Evaluation of agricultural carbon emissions in Xinjiang and analysis of driving factors based on machine learning algorithms
-
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.
-
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. 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% -
[1] 政府间气候变化专门委员会(IPCC). 第四次报告[R]. https://www.ipcc.ch/.Intergovernmental Panel on Climate Change (IPCC). Fourth Report[R]. https://www.ipcc.ch/. [2] JOHNSON J M F, FRANZLUEBBERS A J, WEYERS S L, et al. Agricultural opportunities to mitigate greenhouse gas emissions[J]. Environmental Pollution, 2007, 150(1): 107−124 doi: 10.1016/j.envpol.2007.06.030 [3] THAMO T, KINGWELL R S, PANNELL D J. Measurement of greenhouse gas emissions from agriculture: economic implications for policy and agricultural producers[J]. Australian Journal of Agricultural and Resource Economics, 2013, 57(2): 234−252 doi: 10.1111/j.1467-8489.2012.00613.x [4] 李波, 张俊飚, 李海鹏. 中国农业碳排放时空特征及影响因素分解[J]. 中国人口·资源与环境, 2011, 21(8): 80−86LI B, ZHANG J B, LI H P. Research on spatial-temporal characteristics and affecting factors decomposition of agricultural carbon emission in China[J]. China Population, Resources and Environment, 2011, 21(8): 80−86 [5] 闵继胜, 胡浩. 中国农业生产温室气体排放量的测算[J]. 中国人口·资源与环境, 2012, 22(7): 21−27MIN J S, HU H. Calculation of greenhouse gases emission from agricultural production in China[J]. China Population, Resources and Environment, 2012, 22(7): 21−27 [6] 高鸣, 宋洪远. 中国农业碳排放绩效的空间收敛与分异−基于Malmquist-luenberger指数与空间计量的实证分析[J]. 经济地理, 2015, 35(4): 142−148, 185GAO M, SONG H Y. Dynamic changes and spatial agglomeration analysis of the Chinese agricultural carbon emissions performance[J]. Economic Geography, 2015, 35(4): 142−148, 185 [7] 田云, 张俊飚, 李波. 中国农业碳排放研究: 测算、时空比较及脱钩效应[J]. 资源科学, 2012, 34(11): 2097−2105TIAN Y, ZHANG J B, LI B. Agricultural carbon emissions in China: calculation, spatial-temporal comparison and decoupling effects[J]. Resources Science, 2012, 34(11): 2097−2105 [8] 田云, 张俊飚, 丰军辉, 等. 中国种植业碳排放与其产业发展关系的研究[J]. 长江流域资源与环境, 2014, 23(6): 781−791 doi: 10.11870/cjlyzyyhj201406006TIAN Y, ZHANG J B, FENG J H, et al. Relationship between planting industry carbon emissions and its industry development in China[J]. Resources and Environment in the Yangtze Basin, 2014, 23(6): 781−791 doi: 10.11870/cjlyzyyhj201406006 [9] 张颂心. 中国农业碳排放量测算及影响因素分析−基于省级面板数据的研究[J]. 湖北农业科学, 2021, 60(1): 60−64, 95ZHANG S X. Calculation of agricultural carbon emission and analysis of influencing factors in China: research based on the data of provincial panel[J]. Hubei Agricultural Sciences, 2021, 60(1): 60−64, 95 [10] 张丽琼, 何婷婷. 1997—2018年中国农业碳排放的时空演进与脱钩效应−基于空间和分布动态法的实证研究[J]. 云南农业大学学报(社会科学), 2022, 16(1): 78−90 doi: 10.12371/j.ynau(s).202103015ZHANG L Q, HE T T. Spatio-temporal of agricultural carbon emission and decoupling in China during 1997−2018: an empirical research based on spatial and distribution dynamics method[J]. Journal of Yunnan Agricultural University (Social Science), 2022, 16(1): 78−90 doi: 10.12371/j.ynau(s).202103015 [11] 万运帆, 李玉娥, 林而达, 等. 静态箱法测定旱地农田温室气体时密闭时间的研究[J]. 中国农业气象, 2006, 27(2): 122−124WAN Y F, LI Y E, LIN E D, et al. Studies on closing time in measuring greenhouse gas emission from dry cropland by static chamber method[J]. Chinese Journal of Agrometeorology, 2006, 27(2): 122−124 [12] LAL R. Carbon emission from farm operations[J]. Environment International, 2004, 30(7): 981−990 doi: 10.1016/j.envint.2004.03.005 [13] 李国志, 李宗植, 周明. 碳排放与农业经济增长关系实证分析[J]. 农业经济与管理, 2011(4): 32−39 doi: 10.3969/j.issn.1674-9189.2011.04.005LI G Z, LI Z Z, ZHOU M. Empirical analysis on relationship between carbon emissions and agricultural economic growth[J]. Agricultural Economics and Management, 2011(4): 32−39 doi: 10.3969/j.issn.1674-9189.2011.04.005 [14] 程琳琳, 张俊飚, 何可. 农业产业集聚对碳效率的影响研究: 机理、空间效应与分群差异[J]. 中国农业大学学报, 2018, 23(9): 218−230CHENG L L, ZHANG J B, HE K. Different spatial impacts of agricultural industrial agglomerations on carbon efficiency: Mechanism, spatial effects and groups differences[J]. Journal of China Agricultural University, 2018, 23(9): 218−230 [15] 程琳琳, 张俊飚, 何可. 空间视角下城镇化对农业碳生产率的直接作用与间接溢出效应研究[J]. 中国农业资源与区划, 2019, 40(11): 48−56CHENG L L, ZHANG J B, HE K. The direct influence and indirect spillover effect of urbanization on agricultural carbon productivity base on the spatial durbin model[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2019, 40(11): 48−56 [16] 武春桃. 城镇化对中国农业碳排放的影响−省际数据的实证研究[J]. 经济经纬, 2015, 32(1): 12−18 doi: 10.3969/j.issn.1006-1096.2015.01.003WU C T. The impact of urbanization on agricultural carbon emissions in China — An empirical study based on provincial data[J]. Economic Survey, 2015, 32(1): 12−18 doi: 10.3969/j.issn.1006-1096.2015.01.003 [17] 董明涛. 我国农业碳排放与产业结构的关联研究[J]. 干旱区资源与环境, 2016, 30(10): 7−12DONG M T. The association of agricultural carbon emissions and industrial structure in China[J]. Journal of Arid Land Resources and Environment, 2016, 30(10): 7−12 [18] 魏玮, 文长存, 崔琦, 等. 农业技术进步对农业能源使用与碳排放的影响−基于GTAP-E模型分析[J]. 农业技术经济, 2018(2): 30−40WEI W, WEN C C, CUI Q, et al. The impacts of technological advance on agricultural energy use and carbon emission—an analysis based on GTAP-E model[J]. Journal of Agrotechnical Economics, 2018(2): 30−40 [19] 王惠, 卞艺杰. 农业生产效率、农业碳排放的动态演进与门槛特征[J]. 农业技术经济, 2015(6): 36−47WANG H, BIAN Y J. Dynamic evolution and threshold characteristics of agricultural production efficiency and agricultural carbon emissions[J]. Journal of Agrotechnical Economics, 2015(6): 36−47 [20] 仇伟, 卢东宁. 基于VAR模型的农业碳排放影响因素及其动态响应机制分析[J]. 湖北农业科学, 2019, 58(24): 271−276QIU W, LU D N. Analysis of factors affecting agricultural carbon emission based on VAR model and its dynamic response mechanism[J]. Hubei Agricultural Sciences, 2019, 58(24): 271−276 [21] 刘丽辉, 徐军. 基于扩展的STIRPAT模型的广东农业碳排放影响因素分析[J]. 科技管理研究, 2016, 36(6): 250−255 doi: 10.3969/j.issn.1000-7695.2016.06.046LIU L H, XU J. Analysis of influencing factors of agricultural carbon emission in Guangdong Province with the extended STIRPAT model[J]. Science and Technology Management Research, 2016, 36(6): 250−255 doi: 10.3969/j.issn.1000-7695.2016.06.046 [22] BAI Y P, DENG X Z, JIANG S J, et al. Relationship between climate change and low-carbon agricultural production: a case study in Hebei Province, China[J]. Ecological Indicators, 2019, 105: 438−447 doi: 10.1016/j.ecolind.2018.04.003 [23] 王小彬, 武雪萍, 赵全胜, 等. 中国农业土地利用管理对土壤固碳减排潜力的影响[J]. 中国农业科学, 2011, 44(11): 2284−2293 doi: 10.3864/j.issn.0578-1752.2011.11.010WANG X B, WU X P, ZHAO Q S, et al. Effects of cropland-use management on potentials of soil carbon sequestration and carbon emission mitigation in China[J]. Scientia Agricultura Sinica, 2011, 44(11): 2284−2293 doi: 10.3864/j.issn.0578-1752.2011.11.010 [24] 冉锦成, 马惠兰, 苏洋. 西北五省农业碳排放测算及碳减排潜力研究[J]. 江西农业大学学报, 2017, 39(3): 623−632RAN J C, MA H L, SU Y. A study on agricultural carbon emission and carbon emission reduction potential in five provinces in Northwest China[J]. Acta Agriculturae Universitatis Jiangxiensis, 2017, 39(3): 623−632 [25] 周一凡, 李彬, 张润清. 县域尺度下河北省农业碳排放时空演变与影响因素研究[J]. 中国生态农业学报(中英文), 2022, 30(4): 570−581 doi: 10.12357/cjea.20210624ZHOU Y F, LI B, ZHANG R Q. Spatiotemporal evolution and influencing factors of agricultural carbon emissions in Hebei Province at the County scale[J]. Chinese Journal of Eco-Agriculture, 2022, 30(4): 570−581 doi: 10.12357/cjea.20210624 [26] 刘杨, 刘鸿斌. 山东省农业碳排放特征、影响因素及达峰分析[J]. 中国生态农业学报(中英文), 2022, 30(4): 558−569 doi: 10.12357/cjea.20210582LIU Y, LIU H B. Characteristics, influence factors, and prediction of agricultural carbon emissions in Shandong Province[J]. Chinese Journal of Eco-Agriculture, 2022, 30(4): 558−569 doi: 10.12357/cjea.20210582 [27] 祝宏辉, 李晓晓. 新疆农业碳排放的脱钩效应及驱动因素分析[J]. 生态经济, 2018, 34(9): 31−35, 115ZHU H H, LI X X. Analysis of decoupling effect and driving factors of agriculture carbon emission in Xinjiang[J]. Ecological Economy, 2018, 34(9): 31−35, 115 [28] 苏洋, 马惠兰, 李凤. 新疆农牧业碳排放及其与农业经济增长的脱钩关系研究[J]. 干旱区地理, 2014, 37(5): 1047−1054SU Y, MA H L, LI F. Xinjiang agriculture and animal husbandry carbon emissions and its decoupling relationship with agricultural economic growth[J]. Arid Land Geography, 2014, 37(5): 1047−1054 [29] 冉锦成, 苏洋, 胡金凤, 等. 新疆农业碳排放时空特征、峰值预测及影响因素研究[J]. 中国农业资源与区划, 2017, 38(8): 16−24 doi: 10.7621/cjarrp.1005-9121.20170803RAN J C, SU Y, HU J F, et al. Temporal and spatial characteristics, peak value forecast and influencing factors of agricultural carbon emissions in Xinjiang[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2017, 38(8): 16−24 doi: 10.7621/cjarrp.1005-9121.20170803 [30] WU X R, ZHANG J B, YOU L Z. Marginal abatement cost of agricultural carbon emissions in China: 1993−2015[J]. China Agricultural Economic Review, 2018, 10(4): 558−571 doi: 10.1108/CAER-04-2017-0063 [31] WEST T O, MARLAND G. A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture: comparing tillage practices in the United States[J]. Agriculture, Ecosystems & Environment, 2002, 91(1/2/3): 217−232 [32] 黄华, 倪鹏, 葛中全. 四川省农业生态系统碳排放测算及影响因素分析[J]. 乐山师范学院学报, 2012, 27(5): 22−25 doi: 10.3969/j.issn.1009-8666.2012.05.009HUANG H, NI P, GE Z Q. Calculation and influencing factors analysis of carbon emissions from agricultural ecosystems in Sichuan Province[J]. Journal of Leshan Teachers College, 2012, 27(5): 22−25 doi: 10.3969/j.issn.1009-8666.2012.05.009 [33] 吴昊玥, 黄瀚蛟, 何宇, 等. 中国农业碳排放效率测度、空间溢出与影响因素[J]. 中国生态农业学报(中英文), 2021, 29(10): 1762−1773WU H Y, HUANG H J, HE Y, et al. Measurement, spatial spillover and influencing factors of agricultural carbon emissions efficiency in China[J]. Chinese Journal of Eco-Agriculture, 2021, 29(10): 1762−1773 [34] 刘其涛. 中国农业碳排放效率的区域差异−基于Malmquist-Luenberger指数的实证分析[J]. 江苏农业科学, 2015, 43(9): 497−501LIU Q T. Regional differences in China’s agricultural carbon emission efficiency: an empirical analysis based on Malmquist-Luenberger index[J]. Jiangsu Agricultural Sciences, 2015, 43(9): 497−501 [35] 张俊飚, 程琳琳, 何可. 中国农业低碳经济效率的时空差异及影响因素研究−基于“碳投入”视角[J]. 环境经济研究, 2017, 2(2): 36−51ZHANG J B, CHENG L L, HE K. The difference of China’s agricultural low-carbon economic efficiency in spatial and temporal and its influencing factors: a perspective of carbon input[J]. Journal of Environmental Economics, 2017, 2(2): 36−51 [36] 田云, 王梦晨. 湖北省农业碳排放效率时空差异及影响因素[J]. 中国农业科学, 2020, 53(24): 5063−5072 doi: 10.3864/j.issn.0578-1752.2020.24.009TIAN Y, WANG M C. Research on spatial and temporal difference of agricultural carbon emission efficiency and its influencing factors in Hubei Province[J]. Scientia Agricultura Sinica, 2020, 53(24): 5063−5072 doi: 10.3864/j.issn.0578-1752.2020.24.009 [37] FRANKLIN J. The elements of statistical learning: data mining, inference and prediction[J]. The Mathematical Intelligencer, 2005, 27: 83−85 [38] 张杰. R语言数据可视化之美: 专业图表绘制指南[M]. 北京: 电子工业出版社, 2019ZHANG J. The Beauty of R Language Data Visualization-professional Chart Drawing Guide[M]. Beijing: Publishing House of Electronics industry, 2019 [39] 赵宇铭, 邱新法, 朱晓晨, 等. 1971—2010年中国干湿区降雨资源变化特征分析[J]. 长江科学院院报, 2019, 36(5): 34−41ZHAO Y M, QIU X F, ZHU X C, et al. Characteristics of rainfall amount variations in wet and dry partitions of China from 1971 to 2010[J]. Journal of Yangtze River Scientific Research Institute, 2019, 36(5): 34−41 [40] 朱永彬, 马晓哲, 史雅娟. 县级尺度下河南省农业投入产出效率与减排潜力分析[J]. 中国生态农业学报(中英文), 2022, 30(11): 1−11ZHU Y B, MA X Z, SHI Y J. Agricultural input-output efficiency and the potential reduction of emissions in Henan Province at the county scale[J]. Chinese Journal of Eco-Agriculture, 2022, 30(11): 1−11 [41] 段华平, 张悦, 赵建波, 等. 中国农田生态系统的碳足迹分析[J]. 水土保持学报, 2011, 25(5): 203−208DUAN H P, ZHANG Y, ZHAO J B, et al. Carbon footprint analysis of farmland ecosystem in China[J]. Journal of Soil and Water Conservation, 2011, 25(5): 203−208 [42] 田成诗, 陈雨. 中国省际农业碳排放测算及低碳化水平评价−基于衍生指标与TOPSIS法的运用[J]. 自然资源学报, 2021, 36(2): 395−410 doi: 10.31497/zrzyxb.20210210TIAN C S, CHEN Y. China’s provincial agricultural carbon emissions measurement and low carbonization level evaluation: based on the application of derivative indicators and TOPSIS[J]. Journal of Natural Resources, 2021, 36(2): 395−410 doi: 10.31497/zrzyxb.20210210 [43] 田云, 张俊飚, 尹朝静, 等. 中国农业碳排放分布动态与趋势演进−基于31个省(市、区) 2002—2011年的面板数据分析[J]. 中国人口·资源与环境, 2014, 24(7): 91−98TIAN Y, ZHANG J B, YIN C J, et al. Distributional dynamics and trend evolution of China’s agricultural carbon emissions — An analysis on panel data of 31 provinces from 2002 to 2011[J]. China Population, Resources and Environment, 2014, 24(7): 91−98 [44] 何艳秋, 陈柔, 吴昊玥, 等. 中国农业碳排放空间格局及影响因素动态研究[J]. 中国生态农业学报, 2018, 26(9): 1269−1282HE Y Q, CHEN R, WU H Y, et al. Spatial dynamics of agricultural carbon emissions in China and the related driving factors[J]. Chinese Journal of Eco-Agriculture, 2018, 26(9): 1269−1282 [45] 胡婉玲, 张金鑫, 王红玲. 中国农业碳排放特征及影响因素研究[J]. 统计与决策, 2020, 36(5): 56−62HU W L, ZHANG J X, WANG H L. Characteristics and influencing factors of agricultural carbon emission in China[J]. Statistics & Decision, 2020, 36(5): 56−62 [46] 张广胜, 王珊珊. 中国农业碳排放的结构、效率及其决定机制[J]. 农业经济问题, 2014, 35(7): 18−26, 110ZHANG G S, WANG S S. China’s agricultural carbon emission: structure, efficiency and its determinants[J]. Issues in Agricultural Economy, 2014, 35(7): 18−26, 110 [47] 孟军, 范婷婷. 黑龙江省农业碳排放动态变化影响因素分析[J]. 生态经济, 2020, 36(12): 34−39MENG J, FAN T T. Research on affecting factors decomposition of agricultural CO2 emission in Heilongjiang Province[J]. Ecological Economy, 2020, 36(12): 34−39 -