Abstract: Digital-intelligent technology empowerment for agricultural green and low-carbon development is a pivotal driver for advancing agricultural and rural modernization and achieving China’s “dual carbon goals”. China’s agricultural sector has long grappled with severe resource and environmental constraints stemming from extensive development characterized by “high energy consumption, high input, and high emissions”, which has led to cultivated land degradation, non-point source pollution, and other pressing challenges. As a core driver of the global technological revolution and industrial transformation, digital-intelligent technology serves as a transformative solution to overcome the lock-in effect of traditional agricultural practices.Grounded in the practical demands of agricultural green and low-carbon transformation, this study adopts an integrated analytical framework of “digital-intelligent technology—green low-carbon development—system dynamics simulation”. First, it clarifies the core connotation of digital-intelligent technology: a “digital infrastructure-intelligent application” collaborative system tailored to agricultural contexts, which is data-driven, computing power-supported, and algorithm-optimized. Encompassing four core modules (perception interaction, data processing, intelligent decision-making, and execution application), it forms a closed-loop “perception-decision-execution” workflow. Its technical architecture comprises three layers: 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 new energy agricultural machinery).Second, the study dissects the underlying driving mechanisms from four dimensions: 1) Enhancing production efficiency and emission reduction potential through intelligent agricultural machinery and variable-rate application technologies; 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 through integrated monitoring matrices and digital twin technology; 4) Establishing three core feedback loops: intelligent planting efficiency loop, circular planting emission reduction loop, and intelligent equipment upgrade loop.Third, a system dynamics model was constructed, encompassing 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%—demonstrating the model’s high goodness of fit and reliability.Finally, multi-scenario simulations were conducted across four development modes: natural mode, intelligent decision-making mode, intelligent circular mode, and intelligent joint mode. The results indicate that: 1) In terms of agricultural carbon emissions, the intelligent joint mode achieves a cumulative reduction of 23.2%, declining from 3.02×10¹⁰ kg in 2016 to a stable range of 2.31-2.32×10¹⁰ kg after 2020; 2) Regarding the agricultural green development index, the mode attains a 126.1% growth rate, exhibiting a steady and healthy upward trajectory; 3) For grain production, it demonstrates the highest stability (with fluctuations of less than 0.5% after 2020), and its output curve consistently outperforms those of other modes. The synergistic effect of this integrated mode addresses the limitations of single-technical-path approaches.This study makes three key theoretical contributions: 1) Bridging the research gap in systematically explaining the coupling mechanism between digital-intelligent technology and agricultural green low-carbon development; 2) Establishing a robust “mechanism-path-effect” analytical framework; 3) Enriching the “technology empowerment-simulation-effect verification” research paradigm in the field of agricultural sustainability. Practically, the proposed intelligent joint mode provides actionable guidance for advancing agricultural green and low-carbon transformation. Future research will focus on cross-regional and cross-scale quantitative evaluation of technology empowerment effects, as well as the construction of supportive policy and institutional systems.