Modeling the coevolution of information diffusion and garbage classification behavior on simplicial complex networks

Garbage classification is crucial for environmental sustainability , requiring widespread public participation for effective policy implementation. While existing studies focus on psychological and social factors influencing individual behavior, quantitative research, particularly on the impact of higher-order interactions, remains limited. To fill this gap, we propose the UAU-NCN model (Unaware-Aware-Unaware-Nonclassified-Classified-Nonclassified), which integrates group effects through simplicial complexes and accounts for mass media influence to examine the dynamics of information diffusion and garbage classification behavior contagion. Using the microscopic Markov chain approach (MMCA), we derive the adoption threshold for garbage classification behavior and validate our theoretical results through Monte Carlo (MC) simulations. We further assess the model’s effectiveness by constructing behavioral and information-layer networks from real-world waste disposal data. Our experiments on both synthetic and empirical networks show that information diffusion is a key driver of behavior adoption, with even minor changes significantly affecting spread. Enhancing information diffusion lowers the adoption threshold, encouraging broader participation. Additionally, group effects accelerate the adoption process, increasing both the speed and proportion of individuals engaged in classification practices. This study emphasizes the importance of higher-order interactions in shaping effective waste management strategies and offers insights for optimizing policy interventions and increasing community involvement in environmental initiatives. • A multilayer model with simplex/media effects to promote waste-sorting is proposed. • Using MMCA, derive the model contagion threshold and validate via MC simulations. • Experiments on networks show group and media effects boost individual waste-sorting.