Evaluation of agricultural carbon emissions in Xinjiang and analysis of driving factors based on machine learning algorithms
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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。Abstract: Agriculturl carbon emissions are the second-largest source of carbon in the world. Therefore, clarifying the patterns of agricultural carbon emissions is crucial for achieving carbon peaks and neutrality. To explore the law of agricultural carbon emissions in Xinjiang and promote agricultural carbon emission reduction, agricultural carbon emissions in Xinjiang were measured based on carbon emission coefficients published according to the carbon emission links generated in the process of agricultural production. Furthermore, spatial correlation models, such as the Moran and learned index structure for spatial data (LISA) indices, were used to measure the spatial clustering patterns of agricultural carbon emissions in Xinjiang. A random forest machine learning model was then used to quantitatively analyze the factors influencing the efficiency of agricultural carbon emissions. The results indicated that: 1) agricultural carbon emissions grew slowly from 2010 to 2019, from 292.24×04 t to 379.69×104 t, with an average annual growth rate of 3.33%. 2) Applications of chemical fertilizers and agricultural films were the main sources of agricultural carbon emissions in Xinjiang, accounting for 58.06% and 39.03%, respectively. 3) Xinjiang’s agricultural carbon emission efficiency increased steadily, with a faster growth from 2010 to 2013 and a slower growth from 2014 to 2019. The main distribution range of carbon emissions efficiency increased from less than 50 ¥∙t−1 to 50–100 ¥∙t−1. 4) The agricultural output values in the high-high agglomeration areas of Xinjiang with high agricultural carbon emission efficiency were relatively low because of the low material input. In contrast, the agricultural output values in the low-low agglomeration areas were relatively high, however, where the level of technology and management was low, and the material input was extremely high. The efficiency of agricultural carbon emissions in Xinjiang has room for improvement. 5) Overall agricultural carbon emission efficiency was higher in the southern region with lower precipitation, whereas the northern region with higher precipitation exhibited moderate emissions. Precipitation may indirectly affect agricultural carbon emission efficiency by affecting the level of agricultural development and production technology. 6) Carbon emission efficiency decreased sharply with increased agricultural scale when the agricultural scale was between 0.12 and 2.02 hm2 per person. Moreover, the influence on agricultural carbon emissions efficiency decreased when the agricultural scale exceeded 2.02 hm2 per person. There was a significant negative effect on agricultural carbon emission efficiency when cultivated land was between 120 and 17 220 hm2. In contrast, its’ effect on agricultural carbon emission efficiency was more moderate when cultivated land was larger than 17 220 hm2. Rural economic development level had a positive effect on carbon emission efficiency. Furthermore, carbon emission efficiency exhibited a “U” shaped pattern as a function of agricultural electrification degree. Comprehensively considering the two aspects of improving agricultural output value and agricultural carbon emission efficiency, the degree of agricultural scale and the scale of arable land should be further improved to increase agricultural output value, and the level of rural economic development and the degree of agricultural electrification should be further improved to increase the efficiency of agricultural carbon emissions.
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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
Fertilizer0.90 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 film5.18 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 machineryP×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 irrigation2.6648 kg(C)·hm−2 West, et al.[31] 由统计年鉴数据折算
Converted from Statistical Yearbook data农业翻耕
Agricultural ploughing3.1260 kg(C)·hm−2 黄华等[32]
Huang, et al.[32]统计年鉴
Statistical YearbookP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. 
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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 personnelhm2·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 areakW·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%
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