How the impact and mechanisms of digital financial inclusion on agricultural carbon emission intensity: new evidence from a double machine learning model - Zheng, Fengtian; Chen, Siyu; Wang, Xizhao (Frontiers in Environmental Science, 2025)
The advancement of the digital economy is vital for decreasing agricultural carbon emissions and fostering high-quality agricultural development. Using panel data from 31 Chinese provinces between 2000 and 2021, this paper employs a dual machine learning model for causal inference to analyze the impact of digital financial inclusion on agricultural carbon emissions intensity, its underlying mechanisms, and the characteristics of heterogeneity. The study finds that digital inclusive finance significantly reduces agricultural carbon intensity through two main channels: enhancing scientific and technological innovation and narrowing the urban-rural income gap. Additionally, the expansion of arable land management and the acceleration of economic structural transformation positively moderate these effects. These conclusions remain robust after a series of robustness tests. Further combining factors such as resource endowment, geographic location, economic concentration, and food production areas in the heterogeneity test, the study found that regional differences significantly influence the effect of financial inclusion on agricultural carbon intensity. Therefore, it is essential to enhance the development of inclusive finance, break down regional barriers to promote synergistic development, actively support economic transformation and large-scale operations, strengthen scientific and technological innovation, and narrow the urban-rural income gap to support China’s agricultural green transformation.