A digital twins-based policy simulation framework for rural photovoltaic entrepreneurship in China

Rural photovoltaic entrepreneurship in China faces critical challenges in aligning rapid technological advancements with lagging market responses, where 63% of technology adoption failures originate from mismatches between innovation maturity and regional policy adaptability. To address this, we propose a policy simulation-driven digital twins framework integrating three core innovations: (1) denoising diffusion models that reduce technology adoption prediction errors to <5% for mainstream photovoltaic technologies; (2) a dynamic policy sandbox identifying intervention thresholds through 10⁴ Monte Carlo simulations, revealing the ¥850 million subsidy ceiling that triggers 23% ROI decline; and (3) multi-agent coordination mechanisms optimizing resource allocation across 1,200 + entrepreneurial nodes. Empirical validation across 16 Anhui counties demonstrates the system's effectiveness: 18% reduction in entrepreneurial failure rates through real-time policy adaptation, 12% annual growth in photovoltaic installed capacity, and ¥1.41 billion net policy-driven income. Crucially, our analysis establishes a 12% regional GDP threshold for subsidy intensity, beyond which land price inflation offsets entrepreneurial benefits. This framework provides actionable insights for synchronizing technological roadmaps with localized policy design in rural energy transitions.