Abstract: Digital technology has greatly contributed to green and low-carbon agriculture in recent years. It is one of the most important driving factors to advance agricultural and rural modernization under the dual carbon goals. However, the extensive development is characterized by “high energy consumption, high input, and high emissions”, leading to cultivated land degradation and non-point source pollution. The severe resource and environmental constraints have confined the global technological revolution and industrial transformation. Fortunately, digital technology can be expected to serve as a transformative solution to overcome the lock-in effect of the conventional agricultural practices. It is also the practical demand of green and low-carbon agriculture. In this study, an analytical framework was adopted to integrate the “digital technology—green low-carbon development—system dynamics simulation”. Firstly, the connotation of the digital technology was clarified by a “digital infrastructure-intelligent application” collaborative system under agricultural contexts, which involved data-driven, computing power and algorithm optimization. Four modules (perception interaction, data processing, intelligent decision-making, and execution application) formed the “perception-decision-execution” workflow. Technical architecture comprised the perception layer (a space-air-ground integrated monitoring network), the decision layer (integration of agricultural large models and mechanistic models), and the execution layer (intelligent equipment, such as the new energy agricultural machinery). Secondly, the underlying driving mechanisms were obtained from four dimensions: 1) Enhancing production efficiency and emission reduction potential using intelligent agricultural machinery and variable-rate application; 2) Facilitating agricultural waste resource recycling via the integration of the Internet of Things, big data analytics, and artificial intelligence; 3) Advancing ecological optimization and intelligent decision-making using monitoring matrices and digital twin technology; 4) Establishing three feedback loops: intelligent planting efficiency, circular planting emission reduction, and intelligent equipment upgrade. Thirdly, a dynamic model was constructed with the five interrelated subsystems: population, environment, economy, agricultural energy, and planting production. The model was validated using statistical data on Henan Province’s GDP and cultivated land area from 2016 to 2023. The average relative errors for the two variables were 0.92% and 0.45%, respectively, with all annual relative errors below 5%—indicating the high goodness of fit and reliability. Finally, the multi-scenario simulations were conducted over the natural, intelligent decision-making, intelligent circular, and intelligent joint modes. The results indicate that: 1) In terms of the agricultural carbon emissions, the intelligent joint mode was reduced by 23.2%, declining from 3.02×10¹⁰ kg in 2016 to a stable range of 2.31×10¹⁰-2.32×10¹⁰ kg after 2020; 2) In the agricultural green development index, a 126.1% growth rate was exhibited a steady and healthy upward trajectory; 3) In grain production, the highest stability (with the fluctuations of less than 0.5% after 2020) and its output curve consistently outperformed those of the rest modes. The synergistic effect of the integrated mode overcame the limitations of the single-technical-path approaches. Three theoretical contributions were: 1) To systematically explain the coupling mechanism between digital technology and low-carbon agriculture; 2) To establish a robust “mechanism-path-effect” analytical framework; and 3) to enrich the “technology empowerment-simulation-effect verification” paradigm in the field of agricultural sustainability. Practically, the intelligent joint mode can provide actionable guidance to advance green and low-carbon agriculture. Future research can be expected to focus on the cross-regional and cross-scale quantitative evaluation of the technology empowerment in the supportive policy and institutional systems.