Identification and Forecasting of Key Influencing Factors in China’s Agricultural Carbon Emissions: Based on Machine Learning Method - Juntong Liu; Xiong Peng; Malan Huang; Yuzhou Ma; Cancan Jiang; Wanling Hu; Jinxin Zhang (Systems)
Identifying the key factors influencing agricultural carbon emissions and accurately predicting future trends are essential for achieving carbon peak and carbon neutrality goals. This study aims to assess carbon emissions in agriculture from 1997 to 2022, construct an accurate model to identify the key influencing factors, and predict carbon emissions in agriculture from 2023 to 2030 with an intelligent prediction system to discuss risk management. Additionally, the Dagum method was employed to explore regional differences in agricultural carbon emissions across China. The results reveal that China’s agricultural carbon emissions exhibited a fluctuating trend from 1997 to 2022, peaking in 2015, followed by a period of decline and a moderate rebound in recent years. Elastic Net Regression identified eleven key variables, including Agricultural Machinery Level (MA), Numbers of Agricultural Tools (AT), and Agricultural Industrial Structure Upgrading (AICE), as major determinants of agricultural carbon emissions. Furthermore, the RF-PSO method demonstrated the highest predictive accuracy, forecasting a minor peak in agricultural carbon emissions in 2027, followed by stabilization. Regionally, imbalances in emissions were observed, with the intensity of transvariation accounting for 37.078% of the disparity. Therefore, the Chinese government is advised to implement region-specific strategies for controlling agricultural carbon emissions, cultivate new high-quality agricultural productivity, and promote advanced technologies.