Predicting Sustainable Consumption Behavior from HEXACO Traits and Climate Worry: A Bayesian Modelling Approach

Addressing climate change requires deeper insight into the psychological drivers of pro-environmental behavior. This study investigates how personality traits, climate-related emotions, and demographic factors can predict sustainable consumption and climate action participation using a Bayesian regression approach. Drawing from the HEXACO personality model and key emotional predictors—Climate Change Worry (CCW) and environmental empathy (EE)—we analyzed data from 604 adults in Greece to assess both private and public climate-related behaviors. This research is novel in its integrative approach, combining dispositional traits and affective states within a Bayesian analytical framework to simultaneously predict both sustainable consumption and climate action. Bayesian model testing highlighted education as the most powerful and reliable predictor of sustainable consumption, with increasing levels—namely Doctoral education—linked to more environmentally responsible action. CCW produced small but reliable effects, supporting hypotheses that moderate emotional concern will lead to sustainable behavior when linked to efficacy belief. The majority of HEXACO traits, e.g., Honesty–Humility and Conscientiousness, produced limited predictive power. This indicates in this case that structural and emotional considerations were stronger than dispositional personality traits. For climate action involvement, Bayesian logistic models found no considerable evidence of any predictor, corroborating the perspective that public participation in high effort action is most likely to rely on contextual enablers instead of internal sentiments or attributes. A significant interaction effect between education and gender also indicated that the sustainability effect of education is moderated by sociocultural identity. Methodologically, this research demonstrates the strengths of Bayesian analysis in sustainability science to make sensitive inference and model comparison possible. The results highlight the importance of affect-related structural variables in behavioral models and have applied implications for theory-informed and targeted climate education and communication interventions to enable different populations to act sustainably.