Abstract: Agriculture represents a significant contributor to non-carbon dioxide (CO₂) greenhouse gas (GHG) emissions in China. Effectively managing non-CO₂ GHG emissions from agriculture is crucial for achieving China's "dual-carbon" targets, providing valuable insights into guiding the green transformation of agriculture and promoting sustainable development. Utilizing statistical data on crop yield, fertilizer input, and paddy sowing area in the Xinjiang Uygur Autonomous Region from 2000 to 2020, the authors applied the IPCC coefficient method to assess total agricultural non-CO₂ GHG emissions. The Logarithmic Logarithmic Mean Divisia Index (LMDI) model was used to analyze the drivers of non-CO₂ GHG emissions from agricultural sources. Additionally, a Monte Carlo simulation, coupled with scenario analysis, was conducted to predict the emission trends of agricultural non-CO₂ GHG emissions in Xinjiang during the study period. The findings revealed that: (1) agricultural non-CO₂ GHG emissions in Xinjiang exhibited a fluctuating upward trend, experiencing a 34.43% increase, with animal husbandry identified as the primary emission source. (2) Key contributors to agricultural non-CO₂ GHG emissions were identified as the level of agricultural economic development and urbanization, contributing to emissions of 4211.74×10⁴tCO₂eq and 1016.08×10⁴tCO₂eq, respectively. Conversely, the efficiency of Agricultural non-CO₂ GHG emission intensity, rural population, and the agricultural structure acted as inhibitors, contributing to emission reductions of 4163.36×10⁴tCO₂eq, 224.84×10⁴tCO₂eq and 130.64×10⁴tCO₂eq , respectively, during the period from 2000 to 2020. (3) Agricultural non-CO₂ GHG emissions exhibited an increasing trend in both baseline and planning scenarios. However, the growth rate in the planning scenario surpassed that in the baseline scenario, and a slowdown in the growth rate is anticipated in the low-carbon scenario, with the potential for negative growth by 2035. (4) To effectively control non-CO₂ GHG emissions from agriculture, there is a need to reinforce the implementation of emission reduction policies, continually enhance the efficiency of agricultural production, improve incentive mechanisms for emission reduction, and encourage technological breakthroughs. These measures are essential to guide the green transformation of Xinjiang's agriculture and foster its sustainable development.

Key words: Agricultural non-CO₂ GHG, Drivers, Scenario projections, LMDI model