薛媛, 李春华, 李静雯, 吕慧, 赖清芸, 康芝琳, 姚鹏, 李家会. 中国农业碳排放时空特征及驱动因素分析[J]. 中国生态农业学报 (中英文), 2024, 32(10): 1−13. DOI: 10.12357/cjea.20240038
引用本文: 薛媛, 李春华, 李静雯, 吕慧, 赖清芸, 康芝琳, 姚鹏, 李家会. 中国农业碳排放时空特征及驱动因素分析[J]. 中国生态农业学报 (中英文), 2024, 32(10): 1−13. DOI: 10.12357/cjea.20240038
XUE Y, LI C H, LI J W, LV H, LAI Q Y, KANG Z L, YAO P, LI J H. Analysis of spatial and temporal characteristics and drivers of agricultural carbon emissions in China[J]. Chinese Journal of Eco-Agriculture, 2024, 32(10): 1−13. DOI: 10.12357/cjea.20240038
Citation: XUE Y, LI C H, LI J W, LV H, LAI Q Y, KANG Z L, YAO P, LI J H. Analysis of spatial and temporal characteristics and drivers of agricultural carbon emissions in China[J]. Chinese Journal of Eco-Agriculture, 2024, 32(10): 1−13. DOI: 10.12357/cjea.20240038

中国农业碳排放时空特征及驱动因素分析

Analysis of spatial and temporal characteristics and drivers of agricultural carbon emissions in China

  • 摘要: 为探究中国农业碳排放的时空分布特征及驱动因素, 基于2000—2021年中国31个省(自治区、直辖市, 不包括香港、澳门和台湾, 下同)农业年鉴数据, 考察水利用、土地利用和能源消耗的碳排放, 利用联合国政府间气候变化专门委员会(IPCC)碳排放因子建立2000—2021年水、土地和能源3个子系统相关变量, 计算各省(自治区、直辖市)农业年碳排放总量, 结合莫兰指数对农业碳排放时空演变趋势及空间关联特征进行分析, 并运用对数均值迪氏分解法(Logarithmic Mean Divisia Index, 简称LMDI)探析农业碳排放的主要驱动因素。结果表明: 1)从时序变化看, 农业碳排放量整体呈倒“V”型变化趋势。2)碳排放量较大的省(自治区、直辖市)主要集中在黄淮海区域以及中部平原地区, 西部地区与中心地级市农业碳排放量较少; 从农业碳排放来源看, 化肥碳排放量占比最高; 土地资源及水资源条件好的地区对应的农业碳排放量大; 农业碳排放具有碳排量高的地区往北边蔓延趋势, 在空间上具有集聚效应并随时间推移显著性降低; 河南、安徽、山东等省份(自治区、直辖市)具有显著的“高-高集聚”; 北京、天津、青海等省份(自治区、直辖市)具有显著的“低-低集聚”。3)农业水资源经济产出因素和农业劳动力密集度因素为正向驱动因素, 农业水资源经济产出因素为中国农业碳排放增加的最主要的原因; 农业生产效率因素、劳动力规模因素和农业水土匹配度因素为碳排放负向驱动因素, 其中农业生产效率因素的碳减排贡献率最高, 为中国农业碳排放减少的最主要驱动因素。基于以上结果, 本文针对中国农业减排决策提出以下建议: 政府应加大对低碳农业的投入, 支持新型肥料和新能源农机的研发, 提高水土资源利用效率。同时, 要利用农业碳排放的集聚效应, 推动农业集中发展和区域间合作, 普及理念, 培养新型农业人才。

     

    Abstract: Against the background of global warming, China has taken a large number of emission reduction measures. The in-depth discussions of the sources, structure, drivers and emission reduction strategies of agricultural carbon emissions is of great significance in promoting the low-carbon transformation of China’s agricultural industry. In order to explore the spatial and temporal distribution characteristics and driving factors of China’s agricultural carbon emissions, the study used the agricultural yearbook data of 31 provinces (autonomous regions and municipalities, not including Hong Kong, Macao, and Taiwan of China, the same below) in China from 2000 to 2021, excluding Taiwan, Hong Kong and Macao. The study investigated the carbon emissions of water use, land use, and energy consumption, and used IPCC carbon emission factors to establish the carbon emissions of water, land and energy consumption from 2000 to 2021, and selecting the carbon sources of chemical fertilizers, pesticides, agricultural films, diesel oil, irrigation, and ploughing. The three subsystem-related variables were used to calculate the total annual agricultural carbon emission of each province (autonomous region and municipality). Then analyzing the results of the carbon emissions from agriculture in terms of uncertainty using Monte Carlo simulation. The spatiotemporal evolution trend and spatial correlation characteristics of agricultural carbon emission were analyzed by combining the Moran’s index. The main driving factors of agricultural carbon emission were analyzed by using Logarithmic Mean Divisia Index (LMDI). The results show that: 1) From the perspective of time-ordered change, the overall trend of agricultural carbon emissions is inverted “V”-shaped. 2) The provinces with large carbon emissions are mainly concentrated in the Huang-Huai-Hai Region and the central plain, while the western region and the central prefecture-level cities have less agricultural carbon emissions. From the perspective of agricultural carbon emission sources, chemical fertilizer carbon emissions account for the highest proportion. The areas with good land resources and water resources conditions correspond to large agricultural carbon emissions. Changes in areas with high carbon emissions tend to expand to the north. Henan, Anhui and Shandong and other provinces (autonomous regions and municipalities) show significant high clustering effect; Beijing, Tianjin, Qinghai and other provinces (autonomous regions and municipalities) show significant low clustering effect. 3) The economic output factor of agricultural water resources and the factor of agricultural labor intensity are positive driving factors, while the economic output factor of agricultural water resources is the most important reason for the increase of agricultural carbon emissions in China. The agricultural production efficiency factor, the labor scale factor, and the agricultural water-land matching factor are negative driving factors of carbon emissions. Among these factors, the agricultural production efficiency factor has the highest contribution rate to carbon emission reduction, which is the most important driving factor for reducing agricultural carbon emissions in China. The findings of the study provide recommendations for China's decision-making on agricultural emissions reduction: the government should increase investment in low-carbon agriculture, support the research and development of new fertilizers and agricultural machinery, improve the efficiency of soil and water resources, and enhance the quality of labor. At the same time, it is necessary to take advantage of the agglomeration effect of agricultural carbon emissions to promote centralized agricultural development and inter-regional cooperation, popularize the concept, and cultivate new agricultural talents.

     

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