Deciphering the "Pseudo-Recycling" Dilemma: A Study on the Triangular Evolutionary Game of AI-Driven Intelligent Recycling of Electronic Waste

This dataset comprises the numerical simulation results and corresponding code from the study titled **“Deciphering the ‘Pseudo-Recycling’ Dilemma: A Study on the Triangular Evolutionary Game of AI-Driven Intelligent Recycling of Electronic Waste.”** The research investigates how artificial intelligence technologies—particularly when integrated with blockchain—mitigate fraudulent recycling behaviors and facilitate the transition toward an intelligent circular economy by reshaping the strategic interactions among government, recycling enterprises, and financial institutions. The core methodology is built on a tripartite evolutionary game model, which captures the dynamic strategy adjustments of these three stakeholders under various parameter settings reflecting the influence of AI-enabled supervision, cost structures, and incentive mechanisms. Data supporting the model were sourced from authoritative environmental reports, policy documents, and case studies on e-waste management, enabling empirical calibration of key parameters such as regulatory costs, penalty intensity, and recycling efficiency. The simulation codes—developed and executed manually in MATLAB—include time-series plots illustrating the evolution of strategy probabilities, phase diagrams of system stability, and scripts for equilibrium analysis, all designed to ensure reproducibility of the model’s findings. The dataset is systematically structured and comprehensive, offering a valuable reference and practical toolkit for researchers in the fields of evolutionary game theory, circular economy, and AI-driven environmental governance.