015), is considered, whereas local community and society are included only to a less extent (Blom and Solmar 2009).

The materiality principle is well rooted into the sector, and it should be the guiding principle for defining indicators in any application.

According to the GRI approach, the starting point of the SRs is the materiality analysis to identify key issues and sustainability aspects that could represent both opportunities and risks to the company. The material aspects need to be identified by considering “the impacts related to all of its activities, products, services, and relationships, regardless of whether these impacts occur within or outside the organization” (Global Reporting Initiative 2013). When the relevant topics are identified the organization has to prioritize them. According to the GRI guidelines, this can be done by means of the materiality matrix where the x-axis represents the significance of the organization’s economic, environmental, and social impacts and the y-axis the influence on stakeholder assessments and decisions (Global Reporting Initiative 2013).

As far as the social issues are concerned, within the context of SR, it is possible to distinguish different classifications of the material issues. Table 4 shows a list of the material issues which can be ascribed to social area, extrapolated and further elaborated from the materiality analysis of the companies (i.e., BMW Group 2014; Volkswagen 2014). Three main aspects can be noticed as follows: (i) in some cases, the material issues are gathered according to categories internally defined by the companies; (ii) these categories can be ranked according to three keywords—society, workplace, and supply chain; and (iii) a homogeneous terminology can be observed only for the workplace category. In addition to that, a clear definition of them is not always present. As a consequence, an objective, transparent, and robust identification of what matters in the sector becomes challenging. Moreover, with a few exception (PSA Peugeot Citroen 2014), it is not clear how the importance for external and internal stakeholder is evaluated and which are the scoring processes and the reference scale. In some case a scale between 0 and 100 is used to locate issues in the matrix (Daimler 2014), and in another case, a scoring process involving different weightings for internal and external groups is used (i.e., evaluation of the legitimacy and level of influence of stakeholders by issue category, likelihood of the impact) (PSA Peugeot Citroen 2014).
Table 4

Synthesis of the social material issues of the automotive sector stemmed from the materiality analysis of the corporate-related documents

Categorya

Material issues

Source

Societal

Sponsorship and philanthropy

(PSA Peugeot Citroen 2014)

Responsible marketing

Management of customers’ personal data

Socially responsible mobility

Involvement in host communities

Work forced related

Human rights and union rights

Diversity and equal opportunity

Attracting, developing and retaining talent

Health and safety at work and working conditions

Social dialog and responsible management of jobs and skills

No categories

Environmental and social standards in the supply chain

(BMW Group 2014)

Anti-corruption/compliance

Product safety

Human rights

Occupational health and safety

Demographic change

Life balance

Further education and training

Diversity

Donation/sponsorship

Society

Community engagement

(FIAT 2013)

Commercial partner engagement

Human rights in the value chain

Ethics in business relation

Occupational health and safety

Customer satisfaction

Responsible management and development of employee

Diversity and equal opportunity for employee

Labor unions engagement

No categories

Product safety

(GM 2014)

Employee relations

Human rights

Employee equal opportunity and diversity

Local community

People

Attractiveness as an employer

(Volkswagen 2014)

Training

Participation (employment, equal remuneration for women and men, labor/management relations)

Health

Diversity and equality

Corporate responsibility (indirect economic impacts, local communities, indigenous rights)

Social responsibility

Support of social sustainability initiative

(Daimler 2014)

Regional commitment at our locations

Cross-regional commitment for social issues

Support of voluntary employee commitment

Commitment through our foundation efforts

Company-initiated projects

Ethical responsibility

Human rights

Data protection

Compliance

Integrity

Employee responsibility

Employer attractiveness

Training and continuing education

Occupational health and safety

Generation management

Co-determination

Diversity management

Governance

Human rights strategy

(Ford Motor Company 2013)

Ethical business practices

Supply chain sustainability

Human rights in the supply chain

Sustainable raw materials

Identifying and managing sustainability-related supply chain risks

Workplace

Workplace health and safety

Employee morale and teamwork

Employee labor practices /decent work

Diversity/equal opportunity

Community engagement

Community engagement

Community impacts and contributions

aCategory defined the company in the SR

In some of the reviewed SRs, the progress, priorities, and goal related to the material issues are described in a qualitative way (GM 2014; Ford Motor Company 2013), but the lack of specific and harmonized indicators able to measure them is highlighted. Even if some SRs show a final list of social issues according to the GRI indicators (e.g., number of employees, average age, female, employee satisfaction index), nevertheless, a direct correlation with material issues is not easy to be established.

The stakeholders’ engagement is another common aspect in these SRs, according to the GRI guidelines. It includes identification and selection of stakeholder groups, approaches and frequency of engagement, key topics, and concerns raised during the engagement process (Global Reporting Initiative 2013).

Along with some general description of different forms of engagement—such as quantitative consumer research studies, employee focus groups, congressional testimony, blogs, community meetings (GM 2014), direct contact, philanthropic programs, plant visits, endowed courses, events, assistance via foundations, website (NISSAN Motor Corporation 2014)—there are some SRs where these approached are detailed for each stakeholder group as well as the number of people involved (Ford Motor Company 2013).

In conclusion, to make the materiality principle and stakeholder engagement the guiding principles of the indicators selection, practitioners have to clearly state in the study how the materiality is defined, and discuss it also considering the level of influence in addressing the social aspects.

4 Conclusions

Most of the scientific articles published so far have addressed the applicability of S-LCA by focusing on the selection of suitable and relevant indicators, and on data collection, relying upon the existing guidelines and without questioning key aspects that make the analysis challenging, such as functional unit, system boundary definition, and the scope of the assessment (company vs. product), just to mention some.

Thus, a critical review has been undertaken on how the key elements affecting the inventory phase of S-LCA applications have been dealt with, with the ultimate purpose of identifying and developing a structured approach to S-LCA.

As a result, a conceptual map has been elaborated in which all the elements pointed out by the review have been grouped into seven nodes. Each node represents a crucial point where a decision needs to be taken in order to carry out the analysis. Specific questions that may aid practitioner to clarify the meaning of each node and to go ahead in a more aware assessment are shown, where relevant. The nodes are then placed into four steps representing a suggestion for an orderly procedure of analysis. The aim of the conceptual map is not to solve open methodological issues but to push practitioners in critically facing all of them and therefore contribute to the enhancement of the research in the S-LCA field.

The conceptual map has then been analyzed with respect to the automotive sector, with the ultimate goal of contributing to the development of the S-LCA methodology tailored to the peculiarities and needs of the sector. Overall, the automotive industry was found to have a high maturity in the life cycle-based sustainability assessment; however, the few number of S-LCA applications did not allow to answer all the conceptual map nodes thus pointing out the next challenges and directions which need to be faced.

In fact, the analysis of the sector and its contribution to further tailoring the conceptual map to it highlighted that, when both complex products and value chains are involved, such as in the automotive sector or in the electronic and electrical equipment, just to mention some, both the information on social performances at product and company level are relevant. The latter provides a measure of the degree to which a company is able to manage the social aspects of concern along the value chain, independently from the product/service delivered, and according to its level of influence. This information is relevant also in light of the Directive 2014/95/EU on disclosure of non-financial and diversity information by certain large companies and groups: the companies concerned will be called to disclose information on policies, risks, and outcomes as regards environmental matters, social, and employee-related aspects, respect for human rights, anti-corruption, and bribery issues. S-LCA can already support this requirement, as present applications adopt a company-driven approach: also when a specific product is the object of the analysis, the reporting of the results in relation to the functional unit seems an artifice, as indeed they do not bring a product-specific information but simply the information is allocated to the product.

Regarding the social information at product level, this is considered relevant too for two main purposes: to build the profile of products also in relation to the social aspects, besides the technical, quality-related, and environmental ones, and to be able to better conceive and design products and services taking into account also the social variable.

In order to consider both dimensions in S-LCA, the authors propose to re-define its approach and framework according to the organizational perspective, as laid down in the recent Organization Environmental Footprint and Organizational LCA, which accounts for both organization activities and product portfolio. This implies that social aspects would be evaluated both in relation to the organization behavior and to the basket of products, thus reconciling the need to keep together the conduct-of-a-company perspective, typical of social evaluations, and the product-oriented approach, inherent to the life cycle and in particular to the functional unit concept.

As a next step, the conceptual map will be tested and applied to the case study of an automotive component, with the ultimate goal of making it fully tailored to the automotive sector. This means addressing each nodes with reference to the peculiarities of the sector, to identify sector-specific rules from the social point of view as a step forward towards consistent results. For achieving this goal, actors of the automotive sector, together with a pool of experts in S-LCA, will be involved by means of semi-structured interviews on the nodes of the conceptual map.

Footnotes

  1. 1.

    Tier 1 suppliers are those who supply materials or components directly to the company.

  2. 2.

    The methodology proposed within the Roundtable for Product Social Metrics initiative tries to indirectly tackle social impacts of the existence of the product on stakeholder groups throughout its life cycle by including social topics and performance indicators that reflect positive and negative impacts of the product. The procedure to allocate the general organizational performance to the product level is clearly described in the handbook.

  3. 3.

    Material aspects are those that reflect the organization’s significant economic, environmental, and social impacts or that substantively influence the assessments and decisions of stakeholders (Global Reporting Initiative 2015).

References

  1. ACEA (2015a) Statistics | ACEA - European Automobile Manufacturers’ Association. http://www.acea.be/statistics. Accessed 16 Jul 2015
  2. ACEA (2015b) Automotive OEMs launch joint sustainability self-assessment for suppliers - ACEA - European Automobile Manufacturer’s Association. http://www.acea.be/news/article/automotive-oems-launch-joint-sustainability-self-assessment-for-suppliers. Accessed 7 May 2015
  3. Alves C, Ferrão PMC, Silva AJ, et al. (2010) Ecodesign of automotive components making use of natural jute fiber composites. J Clean Prod 18:313–327CrossRefGoogle Scholar
  4. Baumann H, Arvidsson R, Tong H, Wang Y (2013) Does the production of an airbag injure more people than the airbag saves in traffic? J IndEcol 17:517–527Google Scholar
  5. Beaulieu L, Russo-Garrido S, Hamaide F, Revéret J-P (2014) From potential hotspots identification to social issues prioritization. Proceedings of the 4th Int.Semin. Soc. LCA, 19–21 November Montpellier, France,pp 115–122Google Scholar
  6. Bein T, Mayer D, Hagebeuker L, Bachinger A, Bassan D, Pluymers, B, Delogu M (2016) Enhanced Lightweight Design – First Results of the FP7 Project ENLIGHT. Proceedings of 6th Transport Research Arena, 18-21 April,Warsaw, PolandGoogle Scholar
  7. Benoît Norris C, Ausilio D, Hallisey-Kepka C, et al (2012) Social Scoping Prototype Report Product Category 7 Strawberry YogurtGoogle Scholar
  8. Benoît Norris C, Norris GA (2014) Can conducting a social LCA helps meeting major social responsibility standards requirements? Proceedings of the 4th Int.Semin. Soc. LCA, 19- 21November, Montpellier, France, pp 81–89Google Scholar
  9. Berzi L, Delogu M, Giorgetti A, Pierini M (2013) On-field investigation and process modelling of end-of-life vehicles treatment in the context of Italian craft-type authorized treatment facilities. Waste Manag 33:892–906CrossRefGoogle Scholar
  10. Berzi L, Delogu M, Pierini M, Romoli F (2016) Evaluation of the end-of-life performance of a hybrid scooter with the application of recyclability and recoverability assessment methods. Resour Conserv Recycl 108:140–155. doi: 10.1016/j.resconrec.2016.01.013
  11. Blok K, Huijbregts M, Roes L et al (2013) A novel methodology for the sustainability impact assessment of new technologies. Report prepared within the EC 7th framework project. ProsuiteGoogle Scholar
  12. Blom M, Solmar C (2009) How to socially assess biofuels: a case study of the UNEP/SETAC Code of Practice for social-economical LCAGoogle Scholar
  13. Blume T, Walther M (2013) The end-of-life vehicle ordinance in the German automotive industry—corporate sense making illustrated. J Clean Prod 56:29–38CrossRefGoogle Scholar
  14. BMW Group (2014) Sustainable value report. https://www.bmwgroup.com/en/responsibility/sustainable-value-report.html Accessed 7 May 2015
  15. Bocoum I, Macombe C, Revéret J-P (2015) Anticipating impacts on health based on changes in income inequality caused by life cycles. Int J Life Cycle Assess 20:405–417CrossRefGoogle Scholar
  16. Braithwaite, P (2001) Sustainability Assessment of the Development of the New X-TYPE Jaguar. SAE Technical Paper 2001–01-3767. doi: 10.4271/2001-01-3767
  17. Busset G, Belaud J-P, Montréjaud-Vignoles M, Sablayrolles C (2014) Integration of social LCA with sustainability LCA: a case study on virgin olive oil production. Proceedings of the 4th Int.Semin. Soc. LCA, 19–21 November, Montpellier, France, pp 73–80Google Scholar
  18. Capitano C, Traverso M, Rizzo G, Finkbeiner M (2010) Life cycle sustainability assessment: an implementation to marble products. Proceeding of Life Cycle Management Conference, 28–31 August, Berlin, GermanyGoogle Scholar
  19. Chamon M, Mauro P, Okawa Y (2008) Mass car ownership in the emerging market giants. Econ Policy 23:244–296CrossRefGoogle Scholar
  20. Chang Y-J, Sproesser G, Neugebauer S, et al. (2015) Environmental and social life cycle assessment of welding technologies. Procedia CIRP 26:293–298CrossRefGoogle Scholar
  21. Chatzinikolaou SD, Ventikos NP (2015) Holistic framework for studying ship air emissions in a life cycle perspective. Ocean Eng. doi: 10.1016/j.oceaneng.2015.05.042 Google Scholar
  22. Cichowicz J, Theotokatos G, Vassalos D (2015) Dynamic energy modelling for ship life-cycle performance assessment. Ocean Eng. doi: 10.1016/j.oceaneng.2015.05.041 Google Scholar
  23. Daimler (2014) Sustainability Report. http://sustainability.daimler.com/. Accessed 25 May 2015
  24. Dattilo CA, Delogu M, Berzi L, Pierini M (2016) A sustainability analysis for electric vehicles batteries including aging phenomena. Proceedings of the 17th IEEE International Conference on Environment and Electrical Engineering, 7–10 June, Florence, ItalyGoogle Scholar
  25. De Luca AI, Iofrida N, Strano A, et al. (2015) Social life cycle assessment and participatory approaches: a methodological proposal applied to citrus farming in Southern Italy. Integr Environ Assess Manag 11:383–396CrossRefGoogle Scholar
  26. Del Duce A, Egede P, Öhlschläger G, Dettmer T, Althaus HJ, Bütler T, Szczechowicz E (2013) eLCAr Guidelines for the LCA of electric vehicles. Deliverable: D2.1 Guidebook for LCA studies in the context of e-mobilityGoogle Scholar
  27. Del Pero F, Delogu M, Pierini M, Bonaffini D (2015) Life cycle assessment of a heavy metro train. J Clean Prod 87:787–799CrossRefGoogle Scholar
  28. Delogu M, Del Pero F, Romoli F, Pierini M (2015) Life cycle assessment of a plastic air intake manifold. Int J Life Cycle Assess 20:1429–1443CrossRefGoogle Scholar
  29. Delogu M, Zanchi L, Maltese S, Bonoli A, Pierini M (2016) Environmental and Economic Life Cycle Assessment of a lightweight solution for an automotive component: a comparison between talc-filled and hollow glass microspheres-reinforced polymer composites. (submitted to Journal of Cleaner Production)Google Scholar
  30. Dreyer L, Hauschild M, Schierbeck J (2005) A framework for social life cycle impact assessment (10 pp). Int J Life Cycle Assess 11:88–97CrossRefGoogle Scholar
  31. Dreyer LC, Hauschild MZ, Schierbeck J (2010) Characterisation of social impacts in LCA. Part 2: implementation in six company case studies. Int J Life Cycle Assess 15:385–402CrossRefGoogle Scholar
  32. EC - European Commission (2013) COM (2013) 196 final. Communication from the commission to the European parliament and the Council. Building the Single Market for Green Products Facilitating better information on the environmental performance of products and organisations Building the Single Market for Green Products Facilitating better information on the environmental performance of products and organisationsGoogle Scholar
  33. EC- European Commission (2011) COM (2011) 681. Communication from the commission to the European parliament, the Council, the European economic and social committee and the committee of the regions. A renewed EU strategy 2011–14 for Corporate Social Responsibility.Google Scholar
  34. EC- European Commission (2014a) COM (2014) 14 final. Communication from the commission to the European parliament, the Council, the European economic and social committee and the committee of the regions. For a European Industrial RenaissanceGoogle Scholar
  35. EC - European Commission (2014b) Directive 2014/95/EU of the European parliament and of the council - of 22 October 2014 - amending Directive 2013/34/EU as regards disclosure of non-financial and diversity information by certain large undertakings and groupsGoogle Scholar
  36. Edwards KL (2004) Strategic substitution of new materials for old: applications in automotive product development. Mater Des 25:529–533CrossRefGoogle Scholar
  37. Ekener-Petersen E, Finnveden G (2013) Potential hotspots identified by social LCA—part 1: a case study of a laptop computer. Int J Life Cycle Assess 18:127–143CrossRefGoogle Scholar
  38. Ekener-Petersen E, Höglund J, Finnveden G (2014) Screening potential social impacts of fossil fuels and biofuels for vehicles. Energ Policy 73:416–426CrossRefGoogle Scholar
  39. Elghali L, Clift R, Sinclair P, et al. (2007) Developing a sustainability framework for the assessment of bioenergy systems. Energ Policy 35:6075–6083CrossRefGoogle Scholar
  40. Environmental Product Declaration (EPD) (2015) http://www.environdec.com/en/. Accessed 22 December 2015
  41. Feschet P, Macombe C, Garrabé M, et al. (2012) Social impact assessment in LCA using the Preston pathway. Int J Life Cycle Assess 18:490–503CrossRefGoogle Scholar
  42. FIAT (2013) Sustainability Report at 31 december 2013. http://2013interactivereports.fcagroup.com/en/. Accessed 6 Jul 2015
  43. Finkbeiner M, Hoffmann R (2006) Application of life cycle assessment for the environmental certificate of the Mercedes-Benz S-class. Int J Life Cycle Assess 11:240–246CrossRefGoogle Scholar
  44. Finkbeiner M, Schau EM, Lehmann A, Traverso M (2010) Towards life cycle sustainability assessment. Sustainability 2:3309–3322Google Scholar
  45. Foolmaun RK, Ramjeeawon T (2012) Comparative life cycle assessment and social life cycle assessment of used polyethylene terephthalate (PET) bottles in Mauritius. Int J Life Cycle Assess 18:155–171CrossRefGoogle Scholar
  46. Ford Motor Company (2013) Sustainability Report 2013/14. http://corporate.ford.com/microsites/sustainability-report-2013-14/default.html. Accessed 8 June 2015
  47. Franze J, Ciroth A (2011) A comparison of cut roses from Ecuador and the Netherlands. Int J Life Cycle Assess 16:366–379CrossRefGoogle Scholar
  48. Garrido SR, Parent J, Beaulieu L, Revéret J-P (2016) A literature review of type I SLCA—making the logic underlying methodological choices explicit. Int J Life Cycle Assess. doi: 10.1007/s11367-016-1067-z Google Scholar
  49. Global Reporting Initiative (2015) https://www.globalreporting.org/Pages/default.aspx. Accessed 7 May 2015
  50. Global Reporting Initiative (2013) GRIG4 Part2 Implementation Manual. https://www.globalreporting.org/standards/g4/Pages/default.aspx. Accessed 6 July 2015
  51. GM (2014) A Drive Force, Sustainability Report 2014. http://www.gmsustainability.com/#. Accessed 6 July 2015
  52. Golinska P, Kosacka M (2014) Environmental friendly practices in the automotive industry. In: Golinska P (ed) Environmental issues in automotive industry. Springer, Berlin Heidelberg, pp. 3–22CrossRefGoogle Scholar
  53. Hofstetter P, Norris GA (2003) Why and how should we assess occupational health impacts in integrated product policy? Environ Sci Tech 37(10):2025–2035Google Scholar
  54. Hosseinijou SA, Mansour S, Shirazi MA (2013) Social life cycle assessment for material selection: a case study of building materials. Int J Life Cycle Assess 19:620–645CrossRefGoogle Scholar
  55. Jørgensen A, Dreyer LC, Wangel A (2012) Addressing the effect of social life cycle assessments. Int J Life Cycle Assess 17:828–839CrossRefGoogle Scholar
  56. Jørgensen A, Lai LCH, Hauschild MZ (2009) Assessing the validity of impact pathways for child labour and well-being in social life cycle assessment. Int J Life Cycle Assess 15:5–16CrossRefGoogle Scholar
  57. Kim HC, Wallington TJ (2013) Life cycle assessment of vehicle lightweighting: a physics-based model of mass-induced fuel consumption. Environ Sci Technol 47:14358–14366CrossRefGoogle Scholar
  58. Kim I, Hur T (2009) Integration of working environment into life cycle assessment framework. Int J Life Cycle Assess 14:290–301Google Scholar
  59. Koplin J, Seuring S, Mesterharm M (2007) Incorporating sustainability into supply management in the automotive industry—the case of the Volkswagen AG. J Clean Prod 15:1053–1062CrossRefGoogle Scholar
  60. Kruse SA, Flysjö A, Kasperczyk N, Scholz AJ (2008) Socioeconomic indicators as a complement to life cycle assessment—an application to salmon production systems. Int J Life Cycle Assess 14:8–18CrossRefGoogle Scholar
  61. Lagarde V, Macombe C (2013) Designing the social life cycle of products from the systematic competitive model. Int J Life Cycle Assess 18:172–184CrossRefGoogle Scholar
  62. Larson ED (2006) A review of life-cycle analysis studies on liquid biofuel systems for the transport sector. Energy Sustain Dev 10:109–126CrossRefGoogle Scholar
  63. Macombe, C (2014) Searching for social peace: a theory of justice to determine the nature of impacts in social LCA. Proceedings of the 4th Int.Semin. Soc LCA, 19- 21November, Montpellier, France, pp 56–62Google Scholar
  64. Macombe C, Leskinen P, Feschet P, Antikainen R (2013) Social life cycle assessment of biodiesel production at three levels: a literature review and development needs. J Clean Prod 52:205–216CrossRefGoogle Scholar
  65. Mancini L, Benini L, Sala S (2016) Characterization of raw materials based on supply risk indicators for Europe. Int J Life Cycle Assess. doi: 10.1007/s11367-016-1137-2 Google Scholar
  66. Manik Y, Leahy J, Halog A (2013) Social life cycle assessment of palm oil biodiesel: a case study in Jambi Province of Indonesia. Int J Life Cycle Assess 18:1386–1392CrossRefGoogle Scholar
  67. Martínez-Blanco J, Lehmann A, Muñoz P, et al. (2014) Application challenges for the social life cycle assessment of fertilizers within life cycle sustainability assessment. J Clean Prod 69:34–48CrossRefGoogle Scholar
  68. Martinuzzi A, Kudlak R, Faber C, Wiman A (2011) CSR Activities and Impacts of the Automotive Sector. Res Inst Manag Sustain RIMAS Vienna Univ Econ Bus Franz Klein Gasse 1–1190 Vienna Austria 1–31Google Scholar
  69. Masoni P, Zamagni A (2011) Guidance document for performing LCA on fuel cells. Deliverable D3.3–Final guidance documentGoogle Scholar
  70. Mathe S (2014) Integrating participatory approaches into social life cycle assessment: the S-LCA participatory approach. Int J Life Cycle Assess 19:1506–1514CrossRefGoogle Scholar
  71. Mathieux F, Froelich D, Moszkowicz P (2008) ReSICLED: a new recovery-conscious design method for complex products based on a multicriteria assessment of the recoverability. J Clean Prod 16:277–298CrossRefGoogle Scholar
  72. Mayyas A, Qattawi A, Omar M, Shan D (2012) Design for sustainability in automotive industry: a comprehensive review. Renew Sust Energ Rev 16:1845–1862CrossRefGoogle Scholar
  73. Neugebauer S, Blanco JM, Scheumann R, Finkbeiner M (2015) Enhancing the practical implementation of life cycle sustainability assessment - proposal of a tiered approach. J Clean Prod 102:165–176CrossRefGoogle Scholar
  74. Neugebauer S, Traverso M, Scheumann R, et al. (2014) Impact pathways to address social well-being and social justice in SLCA—fair wage and level of education. Sustainability 6:4839–4857CrossRefGoogle Scholar
  75. NISSAN Motor Corporation (2014) Sistainability Report 2014. http://www.nissan-global.com/EN/CSR/SR/2014/ Accessed 8 June 2015
  76. Norris CB (2013) Data for social LCA. Int J Life Cycle Assess 19:261–265CrossRefGoogle Scholar
  77. Norris GA (2006) Social impacts in product life cycles—towards life cycle attribute assessment. Int J Life Cycle Assess 11:97–104CrossRefGoogle Scholar
  78. Parent J, Cucuzzella C, Revéret J-P (2010) Impact assessment in S-LCA: sorting the sLCIA methods according to their outcomes. Int J Life Cycle Assess 15:164–171CrossRefGoogle Scholar
  79. Peiró-Signes A, Payá-Martínez A, Segarra-Oña M-V, de-Miguel-Molina M (2014) What is influencing the sustainable attitude of the automobile industry? In: Golinska P (ed) Environmental issues in automotive industry. Springer, Berlin Heidelberg, pp. 47–63CrossRefGoogle Scholar
  80. Petti L, Ugaya CML, Di Cesare S (2014) Systematic review of Social-Life Cycle Assessment (S-LCA) case studies. Proceedings of the 4th Int.Semin. Soc. LCA, 19- 21November, Montpellier, France, pp 34–41Google Scholar
  81. PRé Sustainability (2014) Handbook for Product Social Impact Assessment. http://product-social-impact-assessment.com/ Accessed 9 November 2015
  82. PSA Peugeot Citroen (2014) Corporate Social Responsability. http://www.psa-peugeot-citroen.com/en/corporate-social-responsibility/social-policy. Accessed 8 June 2015
  83. Raugei M, Morrey D, Hutchinson A, Winfield P (2015) A coherent life cycle assessment of a range of lightweighting strategies for compact vehicles. J Clean Prod 108(Part A):1168–1176CrossRefGoogle Scholar
  84. Reitinger C, Dumke M, Barosevcic M, Hillerbrand R (2011) A conceptual framework for impact assessment within S-LCA. Int J Life Cycle Assess 16:380–388CrossRefGoogle Scholar
  85. Renault (2011) FLUENCE and FLUENCE Z.E. LIFE CYCLE ASSESSMENT. https://group.renault.com/wp-content/uploads/2014/09/fluence-acv-2011.pdf. Accessed 8 June 2015
  86. Reuter B, Schulz C, Schlagenhalft G, Lienkamp M (2014) Social risk analysis related to the material selection during an early stage of product development.Presentationat the 20thSETAC Europe LCA Case Study Symposium, 24–26 November, Novi Sad, SerbiaGoogle Scholar
  87. Rugani B, Benetto E, Igos E, et al. (2014) Towards prospective life cycle sustainability analysis: exploring complementarities between social and environmental life cycle assessments for the case of Luxembourg’s energy system. Mater Tech 102:605CrossRefGoogle Scholar
  88. Salvado M, Azevedo S, Matias J, Ferreira L (2015) Proposal of a sustainability index for the automotive industry. Sustainability 7:2113–2144CrossRefGoogle Scholar
  89. Schau EM, Traverso M, Finkbeiner M (2012) Life cycle approach to sustainability assessment: a case study of remanufactured alternators. J Remanufacturing 2:1–14CrossRefGoogle Scholar
  90. Simboli A, Taddeo R, Morgante A (2014) Analysing the development of industrial symbiosis in a motorcycle local industrial network: the role of contextual factors. J Clean Prod 66:372–383CrossRefGoogle Scholar
  91. Singh, KR (2014) An approach for operationalization of social life cycle assessment in steel industry.Presentation at the 20thSETAC Europe LCA Case Study Symposium, 24–26 November, Novi Sad, SerbiaGoogle Scholar
  92. Spielmann M, Scholz R (2004) Life cycle inventories of transport services: background data for freight transport (10 pp). Int J Life Cycle Assess 10:85–94CrossRefGoogle Scholar
  93. Sullivan JL, Burnham A, Wang MQ (2013) Model for the part manufacturing and vehicle assembly component of the vehicle life cycle inventory. J Ind Ecol 17:143–153CrossRefGoogle Scholar
  94. Traverso M, Asdrubali F, Francia A, Finkbeiner M (2012a) Towards life cycle sustainability assessment: an implementation to photovoltaic modules. Int J Life Cycle Assess 17:1068–1079CrossRefGoogle Scholar
  95. Traverso M, Finkbeiner M, Jørgensen A, Schneider L (2012b) Life cycle sustainability dashboard. J Ind Ecol 16:680–688CrossRefGoogle Scholar
  96. Traverso M, Wagner V, Trouvay B et al (2013) A comprehensive approach of sustainability assessment of product in the automobile sector: challenges and benefits. Proceedings of the 6th International conference of the Life Cycle Management, 25–28 August Gothenburg, SwedenGoogle Scholar
  97. Umair S, Björklund A, Petersen EE (2015) Social impact assessment of informal recycling of electronic ICT waste in Pakistan using UNEP SETAC guidelines. Resour Conserv Recycl 95:46–57CrossRefGoogle Scholar
  98. UNEP/SETAC (2009) Guidelines for Social Life Cycle Assessment of Products. http://www.unep.org/pdf/DTIE_PDFS/DTIx1164xPA-guidelines_sLCA.pdf. Accessed 9 November 2015
  99. UNEP/SETAC (2013) The methodological sheets for subcategories in social Life Cycle Assessment (S-LCA). http://www.lifecycleinitiative.org/wp-content/uploads/2013/11/S-LCA_methodological_sheets_11.11.13.pdf. Accessed 9 November 2015
  100. UNEP/SETAC (2011) Towards a Life cycle sustainability assessment: making informed choices on products UNEP/SETAC Life Cycle Initiative. http://www.unep.org/pdf/UNEP_LifecycleInit_Dec_FINAL.pdf. Accessed 9 November 2015
  101. van Haaster B, Ramirez A, Ciroth A, Fontes J (2013) Practical Guidance Document for Social Assessments. PROSUITEproject.http://prosuite.org/web/guest/home. Accessed 7 May 2015
  102. Veldhuizen LJL, Berentsen PBM, Bokkers EAM, de Boer IJM (2015) Social sustainability of cod and haddock fisheries in the Northeast Atlantic: what issues are important? J Clean Prod 94:76–85CrossRefGoogle Scholar
  103. Vermeulen I, Block C, Van Caneghem J, et al. (2012) Sustainability assessment of industrial waste treatment processes: the case of automotive shredder residue. Resour Conserv Recycl 69:17–28CrossRefGoogle Scholar
  104. Vinyes E, Oliver-Solà J, Ugaya C, et al. (2012) Application of LCSA to used cooking oil waste management. Int J Life Cycle Assess 18:445–455. doi: 10.1007/s11367-012-0482-z CrossRefGoogle Scholar
  105. Volkswagen (2014) Sustainability Report 2014. http://www.volkswagenag.com/content/vwcorp/content/en/sustainability_and_responsibility.html. Accessed 8 June 2015
  106. Weidema BP (2006) The integration of economic and social aspects in life cycle impact assessment. Int J Life Cycle Assess 11:89–96CrossRefGoogle Scholar
  107. Wu R, Yang D, Chen J (2014) Social life cycle assessment revisited. Sustainability 6:4200–4226CrossRefGoogle Scholar
  108. Zah R, Hischier R, Leão AL, Braun I (2007) Curauá fibers in the automobile industry—a sustainability assessment. J Clean Prod 15:1032–1040CrossRefGoogle Scholar
  109. Zamagni A, Amerighi O, Buttol P (2011) Strengths or bias in social LCA? Int J Life Cycle Assess 16:596–598CrossRefGoogle Scholar
  110. Zanchi L, Delogu M, Ierides M, Vasiliadis H (2016) Life cycle assessment and life cycle costing as supporting tools for EVs lightweight design. In: Setchi R, Howlett RJ, Liu Y, Theobald P (eds) Sustainable design and manufacturing 2016. Springer International Publishing, pp. 335–348Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Laura Zanchi
    • 1
  • Massimo Delogu
    • 1
  • Alessandra Zamagni
    • 2
  • Marco Pierini
    • 1
  1. 1.Department of Industrial EngineeringUniversity of FlorenceFlorenceItaly
  2. 2.Ecoinnovazione srl, Spin-off ENEAPadovaItaly

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