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基于机器学习算法的新疆农业碳排放评估及驱动因素分析

邓路 袁圣博 白萍 李会芳

邓路, 袁圣博, 白萍, 李会芳. 基于机器学习算法的新疆农业碳排放评估及驱动因素分析[J]. 中国生态农业学报 (中英文), 2023, 31(2): 265−279 doi: 10.12357/cjea.20220501
引用本文: 邓路, 袁圣博, 白萍, 李会芳. 基于机器学习算法的新疆农业碳排放评估及驱动因素分析[J]. 中国生态农业学报 (中英文), 2023, 31(2): 265−279 doi: 10.12357/cjea.20220501
DENG L, YUAN S B, BAI P, LI H F. Evaluation of agricultural carbon emissions in Xinjiang and analysis of driving factors based on machine learning algorithms[J]. Chinese Journal of Eco-Agriculture, 2023, 31(2): 265−279 doi: 10.12357/cjea.20220501
Citation: DENG L, YUAN S B, BAI P, LI H F. Evaluation of agricultural carbon emissions in Xinjiang and analysis of driving factors based on machine learning algorithms[J]. Chinese Journal of Eco-Agriculture, 2023, 31(2): 265−279 doi: 10.12357/cjea.20220501

基于机器学习算法的新疆农业碳排放评估及驱动因素分析

doi: 10.12357/cjea.20220501
基金项目: 新疆维吾尔自治区社科联重点项目(2021ZJFLZ17)资助
详细信息
    通讯作者:

    邓路, 研究方向为生态经济和农业经济。E-mail: 119311793@qq.com

  • 中图分类号: F323

Evaluation of agricultural carbon emissions in Xinjiang and analysis of driving factors based on machine learning algorithms

Funds: This study was supported by the Key Projects of Federation of Social Sciences in Xinjiang Uygur Autonomous Region of China (2021ZJFLZ17)
More Information
  • 摘要: 农业是全球第二大碳源, 明确农业碳排放规律对于碳达峰、碳中和具有重要意义。为探究新疆农业碳排放规律, 促进农业碳减排, 本研究根据农业生产过程中的碳排放环节, 结合国内外发布的碳排放系数, 测算了新疆的农业碳排放量; 利用莫兰指数、LISA指数等空间相关性模型测算了新疆农业碳排放的空间集聚规律; 利用机器学习中的随机森林模型对农业碳排效率影响因素进行了动态量化分析。结果显示: 1) 2010—2019年新疆农业碳排放量缓慢增长, 从292.24万t增长到379.69万t, 年均增速3.33%。2)化肥和农膜的使用是新疆农业碳排放的主要来源, 占比分别为58.06%和39.03%。3)新疆农业碳排放效率在不断提升, 2010—2013年增速较快, 2014—2019年增速较慢, 碳排放效率的主要分布区间从小于50元∙t−1变为50~100元∙t−1。4)新疆农业碳排放效率高高聚集区域农业产值不高, 主要是由于物质投入低; 低低聚集区域农业产值相对较高, 但科技、管理水平低, 物质投入过多。5)降水量较低的南疆区域, 农业碳排放效率整体较高, 降水量较高的北疆区域, 农业碳排放效率处于中等水平。6)农业规模化程度在0.12~2.02 hm2∙人−1时, 碳排放效率随着农业规模化程度提高急剧降低, 当农业规模化程度高于2.02 hm2∙人−1时, 对农业碳排放效率的影响力降低; 耕地规模在120~17 220 hm2时, 对农业碳排放效率有一个显著的负向影响, 当耕地规模大于17 220 hm2时, 对农业碳排放效率的影响较为平缓。农村经济发展水平对碳排放效率具有正向影响, 农业电器化程度对碳排放效率呈现出正“U”型影响。
  • 图  1  研究区概况

    Figure  1.  Studying area

    图  2  随机森林计算过程示意图

    Figure  2.  Schematic diagram of random forest calculation process

    图  3  2010—2019年新疆农业累积碳排放量

    Figure  3.  Cumulative carbon emissions from agriculture in Xinjiang from 2010 to 2019

    图  4  2010—2019年新疆农业碳排放结构

    Figure  4.  Xinjiang’s agricultural carbon emission structure from 2010 to 2019

    图  5  2010—2019年新疆农业碳排放效率时空格局演化过程

    Figure  5.  Spatiotemporal pattern of agricultural carbon emission efficiency in Xinjiang from 2010 to 2019

    图  6  2019年新疆农业碳排放效率的LISA集聚图

    Figure  6.  LISA agglomeration map of Xinjiang’s agricultural carbon emission efficiency in 2019

    图  7  决策树数量对袋外误差的影响

    Figure  7.  Effect of the number of decision trees on the out-of-bag error

    图  8  决策树候选变量(影响因素)个数对农业碳排放量预测均方误差的影响

    Figure  8.  Influence of the number of candidate affecting factors (variables) in decision tree on the mean square error of agricultural carbon emission prediction

    图  9  农业碳排放模型预测值与实际值对比

    Figure  9.  Comparison of model predicted value and actual value of agricultural carbon emission

    图  10  基于训练样本的农业碳排放效率的影响因素重要性(百分比越高, 重要性越大)

    Figure  10.  Importance of influcening factors of agricultural carbon emission efficiency based on training samples (the factor with higher percentage is more important)

    图  11  农业规模化程度(a)、农村经济发展水平(b)、耕地规模(c)、农业电气化程度(d)对农业碳排放效率的影响

    Figure  11.  Effects of process of large-scale agricultural production (a), rural economic development level (b), cultivated land scale (c) and degress of agricultural electrification (d) on agricultural carbon emission efficiency

    表  1  农业碳排放源及碳排放系数

    Table  1.   Agricultural carbon emission sources and carbon emission coefficients

    碳源
    Carbon source
    碳排放系数
    Carbon emission coefficient
    参考来源
    Reference source
    数据来源
    Data source
    化肥
    Fertilizer
    0.90 kg(C)·kg−1美国橡树岭国家实验室
    Oak Ridge National Laboratory, USA
    统计年鉴
    Statistical Yearbook
    农膜
    Agriculture film
    5.18 kg(C)·kg−1南京农业大学农业资源与生态环境研究所
    Institute of Agricultural Resources and Ecological Environment,
    Nanjing Agricultural University
    由统计年鉴数据折算
    Converted from Statistical Yearbook data
    农业机械
    Agricultural machinery
    P×16.47 kg(C)·hm−2+
    W×0.18 kg(C)·kW−1
    中国碳排放交易网
    China Carbon Emission Trading Network
    统计年鉴
    Statistical Yearbook
    农业灌溉
    Agricultural irrigation
    2.6648 kg(C)·hm−2West, et al.[31]由统计年鉴数据折算
    Converted from Statistical Yearbook data
    农业翻耕
    Agricultural ploughing
    3.1260 kg(C)·hm−2黄华等[32]
    Huang, et al.[32]
    统计年鉴
    Statistical Yearbook
      P为农业播种面积, W为农业机械总动力。P is the agricultural planting area, W is the total power of agricultural machinery.
    下载: 导出CSV

    表  2  农业碳排放影响因素指标体系

    Table  2.   Index system of influencing factors of agricultural carbon emission

    指标 Index度量方式 Measurement单位 Unit
    耕地规模
    Cultivated land scale
    农作物播种面积
    Crop planting area
    ×103 hm2
    非城镇化水平
    Non-urbanization level
    乡村人口/总人口
    Rural population/total population
    %
    农村经济发展水平
    Rural economic development level
    农业产值/从事农业人员
    Agricultural output/persons engaged in agriculture
    ×104·person−1
    农业规模化程度
    Process of large-scale argricultural production
    农作物播种面积/从事农业人员
    Crop sown area/agricultural personnel
    hm2·person−1
    农业电气化程度
    Agricultural electrification degree
    农村用电量/乡村人口
    Rural electricity consumption/rural population
    ×104 kWh·person−1
    经济发展水平
    Economic development level
    地区生产总值
    Regional GDP
    ×104 ¥·person−1
    农业机械化水平
    Agricultural mechanization level
    机械总动力/农作物播种面积
    Total mechanical power/crop planting area
    kW·hm−2

    产业结构高级化
    Advanced industrial structure
    第二、三产业/地区生产总值
    Secondary and tertiary industries/regional GDP
    %
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-06-29
  • 录用日期:  2022-09-12
  • 修回日期:  2022-09-12
  • 网络出版日期:  2022-11-07
  • 刊出日期:  2023-02-10

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