Establishing a Digital Twin Archetype Through Climate Concerns and Quality of Life Data

This study explores climate anxiety as an emerging psychological phenomenon, using national quality-of-life survey data to identify archetypes reflecting public sentiment toward climate change. Clusters derived through K-Means represent diverse responses shaped by stress, trust, centrality, and broader socio-economic and environmental contexts. The results suggest a foundational model of digital twins or their analogs, highlighting how climate concerns intersect with health and economic vulnerability. These archetypes can support more targeted psychological and policy interventions to address differentiated climate-related experiences in the population. Future research could enhance these archetypes by integrating additional variables, such as wearable or sensor data and medical records, to improve their accuracy and applicability. This would offer a more holistic view of climate-related sentiment, strengthen predictive models of behavior, and inform adaptive strategies for managing the complex and multi-dimensional impacts of climate change on human well-being.