【摘要】 基于中国2012—2022年省级面板数据，采用SBM-GML模型测度林业生态全要素生产率，并运用双重机器学习模型实证检验数字金融与绿色金融协同的影响及作用机制。研究结果表明：第一，数字金融与绿色金融的协同效应对林业生态全要素生产率具有显著促进作用，并且在经过稳健性与内生性检验后结论依旧成立；第二，机制分析发现，两者的协同效应通过林业土地效率和林业劳动力效率两条路径实现对林业生态全要素生产率的提升；第三，异质性分析显示，协同效应在高经济水平区域、低森林资源禀赋区域和环境规制强度高的区域更为显著。因此，提出了要构建数字金融与绿色金融协同的顶层制度框架，设计精准化金融工具以及实施区域差异化策略以提升林业生态全要素生产率的建议。更多还原

【Abstract】 (1) Background——As an important pillar of ecological civilization construction, forestry plays an irreplaceable role in maintaining ecological security, increasing carbon sinks, and promoting green, low-carbon development. Enhancing forestry ecological total factor productivity(FETFP) is the core issue for the high-quality development of forestry. Although existing studies have revealed that green finance or digital finance has certain promoting effects on the green development of forestry, systematic research on their synergistic mechanisms from the perspective of FETFP remains scarce, particularly regarding influence paths, boundary conditions, and regional heterogeneity.(2) Methods——This paper first proposed the research hypotheses based on theoretical analysis. Then, using the annual panel data of 30 provinces(municipalities, autonomous regions) in China from 2012 to 2022, it measured the forestry ecological total factor productivity across these regions by employing the SBM-GML model. Furthermore, it utilized the Double Machine Learning Model to empirically analyze the impact of the synergy between digital finance and green finance on forestry ecological TFP, along with its underlying mechanisms. The research data were primarily sourced from the China Statistical Yearbooks, ERA5-Land Datasets, China Statistical Yearbooks on Science and Technology, Almanacs of China’s Finance and Banking, etc.(3) Results——First, the synergy between digital finance and green finance can significantly enhance the forestry ecological total factor productivity. The two have a high degree of complementarity in aspects such as resources allocation, risks identification, and ecological performance assessment and this synergistic effect helps overcome the traditional obstacles in forestry financing. Second, the synergy between digital finance and green finance enables the precise identification of the marginal benefits of lands and labors through digital means. This effectively enhances the utilization efficiency of forest lands and the allocation efficiency of forestry labors, thereby promoting the growth of the forestry ecological total factor productivity. The role of financial tools is embedded in the value enhancement paths of lands and labors through a chain mechanism of “data-credit-factor efficiency”. Third, the impact of synergy between digital finance and green finance on the forestry ecological total factor productivity exhibits regional heterogeneity. This synergistic effect demonstrates a more pronounced positive influence in regions characterized by higher economic development levels, lower forest resource endowments, and more stringent environmental regulations.(4) Conclusions and Discussions——Based on the above research conclusions, the following policy suggestions are proposed: First, strengthen the top-level institutional supply and establish a macro-policy framework for the synergistic development of digital finance and green finance in order to promote their integration in the forestry sector. This includes developing standardized metrics for evaluating ecological performance and creating interoperable digital platforms for forest resource management and financial transactions. Second, focus on the core transmission mechanism, design and implement precise financial tools to enhance the efficiency of production factors, and develop financial products catered to the needs of different forestry stakeholders, such as a blockchain-based forest carbon credit trading system and a digital microcredit product specifically designed for small-scale forestry farmers. Third, implement differentiated regional strategies and guide financial resources to achieve structural optimization and precise allocation.更多还原