Policy-Driven Dynamics in Sustainable Recycling: Evolutionary Dynamics on Multiple Networks with Case Insights from China

Abstract

Residents have substantial control over their daily lifestyles, and their behavior change has a considerable potential to reduce emissions. Understanding the adoption of sustainable lifestyles and recycling behaviors and how behavioral policies might shape this decision-making is critical to the transition to sustainable consumption patterns. This paper developed a multi-agent model on a multiplex complex network that integrates evolutionary game theory to simultaneously capture information transmission and behavioral interaction dynamics. In this model, residents determine decision utilities under the influence of internal and external factors and information transmission and then perform social interactions according to an evolutionary dynamics model. A real case of residents’ green express packaging purchase decisions in China was used for parameter initialization. Explorative simulations and scenario analyses were conducted to investigate the adoption patterns of sustainable lifestyles under different policy scenarios. Results indicate that the dynamic evolution of residents’ sustainable lifestyle decisions relies on social interactions and social networks. Government subsidies are effective in fostering sustainable decisions, but this effect is sensitive to the size of complex networks. Information campaigns and government subsidies have a significant marginal contribution to promoting sustainable lifestyles than green labeling schemes. Implementing subsidies and information campaigns as policy mixes can exert complementary effects and improve aggregate outcomes of policy packages.