Primary data-based social life cycle assessment: challenges and solutions

Sustainability follows a three-pillar approach, comprising environmental, social, and economic dimensions. Social sustainability is a key aspect to assess sustainability in a holistic manner. Social life cycle assessment (S-LCA) is a method that assesses the social sustainability of a product or service from the life cycle perspective. S-LCA has evolved over the last two decades but still lacks maturity and faces more methodological challenges as compared to its environmental counterpart, life cycle assessment (LCA). One of the main challenges of S-LCA relates to the need for primary, site-specific data. Primary, site-specific data is preferred for use in S-LCA because social impacts are closely related to the specific company decisions and policies. Average performance data based on the physical characteristics of a product is less representative of social impacts. Despite the recognized value of primary data, many S-LCA case studies rely on secondary generic data which can only indicate social risk. This dissertation aims to identify the methodological challenges of conducting primary data-based S-LCA and provide potential solutions that can be used to increase primary data-based S-LCA’s applicability and quality. To better understand the practical challenges of conducting primary data-based S-LCA, two case studies were conducted with a focus on primary data collection through interviews and direct collaboration with companies. Both studies applied the reference scale approach based on local laws and international best practices for subcategories from the Local community, Worker, and Society stakeholder groups. The studies were conducted on lithium production in the Salar de Atacama, Chile and chocolate production in Colombia. Reference scales had to be developed for many subcategories to conduct the two case studies since there is a lack of standardized reference scales for use in S-LCA. For example, for the subcategory Prevention and mitigation of armed conflict, there was no existing reference scale and no clear standard or best practices on which to base the performance reference points. A further challenge was that although the system boundaries were set as “cradle-to-gate” in both studies, the primary data collection and assessment focused only on the foreground processes. For instance, lithium production included impacts from lithium brine but not social impacts associated with other inputs such as diesel and electricity, while chocolate production included impacts from the cocoa production and processing but not from complementary ingredients such as sugar. These choices were made to maintain the quality of data used; however, this choice led to limited coverage of the life cycle. The strong sensitivity of results to the choice of scoring approach was also recognized through the lithium case study. Conducting primary data-based S-LCA case studies helped to identify areas where further methodological development is needed. To gain a deeper understanding of the methodological challenges in primary data-based S-LCA, a systematic literature review was conducted to identify challenges as well as potential solutions utilized by S-LCA practitioners. 68 primary data-based S-LCA case studies from 2015 through 2024 were reviewed. 65 challenges were identified across all four S-LCA phases, with the most in the goal and scope phase (19 challenges), followed by the social life cycle inventory (17 challenges), impact/performance assessment (16 challenges), and interpretation (13 challenges). Core challenges related to defining the functional unit and connecting it to the results, including the full life cycle within the system boundaries, primary data collection (e.g., due to potential bias, limited sample size, logistical concerns to set up interviews, etc.), an absence of standardized reference scales and scoring or weighting approaches, and a lack of transparency on methodological decisions. The review of literature also led to the proposal of good practices to ensure quality in S-LCA studies moving forward. Good practices include following the ISO 14075 standard, defining the functional unit and its role in the study, performing and documenting all phases of S-LCA, addressing the complete life cycle, critically reflecting on data specificity, using precise and consistent terminology, documenting and justifying methodological choices, and discussing encountered challenges openly. Following this set of good practices can help to provide a more meaningful and consistent approach for S-LCA. One challenge that was identified through both the lithium case study and the literature review is the varying levels of data specificity within one S-LCA case study. Since primary data is time consuming to collect and faces additional challenges, many case studies are complemented by secondary data, leading to variation in data specificity. The definitions of “primary” data were reviewed from various sustainability guidelines. Then the primary data share (PDS) indicator from product carbon footprinting (PCF) was adapted for use in S-LCA, leading to PDSS-LCA, site-specific data share (SDSS-LCA), and company-specific data share (CDSS-LCA) indicators. The proposed indicators were applied to the lithium case study and showed potential to improve transparency on the data specificity aspect of data quality in S-LCA. By contributing towards a deeper understanding of the practical methodological challenges of conducting primary data-based S-LCA and providing potential solutions to some of the core challenges, this dissertation can further the application of S-LCA. However, future research is needed to test the proposed good practices and PDSS-LCA indicator in further S-LCA studies.