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西南地区农业碳排放时空演变特征及影响因素研究

Spatial-temporal Evolution Characteristics and Influencing Factors of Agricultural Carbon Emissions in Southwest China

  • 摘要: 研究西南地区农业碳排放的时空演变特征及其影响因素对我国西南地区的农业低碳生产转型和国家“双碳”目标战略的实现具有重要意义。因此,基于2005—2023年19年的西南地区5个省份数据对农业碳排放量进行测算,并对农业碳排放量的强度、结构及其时空分布特征进行分析。同时,采用LMDI模型探究影响农业碳排放的因素,运用灰色预测模型GM (1,1)预测未来7年农业碳排放量。结果表明,2005—2023年西南地区农业碳排放总量变化范围为3 219.39×104 t~4 315.15×104 t,总体呈波动上升趋势,而碳排放强度呈“V”型波动降低趋势。2005—2023年西藏自治区农业碳排放强度均为V级,其余四省农业碳排放强度由2005年的V级降低为2023年的I级。西南地区各省市农业碳排放总量区域差异较大,四川省农业碳排放量最大,其次是云南省、贵州省、重庆市和西藏自治区。畜禽养殖是西南地区农业碳排放最主要的碳源,占年平均总量的48.99%,再次是农田土壤利用(26.20%)和农资投入(24.80%)。农业生产效率及农业劳动力规模两个因素对农业碳排放起到抑制作用,农业经济发展水平因素对农业碳排放起促进作用,而农业产业结构因素根据各省农业生产结构是否合理,对农业碳排放起着不同的作用。预测结果表明西南地区农业碳排放量在2024—2030年将仍保持增长趋势,与2023年相比,年均增加13.29×104 t。其中,四川省在前期保持高农业碳排放量的情况下呈下降趋势,而其余省份均呈现缓慢上升的趋势。

     

    Abstract: It is essential for the transformation of low-carbon agricultural practices and for achieving the national dual-carbon goals to study the spatial-temporal variation of agricultural carbon emissions and the factors influencing these emissions in Southwest China. We calculated agricultural carbon emissions, and explored the structure, intensity, and spatial-temporal variation of agricultural carbon emissions in the five provinces of Southwest China from 2005 to 2023. Moreover, the LMDI model was employed to identify the driving factors of agricultural carbon emissions across both the entire southwestern region and its individual provinces. The grey prediction model GM(1,1) was used to forecast emissions for the next seven years. The results indicate that during from 2005 to 2023, total agricultural carbon emissions in Southwest China ranged from 32. 1939 to 43. 1515 million tons, exhibiting an overall fluctuating upward trend, while the carbon emission intensity exhibited a V-shaped fluctuating decline. Throughout this period, the Tibet Autonomous Region consistently maintained Level V emission intensity, whereas the other four provinces reduced their intensity from Level V in 2005 to Level I in 2023. There were notable regional disparities, with Sichuan Province having the highest emissions, followed by Yunnan, Guizhou, Chongqing, and Tibet. Livestock farming constituted the predominant carbon source (48. 99% of annual average), followed by cropland soil utilization (26. 20%) and agricultural inputs (24. 80%). Agricultural production efficiency and labor scale had an inhibiting effect on emissions, whereas the level of agricultural economic development acted as a promoting factor; the impact of agricultural industrial structure varied contingent upon the rationality of provincial production systems. Projections suggest emissions will maintain growth during 2024—2030 with an annual average increase of 132, 900 tons relative to 2023 levels, with Sichuan demonstrating declining trends despite its high baseline emissions while other provinces exhibit gradual increases.

     

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