Prospective Social Life Cycle Assessment of Emerging Technologies: A Case Study of a Current Limiting Device

Climate change stands as one of the most urgent global challenges, demanding innovative solutions for renewable energy systems. These systems are rapidly evolving and creating demand for new technologies. Within the EU-funded Novetrol project, a Current Limiting (CL) device for Low Voltage Direct Current (LVDC) microgrids has been developed to enhance the efficiency and safety of renewable energy networks. As an emerging technology still at low Technology Readiness Level (TRL), it is important to assess potential social impacts at an early stage to ensure responsible innovation. While prospective environmental Life Cycle Assessment (p-LCA) has been increasingly applied to estimate future environmental impacts, the application of prospective Social Life Cycle Assessment (p-S-LCA) is still limited, especially for technologies at low TRL. This thesis explores the feasibility of conducting a pSLCA framework to such emerging technologies, using the Novetrol CL device as a case study. The assessment was performed using OpenLCA 2.1.1 together with PSILCA database. Two scenarios were modeled, Case 1 representing today’s baseline (2025) at the lab scale and Case 2 representing a prospective scenario in 2030 with some assumptions on system optimization. The results were consistent for both scenarios, identifying worker with fair salary as the most impacted stakeholder and critical indicator, followed by value chain actors with public sector corruption. Sensitivity analysis on the supply chain confirmed that the highest impacted stakeholder and social indicator remained the same, while the second highest shifted to the local community with sanitation coverage. At the process level, the highest impact is caused by the photoresist coating process, with InSb wafer as the main material, followed by metal layer deposition and PCB integration. These findings highlight that social risks in this study are mainly influenced by the geographical sourcing of the key materials rather than the process-level system optimization.