The dairy industry, one of the major sources of agricultural greenhouse gas emissions, faces the challenge of achieving an in-depth low-carbon transformation while satisfying the ever-growing demand. Green finance, as a vital instrument to channel capital into environmentally sustainable activities, is increasingly recognized as a key measure to drive this transformation. However, empirical evidence remains insufficient regarding its specific impacts and transmission mechanisms in animal husbandry, particularly in dairy farming. This study aims to probe into the impact of green finance on carbon emissions from dairy farming in China. Based on the panel data of 31 provinces in China from 2012 to 2023, a provincial-level comprehensive green finance index has been constructed, and carbon emissions from dairy farming were measured using the IPCC methodology combined with localized coefficients. A two-way fixed effects model was employed to reveal that the development of green finance exerts a significant inhibitory effect on carbon emissions from dairy farming. This conclusion remains valid even after endogeneity tests with instrumental variables as well as a series of robustness tests. The mechanism analysis further identified two primary transmission pathways: promoting the development of the technology market to accelerate the application and dissemination of feeding practices and manure management technologies that feature low carbon emissions on the one hand; optimizing the energy mix of farms, and facilitating the shift from fossil energy to clean energy sources like biogas on the other hand. The heterogeneity analysis indicates that the carbon emission reduction effect of green finance is more pronounced in non-main livestock production areas and green finance reform and innovation pilot zones. This finding suggests that regional policy orientations and institutional support play a regulatory role in the implementation effectiveness of green finance. This study has offered systematic empirical evidence demonstrating that green finance serves as an effective policy instrument for promoting emission reduction in the dairy industry, and further enriched the theoretical understanding of green finance’s role in agricultural carbon emission reduction by clarifying its specific functional mechanisms. The research findings exhibit reference significance for policymakers and have designed differentiated and context-specific green financial products and supporting policies, thereby enabling the financial market to better guide the dairy industry toward a sustainable and low-carbon future while balancing production and environmental objectives.

This study examines the profound impact of artificial intelligence (AI) adoption on corporate green innovation through a comprehensive empirical analysis, focusing on manufacturing firms in China. Using panel data from A-share manufacturing companies listed on the Shanghai and Shenzhen stock exchanges between 2016 and 2022, we measure AI adoption through text analysis of annual reports and green innovation via authorized green patents. Results reveal that AI adoption significantly promotes corporate green innovation. The results are reliable according to robustness tests. Heterogeneity analysis indicates that the effect is more pronounced in eastern and central regions, less polluting firms, and state-owned enterprises. Importantly, the effect is positive across all manufacturing industries but substantially stronger in high-technology industries than in medium and low-technology ones. Furthermore, mechanism analysis shows that both environmental regulation and subsidies have moderating effects. Specifically, regulations weaken the positive relationship, whereas environmental subsidies strengthen it. Based on these findings, we put forward three suggestions: enhancing the policy coordination between AI and green innovation, establishing a linkage mechanism between environmental subsidies and innovation performance, and advancing the transformation of environmental regulation toward greater intelligence and precision.