Artificial intelligence empowers port pollution control to achieve sustainable development

Description

This dataset comprises the numerical simulation results and corresponding code from the study titled “Artificial Intelligence Empowers Port Pollution Control to Achieve Sustainable Development.” The research investigates how artificial intelligence technologies reshape the strategic interactions among governments, port authorities, and shipping enterprises to facilitate the green transition of the maritime sector. 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 empirical case studies on port pollution governance, enabling the calibration of key parameters such as regulatory costs, penalty intensity, AI recognition accuracy, and emission reduction 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, Sobol global sensitivity analysis, and scripts for equilibrium stability 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, green port governance, and AI-driven environmental regulation.

Institutions

• Anhui University of Finance and Economics

  Anhui, Bengbu

Categories

Licence