黄河流域农业碳排放时空演化特征及其影响因素分析

doi:10.12118/j.issn.1000-6060.2025.240

摘要/Abstract

摘要：

为深入探讨黄河流域农业碳排放的时空分布特征，采用联合国政府间气候变化专门委员会提供的碳排放因子，计算2013—2023年黄河流域各省（区）农业碳排放总量。运用莫兰自相关指数（Moran’s I）分析其空间自相关性，借助对数平均迪氏指数（Logarithmic mean divisia index，LMDI）对农业碳排放的影响因素进行定量分解，深入探讨了各因素对碳排放的驱动与抑制作用。结果表明：（1） 2013—2023年黄河流域农业碳排放总量呈现“缓慢上升-逐年下降-略有回升”趋势。空间上呈现出“南北高、中部低”的分布格局。（2）各省（区）农业碳排放强度总体呈下降趋势。（3）全局Moran’s I除2016年和2017年外，整体呈现出显著的正空间相关性，且这一空间集聚效应逐年增强。局部Moran’s I散点图进一步证实农业碳排放强度在该地区的显著空间自相关性。（4）经济效应和结构效应对农业碳排放具有正向驱动作用，而人口效应、产业效应和技术效应则对碳排放具有负向抑制作用。识别农业碳排放的主导因素，从农业生产的各个环节有效抑制碳排放，从而进行精准碳减排。

关键词: 时空演变, 农业碳排放, 空间自相关性, LMDI模型, 影响因素, 黄河流域

Abstract:

To investigate the spatial and temporal distribution characteristics of agricultural carbon emissions in the Yellow River Basin, this study calculates the total agricultural carbon emissions of the provinces (autonomous regions) in the Yellow River Basin from 2013 to 2023 using carbon emission factors provided by the Intergovernmental Panel on Climate Change. The intensity of agricultural carbon emissions in each province (autonomous region) is determined, spatial autocorrelation is examined using the Moran’s index, and the influencing factors of agricultural carbon emissions are quantitatively decomposed using the logarithmic mean Divisia index (LMDI) method, in order to further explore the driving and inhibiting effects of these factors on carbon emissions. The results show that (1) From 2013 to 2023, agricultural carbon emissions in the nine provinces (autonomous regions) of the Yellow River Basin generally exhibited a trend of “slow increase-year-by-year decline-slight rebound”. Animal husbandry is the dominant source of agricultural carbon emissions. The total agricultural carbon emissions display a spatial distribution pattern characterized by “high in the north and south, low in the center”, with relatively high emissions in Inner Mongolia Autonomous Region, Sichuan Province, Henan Province, and Shandong Province, and relatively low emissions in Qinghai Province, Gansu Province, Ningxia Hui Autonomous Region, Shaanxi Province, and Shanxi Province. (2) The overall trend of agricultural carbon emission intensity in each province (autonomous region) is declining; however, the intensity in Qinghai Province has remained consistently high. (3) The global Moran’s index indicates significant positive spatial autocorrelation of agricultural carbon emission intensity in the Yellow River Basin overall, except in 2016 and 2017, and this spatial clustering effect has strengthened year by year. The local Moran scatter plots further confirm that most provinces are located in the first and third quadrants, with only a few outliers, revealing significant spatial autocorrelation of agricultural carbon emission intensity in the region. (4) The LMDI decomposition results indicate that economic and structural effects exert positive driving effects on agricultural carbon emissions, whereas demographic, industrial structure, and technological effects exert negative inhibitory effects on carbon emissions.

Key words: spatial and temporal evolution, agricultural carbon emissions, spatial autocorrelation, LMDI model, influencing factors, Yellow River Basin

陈文宣, 陈钰, 王生隆. 黄河流域农业碳排放时空演化特征及其影响因素分析[J]. 干旱区地理, 2026, 49(4): 713-726.

CHEN Wenxuan, CHEN Yu, WANG Shenglong. Characteristics of spatial and temporal evolution of agricultural carbon emissions in the Yellow River Basin and analysis of its influencing factors[J]. Arid Land Geography, 2026, 49(4): 713-726.

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碳源指标	碳排放系数	参考来源
化肥/kg·kg^-1	0.8956	美国橡树岭国家实验室^[23]
农药/kg·kg^-1	4.9341	美国橡树岭国家实验室^[23]
农用薄膜/kg·kg^-1	5.1800	南京农业大学农业资源与环境研究所^[24]
农用柴油/kg·kg^-1	0.5927	政府间气候变化专门委员会^[24]
农业播种面积/kg·hm^-2	3.1260	中国农业大学生物与技术学院^[9]
农业灌溉面积/kg·hm^-2	25.0000	政府间气候变化专门委员会^[24]

碳源指标/kg·头·a^-1	CH₄排放系数		N₂O排放系数	参考来源
碳源指标/kg·头·a^-1	肠胃发酵	粪便发酵	N₂O排放系数	参考来源
猪	1	3.50	0.53	[13]
牛	51.4	1.50	1.39	[13]
马	18	1.64	1.39	[13]
驴	10	0.90	1.39	[13]
骡	10	0.90	0.33	[13]
山羊	5	0.17	0.33	[13]
绵羊	5	0.15	0.33	[13]
骆驼	46	1.92	1.39	[13]

年份	碳源类型	青海	甘肃	四川	宁夏	内蒙古	山西	陕西	河南	山东
2013	种植业	17.62	233.84	357.66	60.88	288.02	170.34	302.33	858.98	782.27
2013	畜牧业	1175.06	1434.61	3471.31	322.09	2602.20	1171.44	647.53	3208.48	2162.11
2014	种植业	17.83	244.33	358.09	59.47	315.08	170.28	292.35	866.93	769.98
2014	畜牧业	1175.21	1507.10	3272.89	343.33	2708.24	1290.98	669.73	3237.93	2152.94
2015	种植业	18.42	251.19	358.85	60.06	327.34	168.73	295.58	874.41	760.09
2015	畜牧业	1173.02	1489.88	3338.68	342.35	2825.23	1259.46	654.13	3259.38	2164.02
2016	种植业	17.37	240.62	357.96	60.55	334.26	167.45	297.29	872.01	749.22
2016	畜牧业	1212.94	1462.58	3429.76	350.57	2724.78	1184.21	637.88	3133.79	2118.10
2017	种植业	17.55	221.40	349.94	60.27	331.29	162.21	297.05	856.25	722.12
2017	畜牧业	1298.66	1350.08	3107.37	345.72	2806.74	1344.22	693.48	2219.99	1926.75
2018	种植业	16.78	207.57	336.36	57.78	320.40	157.93	294.09	835.12	687.97
2018	畜牧业	1230.54	1385.44	2992.52	360.69	2707.25	1340.85	686.54	2219.01	1884.53
2019	种植业	14.88	199.67	324.47	58.04	314.55	155.27	270.69	805.35	649.94
2019	畜牧业	1183.50	1419.56	2675.19	396.05	2700.21	1171.44	663.67	1960.92	1644.22
2020	种植业	14.03	197.85	309.60	58.04	302.50	153.49	270.88	784.76	627.47
2020	畜牧业	1471.29	1543.07	2998.60	470.77	2825.57	1421.10	693.17	2622.94	1622.45
2021	种植业	13.46	197.00	304.40	58.23	339.18	150.15	269.75	753.40	606.15
2021	畜牧业	1464.99	1670.05	3009.79	538.74	2947.54	1421.10	701.57	2798.56	1674.73
2022	种植业	13.24	199.41	301.01	58.41	329.29	146.51	264.12	714.97	586.54
2022	畜牧业	1458.73	1742.42	3043.93	585.43	3107.02	1927.73	708.46	2840.39	1639.12
2023	种植业	13.02	198.13	297.64	58.94	327.63	144.55	264.04	696.10	568.13
2023	畜牧业	1431.97	1833.69	3162.77	614.18	3339.66	1955.51	716.16	2700.07	1534.57

年份	全局Moran’s I	Z值
2013	0.011^***	2.814
2014	0.019^***	2.838
2015	0.000^***	2.580
2016	-0.011^***	2.466
2017	-0.042^**	1.477
2018	0.004^**	1.754
2019	0.091^*	1.466
2020	0.047^*	1.316
2021	0.083^*	1.483
2022	0.094^*	1.460
2023	0.103^*	1.484

年份	农业碳排放变化量	人口效应	经济效应	产业效应	结构效应	技术效应
2014	104.46	-620.33	2051.37	-547.89	-58.56	-720.11
2015	168.14	-866.50	1901.86	-678.55	95.57	-284.24
2016	-269.50	-938.67	2305.93	-833.45	-85.63	-717.67
2017	-1240.22	-856.05	2811.66	-1536.59	-101.71	-1557.53
2018	-389.74	-640.18	2372.28	-833.80	-72.57	-1215.47
2019	-1113.74	-610.45	1786.57	213.33	73.09	-2576.30
2020	1779.97	-848.12	1288.47	1717.00	130.11	-507.48
2021	883.57	-652.24	3264.75	-1389.37	145.89	-485.46
2022	395.55	778.05	477.07	34.54	-24.23	-869.88
2023	190.05	-125.97	937.91	-620.17	38.34	-40.04
总效应	508.55	-5380.50	19197.92	-4474.96	140.29	-8974.20