Impact Factors and Policy Effectiveness of Renewable Energy Generation in China: Insights from a Multi-Scenario Bayesian Analysis

This study develops a hybrid analytical framework that bridges data-driven K2 structural learning with expert-informed Bayesian Networks to decrypt the intricate interdependencies among policy instruments, resource endowments, and socio-economic variables across China’s hydropower, wind, and solar power. The results demonstrate a fundamental paradigm shift from resource-bound growth to institutional-steered expansion, notably in the solar sector where the Renewable Portfolio Standard (RPS) has superseded natural radiation as the primary determinant for capacity scaling. Forward sensitivity analysis and backward diagnostic attribution reveal that achieving high-growth milestones requires a synergistic convergence of tech-cost reductions and mandatory consumption quotas, whereas the absence of RPS leads to a catastrophic 64% degradation in systemic causal connectivity. These findings underscore the necessity of transitioning from price-side stimuli to structural consumption-side mandates to ensure a resilient and certain energy transition under stringent carbon constraints.