Skip to main content
SpringerLink
Log in

A literature review of type I SLCA—making the logic underlying methodological choices explicit

  • SOCIAL LCA IN PROGRESS
  • Published:
The International Journal of Life Cycle Assessment Aims and scope Submit manuscript

Abstract

Purpose

The Social Life Cycle Assessment guidelines (UNEP-SETAC 2009) distinguish two different SLCA approaches, type I and type II. Few comprehensive and analytical reviews have been undertaken to examine the multiplicity of approaches that have been developed within type I SLCA. This paper takes on the task of exploring the evaluation methods used in type I SLCA methods.

Methods

In order to tackle this work, a critical literature review was undertaken, covering a total of 32 reviewed articles, ranging from 2006 to 2015. Those articles have been selected for they make explicit reference to type I, performance reference points (PRPs), corporate behavior assessment, and social performance assessment or if their assessment methods generated a result located at the same point as the inventory data, with regards to the impact pathway. The selected articles were analyzed with a focus on the inventory data used, the aggregation of inventory data on the functional unit, and the type of characterization and weighting methods used. This analysis allowed to make explicit the often implicit logic underlying the evaluation methods and to identify the common denominators of type I SLCA.

Results and discussion

The analysis highlighted the multiplicity of approaches that are comprised within type I SLCA today, both in terms of the data collected (in particular, its positioning along the impact pathway); the presence of some optional steps, such as the scaling of inventory data on the functional unit (FU); and in terms of the different characterization and weighting steps. With regards to data collection, this review has highlighted that the furthest indicators are positioned along the impact pathway, the hardest it is to justify the link between them and the activities of companies in the product system. The analysis also suggested that an important differentiating factor among type I SLCA methods lies in “what the inventory data is assessed against” at the characterization step and how it is ultimately weighted. To illustrate this, a typology of six characterization methods and five types of weighting methods was presented.

Conclusions

It is interesting to identify which approaches are most appropriate to respond to the various questions that SLCA aims to respond to. A question that arises is what approaches are most likely to tell us anything about the impact of a product system on social well-being? This question is particularly relevant in the absence of well-documented impact pathways between activities within product systems and impact on social well-being.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. Wu et al. (2014) also identify the final aggregation chosen, the weighting approach, and the geographic and product system specification.

  2. Evaluation methods are often referred to as impact assessment methods or characterization models in the literature.

  3. The causal chain, in the context of assessment of socioeconomic impacts, is often represented by a chain going from input to impact. Inputs are the necessary resources to carry an activity, the activity is the source of impact and can range from product or service sales to compliance with norms, the output is the result of the activity, the output is a change in the lives of the target population, and the impact is goal-level oriented (WBCSD 2013). The larger arrows strive to represent the same idea with a more intuitive wording and for the specific case of the assessment of a product system.

  4. This figure may seem similar to Fig. 3 in Wu et al. (2014). Indeed, both figures show differences between type I and type II; however, Wu’s figure focuses on the presence or not of causal relations and the mathematical approach used in both methods (Σ vs. f(x)). Meanwhile, Fig. 1 focuses on where the data and evaluation results in both types of SLCA can be found along the impact pathway. It puts to the fore the idea of assumed hypothetical impact pathway in type I SLCA and verified and measured impact pathways in type II SLCA.

  5. Inventory data and inventory indicators are here used interchangeably as the data are, most of the time, collected in the unit the inventory indicator report them; there is no manipulation modifying the collected data in order to express it into the indicator units.

  6. For the sake of simplicity, we discuss here company activities. However, the same logic could be adopted for studies focusing on the sector level and country level, which are found in the literature.

  7. While the term company activity is used here, we recognize that these activities are not always wholly attributable to company decisions. They can also be heavily influenced by sector-specific or geographical factors. For example, job hazard might have a lot to do with sector of activity rather than practices, and tax contributions heavily depend on national tax structure. However, they are, to a certain extent, influenced by company governance.

  8. Dreyer et al. (2006) refer to the company activities as “indirect effects” and to the immediate/further effects as “direct effects.”

  9. Wu et al. (2014) suggested that only data representing features of unit processes can be linked to a FU. In the literature, data representing phenomena that are not features of unit processes but rather related to organizations’ practices where the unit process unfolds (e.g., hours of training) are however also scaled on FU.

  10. This indicator takes into account the company’s guidelines and practices, communication and delegation of responsibility, and systematic active control of the integration of the measure into daily work, ultimately calculating a score reflecting the company’s performance (Dreyer et al. 2010).

  11. This includes “the existence and enforcement of national legislation concerning the issue and social, cultural, economic, and political practices at the location” (Dreyer et al. 2010, p. 253), as well as the frequency and severity of violations at a particular location and in a specific industry.

  12. This includes value chain actors, workers, local community, and the general public (Manik et al. 2013).

  13. An example of the questions asked are “Using the scale from 1 to 7, where 1 means unimportant and 7 means very important, how do you rate the importance of the access to material resources?” and “Using the scale from 1 to 7, where 1 means totally disagree and 7 means totally agree, how do you rate the statement that the actual process of palm oil biodiesel is ensuring the access to material resources?” (Manik et al. 2013, p. 1389).

  14. More specifically, they ask experts to rate, on a scale of 1 to 6 (corresponding to very negative effect all the way to positive effect), the likely effects of those materials on a range of stakeholders (Hosseinijou et al. 2014).

  15. Both authors proceed in fact to two steps of weighting in their study; Hosseinijou et al.’s (2014) hot spot assessment calls upon MFA to perform an initial weighting and subsequently weighs subcategories with the aid of an expert panel, and Manik et al. (2013) proceeds to a weighting process right at data collection stage, when directly affected stakeholders are asked to rank their expectations, and then they proceed to another weighting through a stakeholder panel.

  16. This includes institutions that decide about the design of the water or packaging waste management systems investigated in the study.

  17. For example, the number of loss days due to injuries could be used to weight different process. However, one could ask the relevance of giving more relative importance to a unit process associated with more loss days.

  18. Their unit processes are at the country level—data are collected at that level—and a single country can host unit processes found in different life cycle steps. Therefore, weighting the country according to their relative importance in a product system is a little different from weighting the unit processes or the life cycle steps.

References

  • Agéco (2013) Environmental and socioeconomic life cycle assessment of the Quebec auto parts recycling sector Montreal, L’Association des recycleurs de pièces d’autos et de camions inc (ARPAC). 187 p

  • Andrews E, Lesage P, Benoît C, Parent J, Noris G, Revéret J-P (2009) Life cycle attribute assessment. J Ind Ecol 12(4):565–578

    Article  Google Scholar 

  • Aparcana S, Salhofer S (2013) Development of a social impact assessment methodology for recycling systems in low-income countries. Int J Life Cycle Assess 18(5):1106–1115

    Article  Google Scholar 

  • Beaulieu L, Russo Garrido S, Hamaide F, Revéret J-P (2014) From potential hotspots identification to social issues prioritization. Social LCA in progress. C. Macombe et D. Loeillet. Montpellier, CIRAD, p 115–122

  • Benoît Norris C, Norris GS, Aulisio Cavan D (2013) Social hotspots database—supporting documentation. New Earth, 81 p

  • Benoît C, Cavan DA, Norris GA (2012) Strawberry yogurt social scoping prototype—report product category 7, Arizona State University and University of Arkansas

  • Benoît-Norris C (2014) Data for social LCA. Int J Life Cycle Assess 19:261–265

  • Blok K, Huijbregts M. Roes L, van Haaster B, Patel M, Hertwich E, Wood R, Hauschild MZ, Sellke P, Antunes P, Hellweg S, Ciroth A, Harmelink M (2013) Prosuite: handbook on a novel method for the sustainable impact assessment of new technologies. Brussles, Report prepared within the EC 7th framework project PROSUITE (Development and application of a standardized methodology for the PROspective SUstaInability assessment of TEchnologies), 62 p

  • Bouzid A, Padilla M (2014) Analysis of social performance of the industrial tomatoes food chain in Algeria. Mediterranean journal of economics, agriculture and environment. Rev Méditerr Économie, Agric Environ 13(1):p60–p65

    Google Scholar 

  • Chhipi-Shrestha GK, Hewage K, Sadiq R (2014) ‘Socializing’ sustainability: a critical review on current development status of Social Life Cycle Impact Assessment method. Clean Techn Envir 17(3):579–596

    Article  Google Scholar 

  • CIRAIG–AGÉCO (2011) Analyse du cycle de vie environnemental et social de deux options de gestion de matériel informatique en fin de vie. Recyc-Québec, Montréal

    Google Scholar 

  • Ciroth A, Franze J (2011) LCA of an ecolabeled notebook: consideration of social and environmental impacts along the entire life cycle. Federal Public Planning Service Sustainable Development, Brussels

    Google Scholar 

  • Dreyer LC, Hauschild MZ, Schierbeck J (2006) A framework for Social Life Cycle Impact Assessment. Int J Life Cycle Assess 11(2):88–97

    Article  Google Scholar 

  • Dreyer LC, Hauschild MZ, Schierbeck J (2010) Characterisation of social impacts in LCA. Int J Life Cycle Assess 15(3):247–259

    Article  CAS  Google Scholar 

  • 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(1):127–143

    Article  Google Scholar 

  • Fontes J (2014) Handbook for product social impact assessment roundtable for product social metrics. 137p

  • 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(1):155–171

  • Hosseinijou SA, Mansour S, Shirazi MA (2014) Social Life Cycle Assessment for material selection: a case study of building materials. Int J Life Cycle Assess 19(3):620–645

    Article  Google Scholar 

  • Jørgensen A, Le Bocq A, Nazarkina L, Hauschild M (2008) Methodologies for Social Life Cycle Assessment. Int J Life Cycle Assess 13(2):96–103

    Article  Google Scholar 

  • 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(1):8–18

    Article  Google Scholar 

  • Lehmann A, Russi D, Bala A, Finkbeiner M, Fullana-i-Palmer P (2011) Integration of social aspects in decision support, based on life cycle thinking sustainability. Int J Life Cycle Assess 3(4):562–577

    Google Scholar 

  • Manhart A, Griesshammer R (2006) Social impacts of the production of notebook PCs, Öko-Institute e.V

  • 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(7):1386–1392

    Article  Google Scholar 

  • Martínez-Blanco J, Lehmann A, Muňoz P, Antón A, Traverso M, Rieradevall J, Finbeiner M (2014) Application challenges for the Social Life Cycle Assessment of fertilizers within life cycle sustainability assessment. J Clean Prod 69:34–48

    Article  Google Scholar 

  • Macombe C, Lagarde V, Falque A, Feschet P, Garrabé M, Gillet C, Loeillet D (2013) Social LCAs. Socio-economic effects in value chains. FruiTrop Journal Thema Collection, published by CIRAD. 172 pp

  • Morberg Å, Picha M, Erlandsson-Segertröm B, Karagianni C, Malmodin J, Wiklund J (2009) Using a life-cycle perspective to assess potential social impacts of ICT services—a pre-study Stockholm, KTH Centre for Sustainable Communications. 44p

  • Norris GA (2006) Social impacts in product life cycles—towards life cycle attribute assessment. Int J Life Cycle Assess 11(S1):97–104

    Article  Google Scholar 

  • Parent J, Cucuzzella C, Reveret J-P (2010) Impact assessment in SLCA: sorting the sLCIA methods according to their outcomes. Int J Life Cycle Assess 15(2):164–171

    Article  Google Scholar 

  • Parent J, Cucuzella C, Reveret J-P (2012) Revisiting the role of LCA and SLCA in the transition towards sustainable production and consumption. Int J Life Cycle Assess Published online: 09 August 2012. doi:10.1007/s11367-012-0485-9

  • Prakash S, Manhart A, Amoyaw-Osei Y, Agyekum OO (2010) Socio-economic assessment and feasibility study on sustainable e-waste management in Ghana Freiburg, Öko-Institut eV in cooperation with Ghana Environmental Protection Agency (EPA) & Green Advocacy Ghana, Ministry of Housing, Spatial Planning and the Environment, VROM-Inspectorate. 110 p

  • Quantis, Agéco, Ciraig (2012) Environmental and socioeconomic life cycle analysis of Canadian milk. Montréal. 253 p

  • Revéret J-P, Parent J (2012) L’analyse sociale et socioéconomique du cycle de vie des produits: états des lieux et défis. Développement durable et économie environnementale régionale. p 79–90

  • Revéret J-P, Couture J-M, Parent J (2015) Socioeconomic LCA of milk production in Canada. Environmental Footprints and Eco-design of Products and Processes. S.S. Muthu. Singapore, Springer Science + Business Media. p 25–69

  • Sanchez Ramirez PKS, Petti L, Haberland NT, Lie Ugaya CM (2014) Subcategory assessment method for Social Life Cycle Assessment. Part 1: methodological framework. Int J Life Cycle Assess 19(8):1515–1523

    Article  Google Scholar 

  • Tsurukawa N, Prakash S, Manhart A (2011) Social impacts of artisanal cobalt mining in Katanga, Democratic Republic of Congo Freiburg, Öko-Institut eV. 75 p

  • UNEP-SETAC (2009) Guidelines for Social Life Cycle Assessment of products. Edited by Benoit C, Mazjin B, UNEP, Paris

  • WBCSD (2013) Measuring socio-economic impact—a guide for business. World Business Council for Sustainable Development. 39 p

  • Weldegiogis GS, Franks DM (2014) Social dimensions of energy supply alternatives in steelmaking: comparison of biomass and coal production scenarios in Australia. J Clean Prod 84:281–288

    Article  Google Scholar 

  • Wu SR, Yang D, Chen J (2014) Social Life Cycle Assessment revisited. Sustainability 6(7):4200–4226

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CIRAIG-UQAM, International Life Cycle Chair, 3333 Chemin Queen-Mary, Bureau 310, Montréal, Québec, H3C 3A7, Canada

    Sara Russo Garrido, Luce Beaulieu & Jean-Pierre Revéret

  2. Groupe AGECO, 395, Avenue Laurier Ouest, Montréal, Québec, H2V 2K3, Canada

    Julie Parent & Jean-Pierre Revéret

Authors
  1. Sara Russo Garrido
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julie Parent
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luce Beaulieu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jean-Pierre Revéret
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sara Russo Garrido.

Additional information

Responsible editor: Catherine Macombe

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Russo Garrido, S., Parent, J., Beaulieu, L. et al. A literature review of type I SLCA—making the logic underlying methodological choices explicit. Int J Life Cycle Assess 23, 432–444 (2018). https://doi.org/10.1007/s11367-016-1067-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11367-016-1067-z

Keywords

Search

Navigation