Reconciling the Complexities and Ambiguities of Social Life Cycle Assessment Stelvia Matos, Jeremy Hall, Napoleao E. Beltrao Recent trends in the literature have emphasized the importance of Social Life Cycle Assessment (S-LCA) in taking a broader perspective when dealing with product stewardship, including environmental impacts created by extended value-chain members. It is thus widely accepted that social issues play a major role in the sustainability of technologies. However, when conducting S-LCA studies, a key challenge faced by researchers and practitioners is how to enhance reliability and establish validity of qualitative data collected. Following Matos and Hall (2007), we argue that S-LCA involve a wide range of stakeholders, many of whom are not directly involved with the organization. Researchers and practitioners are thus likely to have significant difficulties in conducting these studies as sometimes it is not possible to identify key parameters or stakeholders have different views, interests that are sometimes conflicting or difficult to reconcile. Such challenges make the sole application of quantitative data collection techniques unsuitable, as the estimation of probabilities through for example actuarial sciences and simulations and cost-benefit analysis would be based on unacceptably high degrees of imperfect information. We propose multi-method approaches of data collection and analysis to help researchers strengthen the foundations of their S-LCA studies. Based on an illustrative case study of naturally colored cotton production in Brazil, we argue that sustainable development pressures have increased complexities and presented ambiguous challenges that scientific methods based only in mathematical or statistical reasoning are inadequate. We provide a framework that addresses these deficiencies and discuss implications for researchers and LCA practitioners.
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Reconciling the Complexities and Ambiguities of Social Life Cycle Assessment
Stelvia Matos, Jeremy Hall, Napoleao E. Beltrao
Recent trends in the literature have emphasized the importance of Social Life Cycle Assessment (S-LCA) in taking a broader perspective when dealing with product stewardship, including environmental impacts created by extended value-chain members. It is thus widely accepted that social issues play a major role in the sustainability of technologies. However, when conducting S-LCA studies, a key challenge faced by researchers and practitioners is how to enhance reliability and establish validity of qualitative data collected. Following Matos and Hall (2007), we argue that S-LCA involve a wide range of stakeholders, many of whom are not directly involved with the organization. Researchers and practitioners are thus likely to have significant difficulties in conducting these studies as sometimes it is not possible to identify key parameters or stakeholders have different views, interests that are sometimes conflicting or difficult to reconcile. Such challenges make the sole application of quantitative data collection techniques unsuitable, as the estimation of probabilities through for example actuarial sciences and simulations and cost-benefit analysis would be based on unacceptably high degrees of imperfect information. We propose multi-method approaches of data collection and analysis to help researchers strengthen the foundations of their S-LCA studies. Based on an illustrative case study of naturally colored cotton production in Brazil, we argue that sustainable development pressures have increased complexities and presented ambiguous challenges that scientific methods based only in mathematical or statistical reasoning are inadequate. We provide a framework that addresses these deficiencies and discuss implications for researchers and LCA practitioners.
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