An interpretable framework for agricultural carbon decoupling: integrating Tapio, LMDI, and spatial Markov models with CoT-enhanced LLMs

Understanding whether agricultural economic growth can be achieved without increasing carbon emissions is essential for China’s low-carbon transition. This study examines the decoupling relationship between agricultural carbon emissions and agricultural economic output across 31 Chinese provinces from 2011 to 2023. An integrated framework combining Tapio elasticity, logarithmic mean Divisia index (LMDI) decomposition, spatial Markov chains, and a Chain-of-Thought-enhanced large language model is used to identify decoupling types, driving factors, spatial transition patterns, and policy implications. Results show that national agricultural carbon emissions peaked around 2015–2016 and then declined steadily. Fertilizer was the dominant emission source, accounting for 54.5% of total emissions. Provincial decoupling improved markedly after 2015, with more than 80% of provinces achieving strong decoupling by 2017. LMDI results indicate that economic scale was the main emission-increasing factor, while improvements in input intensity contributed substantially to emission reduction. Spatial analysis reveals persistent high-emission clusters and spatially conditioned decoupling transitions among neighboring provinces. The CoT-enhanced module translates quantitative results into structured, region-specific policy implications. These findings highlight fertilizer reduction, energy-efficiency improvement, and spatially coordinated mitigation as key priorities for sustaining agricultural carbon decoupling in China.