A Simulation-Based Conceptual Model for Tokenized Recycling: Integrating Blockchain, Market Dynamics, and Behavioral Economics

Abstract: This study develops a conceptual simulation model for a tokenized recycling incentive system that integrates blockchain infrastructure, market-driven pricing, behavioral economics, and carbon credit mechanisms. The model aims to address the limitations of traditional recycling systems, which often rely on static government subsidies and fail to generate sustained public participation. By introducing dynamic token values linked to real-world supply and demand conditions, as well as incorporating non-monetary behavioral drivers (e.g., social norms, reputational incentives), the framework creates a dual-incentive structure that can adapt over time. The model uses Monte Carlo simulations to estimate outcomes under a range of scenarios involving operational costs, carbon pricing, token volatility, and behavioral adoption rates. Due to the absence of real-world implementations of such integrated blockchain-based recycling systems, the paper remains theoretical and simulation-based. It is intended as a prototype framework for future policy experimentation and pilot projects. The model provides insights for policymakers, urban planners, and technology developers aiming to explore decentralized and market-responsive solutions to sustainable waste management. Future work should focus on validating the model through field trials or behavioral experiments.