Impact Factors and Policy Effectiveness of Renewable Energy Generation in China

As China accelerates toward carbon neutrality, decrypting the causal drivers of renewable energy expansion is paramount for effective policy design. We develop a hybrid analytical framework bridging data-driven K2 structural learning with expert-informed Bayesian Networks to map the intricate interdependencies between policy instruments, resource endowments, and socio-economic variables. This causal mapping reveals a fundamental paradigm shift from resource-bound growth to institutional-steered expansion, particularly in the solar sector where the Renewable Portfolio Standard (RPS) has superseded natural radiation as the primary determinant for capacity scaling. Forward sensitivity and backward diagnostic analyses demonstrate that achieving high-growth milestones requires a synergistic convergence of technological cost reductions and mandatory consumption quotas; conversely, the absence of RPS leads to a 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 energy transition. Ultimately, this framework and the identified causal pathways provide a strategic blueprint for other emerging economies navigating the complex transition from subsidy-dependent to market-resilient renewable energy landscapes under stringent climate constraints.