中国农业碳排放时空演进及驱动因素

Spatiotemporal evolution and driving factors of China’s agricultural carbon emissions

  • 摘要: 本文基于种植业和畜牧业数据, 利用IPCC碳核算方法对2010—2021年中国30个省(直辖市、自治区, 不包括香港、澳门、台湾和西藏地区)的农业碳排放总量和强度进行测算。采用基尼系数地区分解法、结构分解法对农业碳排放强度的地区差异、结构差异进行分析, 并利用全局莫兰指数进行空间相关性讨论。此外, 利用对数均值迪氏分解法 (Logarithmic Mean Divisia Index, 简称LMDI)探讨农业碳排放的驱动因素, 分析农业生产技术、农业产业结构、农业发展规模、农业从业人员结构和劳动力规模对农业碳排放的影响。结果表明: 1)与2010年相比, 多数省份2021年农业碳排放总量和碳排放强度有所下降, 总体排名变化不大。2)按照粮食主产区、主销区和产销平衡区划分, 对碳排放强度进行基尼系数地区分解, 发现与2010年相比, 2021年基尼系数减小; 粮食主产区地区间农业碳排放差异最大, 粮食主销区地区间差异最小。结构分解结果表明, 水稻种植对碳排放强度贡献最大, 占70%以上。碳排放强度空间相关性不强。3)农业碳排放的关键抑制因素为农业生产技术和农业从业人员结构。分析农业碳排放特征及驱动因素有助于了解各省份特点, 制定差异化农业碳减排策略, 推动碳减排和农业可持续发展。

     

    Abstract: Relevant research on China’s agricultural carbon emission reduction is of great practical significance for optimizing the agricultural structure, improving agricultural production efficiency, and promoting sustainable agricultural development. Based on data from the planting and animal husbandry sectors, this study utilized the IPCC carbon accounting method to calculate the total amount and intensity of agricultural carbon emissions in the selected 30 provinces in China (excluding Hong Kong of China, Macao of China, Taiwan of China, and Xizang of China) from 2010 to 2021. The following five sources of agricultural carbon emissions were considered: rice cultivation, land tillage, agricultural material inputs, energy use, and animal husbandry. The Dagum Gini coefficient regional decomposition and equal-weighted Gini coefficient structural decomposition methods were used to analyze the regional and structural differences. This study also discussed the spatial correlation of agricultural carbon emission intensity using the global Moran’s Index method. Furthermore, influencing factor decomposition was conducted based on the Logarithmic Mean Divisia Index (LMDI). This method was employed to explore the factors driving agricultural carbon emissions by analyzing the effects of agricultural production technology, agricultural industry structure, agricultural development scale, agricultural employment structure, and labor force scale on agricultural carbon emissions. The results indicate the following: 1) Compared to 2010, the total agricultural carbon emissions and agricultural carbon emission intensity of most provinces have declined in 2021, with minor changes in the overall rankings. 2) The Gini coefficient regional decomposition revealed that a decrease in Gini coefficient in 2021 compared to 2010. This study analyzed regional differences in agricultural carbon emission intensity and concluded that the greatest disparity existed among main producing areas, while the smallest disparity was found among main sales areas. The structural decomposition results showed that rice cultivation contributed the most to carbon emission intensity, accounting for over 70%. The spatial correlation of carbon emissions intensity was weak. 3) The key factors inhibiting agricultural carbon emissions include agricultural production technology and agricultural employment structure. This study provided insight into the characteristics and factors influencing agricultural carbon emissions. This study aimed to assist provinces in conducting in-depth analyses of their agricultural development characteristics. These provinces should formulate different strategies for agricultural carbon reduction, thereby promoting carbon reduction and sustainable agricultural development.

     

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