Owing to the contradiction between agricultural production and environmental development, the issues of food security and carbon mitigation cannot be isolated, and achieving coupled and coordinated development is the key to agricultural sustainability. This study adopted the coupled coordination model and dynamic qualitative comparative analysis (dynamic QCA) method to measure the coupled coordination degree (CCD) of the food security index (FSI) and agricultural carbon emission efficiency (ACEE) in 31 provinces of China from 2010 to 2021, seeking paths to achieve high coupled coordination from Climate-Smart Agriculture technology, external environment, and incentive dimensions, and simulating path selection differences under various CSA priority scenarios. The results indicated that the CCD of the FSI and ACEE in China significantly increased year-on-year increase, with significant regional differences primarily reflected in the Northeast > East > West > Central regions. Based on the CSA orientation, the \"technology-environmental safeguard\" linkage path and the \"technology-environment-incentive\" hybrid path are proposed. There are differences in CSA practices across regions, which require customization based on their unique socioeconomic, ecological, and political landscapes. When priorities favour food security, the \"technology-environment-incentive\" hybrid pathway supports high CCD, and as priorities increase, the contribution of CSA technologies, centred on water-saving irrigation, increases and the role of the external environment diminishes. When the priority tendency is to mitigate emissions, both paths can achieve high CCD. As the priority tendency for carbon emissions increases, urbanisation and CSA technologies such as water-saving irrigation and straw return become essential factors contributing to higher coupling coordination, and the role of agriculture-related financial expenditures diminishes. These findings provide policy support for safeguarding food security and low-carbon agriculture.

To study the multi-factor linkage effect of carbon trading on green technology innovation, this paper employs the dynamic QCA analysis method and uses panel data from China\'s carbon trading pilot areas. The aim is to explore the causal path considering the time effect. Additionally, the Kruskal-Wallis rank sum test is applied to investigate the provincial coverage difference of the configuration and reveal the variation in configuration preferences between regions from a spatial dimension. The results indicate that a single factor alone does not constitute the necessary conditions for the \"quantity\" and \"quality\" of high-green technology innovation. However, the necessity of carbon trading price exhibits a declining trend over the years, demonstrating the presence of a time effect. Regarding the sufficiency analysis of conditional configuration, it mainly includes a \"price-market scale\" dual effect model and a single market scale effect model, with three configuration paths for each model. Among them, the \"price-market scale\" dual effect model can drive the increase in the quantity of green technology innovation through carbon trading price, market scale, government intervention degree, and other factors. The single market scale effect model can promote the high-quality development of green technology innovation, but the impact of carbon trading price on the quality of green technology innovation is relatively insignificant. In terms of the time dimension, the three configurations still maintain good applicability to green technology innovation under normal conditions. However, when considering the spatial dimension, the coverage distribution of the three configurations exhibits evident regional differences. This study introduces the dynamic panel QCA method into the research field for the first time. It addresses the limitations of the traditional QCA method, which is constrained by cross-section data and lacks the ability to explore the linkage effect between factors over time. Additionally, the study analyzes the effects of carbon trading price and market size on the \"quantity\" and \"quality\" of green technology innovation, considering both time and space dimensions, from a configuration perspective.