1.黄河水利委员会 黄河上中游管理局, 陕西 西安 710021
2.水利部 黄土高原水土保持野外科学观测研究站, 陕西 西安 710000
3.新疆大学 商学院, 新疆 乌鲁木齐830012
4.中陕高标准农田建设集团有限公司, 陕西 西安710000
白桦锐(1996—),女(汉族),陕西省榆林市人,硕士,工程师,研究方向为气候变化与碳减排。Email:514428732@qq.com。
收稿:2024-12-19,
修回:2025-03-24,
纸质出版:2025-08-20
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白桦锐, 裴健宇, 王琪, 等.黄河流域九省区农业碳排放的驱动因素及预测[J].水土保持通报,2025,45(4):244-255.
Bai Huarui, Pei Jianyu, Wang Qi, et al. Driving factors and prediction of agricultural carbon emissions in nine provinces of Yellow River basin [J]. Bulletin of Soil and Water Conservation,2025,45(4):244-255.
白桦锐, 裴健宇, 王琪, 等.黄河流域九省区农业碳排放的驱动因素及预测[J].水土保持通报,2025,45(4):244-255. DOI: 10.13961/j.cnki.stbctb.2025.04.007. CSTR: 32312.14.stbctb.2025.04.007..
Bai Huarui, Pei Jianyu, Wang Qi, et al. Driving factors and prediction of agricultural carbon emissions in nine provinces of Yellow River basin [J]. Bulletin of Soil and Water Conservation,2025,45(4):244-255. DOI: 10.13961/j.cnki.stbctb.2025.04.007. CSTR: 32312.14.stbctb.2025.04.007..
目的
2
科学评估黄河流域农业碳排放的时空变化规律及其主要影响因素,预测未来排放趋势,为制定农业碳减排政策和区域协同治理方案提供数据支持和决策参考。
方法
2
选取黄河流域九省区2001—2021年农业碳排放作为研究对象,运用STIRPAT扩展模型(可拓展的随机性环境影响评估模型)分析其驱动因素,并采用GM(1,1)模型进行预测。
结果
2
①黄河流域各省域之间的农业碳排放存在显著差异,粮食主产区农业碳排放明显高于其他省域。 ②黄河流域农业碳排放随着时间的推移先上升后下降,整体呈“倒U型”,前期环比增速呈现波动上升,在2012年之后开始缓慢下降,直至2017年出现负增长,这表明政策干预在农业碳减排中发挥了重要作用。 ③黄河流域农业碳排放的驱动因素中,农业生产效率、经济发展水平、城镇化水平、农地经营规模、农业机械化水平是导致黄河流域农业碳排放增加的主要因素,农业机械能源强度对碳排放具有抑制作用,技术进步可能因为“回弹效应”抵消部分碳减排效果。 ④2022—2035年黄河流域农业碳排放可能呈下降趋势,但仍保持较高水平,农业碳减排压力仍然较大。
结论
2
黄河流域农业碳减排潜力尚未充分释放,应通过加速新能源技术应用,普及绿色低碳生产技术,构建农业废弃物全链条管理体系,推进农业结构优化与种养耦合模式创新,因地制宜构建生态农业与有机农业协同发展体系等方式,进一步实现农业碳减排。同时,需建立经济增长与减排目标的动态平衡机制,规避因粗放型发展导致的碳排放反弹风险。
Objective
2
The spatiotemporal variation of agricultural carbon emissions and its main influencing factors in the Yellow River basin were scientifically evaluated to predict the future emission trend, and provide data support and decision-making reference for formulating agricultural carbon emission reduction policies and regional collaborative governance plans.
Methods
2
The agricultural carbon emissions from 2001 to 2021 were selected from nine provinces of the Yellow River basin, the STIRPATextended model(stochastic impacts by regression on population, affluence and technology) was used to analyze the driving factors, and the GM (1,1) model was used for forecasting.
Results
2
① Significant differences were observed in agricultural carbon emissions among provinces and regions in the Yellow River basin, and the agricultural carbon emissions in major grain-producing areas were significantly higher than those in other provinces. ② Agricultural carbon emissions in the Yellow River basin first increased and then decreased over time, showing an overall ‘inverted U-shape’. The quarter-on-quarter growth rate showed a fluctuating rise in the early stage and then began to decline slowly after 2012 until a negative growth in 2017, indicating that policy intervention played an important role in agricultural carbon emission reduction. ③ Among the driving factors of agricultural carbon emissions in the Yellow River basin, agricultural production efficiency, economic development level, urbanization level, agricultural land management scale and agricultural mechanization level were the main factors leading to the increase of agricultural carbon emissions. The energy intensity of agricultural machinery had a restraining effect on carbon emissions, and the technological progress may offset part of the carbon emission reduction effect due to the ‘rebound effect’. ④ From 2022 to 2035, agricultural carbon emissions in the Yellow River basin may exhibit a downward trend, while maintaining a high level, and agricultural carbon emission reduction pressure would remain large.
Conclusion
2
The potential of agricultural carbon emission reduction in the Yellow River basin has not been fully realized, and agricultural carbon emission reduction should be further achieved by accelerating the application of new energy technologies, popularizing green and low-carbon production technologies, building a whole-chain management system of agricultural waste, promoting the optimization of agricultural structure and the innovation of the coupling model of planting and breeding, and building a collaborative development system of ecological and organic agriculture based on local conditions. At the same time, it is necessary to establish a dynamic balance mechanism between economic growth and emission reduction targets to avoid the risk of carbon emissions rebound caused by extensive development.
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