A Social Digital Twin for Sodium-Ion Battery Circular Economy: Integrating S-LCA, Stakeholder Diffusion Modeling, and Correlation Analysis

The global transition to electrified transportation has intensified demand for advanced battery technologies, with Sodium-Ion (Na-ion) batteries emerging as a compelling alternative to lithium-ion systems due to the abundance, lower cost, and geopolitical accessibility of sodium precursors. While environmental life cycle assessment (E-LCA) of battery technologies has received growing attention, the social dimensions—labor conditions in material sourcing, community impacts of manufacturing, equity in access to circular economy benefits, and stakeholder acceptance of end-of-life strategies—remain critically underexplored. Social Life Cycle Assessment (S-LCA), guided by the UNEP/SETAC Guidelines (2020), provides a structured methodology for evaluating these dimensions, yet its application to next-generation battery systems is nascent. A recent systematic review found only 45 published studies (2009–2023) evaluating social consequences of circular economy using S-LCA, with most focusing on material recycling rather than reuse, remanufacturing, or product-service systems (Petti et al., 2018; Mancini et al., 2025). Simultaneously, digital twin technology—virtual replicas of physical systems enabling real-time monitoring, simulation, and optimization—is transforming battery lifecycle management. Industry leaders such as Siemens have deployed Battery Passport systems powered by digital twins for technical circularity tracking, and the Fraunhofer-led DiVaBatt project (launched February 2026) develops AI-supported LCA for the battery value chain. However, a critical gap persists: no existing digital twin framework integrates social impact indicators alongside technical and environmental performance for battery systems. This research proposes to bridge this gap by developing a proof-of-concept Social Digital Twin for Naion battery circular economy. While industrial digital twins operate on millisecond timescales for process control via IoT sensors, this framework conceptualizes a Strategic Social Digital Twin. Unlike operational twins, this model functions on policy and behavioral timescales, where "real-time" monitoring refers to the dynamic updating of social risk indicators and stakeholder states rather than physical sensor streams. Fed by empirical survey data, S-LCA indicators, and policy datasets, it enables scenario-based exploration of “what-if” questions (e.g., “What happens to social outcomes if a jurisdiction mandates battery recycling quotas?”). This approach extends the digital twin paradigm from technical asset management to strategic socio-technical systems analysis.