Nonlinear effects of green finance on net primary productivity under environmental regulation constraints

Net Primary Productivity (NPP) is a critical indicator for assessing ecosystem health and productivity, as it directly reflects carbon balance and biodiversity. Based on panel data from 31 Chinese provinces from 2001 to 2022, this study constructs a Panel Smooth Transition Regression (PSTR) model to empirically examine the nonlinear effects of green finance on NPP under varying levels of environmental regulation. The results show that the effect of green finance on NPP depends on the intensity of environmental regulation. When regulatory intensity is below 0.121, green finance exhibits a stronger positive correlation with NPP (coefficient = 0.370). However, when the regulatory intensity exceeds 0.121, the coefficient declines to 0.283, suggesting that stronger regulation may partially substitute for green finance incentives. Furthermore, nonlinear marginal analysis demonstrates notable spatial and temporal variation in the relationship, with the ecological benefits of green finance being more pronounced in regions with weaker environmental regulation. The study also finds that, overall, NPP in China has increased substantially over the past two decades, but with evident regional disparities: the eastern region has seen the largest improvement, followed by the central region, while the western region has experienced more limited growth. Based on these findings, the study proposes several policy recommendations: adopting flexible green finance instruments tailored to varying levels of environmental regulation; implementing differentiated green finance strategies to address regional imbalances; and dynamically adjusting green finance policies to reflect their nonlinear effects on NPP, thereby promoting a more coordinated balance between ecological conservation and economic development.