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东北三省农业碳排放时空分异特征及其关键驱动因素

钱凤魁 王祥国 顾汉龙 王大鹏 李鹏飞

钱凤魁, 王祥国, 顾汉龙, 王大鹏, 李鹏飞. 东北三省农业碳排放时空分异特征及其关键驱动因素[J]. 中国生态农业学报 (中英文), 2023, 31(0): 1−11 doi: 10.12357/cjea.20230225
引用本文: 钱凤魁, 王祥国, 顾汉龙, 王大鹏, 李鹏飞. 东北三省农业碳排放时空分异特征及其关键驱动因素[J]. 中国生态农业学报 (中英文), 2023, 31(0): 1−11 doi: 10.12357/cjea.20230225
QIAN F K, WANG X G, GU H L, WANG D P, LI P F. Spatial-temporal differentiation characteristics and key driving factors of agricultural carbon emissions in the three northeastern provinces of China[J]. Chinese Journal of Eco-Agriculture, 2023, 31(0): 1−11 doi: 10.12357/cjea.20230225
Citation: QIAN F K, WANG X G, GU H L, WANG D P, LI P F. Spatial-temporal differentiation characteristics and key driving factors of agricultural carbon emissions in the three northeastern provinces of China[J]. Chinese Journal of Eco-Agriculture, 2023, 31(0): 1−11 doi: 10.12357/cjea.20230225

东北三省农业碳排放时空分异特征及其关键驱动因素

doi: 10.12357/cjea.20230225
基金项目: 国家自然科学基金项目(42077149, 41671329)资助
详细信息
    作者简介:

    钱凤魁, 从事耕地保护与评价研究。E-mail: fkqian@163.com

    通讯作者:

    顾汉龙, 从事土地经济与资源利用研究。E-mail: allenguhan@126.com

  • 中图分类号: F301.21

Spatial-temporal differentiation characteristics and key driving factors of agricultural carbon emissions in the three northeastern provinces of China

Funds: This study was supported by the National Natural Science Foundation of China (42077149, 41671329).
More Information
  • 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。
  • 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
    Pesticides
    4.9341 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 fertilizer
    0.8956 kg(CO2eq)∙kg−1West, 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 film
    5.1800 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
    Irrigation
    20.4760 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
    Ploughing
    3.1260 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 machinery
    0.5927 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]
    下载: 导出CSV

    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 weakening
    q(Xi∩Xj) < Min[q(Xi), q(Xj)]
    单因子非线性减弱
    Single factor nonlinear weakening
    Min[q(Xi), q(Xj)] < q(Xi∩Xj) <
    Max[q(Xi), q(Xj)]
    双因子增强
    Two-factor enhancement
    q(Xi∩Xj) > Max[q(Xi), q(Xj)]
    相互独立
    Independent
    q(Xi∩Xj) = q(Xi) + q(Xj)
    非线性增强
    Non-linear enhancement
    q(Xi∩Xj) > q(Xi) + q(Xj)
      XiXj: 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).
    下载: 导出CSV

    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

    200020042008201220162019
    PP value0.0010.0010.0010.0010.0010.001
    Moran’s I0.83340.87870.88070.90810.90100.9419
    下载: 导出CSV

    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


    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 Shenyang74.834.692.3913.1677.845.560.770.28870.89
    大连 Dalian48.733.059.6016.7855.0518.040.330.201231.01
    鞍山 Anshan28.511.785.1911.2976.426.250.570.28970.24
    抚顺 Fushun14.210.895.1911.2976.406.250.590.28855.70
    本溪 Benxi6.600.415.1911.2976.426.250.570.28732.46
    丹东 Dandong23.441.475.1811.2776.296.240.750.28930.44
    锦州 Jinzhou51.273.215.1811.2776.246.230.800.281070.44
    营口 Yingkou12.950.815.1511.2275.896.201.260.28848.86
    阜新 Fuxin52.903.315.1911.2976.406.250.590.281229.91
    辽阳 Liaoyang17.731.115.1711.2676.166.230.900.28770.72
    盘锦 Panjin15.700.985.1511.2175.826.201.350.28920.20
    铁岭 Tieling56.673.555.1811.2876.346.240.670.28903.45
    朝阳 Chaoyang52.253.275.1811.2776.266.230.780.28993.86
    葫芦岛 Huludao29.421.845.1911.3076.436.250.560.281005.92
    长春 Changchun108.646.804.374.8082.987.000.480.38691.26
    吉林 Jilin64.334.035.266.6678.478.700.510.38980.86
    四平 Siping71.454.474.132.3386.426.110.560.44759.66
    辽源 Liaoyuan20.241.274.846.1380.448.010.230.351046.50
    通化 Tonghua30.491.915.036.3779.238.320.690.37984.44
    白山 Baishan3.940.257.879.9568.4913.000.120.57605.94
    松原 Songyuan87.595.485.917.4875.109.781.290.43721.66
    白城 Baicheng65.924.136.998.8570.3111.561.790.51801.15
    延边 Yanbian21.561.358.6210.9164.9114.260.670.63631.03
    哈尔滨 Harbin113.007.084.847.1177.608.451.460.53507.38
    齐齐哈尔 Qiqihar92.045.765.264.9972.5814.441.970.75336.73
    鸡西 Jixi14.730.925.456.5668.8615.332.830.97311.27
    鹤岗 Hegang9.970.623.444.1479.359.682.770.61485.46
    双鸭山 Shuangyashan16.011.004.385.2775.9412.321.310.78317.74
    大庆 Daqing34.892.183.584.3178.2210.073.180.64439.21
    伊春 Yichun7.550.475.326.3970.7414.941.660.94346.28
    佳木斯 Jiamusi67.114.208.288.2560.1020.981.870.51560.68
    七台河 Qitaihe7.670.483.954.7578.8711.110.610.70403.39
    牡丹江 Mudanjiang21.501.355.076.0972.5914.241.110.90342.53
    黑河 Heihe33.892.126.247.5067.0717.530.561.11355.02
    绥化 Suihua92.165.773.484.1980.599.791.340.62419.72
    大兴安岭
    Da Hinggan Ling Prefecture
    2.770.1710.6812.8543.8630.030.691.90180.32
    黑龙江农垦总局
    Farms & Land Reclamation Administration in Heilongjiang
    124.417.793.954.7576.3411.103.160.701275.38
      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.
    下载: 导出CSV

    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)
    2001−126.46−11.26123.39−13.58−27.91
    2002−201.13−3.76−253.92−30.19−489.00
    2003−210.69−148.95448.03−45.5742.82
    2004−365.28−124.72645.15−127.3127.83
    2005−406.57−147.33780.07−132.0494.13
    2006−496.11−131.99921.95−131.47162.38
    2007−606.68−112.401132.58−130.02283.48
    2008−803.81−150.481422.80−136.41332.10
    2009−806.27−194.411614.31−140.80472.83
    2010−1053.43−143.421832.20−151.37483.98
    2011−1294.83−120.522151.57−175.59560.63
    2012−1478.83−91.282369.80−43.65756.04
    2013−1559.58−115.232547.68507.851380.72
    2014−1655.41−33.552648.58−210.04749.57
    2015−1627.33−95.592666.12−149.23793.96
    2016−1529.74−110.732625.18−166.57818.15
    2017−1380.87−124.772502.60−183.27813.69
    2018−1522.39−39.192678.61−198.45918.58
    2019−1571.42−45.242672.12−210.61844.86
    下载: 导出CSV

    表  6  交互探测器结果

    Table  6.   Results of interactive detector

    交互因子 Interacting factorAEDL∩APEAEDL∩AISALFS∩APEALFS∩AISALFS∩AEDLAPE∩AIS
    qq value0.82**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.
    下载: 导出CSV
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  • 收稿日期:  2023-04-26
  • 录用日期:  2023-09-25
  • 网络出版日期:  2023-10-15

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