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

史新杰; 谭雪勤; 周茹; 熊晋冉; 李玲; 李栋浩

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中国环境科学 ›› 2025, Vol. 45 ›› Issue (10) : 5885-5893.

碳排放控制

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

史新杰^1,2,3, 谭雪勤¹, 周茹¹, 熊晋冉⁴, 李玲^1,2, 李栋浩^1,2

作者信息 +

Driving factors analysis and scenario prediction of agricultural carbon emissions in Henan Province based on machine learning

SHI Xin-jie^1,2,3, TAN Xue-qin¹, ZHOU Ru¹, XIONG Jin-ran⁴, LI Ling^1,2, LI Dong-hao^1,2

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摘要

选取土地管理、粮食种植、畜牧养殖3类指标对2000~2021年河南省各地市农业碳排放进行了测算,结合局部莫兰指数分析了各地市农业碳排放的时空演变趋势和格局,最后采用随机森林模型识别了主要驱动因素并预测了基准、低碳、高碳情景下的河南省农业碳排放量.结果表明:2000~2021年,河南省农业碳排放先上升后下降,在2006年达到峰值5849.52万t,2021年较2006年下降40.60%;各地市农业碳排放分布不均,整体呈现“东南高、西北低”的特征.土地翻耕面积、农村人口数量、行政区面积以及农业机械总动力是影响河南省农业碳排放最主要的正向驱动因素,城镇化率为负向驱动因素.河南省农业碳排放已于2006年达峰值,在3种情景下,2022~2030年河南省农业碳排放均持续下降,与达峰状态相比下降范围分别为2241.55~2470.73万t,2245.67~2482.22万t,2240.10~2459.74万t.研究结果可以为河南省各地市未来农业碳减排路径提供依据,确保“双碳”目标达成.

Abstract

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.

导出引用

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[J]. 中国环境科学. 2025, 45(10): 5885-5893

SHI Xin-jie, TAN Xue-qin, ZHOU Ru, XIONG Jin-ran, LI Ling, LI Dong-hao. 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

中图分类号： X24

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