Spatial-temporal differentiation characteristics and key driving factors of agricultural carbon emissions in the three northeastern provinces of China
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摘要: 推动农业低碳发展是应对气候威胁和农业面源污染的有效途径。本文基于IPCC和农用物资投入数据核算2000—2019年东北三省农业碳排放, 利用空间自相关等方法分析其时空分异特征, 通过LMDI指数分解模型和地理探测器探究农业碳排放驱动因素及其交互作用关系。结果表明: 1)东北三省2015年农业碳排放总量达到峰值, 约为1759.66万t, 较2000年(1048.19万t)增加67.88%, 年均递增4.53%; 研究期整体呈现“先上升、后下降”的态势, 碳排放增量变动可划分为“波动上升期(2000—2009年)—过渡期(2010—2015年)—平稳下降期(2016—2019年)”3个阶段。化肥施用是主要碳源, 占比75.12%。2)分解模型测算结果表明, 农业生产效率、农业产业结构和农业劳动力规模对碳排放具有抑制作用, 其碳减排比例分别为207.31%、21.56%、20.72%; 农业经济发展水平对碳排放表现出较强的推动作用, 实现349.59%的碳增量。3)相较于单因子来说, 农业经济发展水平、农业生产效率与农业产业结构之间交互结果对农业碳排放的影响呈非线性增强特征, 农业劳动力规模与其他因素叠加均呈现出双因子增强的作用效果。以上研究结果表明东北三省农业碳排放受周边地区影响且影响程度不断加强, 同时碳排放关键驱动因素之间存在协同作用。本研究成果为推动农业低碳发展提供理论基础与政策依据。Abstract: The climate problem caused by increasing carbon dioxide emissions is one of the major challenges facing the world today. Promoting low-carbon development of agriculture is an effective way to deal with climate threats and agricultural non-point source pollution. Accurately measuring the effect of agricultural carbon emission and its spatial and temporal evolution characteristics is the data basis for promoting low-carbon development of agriculture, and studying the key driving factors of agricultural carbon emission and the trade-off and coordination relationship between driving factors is of great significance for formulating regional carbon emission reduction policies in Northeast China. Based on the input data of agricultural materials and the IPCC method, this paper calculates the agricultural carbon emissions of the three northeastern provinces of China from 2000 to 2019, uses the spatial autocorrelation analysis method to clarify the spatial and temporal differentiation characteristics of agricultural carbon emissions, and explores the driving factors of agricultural carbon emissions and their interaction through the LMDI index decomposition model and geographic detector. The results show that 1) the total carbon emissions of the three northeastern provinces of China showed an increasing trend first and then decreased. The incremental changes of carbon emissions can be divided into three stages: fluctuating rising period (2000−2009), transitional period (2010−2015) and steady decline period (2016−2019). In 2015, the total amount of agricultural carbon emissions reached a peak of 17.5966 million tons (an increase of 67.88% compared to 2000), with an average annual increase of 4.53%. During the study period, all carbon sources showed different degrees of growth, and chemical fertilizer application was the main carbon source, accounting for 75.12%. 2) The spatial distribution of total carbon emissions in the three northeastern provinces has significant spatial autocorrelation. The hot spots of carbon emissions are mainly distributed in the northeast plain area, and the trend and scope of agglomeration are expanding. The cold spots of carbon emissions are mainly distributed in the Changbai Mountains and Daxing’an mountains and do not change significantly with time. 3) The total amount of agricultural carbon emissions in the three northeastern provinces is affected by many factors. The improvement of agricultural production efficiency, the optimization of agricultural industrial structure and the reduction of agricultural labor force have an inhibitory effect on carbon emissions, and the proportion of carbon emission reduction is 207.31%, 21.56% and 20.72%, while the level of agricultural economic development has a strong driving effect on carbon emissions, achieving 349.59% carbon increment. The interaction between the level of agricultural economic development, agricultural production efficiency and agricultural structure is more nonlinear than the influence of single factor on carbon emissions. The driving force of the paired combination of agricultural labor force size and other factors exhibits a two-factor enhancement effect. The research results reveal that the carbon emission effect of the three northeastern provinces is easily affected by the surrounding areas and the degree of the influences is increasing. At the same time, there is a strong synergy between the driving factors of carbon emissions.
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图 1 2000—2019年东北三省农业碳排放总量与强度及其环比增速的历史变化
Figure 1. Historical changes of total agricultural carbon emissions and carbon emission intensity of the three northeastern provinces of China and their monthly growth rate from 2000 to 2019
图 2 2000—2019年东北三省农业6个碳源的农业碳排放量历史变化
Figure 2. Historical changes of carbon emissions of six carbon sources in agriculture in the three northeastern provinces of China from 2000 to 2019
图 3 东北三省在特征年份农业碳排放总量的LISA集聚图
Figure 3. The LISA agglomeration map of the total amount of agricultural carbon emissions in the three northeastern provinces of China in the characteristic years
图 4 2001—2019年东北三省农业碳排放驱动因素对农业碳排放量贡献率
APE: 农业生产效率; AIS: 农业产业结构; AEDL: 农业经济发展水平; ALFS: 农业劳动力规模。
Figure 4. The contribution rate of driving factors of agricultural carbon emissions to agricultural carbon emissions in the three northeastern provinces of China from 2001 to 2019
APE: agricultural production efficiency; AIS: agricultural industrial structure; AEDL: agricultural economic development level; ALFS: agricultural labor force size.
表 1 农业碳源碳排放系数
Table 1. Carbon emission factors of different agricultural carbon sources
碳源
Carbon source排放系数
Emission factor包含的过程
Processes included数据来源
Data source农药
Pesticides4.9341 kg(CO2eq)∙kg−1 生产、运输和使用
Production, transportation and use美国橡树岭国家实验室[24]
Oak Ridge National Laboratory, USA [24]化肥
Chemical fertilizer0.8956 kg(CO2eq)∙kg−1 West, et al[25]、美国橡树岭国家实验室[24]
West, et al[25] and Oak Ridge National Laboratory, USA [24]农膜
Agricultural film5.1800 kg(CO2eq)∙kg−1 政府间气候变化专门委员会[26]
Intergovernmental Panel on Climate Change[26]灌溉
Irrigation20.4760 kg(CO2eq)∙hm−2 农作物实际灌溉面积
Actual irrigated area of crops湖北农村发展研究中心[27]
Rural Development Research Center of Hubei[27]翻耕
Ploughing3.1260 kg(CO2eq)∙km−2 农作物实际播种总面积
Actual sown area of crops中国农业大学农学与生物技术学院[28]
College of Agronomy and Biotechnology, China Agricultural University[28]农业机械
Agricultural machinery0.5927 kg(CO2eq)∙kg−1 农用机械消耗柴油量
Diesel fuel consumption by agricultural machinery政府间气候变化专门委员会[26]
Intergovernmental Panel on Climate Change[26]
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表 2 驱动因素交互作用结果类型
Table 2. Types of interaction between two covariates
交互作用
Interaction判别依据
Distinguish basis非线性减弱
Non-linear weakeningq(Xi∩Xj) < Min[q(Xi), q(Xj)] 单因子非线性减弱
Single factor nonlinear weakeningMin[q(Xi), q(Xj)] < q(Xi∩Xj) <
Max[q(Xi), q(Xj)]双因子增强
Two-factor enhancementq(Xi∩Xj) > Max[q(Xi), q(Xj)] 相互独立
Independentq(Xi∩Xj) = q(Xi) + q(Xj) 非线性增强
Non-linear enhancementq(Xi∩Xj) > q(Xi) + q(Xj) Xi和Xj: 农业碳排放关键驱动因素; Min[q(Xi), q(Xj)]: 取q(Xi)和q(Xj)的最小值; Max[q(Xi), q(Xj)]: 取q(Xi)和q(Xj)的最大值; q(Xi) + q(Xj): q(Xi)和q(Xj)求和; q(Xi∩Xj): q(Xi)和q(Xj)交互。Xi, Xj: key driving factors of agricultural carbon emissions; Min[q(Xi), q(Xj)]: minimum value of q(Xi) and q(Xj) ; Max[q(Xi), q(Xj)]: maximum value of q(Xi) and q(Xj); q(Xi) + q(Xj): sum of q(Xi) and q(Xj); q(Xi∩Xj): interaction between q(Xi) and q(Xj).
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表 3 2000—2019年东北三省农业碳排放总量的全域莫兰指数(Moran’s I)与检验
Table 3. Global Moran’s I and test of total agricultural carbon emissions in the three northeastern provinces from 2000 to 2019
2000 2004 2008 2012 2016 2019 P值 P value 0.001 0.001 0.001 0.001 0.001 0.001 Moran’s I 0.8334 0.8787 0.8807 0.9081 0.9010 0.9419
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表 4 2019年东北三省各地级市农业碳排放总量、各碳源排放占比及碳排强度
Table 4. Total amount of agricultural carbon emissions, the proportion of carbon emissions from each carbon source and carbon emission intensity of each city in the three northeastern provinces of China in 2019
市
City碳排放总量
Total carbon
emissions
(×104 t)占比
Proportion
(%)碳排放源排放量占比
Proportion of carbon emissions from each sources (%)碳排放强度
Carbon emission
intensity (kg·hm−2)农药
Pesticides农膜
Agricultural film化肥
Chemical fertilizer农业机械
Agricultural
machinery农业灌溉
Irrigation农业翻耕
Ploughing沈阳 Shenyang 74.83 4.69 2.39 13.16 77.84 5.56 0.77 0.28 870.89 大连 Dalian 48.73 3.05 9.60 16.78 55.05 18.04 0.33 0.20 1231.01 鞍山 Anshan 28.51 1.78 5.19 11.29 76.42 6.25 0.57 0.28 970.24 抚顺 Fushun 14.21 0.89 5.19 11.29 76.40 6.25 0.59 0.28 855.70 本溪 Benxi 6.60 0.41 5.19 11.29 76.42 6.25 0.57 0.28 732.46 丹东 Dandong 23.44 1.47 5.18 11.27 76.29 6.24 0.75 0.28 930.44 锦州 Jinzhou 51.27 3.21 5.18 11.27 76.24 6.23 0.80 0.28 1070.44 营口 Yingkou 12.95 0.81 5.15 11.22 75.89 6.20 1.26 0.28 848.86 阜新 Fuxin 52.90 3.31 5.19 11.29 76.40 6.25 0.59 0.28 1229.91 辽阳 Liaoyang 17.73 1.11 5.17 11.26 76.16 6.23 0.90 0.28 770.72 盘锦 Panjin 15.70 0.98 5.15 11.21 75.82 6.20 1.35 0.28 920.20 铁岭 Tieling 56.67 3.55 5.18 11.28 76.34 6.24 0.67 0.28 903.45 朝阳 Chaoyang 52.25 3.27 5.18 11.27 76.26 6.23 0.78 0.28 993.86 葫芦岛 Huludao 29.42 1.84 5.19 11.30 76.43 6.25 0.56 0.28 1005.92 长春 Changchun 108.64 6.80 4.37 4.80 82.98 7.00 0.48 0.38 691.26 吉林 Jilin 64.33 4.03 5.26 6.66 78.47 8.70 0.51 0.38 980.86 四平 Siping 71.45 4.47 4.13 2.33 86.42 6.11 0.56 0.44 759.66 辽源 Liaoyuan 20.24 1.27 4.84 6.13 80.44 8.01 0.23 0.35 1046.50 通化 Tonghua 30.49 1.91 5.03 6.37 79.23 8.32 0.69 0.37 984.44 白山 Baishan 3.94 0.25 7.87 9.95 68.49 13.00 0.12 0.57 605.94 松原 Songyuan 87.59 5.48 5.91 7.48 75.10 9.78 1.29 0.43 721.66 白城 Baicheng 65.92 4.13 6.99 8.85 70.31 11.56 1.79 0.51 801.15 延边 Yanbian 21.56 1.35 8.62 10.91 64.91 14.26 0.67 0.63 631.03 哈尔滨 Harbin 113.00 7.08 4.84 7.11 77.60 8.45 1.46 0.53 507.38 齐齐哈尔 Qiqihar 92.04 5.76 5.26 4.99 72.58 14.44 1.97 0.75 336.73 鸡西 Jixi 14.73 0.92 5.45 6.56 68.86 15.33 2.83 0.97 311.27 鹤岗 Hegang 9.97 0.62 3.44 4.14 79.35 9.68 2.77 0.61 485.46 双鸭山 Shuangyashan 16.01 1.00 4.38 5.27 75.94 12.32 1.31 0.78 317.74 大庆 Daqing 34.89 2.18 3.58 4.31 78.22 10.07 3.18 0.64 439.21 伊春 Yichun 7.55 0.47 5.32 6.39 70.74 14.94 1.66 0.94 346.28 佳木斯 Jiamusi 67.11 4.20 8.28 8.25 60.10 20.98 1.87 0.51 560.68 七台河 Qitaihe 7.67 0.48 3.95 4.75 78.87 11.11 0.61 0.70 403.39 牡丹江 Mudanjiang 21.50 1.35 5.07 6.09 72.59 14.24 1.11 0.90 342.53 黑河 Heihe 33.89 2.12 6.24 7.50 67.07 17.53 0.56 1.11 355.02 绥化 Suihua 92.16 5.77 3.48 4.19 80.59 9.79 1.34 0.62 419.72 大兴安岭
Da Hinggan Ling Prefecture2.77 0.17 10.68 12.85 43.86 30.03 0.69 1.90 180.32 黑龙江农垦总局
Farms & Land Reclamation Administration in Heilongjiang124.41 7.79 3.95 4.75 76.34 11.10 3.16 0.70 1275.38 黑龙江农垦总局各指标数据是从各地级市指标中剥离出来的, 并独立统计。Data for each indicator in Farms & Land Reclamation Administration in Heilongjiang are separated from that in different cities, and collected independently.
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表 5 2001—2019年东北三省农业碳排放驱动因素分解
Table 5. Decomposition of driving factors of agricultural carbon emissions in the three northeastern provinces of China from 2001 to 2019
年份
Year农业生产效率
Agricultural production
efficiency (×104t)农业产业结构
Agricultural industrial
structure (×104t)农业经济发展水平
Agricultural economic
development level (×104t)农业劳动力规模
Agricultural labour
force size (×104t)总效应
Total effect (×104t)2001 −126.46 −11.26 123.39 −13.58 −27.91 2002 −201.13 −3.76 −253.92 −30.19 −489.00 2003 −210.69 −148.95 448.03 −45.57 42.82 2004 −365.28 −124.72 645.15 −127.31 27.83 2005 −406.57 −147.33 780.07 −132.04 94.13 2006 −496.11 −131.99 921.95 −131.47 162.38 2007 −606.68 −112.40 1132.58 −130.02 283.48 2008 −803.81 −150.48 1422.80 −136.41 332.10 2009 −806.27 −194.41 1614.31 −140.80 472.83 2010 −1053.43 −143.42 1832.20 −151.37 483.98 2011 −1294.83 −120.52 2151.57 −175.59 560.63 2012 −1478.83 −91.28 2369.80 −43.65 756.04 2013 −1559.58 −115.23 2547.68 507.85 1380.72 2014 −1655.41 −33.55 2648.58 −210.04 749.57 2015 −1627.33 −95.59 2666.12 −149.23 793.96 2016 −1529.74 −110.73 2625.18 −166.57 818.15 2017 −1380.87 −124.77 2502.60 −183.27 813.69 2018 −1522.39 −39.19 2678.61 −198.45 918.58 2019 −1571.42 −45.24 2672.12 −210.61 844.86
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表 6 交互探测器结果
Table 6. Results of interactive detector
交互因子 Interacting factor AEDL∩APE AEDL∩AIS ALFS∩APE ALFS∩AIS ALFS∩AEDL APE∩AIS q值 q value 0.82** 0.64** 0.92* 0.91* 0.90* 0.38* APE: 农业生产效率; AIS: 农业产业结构; AEDL: 农业经济发展水平; ALFS: 农业劳动力规模; ∩: 两者交互; *: 双因子增强; **: 非线性增强。双因子增强的交互作用弱于非线性增强的交互作用。APE: agricultural production efficiency; AIS: agricultural industrial structure; AEDL: agricultural economic development level; ALFS: agricultural labor force size; ∩: interaction between two factors; *: two-factor enhancement; **: non-linear enhancement. The interaction of tow-factor enhancement is weaker than that of non-linear enhancement.
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