As a core policy tool for China in addressing climate risks, green finance plays a strategically important role in shaping carbon mitigation outcomes. This study investigates the nonlinear and interaction effects of green finance on carbon emission intensity (CEI) using Chinese provincial panel data from 2000 to 2022. The Climate Physical Risk Index (CPRI) is incorporated into the analytical framework to assess its potential role in shaping carbon outcomes. We employ Bayesian Additive Regression Trees (BART) to capture complex nonlinear relationships and interaction pathways, and use SHapley Additive exPlanations values to enhance model interpretability. Results show that the Green Finance Index (GFI) has a statistically significant inverted U-shaped effect on CEI, with notable regional heterogeneity. Contrary to expectations, CPRI does not show a significant impact on carbon emissions. Further analysis reveals that in high energy consumption scenarios, stronger green finance development contributes to lower CEI. These findings highlight the potential of green finance as an effective instrument for carbon intensity reduction, especially in energy-intensive contexts, and underscore the importance of accounting for nonlinear effects and regional disparities when designing and implementing green financial policies.

翻译：作为中国应对气候风险的核心政策工具，绿色金融对碳减排成效具有重要战略影响。本研究利用2000年至2022年中国省级面板数据，探讨绿色金融对碳排放强度（CEI）的非线性与交互效应。分析框架中纳入气候物理风险指数（CPRI），以评估其对碳排放结果的潜在影响。我们采用贝叶斯加性回归树（BART）捕捉复杂的非线性关系与交互路径，并运用SHapley加性解释值增强模型可解释性。结果表明：绿色金融指数（GFI）对CEI存在统计显著的倒U型影响，且呈现明显的区域异质性；与预期相反，CPRI对碳排放未表现出显著影响。进一步分析发现，在高能耗情景下，更强的绿色金融发展有助于降低CEI。这些发现揭示了绿色金融作为降低碳强度有效工具的潜力（尤其在能源密集型情境中），并强调了在设计与实施绿色金融政策时需充分考虑非线性效应与区域差异的重要性。