Modeling and Control of Sustainable Transitions Through Opinion-Behavior Coupling in Heterogeneous Networks

Abstract

Understanding how sustainable behaviors spread within heterogeneous societies requires the integration of behavioral data, social influence mechanisms, and structured control strategies. In this article, we propose a data-driven computational framework for coupled opinion-adoption dynamics in social systems. Each node in the multilayer network represents a community characterized by age group and mobility level, derived from large-scale survey data on the propensity to adopt electric vehicles (EVs) in Northern Europe. The proposed model captures three mechanisms: behavioral contagion through social and informational diffusion, abandonment driven by dissatisfaction, and feedback between opinions and adoption levels through social influence. By analyzing the equilibria of the coupled system, we identify conditions that enable widespread adoption. The model is empirically calibrated using survey data to generate synthetic populations and social similarity networks, which are then used to evaluate targeted interventions promoting sustainable transitions. We focus on two control strategies: 1) opinion-based interventions acting on the social network layer; and 2) experience- or dissatisfaction-based policies aimed at reducing abandonment. Simulation results indicate that policies targeting dissatisfaction are more effective in ensuring stable and long-term adoption, providing actionable insights for designing interventions in sociotechnical transitions toward sustainability.