An analytic framework for social life cycle impact assessment—part 1: methodology

Abstract

Purpose This study aims to develop a new framework of social life cycle impact assessment (SLCIA) method based on the United Nations Environment Program/Society of Environmental Toxicology and Chemistry (UNEP/SETAC) Guidelines for analyzing the social impact in Taiwan, particularly in the electronics industry. Methods After reviewing the literature on social life cycle assessment (SLCA), we analyzed existing case studies and developed SLCIA methods based on the UNEP/SETAC Guidelines. We thereafter identified stakeholders, subcategories, and indicators in accordance with the current status of SLCA case studies and opinions from ten experts in the Taiwanese electronics industry. Both quantitative and semiquantitative indicators were subsequently proposed to assess the social impact of workers in the Taiwanese electronics sector. Each indicator was given the score of 1 to 5 by classifying the social impact percentage of nine scales. To formulate an analytic framework for SLCIA, the weighting values of each subcategory and indicator were determined using the consistent fuzzy preference relations (CFPR) method. Results and discussion Seven subcategories and 19 qualitative and quantitative indicators of worker stakeholders for the electronics sector were identified based on the UNEP/SETAC Guidelines. A score of 1 to 5 is assigned to each quantitative indicator by classifying the social impact percentage of nine scales. The data obtained from companies for each quantitative indicator were subsequently transformed into social impact percentage in terms of the statistical data on social situations at the country or industry level. With regard to semiquantitative indicators, three implementation levels of management efforts on social performance within five elements were identified. The CFPR method was then employed to determine the weights of each indicator by ten experts. Results indicated that preventing forced work practices, protecting children from having to work, and providing minimum and fair wages for workers are the three most important indicators for assessing social impact. Conclusions A new SLCIA method that incorporates both quantitative and semi-quantitative indicators was proposed for assessing social impact in the electronics sector in Taiwan. Nine quantitative indicators can be easily organized using available social data from government statistics as performance reference points (PRPs) to determine the social impact exerted by companies. The relative weights were determined to allow for an impact assessment and thus solve the limitation of their currently assumed equal weights. The proposed framework is examined to analyze the social impact of three production sites for semiconductor packaging and manufacturing in Taiwan. Responsible editor: Marzia TraversoElectronic supplementary material The online version of this article (

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“…Wang et al [90] To develop a new framework of social life cycle impact assessment (SLCIA) method based on UNEP/SETAC. SO X √ √ √…”

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“…This is consistent with the study conducted by Aparcana and Salhofer (2013), Dreyer et al (2006), Jørgensen et al (2008), and Wang et al 2016 2018and Traverso et al (2012) mention that giving the same importance to the social indicators makes the interpretation transparent and simple for users. In contrast, there are other studies in which a weighting system was applied for SLCA (see, for example, Schmidt et al 2004;Manik et al 2013;Wang et al 2016;van Haaster et al 2017). In fact, defining the weights becomes important if aggregation and/or making composite indicators are the main focus of the analysis in order to allow decisionmaking (Traverso et al, 2012;Manik et al 2013; Chhipi-Shrestha and Rehan Sadiq 2015; Telles do Carmo et al 2016; Garrido et al 2018;Ofori Agyekum et al 2017).…”

“…For the three quantitative indicators, the scoring approach proposed by Wang et al (2016) is considered as the baseline for our study (Table 4). Originally, they proposed a scoring system with 5 levels.…”

“…Originally, they proposed a scoring system with 5 levels. However, the score levels developed by Wang et al (2016) are slightly modified in our study to be in 4 levels. This modification was needed because the scoring in SAM is comprised of 4 levels, and in order to make a comparable scoring between both quantitative and semi-quantitative measurements, the levels for quantitative scoring needed to be modified.…”

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