Amid the pressing challenges of global warming, carbon trading policies have gained increasing importance in advancing green development. This study employs Double Machine Learning (DML) to effectively process high-dimensional data and nonlinear relationships, integrating methods such as Difference-in-Differences (DID) to systematically address endogeneity issues. Using an indicator system for High-Quality Agricultural Development (HQAD) covering 30 provinces, municipalities, and autonomous regions, this study aims to evaluate the impact of the pilot carbon emissions trading (CET) policy on HQAD. The findings are as follows: (1) The pilot CET policy significantly enhances HQAD. Though the positive effect has diminished, multi-dimensional robustness checks confirm the results’ credibility and stability. (2) Mechanism analysis shows that the policy promotes HQAD through two key pathways: strengthening environmental regulation (ER) and improving agricultural energy total factor productivity (AETFP). (3) Regional heterogeneity is evident, with the eastern region showing the most substantial policy effects, followed by the western region, while the central region shows minimal impact. Regarding grain functional zones, the policy effect is significant in the main sales and balance areas but weaker in the main producing area. Based on these findings, this study provides three policy recommendations to inform policymaking, facilitate the green transition, and promote High-Quality Agricultural Development.