Optimization of green economy development efficiency by integrating Bayesian network

Conventional modeling methods of green economy development efficiency are often difficult to balance and realize real-time interaction and multi-objective trade-off. This study puts forward an integrated analytical framework to analyze this problem using a Multi-Agent Independent Actor–Critic (MAIAC) method and a Dynamic Bayesian Network (DBN); this framework makes policy coordination stronger and increases the simulation accuracy of regional green economic development. A green economic efficiency (GEE) index system is built using three things (resource inputs, technical outputs, and environmental restrictions); it uses panel data from 30 provincial-level administrative regions in China between 2012 and 2022; the GEE concept is quantitatively measured. In order to learn and simulate the dynamic causal relationships among key economic variables, a DBN is employed to establish an environmental simulator. A configurable multi-objective reward function then guided the MAIAC algorithm to perform decentralized dynamic decision optimization. In prediction tasks, the model accurately forecasted green technology output indicators (RMSE = 0.047, MAE = 0.034, R² = 0.909). In policy optimization tasks, it significantly enhanced overall green efficiency and clarified the structural trade-offs among different policy preferences, including green priority, economic priority, and balanced development. This study provides a validated macro-level policy analysis tool and establishes a methodological foundation for integrating large-scale real-time IoT data into more refined intelligent decision-support systems.