留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

中国种植业碳排放达峰进程初判及脱钩分析

吴昊玥 周蕾 何艳秋 刘璐 马金山 孟越 郑祥江

吴昊玥, 周蕾, 何艳秋, 刘璐, 马金山, 孟越, 郑祥江. 中国种植业碳排放达峰进程初判及脱钩分析[J]. 中国生态农业学报 (中英文), 2023, 31(0): 1−12 doi: 10.12357/cjea.20220864
引用本文: 吴昊玥, 周蕾, 何艳秋, 刘璐, 马金山, 孟越, 郑祥江. 中国种植业碳排放达峰进程初判及脱钩分析[J]. 中国生态农业学报 (中英文), 2023, 31(0): 1−12 doi: 10.12357/cjea.20220864
WU H Y, ZHOU L, HE Y Q, LIU L, MA J S, MENG Y, ZHENG X J. Peaking process and decoupling analysis of carbon emissions of crop production in China[J]. Chinese Journal of Eco-Agriculture, 2023, 31(0): 1−12 doi: 10.12357/cjea.20220864
Citation: WU H Y, ZHOU L, HE Y Q, LIU L, MA J S, MENG Y, ZHENG X J. Peaking process and decoupling analysis of carbon emissions of crop production in China[J]. Chinese Journal of Eco-Agriculture, 2023, 31(0): 1−12 doi: 10.12357/cjea.20220864

中国种植业碳排放达峰进程初判及脱钩分析

doi: 10.12357/cjea.20220864
基金项目: 国家自然科学基金青年项目(71704127)、四川省科技计划项目(2022JDTD0022)和西南科技大学博士基金(22zx7148)资助
详细信息
    作者简介:

    吴昊玥, 主要研究方向为农业碳排放。E-mail: wuhaoyue@swust.edu.cn

    通讯作者:

    郑祥江, 主要研究方向为农业资源配置与利用。E-mail: 77463015@qq.com

  • 中图分类号: F323

Peaking process and decoupling analysis of carbon emissions of crop production in China

Funds: This study was supported by the National Natural Science Foundation of China (71704127), Sichuan Science and Technology Program (2022JDTD0022), and the Doctoral Foundation of Southwest University of Science and Technology of China (22zx7148).
More Information
  • 摘要: 判断种植业碳达峰进程, 可为温室气体减排提供农业领域的数据支撑。考虑农用物资、水稻种植、土壤管理和秸秆燃烧4类排放源, 本文对2000—2020年中国30省(市、自治区)种植业碳排放进行核算, 分类别、分量级对达峰进程展开初步探索, 利用Tapio脱钩指数探讨种植业碳排放与农业产值之间的关系。结果显示: 全国种植业碳排放量年均为23 326.860万t, 在2015年达到峰值26 264.777万t, 达峰后年均变化率为−1.560%, 尚处于平台期。根据达峰进程, 可将30省(市、自治区)分为下降期(北京、天津等13地)、平台期(山西、重庆等10地)、达峰期(河南、安徽等7地)。从全国层面来看, 种植业碳排放与农业产值的长期关系表现为弱脱钩, 短期关系已由弱脱钩转变为强脱钩。就省域层面而言, 短期关系自多种类型并存格局演化为强脱钩主导的极化态势。应根据达峰阶段及特点, 分区域、分类型制定全局减排策略, 加快我国种植业碳排放达峰转降进程。
  • 图  1  基于指标变化率与弹性值的种植业经济产出的碳排放Tapio脱钩类别划分(ε为脱钩指数, ΔE/E表示种植业碳排放变化率, ΔA/A表示农业产值变化率)

    Figure  1.  Classification of Tapio decoupling states of carbon emission of crop production based on the changing rate and elasticity (ε is decoupling index, ΔE/E is the change rate of carbon emissions of crop production, ΔA/A is the change rate of agricultural output value)

    图  2  2000—2020年中国种植业碳排放演进过程

    Figure  2.  Evolution of carbon emissions of crop production in China from 2000 to 2020

    图  3  2000—2020年中国30省(市、自治区)种植业碳排放总量、结构及达峰进程

    纵轴表示种植业碳排放, 横轴表示年份, 从左至右为2000—2020年。每幅堆积柱状图的纵轴区间统一标注于最左侧, 图幅下方依次呈现对应省份、基期(2000年)排放量(×104 t)→峰值排放量(×104 t)→末期(2020年)排放量(×104 t)和达峰后年均变化率。The vertical axis denotes carbon emissions of crop production, and the horizontal axis denotes time, from 2000 to 2020. The range of the vertical axis of the line graph by province is marked on the left side, and the corresponding provinces, emissions (×104 t) in 2000 → peak emissions (×104 t) → emissions (×104 t) in 2020, and annual average change rates after peaking are presented below each bar graph.

    Figure  3.  Amount, composition, and peaking process of carbon emissions of crop production in 30 Chinese provinces between 2000 and 2020

    图  4  基于种植业碳排放和农业产值的省域类型划分

    1: 湖南; 2: 河南; 3: 安徽; 4: 江苏; 5: 山东; 6: 湖北; 7: 黑龙江; 8: 江西; 9: 广东; 10: 广西; 11: 四川; 12: 河北; 13: 吉林; 14: 云南; 15: 浙江; 16: 辽宁; 17: 福建; 18: 内蒙古; 19: 新疆; 20: 陕西; 21: 山西; 22: 重庆; 23: 贵州; 24: 甘肃; 25: 海南; 26: 宁夏; 27: 上海; 28: 天津; 29: 北京; 30: 青海。1: Hunan; 2: Henan; 3: Anhui; 4: Jiangsu; 5: Shandong; 6: Hubei; 7: Heilongjiang; 8: Jiangxi; 9: Guangdong; 10: Guangxi; 11: Sichuan; 12: Hebei; 13: Jilin; 14: Yunnan; 15: Zhejiang; 16: Liaoning; 17: Fujian; 18: Inner Mongolia; 19: Xinjiang; 20: Shaanxi; 21: Shanxi; 22: Chongqing; 23: Guizhou; 24: Gansu; 25: Hainan; 26: Ningxia; 27: Shanghai; 28: Tianjin; 29: Beijing; 30: Qinghai.

    Figure  4.  Provincial classification based on carbon emissions of crop production and agricultural output value

    图  5  2000—2020年中国种植业碳排放与农业产值的脱钩状态

    Figure  5.  Decoupling states between carbon emissions of crop production and agricultural output value in China between 2000 and 2020

    表  1  2000—2020年中国30省(市、自治区)种植业碳排放与农业产值的脱钩状态

    Table  1.   Decoupling states between carbon emissions of crop production and agricultural output value in 30 Chinese provinces from 2000 to 2020

    省份
    Province
    2000—20052005—20102010—20152015—2020
    ΔE/EΔA/AεsΔE/EΔA/AεsΔE/EΔA/AεsΔE/EΔA/Aεs
    北京 Beijing−0.1970.066−2.973a−0.0110.130−0.088a−0.279−0.1032.713c−0.427−0.3591.190e
    天津 Tianjin0.137−0.020−6.745g0.0880.1990.440b−0.0510.207−0.245a−0.1750.167−1.048a
    河北 Hebei0.3080.3130.985d−0.0160.228−0.072a0.0580.2050.285b−0.1600.170−0.940a
    山西 Shanxi0.2260.0842.682f0.1810.2270.800b0.1230.2370.520b−0.0450.184−0.243a
    内蒙古 Inner Mongolia0.4000.2131.878f0.4730.2052.314f0.3600.3730.963d−0.0130.146−0.088a
    辽宁 Liaoning0.2220.3410.652b0.1710.1441.189d0.0500.4450.112b−0.0710.057−1.252a
    吉林 Jilin0.3100.4100.757b0.1920.2900.662b0.2950.3290.898d−0.0350.259−0.134a
    黑龙江 Heilongjiang0.1360.4780.285b0.7380.2552.890f0.2780.3560.781b−0.0690.192−0.362a
    上海 Shanghai−0.282−0.0903.150c−0.0050.012−0.456a−0.116−0.0871.330c−0.205−0.2160.950e
    江苏 Jiangsu−0.0160.164−0.095a0.0620.1970.317b0.0230.2100.108b−0.0340.108−0.314a
    浙江 Zhejiang−0.1870.113−1.656a−0.0840.167−0.505a−0.0910.111−0.817a−0.0870.168−0.517a
    安徽 Anhui0.0570.0242.414f0.1630.2760.589b0.1510.2500.606b−0.0420.156−0.272a
    福建 Fujian−0.0820.187−0.435a−0.0520.224−0.232a−0.0500.237−0.213a−0.1360.212−0.643a
    江西 Jiangxi0.1240.2090.590b0.1050.1750.604b0.0540.2620.204b−0.0930.247−0.376a
    山东 Shandong0.1120.1860.604b0.0590.1890.311b0.0320.2180.145b−0.0820.214−0.381a
    河南 Henan0.1750.2670.658b0.2430.2790.869d0.1110.2410.460b−0.0300.249−0.121a
    湖北 Hubei0.0510.1440.352b0.1210.2160.559b0.0780.2400.324b−0.1160.231−0.501a
    湖南 Hunan0.0060.2180.026b0.0990.2410.410b0.0550.2100.264b−0.0890.192−0.461a
    广东 Guangdong−0.0780.254−0.308a0.0030.1960.013b0.0070.2220.031a−0.0600.272−0.221a
    广西 Guangxi0.0850.3080.276b−0.0160.289−0.055a0.0150.2960.051b−0.0740.347−0.212a
    海南 Hainan0.0180.3540.050b0.2200.4220.522b0.0010.3480.003a−0.2030.302−0.672a
    重庆 Chongqing0.0360.1990.181b0.0210.3270.064b0.0290.2530.116b−0.0370.239−0.155a
    四川 Sichuan−0.0020.113−0.016a0.0520.1670.309b0.0230.2260.101b−0.0650.350−0.186a
    贵州 Guizhou0.0410.1350.301b0.0560.1880.296b0.1220.3960.309b−0.1780.456−0.391a
    云南 Yunnan0.1130.2450.461b0.1020.3240.315b0.1770.3500.506b−0.1300.400−0.324a
    陕西 Shaanxi0.0550.3720.148b0.2230.3490.641b0.1360.3210.425b−0.0630.261−0.239a
    甘肃 Gansu0.2060.3530.585b0.3400.3201.065d0.3340.3171.056d−0.2010.304−0.659a
    青海 Qinghai−0.0460.182−0.250a0.2800.3720.754b0.1980.2740.721b−0.2360.252−0.936a
    宁夏 Ningxia0.1570.4020.390b0.2790.4730.589b0.0420.2740.155b−0.0300.190−0.156a
    新疆 Xinjiang0.2570.2700.954d0.4650.3921.188d0.4690.4201.118d−0.0270.306−0.087a
      ΔE/E表示种植业碳排放变化率, ΔA/A表示农业产值变化率, ε表示脱钩指数, s表示脱钩状态, a为强脱钩, b为弱脱钩, c为衰退脱钩, d为增长连接, e为衰退连接, f为扩张负脱钩, g为强负脱钩。ΔE/E is the change rate of carbon emissions of crop production, ΔA/A is the change rate of agricultural output value, ε is the decoupling index. s denotes the decoupling states, a denotes the strong decoupling, b denotes the weak decoupling, c denotes the recessive decoupling, d denotes the expansive coupling, e denotes the recessive coupling, f denotes the expansive negative decoupling, and g denotes the strong negative decoupling.
    下载: 导出CSV
  • [1] 潘家华, 廖茂林, 陈素梅. 碳中和: 中国能走多快?[J]. 改革, 2021(7): 1−13

    PAN J H, LIAO M L, CHEN S M. Carbon neutrality: how fast can China go?[J]. Reform, 2021(7): 1−13
    [2] 林斌, 徐孟, 汪笑溪. 中国农业碳减排政策、研究现状及展望[J]. 中国生态农业学报(中英文), 2022, 30(4): 500−515 doi: 10.12357/cjea.20210843

    LIN B, XU M, WANG X X. Mitigation of greenhouse gas emissions in China’s agricultural sector: Current status and future perspectives[J]. Chinese Journal of Eco-Agriculture, 2022, 30(4): 500−515 doi: 10.12357/cjea.20210843
    [3] WEST T O, BRANDT C C, BASKARAN L M, et al. Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory-based carbon accounting[J]. Ecological Applications, 2010, 20(4): 1074−1086 doi: 10.1890/08-2352.1
    [4] ZOU J, HUANG Y, JIANG J, et al. A 3-year field measurement of methane and nitrous oxide emissions from rice paddies in China: Effects of water regime, crop residue, and fertilizer application[J]. Global Biogeochemical Cycles, 2005, 19(2): 1−9
    [5] BERTRAND G, BENOIT G, CLAIRE C, et al. Can N2O emissions offset the benefits from soil organic carbon storage?[J]. Global Change Biology, 2020, 27(2): 237−256
    [6] WANG P, WANG W, ZHANG J. Carbon emission measurement using different utilization methods of waste products: Taking cotton straw resources of south Xinjiang in China as an example[J]. Nature Environment and Pollution Technology, 2018, 17(2): 383−390
    [7] 石祖梁, 贾涛, 王亚静, 等. 我国农作物秸秆综合利用现状及焚烧碳排放估算[J]. 中国农业资源与区划, 2017, 38(9): 32−37 doi: 10.7621/cjarrp.1005-9121.20170905

    SHI Z L, JIA T, WANG Y J, et al. Comprehensive utilization status of crop straw and estimation of carbon from burning in China[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2017, 38(9): 32−37 doi: 10.7621/cjarrp.1005-9121.20170905
    [8] 田云, 陈池波. 市场与政府结合视角下的中国农业碳减排补偿机制研究[J]. 农业经济问题, 2021(5): 120−136 doi: 10.13246/j.cnki.iae.2021.05.013

    TIAN Y, CHEN C B. Research on the compensation mechanism of agricultural carbon emission reduction in China from the perspective of combination of market and government[J]. Issues in Agricultural Economy, 2021(5): 120−136 doi: 10.13246/j.cnki.iae.2021.05.013
    [9] SMITH P, MARTINO D, CAI Z C, et al. Greenhouse gas mitigation in agriculture[J]. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 2008, 363(1492): 789−813 doi: 10.1098/rstb.2007.2184
    [10] TUBIELLO F N, SALVATORE M, ROSSI S, et al. The FAOSTAT database of greenhouse gas emissions from agriculture[J]. Environmental Research Letters, 2013, 8(1): 15009 doi: 10.1088/1748-9326/8/1/015009
    [11] 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 doi: 10.3969/j.issn.1002-2104.2011.08.013

    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 doi: 10.3969/j.issn.1002-2104.2011.08.013
    [12] ZHANG D, SHEN J, ZHANG F, et al. Carbon footprint of grain production in China[J]. Scientific Reports, 2017, 7(1): 4126 doi: 10.1038/s41598-017-04182-x
    [13] 王学婷, 张俊飚. 双碳战略目标下农业绿色低碳发展的基本路径与制度构建[J]. 中国生态农业学报(中英文), 2022, 30(4): 516−526 doi: 10.12357/cjea.20210772

    WANG X T, ZHANG J B. Basic path and system construction of agricultural green and low-carbon development with respect to the strategic target of carbon peak and carbon neutrality[J]. Chinese Journal of Eco-Agriculture, 2022, 30(4): 516−526 doi: 10.12357/cjea.20210772
    [14] 严圣吉, 邓艾兴, 尚子吟, 等. 我国作物生产碳排放特征及助力碳中和的减排固碳途径[J]. 作物学报, 2022, 48(4): 930−941 doi: 10.3724/SP.J.1006.2022.12073

    YAN S J, DENG A X, SHANG Z Y, et al. Characteristics of carbon emission and approaches of carbon mitigation and sequestration for carbon neutrality in China’s crop production[J]. Acta Agronomica Sinica, 2022, 48(4): 930−941 doi: 10.3724/SP.J.1006.2022.12073
    [15] WU H, HUANG H, CHEN W, et al. Estimation and spatiotemporal analysis of the carbon-emission efficiency of crop production in China[J]. Journal of Cleaner Production, 2022, 371: 133516 doi: 10.1016/j.jclepro.2022.133516
    [16] ZHANG Z. Decoupling China’s carbon emissions increase from economic growth: an economic analysis and policy implications[J]. World Development, 2000, 28(4): 739−752 doi: 10.1016/S0305-750X(99)00154-0
    [17] Organisation for Economic Co-operation and Development. Sustainable development: indicators to measure decoupling of environmental pressure from economic growth[R/OL]. Paris: Organisation for Economic Co-operation and Development , (2002-05-16). https://www.oecd.org/env/indicators-modelling-outlooks/indicatorstomeasuredecouplingofenvironmentalpressurefromeconomicgrowth.htm
    [18] TAPIO P. Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001[J]. Transport Policy, 2005, 12(2): 137−151 doi: 10.1016/j.tranpol.2005.01.001
    [19] ZHOU X, ZHANG M, ZHOU M, et al. A comparative study on decoupling relationship and influence factors between China’s regional economic development and industrial energy-related carbon emissions[J]. Journal of Cleaner Production, 2017, 142: 783−800 doi: 10.1016/j.jclepro.2016.09.115
    [20] YANG L, YANG Y, ZHANG X, et al. Whether China’s industrial sectors make efforts to reduce CO2 emissions from production? A decomposed decoupling analysis[J]. Energy, 2018, 160: 796−809 doi: 10.1016/j.energy.2018.06.186
    [21] SHI C. Decoupling analysis and peak prediction of carbon emission based on decoupling theory[J]. Sustainable Computing:Informatics and Systems, 2020, 28: 100424 doi: 10.1016/j.suscom.2020.100424
    [22] LI H, QIN Q. Challenges for China’s carbon emissions peaking in 2030: A decomposition and decoupling analysis[J]. Journal of Cleaner Production, 2019, 207: 857−865 doi: 10.1016/j.jclepro.2018.10.043
    [23] 陈柔, 何艳秋, 朱思宇, 等. 我国农业碳排放双重性及其与经济发展的协调性研究[J]. 软科学, 2020, 34(1): 132−138 doi: 10.13956/j.ss.1001-8409.2020.01.21

    CHEN R, HE Y Q, ZHU S Y, et al. Duality of agricultural carbon emissions and coordination with economic development in China[J]. Soft Science, 2020, 34(1): 132−138 doi: 10.13956/j.ss.1001-8409.2020.01.21
    [24] 田云, 林子娟. 长江经济带农业碳排放与经济增长的时空耦合关系[J]. 中国农业大学学报, 2021, 26(1): 208−218 doi: 10.11841/j.issn.1007-4333.2021.01.21

    TIAN Y, LIN Z J. Spatio-temporal coupling relationship between agricultural carbon emissions and economic growth in the Yangtze River Economic Belt[J]. Journal of China Agricultural University, 2021, 26(1): 208−218 doi: 10.11841/j.issn.1007-4333.2021.01.21
    [25] Intergovernmental Panel on Climate Change. Climate Change 2013 — The Physical Science Basis Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[M]. Cambridge: Cambridge University Press, 2014
    [26] 张立, 万昕, 蒋含颖, 等. 二氧化碳排放达峰期、平台期及下降期定量判断方法研究[J]. 环境工程, 2021, 39(10): 1−7 doi: 10.13205/j.hjgc.202110001

    ZHANG L, WAN X, JIANG H Y, et al. Quantitative evaluation on the status of CO2 emissions: peak period, plateau period, and decline period[J]. Environmental Engineering, 2021, 39(10): 1−7 doi: 10.13205/j.hjgc.202110001
    [27] 孙睿. Tapio脱钩指数测算方法的改进及其应用[J]. 技术经济与管理研究, 2014(8): 7−11

    SUN R. Improving Tapio decoupling measurement method and its applications[J]. Technoeconomics & Management Research, 2014(8): 7−11
    [28] 田云, 尹忞昊. 中国农业碳排放再测算: 基本现状、动态演进及空间溢出效应[J]. 中国农村经济, 2022(3): 104−127

    TIAN Y, YIN M H. Re-evaluation of China’s agricultural carbon emissions: basic status, dynamic evolution and spatial spillover effects[J]. Chinese Rural Economy, 2022(3): 104−127
    [29] 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): 56−62 doi: 10.13546/j.cnki.tjyjc.2020.05.012

    HU W L, ZHANG J X, WANG H L. Characteristics and influencing factors of agricultural carbon emission in China[J]. Statistics and Decision, 2020, 36(5): 56−62 doi: 10.13546/j.cnki.tjyjc.2020.05.012
    [30] 黄晓慧, 杨飞. 碳达峰背景下中国农业碳排放测算及其时空动态演变[J]. 江苏农业科学, 2022, 50(14): 232−239 doi: 10.15889/j.issn.1002-1302.2022.14.033

    HUANG X H, YANG F. Measurement of agricultural carbon emissions in China under the background of peak carbon dioxide emissions and its temporal and spatial dynamic evolution[J]. Jiangsu Agricultural Sciences, 2022, 50(14): 232−239 doi: 10.15889/j.issn.1002-1302.2022.14.033
    [31] 程琳琳. 中国农业碳生产率时空分异: 机理与实证[D]. 武汉: 华中农业大学, 2018

    CHENG L L. Spatio-temporal differentiation of agricultural carbon productivity in China: mechanism and empirical study[D]. Wuhan: Huazhong Agricultural University, 2018.
    [32] 吴义根, 冯开文. 中国省际农业碳排放的时空分异特征及关联效应[J]. 环境科学与技术, 2019, 42(3): 180−190

    WU Y G, FENG K W. Spatial-temporal differentiation features and correlation effects of provincial agricultural carbon emissions in China[J]. Environmental Science and Technology, 2019, 42(3): 180−190
  • 加载中
图(5) / 表(1)
计量
  • 文章访问数:  25
  • HTML全文浏览量:  8
  • PDF下载量:  6
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-11-07
  • 录用日期:  2022-12-27
  • 修回日期:  2023-01-12
  • 网络出版日期:  2023-02-10

目录

    /

    返回文章
    返回