中国省际农作物生产碳公平及其驱动因素

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Abstract On the basis of using the life cycle assessment method to calculate the carbon emissions of crop production in China and 31 provinces from 2006 to 2021, the carbon fairness coefficient of crop production was constructed from the perspective of carbon sink. The XGBoost model was used to identify the key driving factors of carbon fairness and their nonlinear response relationships. The results showed that during the inspection period, the overall carbon fairness of crop production in China tended to decrease, with the carbon fairness coefficient decreasing from 1.025 to 0.944. The regional differences in carbon fairness of crop production in China were significant, with the overall performance being "main grain production area>the production and sales balance area>main grain sales area". The overall degree of carbon fairness was decreasing from northwest to southeast, and there was a trend of weakening high-value aggregation and low-value aggregation. Except for factors such as farmland irrigation conditions, urban-rural income gap, and technological innovation level, other driving factors had complex nonlinear characteristics in their impact on carbon fairness. From the perspective of time effects, agricultural production structure was the most important factor affecting carbon fairness, and the importance of farmland irrigation conditions and grain yield per hectare factors always ranked high. From the perspective of regional effects, agricultural production structure factor ranked high, and there were certain differences in the importance of other driving factors.

Key words： crop production carbon fairness driving factors XGBoost model

Received: 18 May 2024

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	TANG Li-chen
	ZENG Xian-gang
	CHEN Hui
	LI Jie
	CHEN Mi
	ZHANG Zhong-yuan

Cite this article:

TANG Li-chen,ZENG Xian-gang,CHEN Hui等. Carbon fairness in inter provincial crop production and its driving factors in China[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(12): 7063-7078.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I12/7063

[1] 曾贤刚,余畅,孙雅琪.中国农业农村碳排放结构与碳达峰分析[J]. 中国环境科学, 2023,43(4):1906-1918. Zeng X G, Yu C, Sun Y Q. Carbon emission structure and carbon peak of agriculture and rural areas in China [J]. China Environmental Science, 2023,43(4):1906-1918.
[2] 赵明月,刘源鑫,张雪艳.农田生态系统碳汇研究进展[J]. 生态学报, 2022,42(23):9405-9416. Zhao M Y, Liu Y X, Zhang X Y. A review of research advances on carbon sinks in farmland ecosystems [J]. Acta Ecologica Sinica, 2022,42(23):9405-9416.
[3] 罗怀良.国内农业碳源/汇效应研究:视角、进展与改进[J].生态学报, 2022,42(9):3832-3841. Luo H L. Research on domestic agricultural carbon source/sink effect: Perspectives, advances and improvements [J]. Acta Eeulogica Sinica, 2022,42(9):3832-3841.
[4] 邓荣荣,肖湘涛,刘凡璠.公平和效率双重视角下中国粮食主产区农业碳排放研究[J]. 调研世界, 2023,(6):14-25. Deng R R, Xiao X T, Liu F P. Agricultural carbon emissions in major grain-producing areas of China from the perspectives of fairness and efficiency [J]. The World of Survey and Research, 2023,(6):14-25.
[5] 韩冬,钟钰.农业科技创新对粮食生产碳排放的影响——以中国三大粮食功能区为例[J]. 科技导报, 2023,41(16):32-42. Han D, Zhong Y. Research on the impact of agricultural science and technology innovation on carbon emission of grain production: A case study of China's three grain functional areas [J]. Science & Technology Review, 2023,41(16):32-42.
[6] 杨思存,霍琳,王成宝,等.基于STIRPAT模型的甘肃省农业碳排放特征分析[J]. 干旱区地理, 2023,46(9):1493-1502. Yang S C, Huo L, Wang C B, et al. Characteristics of agricultural carbon emissions in Gansu Province based on STIRPAT model [J]. Arid Land Geography, 2023,46(9):1493-1502.
[7] 于卓卉,毛世平.中国农业净碳排放与经济增长的脱钩分析[J]. 中国人口·资源与环境, 2022,32(11):30-42. Yu Z H, Mao S P. Analysis of the decoupling of China's agricultural net carbon emissions from its economic growth [J]. China Population, Resources and Environment, 2022,32(11):30-42.
[8] 尚杰,杨滨键.种植业碳源、碳汇测算与净碳汇影响因素动态分析:山东例证[J]. 改革, 2019,(6):123-134. Shang J, Yang B J. Estimation of carbon source and carbon sequestration in planting industry and dynamic analysis of influencing factors of net carbon sequestration: A case study of Shandong Province [J]. Reform, 2019,(6):123-134.
[9] 吴昊玥,黄瀚蛟,陈文宽.中国粮食主产区耕地利用碳排放与粮食生产脱钩效应研究[J]. 地理与地理信息科学, 2021,37(6):85-91. Wu H Y, Huang H J, Chen W K. Decoupling effects between carbon emissions from cropland use and grain production in the major grain-producing areas in China [J]. Geography and Geo-Information Science, 2021,37(6):85-91.
[10] 徐湘博,李畅,郭建兵,等.土地转入规模、土地经营规模与全生命周期作物种植碳排放——基于中国农村发展调查的证据[J]. 中国农村经济, 2022,(11):40-58. Xu X B, Li C, Guo J B, et.al. Land transfer-in scale, land operation scale and carbon emissions from crop planting throughout the life cycle: Evidence from China rural development survey [J]. Chinese Rural Economy, 2022,(11):40-58.
[11] Hedenus F, Azar C. Estimates of trends in global income and resource inequalities [J]. Ecological Economics, 2005,55(3):351-364.
[12] Duro J A, Padilla E. International inequalities in per capita CO₂ emissions: A decomposition methodology by Kaya factors [J]. Energy Economics, 2006,28(2):170-187.
[13] 陈晖,温婧,庞军,等.基于31省MRIO模型的中国省际碳转移及碳公平研究[J]. 中国环境科学, 2020,40(12):5540-5550. Chen H, Wen J, Pang J, et.al. Research on the carbon transfer and carbon equity at provincial level of China based on MRIO model of 31provinces [J]. China Environmental Science, 2020,40(12):5540- 5550.
[14] 宋德勇,刘习平.中国省际碳排放空间分配研究[J]. 中国人口·资源与环境, 2013,23(5):7-13. Song D Y, Liu X P. Spatial distribution of provincial carbon emissions [J]. China Population, Resources and Environment, 2013,23(5):7-13.
[15] 田云,张俊飚.中国省级区域农业碳排放公平性研究[J]. 中国人口·资源与环境, 2013,23(11):36-44. Tian Y, Zhang J B. Fairness research of agricultural carbon emissions between provincial regions in China [J]. China Population, Resources and Environment, 2013, 23(11):36-44.
[16] 高国生,王奇珍,支海兵.数字普惠金融对农业碳排放强度的影响效应分析[J]. 经济问题, 2024,(1):57-65. Gao G S, Wang Q Z, Zhi H B. Impact of digital financial inclusion on agricultural carbon emission intensity [J]. On Economic Problems, 2024,(1):57-65.
[17] 刘志华,徐军委.碳市场试点对省域碳排放公平性的影响及作用机制——基于多期DID、空间DID与中介效应的实证研究[J]. 自然资源学报, 2024,39(3):697-711. Liu Z H, Xu J W. The influence and mechanism of carbon trading pilot on provincial carbon emissions equity: Empirical analysis based on multi-period DID, spatial DID and intermediary effect [J]. Journal of Natural Resources, 2024,39(3):697-711.
[18] 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):2097-2105. Tian 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.
[19] 张帆,宣鑫,金贵,等.农业源非二氧化碳温室气体排放及情景模拟[J]. 地理学报, 2023,78(1):35-53. Zhang F, Xuan X, Jin G, et.al. Agricultural non-CO₂ greenhouse gases emissions and scenario simulation analysis [J]. Acta Geographica Sinica, 2023,78(1):35-53.
[20] 张国,王效科.我国保护性耕作对农田温室气体排放影响研究进展[J]. 农业环境科学学报, 2020,39(4):872-881. Zhang G, Wang X K. Impacts of conservation tillage on greenhouse gas emissions from cropland in China: A review [J]. Journal of Agro-Environment Science, 2020,39(4):872-881.
[21] 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):80-86. Li 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.
[22] 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):203-208. Duan 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.
[23] 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):21-27. Min 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.
[24] 孙立博,王晓玲,王旭敏,等.淮北平原典型农田土壤N₂O产生途径及相关功能基因丰度研究[J]. 环境科学学报, 2022,42(10):441-451. Sun L B, Wang X L, Wang X M, et al. N₂O production pathways and related functional gene abundance in typical agricultural soils of the Huaibei Plain [J]. Acta Scientiae Circumstantiae, 2022,42(10):441- 451.
[25] Xu Y Q, Huang Z J, Ou J M, et al. Near-real-time estimation of hourly open biomass burning emissions in China using multiple satellite retrievals [J]. Science of the Total Environment, 2022,817: 152777.
[26] 刘慧琳,汪子博,涂智鹏,等.基于多源卫星表征的中国秸秆露天燃烧CO₂排放特征及驱动因子分析[J]. 环境科学学报, 2024,44(2):261- 272. Liu H L, Wang Z B, Tu Z P, et al. CO₂ emission characteristics and driving factors of open crop straw burning in China based on multi-source satellite observation [J]. Acta Scientiae Circumstantiae, 2024,44(2):261-272.
[27] 王修兰.全球农作物对大气CO₂及其倍增的吸收量估算[J]. 气象学报, 1996,(4):466-473. Wang X L. The estimation on crop absorbing CO₂ under current and double CO₂ conditions in the world [J]. Acta Meteorologica Sinica, 1996,(4):466-473.
[28] 韩召迎,孟亚利,徐娇,等.区域农田生态系统碳足迹时空差异分析——以江苏省为案例[J]. 农业环境科学学报, 2012,31(5):1034- 1041. Han Z Y, Meng Y L, Xu J, et al. Temporal and spatial difference in carbon footprint of regional farmland ecosystem: Taking Jiangsu Province as a case [J]. Journal of Agro-Environment Science, 2012, 31(5):1034-1041.
[29] 田云,张俊飚,吴贤荣,等.中国种植业碳汇盈余动态变化及地区差异分析——基于31个省(市、区)2000~2012年的面板数据[J]. 自然资源学报, 2015,30(11):1885-1895. Tian Y, Zhang J B, Wu X R, et al. Research on dynamic change and regional differences of China's planting industry carbon sink surplus [J]. Journal of Natural Resources, 2015,30(11):1885-1895.
[30] 陈罗烨,薛领,雪燕.中国农业净碳汇时空演化特征分析[J]. 自然资源学报, 2016,31(4):596-607. Chen L Y, Xue L, Xue Y. Spatial-temporal characteristics of China's agricultural net carbon sink [J]. Journal of Natural Resources, 2016, 31(4):596-607.
[31] 崔海洋,卓雯君,虞虎,等.基于三阶段DEA模型的农业生产效率及其时空特征研究——以长江经济带为例[J]. 中国生态农业学报(中英文), 2021,29(7):1243-1252. Cui H Y, Zhuo W J, Yu H, et al. Calculation of agricultural production efficiency based on a three-stage DEA model and analysis of the spatial-temporal characteristics: An example from the Yangtze River Economic Belt [J]. Chinese Journal of Eco-Agriculture, 2021,29(7): 1243-1252.
[32] 李红梅,吴喜之,王涛.基于纵向数据与多重共线性数据的神经网络与传统方法比较[J]. 统计与决策, 2020,36(9):22-25. Li H M, Wu X Z, Wang T. Comparisons of neural networks and traditional methods based on longitudinal data and multicollinearity data [J]. Statistics & Decision, 2020,36(9):22-25.
[33] 朱钰,郑屹然,尹默.统计学意义下的多重共线性检验方法[J]. 统计与决策, 2020,36(7):34-36. Zhu Y, Zheng Y R, Yin M. Multicollinearity test under statistical significance [J]. Statistics & Decision, 2020,36(7):34-36.
[34] 黄和平,李紫霞,黄靛,等.“双碳”目标下江西省农业碳排放量测算、影响因素分析与预测研究[J]. 生态与农村环境学报, 2024,40(2): 179-190. Huang H P, Li Z X, Huang D, et al. Research on the measurement, analysis and prediction of agricultural carbon emissions in Jiangxi Province under the "Dual Carbon" goals [J]. Journal of Ecology and Rural Environment, 2024,40(2):179-190.
[35] 刘敏,周健,胡月明,等.基于XGBoost算法的可恢复耕地宜耕性评价——以湘阴县为例[J]. 农业资源与环境学报, 2024,41(1): 49-60. Liu M, Zhou J, Hu Y M, et al. Using XGBoost to evaluate arability on recoverable cultivated land: A case study of Xiangyin County [J]. Journal of Agricultural Resources and Environment, 2024,41(1):49- 60.
[36] 潘梦瑶,任瑛,王思源,等.基于梯度提升算法和SHAP的石家庄PM_2.5和臭氧浓度预测及影响因素分析[J]. 环境科学学报, 2024, 44(7):402-409. Pan M Y, Ren Y, Wang S Y, et al. Prediction of PM_2.5 and ozone concentration in Shijiazhuang and analysis of influencing factors based on gradient boosting algorithm and SHAP [J]. Acta Scientiae Circumstantiae, 2024,44(7):402-409.
[37] 尹忞昊,田云,卢奕亨.中国农业碳排放区域差异及其空间分异机理[J]. 改革, 2023,(10):130-145. Yin M H, Tian Y, Lu Y H. Regional differences and spatial divergence mechanisms of agricultural carbon emissions in China [J]. Reform, 2023,(10):130-145.
[38] 吉雪强,刘慧敏,张跃松.中国省际土地利用碳排放空间关联网络结构演化及驱动因素[J]. 经济地理, 2023,43(2):190-200. Ji X Q, Liu H M, Zhang Y S. Spatiotemporal evolution and driving factors of spatial correlation network structure of China's land-use carbon emission [J]. Economic Geography, 2023,43(2):190-200.
[39] 信猛,陈菁泉,彭雪鹏,等.农业碳排放驱动因素——区域间贸易碳排放转移网络视角[J]. 中国环境科学, 2023,43(3):1460-1472. Xin M, Chen J Q, Peng X P, et al. Driving factors of agricultural carbon emission: From the perspective of interregional trade carbon emission transfer network [J]. China Environmental Science, 2023, 43(3):1460-1472.
[40] 高强,曹翔.农业补贴、资源禀赋与农户收入差距[J]. 财政科学, 2021,(12):66-80. Gao Q, Cao X. Agricultural subsidies, resource endowment and household income gap [J]. Fiscal Science, 2021,(12):66-80.
[41] 许庆,杨青,章元.农业补贴改革对粮食适度规模经营的影响[J]. 经济研究, 2021,56(8):192-208. Xu Q, Yang Q, Zhang Y. The effect of agricultural subsidies reform on the optimum-scale management of grain [J]. Economic Research Journal, 2021,56(8):192-208.
[42] 吴连翠,谭俊美.粮食补贴政策的作用路径及产量效应实证分析[J]. 中国人口·资源与环境, 2013,23(9):100-106. Wu L C, Tan J M. Empirical analysis on yield effect and action path of grain subsidy policy [J]. China Population, Resources and Environment, 2013,23(9):100-106.
[43] 张卫建,严圣吉,张俊,等.国家粮食安全与农业双碳目标的双赢策略[J]. 中国农业科学, 2021,54(18):3892-3902. Zhang W J, Yan S J, Zhang J, et al. Win-Win strategy for national food security and agricultural double-carbon goals [J]. Scientia Agricultura Sinica, 2021,54(18):3892-3902.
[44] 张扬,李涵,赵正豪.中国粮食作物种植变化对省际农业碳排放量的影响研究[J]. 中国农业资源与区划, 2023,44(7):29-38. Zhang Y, Li H, Zhao Z H. Research on effects of grain crop planting changes on agricultural carbon emissions between provinces in China [J]. Chinese Journal of Agricultural Resources and Regional Planning, 2023,44(7):29-38.
[45] 刘天奇,胡权义,汤计超,等.长江中下游水稻生产固碳减排关键影响因素及技术体系[J]. 中国生态农业学报(中英文), 2022,30(4):603- 615. Liu T Q, Hu Q Y, Tang J C, et al. Key influencing factors and technical system of carbon sequestration and emission reduction in rice production in the middle and lower reaches of the Yangtze River [J]. Chinese Journal of Eco-Agriculture, 2022,30(4):603-615.
[46] 王嘉怡,李国煜,方晓倩,等.“社会-生态系统”视角下耕地生态系统服务内涵解析与研究框架[J]. 生态学报, 2024,44(15):6881-6891. Wang J Y, Li G Y, Fang X Q, et al. Exploring the connotation and research framework of cultivated land ecosystem services from the perspective of social-ecological systems [J]. Acta Ecologica Sinica, 2024,44(15):6881-6891.
[47] 杨曼莉.收入差距是否影响环境质量?——国内外研究综述[J]. 中国人口·资源与环境, 2020,30(4):116-124. Yang M L. Does income inequality affect environmental quality? A review of domestic andforeign studies [J]. China Population, Resources and Environment, 2020,30(4):116-124.
[48] 鲁庆尧,杨春红.我国粮食种植碳排放量变化趋势与驱动因素研究[J]. 经济问题, 2023,(1):114-121. Lu Q Y, Yang C H. A study on the changing trend and driving factors of carbon emissions from grain planting in China [J]. On Economic Problems, 2023,(1):114-121.
[49] 贯君,张少鹏,任月,等.中国农业净碳汇时空分异与影响因素演进分析[J]. 中国环境科学, 2024,44(2):1158-1170. Guan J, Zhang S P, Ren Y, et al. Random forest model-assisted evaluation of spatiotemporal differentiation of China's agricultural net carbon sink and evolution of influencing factors [J]. China Environmental Science, 2024,44(2):1158-1170.
[50] 吉雪强,崔益邻,张思阳,等.农地流转对农业碳排放强度影响的空间效应及作用机制[J]. 中国环境科学, 2023,43(12):6611-6624. Ji X Q, Cui Y L, Zhang S Y, et al. The spatial effect and action mechanism of the influence of rural land transfer on agricultural carbon emission intensity [J]. China Environmental Science, 2023, 43(12):6611-6624.
[51] 徐清华,张广胜.农业机械化对农业碳排放强度影响的空间溢出效应——基于282个城市面板数据的实证[J]. 中国人口·资源与环境, 2022,32(4):23-33. Xu Q H, Zhang G S. Spatial spillover effect of agricultural mechanization on agricultural carbon emission intensity: An empirical analysis of panel data from 282cities [J]. China Population, Resources and Environment, 2022,32(4):23-33.
[52] 刘琼,肖海峰.农地经营规模与财政支农政策对农业碳排放的影响[J]. 资源科学, 2020,42(6):1063-1073. Liu Q, Xiao H F. The impact of farmland management scale and fiscal policy for supporting agriculture on agricultural carbon emission [J]. Resources Science, 2020,42(6):1063-1073.
[53] 王辰璇,姚佐文.农业科技投入对农业生态效率的空间效应分析[J]. 中国生态农业学报(中英文), 2021,29(11):1952-1963. Wang C X, Yao Z W. An analysis of the spatial effect of agricultural science and technology investment on agricultural eco-efficiency [J]. Chinese Journal of Eco-Agriculture, 2021,29(11):1952-1963.
[54] 张青青,曲衍波,展凌云,等.中国粮食生产碳排放动态演进及驱动效应[J]. 地理学报, 2023,78(9):2186-2208. Zhang Q Q, Qu Y B, Zhan L Y, et al. Dynamic evolution and driving effects of carbon emissions from grain production in China [J]. Acta Geographica Sinica, 2023,78(9):2186-2208.
[55] 黄伟华,祁春节,聂飞.财政支农、技术溢出与农业碳排放[J]. 软科学, 2023,37(2):93-102. Huang W H, Qi C J, Nie F. Financial support for agriculture, technology spillover and agricultural carbon emissions [J]. Soft Science, 2023,37(2):93-102.
[56] 陈柱康,张俊飚,程琳琳,等.碳排放如何影响水稻全要素生产率[J]. 中国农业大学学报, 2019,24(11):197-213. Chen Z K, Zhang J B, Cheng L L, et al. How carbon emissions affect rice total factor productivity [J]. Journal of China Agricultural University, 2019,24(11):197-213.
[57] 刘金良,王长波,杨子彦,等.基于混合生命周期评价模型的我国食物系统水资源消耗及二氧化碳排放核算[J]. 中国环境管理, 2022, 14(6):88-99. Liu J L, Wang C B, Yang Z Y, et al. Accounting of water consumption and CO₂emissions in china's food system based on an hybrid LCA model [J]. Chinese Journal of Environmental Management, 2022,14(6): 88-99.
[58] 魏梦升,颜廷武,罗斯炫.规模经营与技术进步对农业绿色低碳发展的影响——基于设立粮食主产区的准自然实验[J]. 中国农村经济, 2023,(2):41-65. Wei M S, Yan T W, Luo S X. The impacts of scale management and technological progress on green and low-carbon development of agriculture: A quasi-natural experiment based on the establishment of major grain-producing areas [J]. Chinese Rural Economy, 2023,(2): 41-65.
[59] 李成龙,周宏.农业技术进步与碳排放强度关系——不同影响路径下的实证分析[J]. 中国农业大学学报, 2020,25(11):162-171. Li C L, Zhou H. Relationship between agricultural technology progress and carbon emission intensity: An empirical analysis under different influence paths [J]. Journal of China Agricultural University, 2020,25(11):162-171.
[60] 马艳.基于两阶段Super-NSBM模型的农业生态效率及影响因素研究——以长江经济带为例[J]. 长江流域资源与环境, 2023,32(4): 883-894. Ma Y. Study on agricultural ecological efficiency and its influencing factors based on two-stage super-efficiency network SBM model: A case study of the Yangtze RiverEconomic Belt [J]. Resources and Environment in the Yangtze Basin, 2023,32(4):883-894.