Impact of agricultural subsidy on chemical fertilizer use: Empirical evidence of China’s Organic-Substitute-Chemical-Fertilizer policy based on double machine learning - Lei Deng; Pengcheng Wan; Fangyu Ye; Jingjie Zhao (PLoS ONE, 2025)
The sustainable development of agriculture hinges on effective fertilizer management, and China’s experience with chemical fertilizer overuse highlights the challenges and opportunities in this domain. This study examines the impact of agricultural subsidy policy on chemical fertilizer use across 2319 counties from 2012 to 2022. By treating the “Action Plan for Organic-Substitute-Chemical-Fertilizer (OSCF) for Fruits, Vegetables and Tea” as a quasi-natural experiment, this study uses a Double Machine Learning model to analyze its effects on fertilizer use and the underlying mechanisms, considering technical and scale efficiency as mediating variables. The findings reveal that the OSCF policy has a significant negative effect on chemical fertilizer use, primarily by enhancing both technical and scale efficiency. This study further reveals regional heterogeneity in the policy’s effectiveness. The results imply that while the impact of the OSCF policy is generally beneficial, it is shaped by regional economic development, agricultural production structure and initial level of fertilizer use. This highlights the importance of tailored policy instruments to address regional disparities in agricultural practices and targeted strategies to maximize the OSCF policy’s impact on sustainable agricultural development. This study provides valuable insights for policymakers and farm managers to enhance the sustainability of agricultural practices.