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Driving factors analysis and scenario prediction of agricultural carbon emissions in Henan Province based on machine learning
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Xin-jie SHI1, 2, 3, Xue-qin TAN1, Ru ZHOU1, Jin-ran XIONG4, Ling LI1, 2, Dong-hao LI1, 2, *
China Environmental Science | 2025, 45(10) : 5885 - 5893
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China Environmental Science | 2025, 45(10): 5885-5893
• Carbon Emission Control •
Driving factors analysis and scenario prediction of agricultural carbon emissions in Henan Province based on machine learning
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Xin-jie SHI1, 2, 3, Xue-qin TAN1, Ru ZHOU1, Jin-ran XIONG4, Ling LI1, 2, Dong-hao LI1, 2, *
Affiliations
  • 1.College of Resources and Environment, Henan Agricultural University, Zhengzhou 450046, China
  • 2.Henan Engineering Research Center of Land Consolidation and Ecological Restoration, Zhengzhou 450046, China
  • 3.Postdoctoral station of Crop Science of Henan Agricultural University, Zhengzhou 450046, China
  • 4.Department of Environmental Sciences and Engineering, University of Science and Technology of China, Hefei 230022, China
Published: 2025-10-20
Outline
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This study analyzed agricultural carbon emissions in different cities of Henan Province from 2000 to 2021 by focusing on three indicator categories: land management, grain cultivation, and livestock farming. The analysis of the spatiotemporal evolution trends and patterns of agricultural carbon emissions was conducted using the local Moran I index. Additionally, a random forest model was utilized to determine the primary influencing factors and forecast agricultural carbon emissions in Henan Province across baseline, low-carbon, and high-carbon scenarios. The study findings indicated the following key points: agricultural carbon emissions in Henan Province exhibited a fluctuating trend from 2000 to 2021, reaching a peak of 58.4952million tons in 2006 before declining. By 2021, emissions had decreased by 40.60% compared to the peak year. The spatial distribution of agricultural carbon emissions across cities in the province followed a distinct pattern of being higher in the southeast and lower in the northwest. The primary driving factors positively influencing agricultural carbon emissions in Henan Province were the land plowing area, rural population, administrative area, and total agricultural machinery power. Conversely, the urbanization rate had a negative impact on emissions. Agricultural carbon emissions in Henan Province peaked in 2006 and are projected to continue decreasing from 2022 to 2030 under three scenarios. The anticipated reductions range from 22.4155~24.7073 million tons, 22.4567~24.8222 million tons, and 22.401~24.5974 million tons, respectively, compared to the peak level. These findings offer valuable insights for developing future strategies for reducing agricultural carbon emissions in different cities within Henan Province, thereby contributing to the attainment of the "dual carbon" objectives.

agricultural carbon emissions  /  random forest  /  machine learning  /  Henan province
史新杰, 谭雪勤, 周茹, 熊晋冉, 李玲, 李栋浩. 基于机器学习的河南省农业碳排放驱动因素分析和情景预测. 中国环境科学, 2025 , 45 (10) : 5885 -5893 .
Xin-jie SHI, Xue-qin TAN, Ru ZHOU, Jin-ran XIONG, Ling LI, Dong-hao LI. Driving factors analysis and scenario prediction of agricultural carbon emissions in Henan Province based on machine learning[J]. China Environmental Science, 2025 , 45 (10) : 5885 -5893 .
Year 2025 volume 45 Issue 10
79
17
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Article Info
  • Receive Date:2025-03-10
  • Online Date:2026-10-08
  • Published:2025-10-20
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  • Received:2025-03-10
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Affiliations
    1.College of Resources and Environment, Henan Agricultural University, Zhengzhou 450046, China
    2.Henan Engineering Research Center of Land Consolidation and Ecological Restoration, Zhengzhou 450046, China
    3.Postdoctoral station of Crop Science of Henan Agricultural University, Zhengzhou 450046, China
    4.Department of Environmental Sciences and Engineering, University of Science and Technology of China, Hefei 230022, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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