我国农业碳排放的时空演化、脱钩效应及绩效评估

doi:10.3864/j.issn.0578-1752.2023.20.010

Abstract

Abstract:

【Objective】The temporal characteristics, spatial pattern, evolution mode, decoupling relationship and performance evaluation of China’s agricultural carbon emissions were analyzed scientifically, so as to provide a basis for helping China achieve the goal of “carbon peaking and carbon neutrality” and strengthen the construction of an agricultural power. 【Method】This study constructed an index system for assessing agricultural carbon emissions and agricultural carbon emission performance in China, and measured the systematic measurement index of agricultural carbon emissions in Chinese provinces from 2007 to 2020. The Kernel density estimation and standardized ellipsoidal visualization analysis were used to analyze the regional distribution characteristics and spatial-temporal evolution trends of agricultural carbon emissions, Tapio model was used to examine the decoupling relationship between examining agricultural carbon emissions and economic growth, and the super-efficient SBM model with non-expected output was constructed to report the agricultural carbon emission performance and decomposition efficiency of China and the seven economic regions. 【Result】 From 2007 to 2020, the overall agricultural carbon emissions in China showed an “inverted U-shaped” curve of rising and then declining, with obvious regional differences and stable distribution of ranks. The eastern region had the best emission reduction effect, the central region had a “bipolar” distribution, and the western region had a higher pressure of emission reduction, with the overall spatial pattern dominated by the northeast-southwest direction, and tended to be decentralized to the northeast and northwest. China’s agricultural carbon emissions and agricultural economic development have been maintained at a weakly decoupled level and have made a breakthrough to a strongly decoupled level, which could be divided into two stages: a stable period (2007-2016) and a breakthrough period (2017-2020). The assessment of agricultural carbon emission performance showed a trend of “rapid rise - slow decline - steady improvement”, with the Great Northwest Economic Zone and the Northern Coastal Economic Zone in the first and last positions, respectively, and the contribution of technological change in agricultural production (TC) was more prominent than that of technical efficiency change (EC). 【Conclusion】With 2017 as the inflection point, China’s agricultural carbon emissions as a whole showed a decreasing trend, and the agricultural economic development as a whole was gradually getting rid of the dependence on agricultural carbon emissions, with different agricultural bases and different emission reduction targets in each region and province. It was necessary to reasonably plan the scale and internal structure of agricultural comparative advantage industries according to local conditions, reasonably select the resource endowment production characteristics of industries in the region. At the same time, we should pay attention to technology iteration and updating in the agricultural economic development and energy conservation and emission reduction in the role of promoting, taking into account the regional ecological benefits and economic benefits.

Key words: carbon emissions from agriculture, space-time evolution, decoupling effect, performance evaluation

MENG QingLei, YIN YuXiang, WANG YuHao. Spatial-Temporal Evolution, Decoupling Effect and Performance Evaluation of China’s Agricultural Carbon Emissions[J].Scientia Agricultura Sinica, 2023, 56(20): 4049-4066.

0
/ / Recommend

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.chinaagrisci.com/EN/10.3864/j.issn.0578-1752.2023.20.010

https://www.chinaagrisci.com/EN/Y2023/V56/I20/4049

Figures/Tables 13

Table 1

Table 2

Table 3

Fig. 1

Fig. 2

Fig. 3

Table 4

Fig. 4

Table 5

Table 6

Fig. 5

Table 7

Table 8

References 42

[1]	国家统计局. 2021年全国农业及相关产业增加值占GDP比重为16.05%. (2022-12-30)[2023-05-10]. http://www.stats.gov.cn/xxgk/sjfb/zxfb2020/202212/t20221230_1891328.html.
	National Bureau of Statistics. In 2021, the added value of agriculture and related industries accounted for 16.05% of GDP. (2022-12-30) [2023-05-10]. http://www.stats.gov.cn/xxgk/sjfb/zxfb2020/202212/t20221230_1891328.html. (in Chinese)
[2]	农业科技进步贡献率10年提升7个百分点——科技兴农开花结果. (2022-08-19)[2023-05-10]. http://www.kjs.moa.gov.cn/gzdt/202208/t20220819_6407317.htm.
	The contribution rate of agricultural scientific and technological progress has increased by 7 percentage points in 10 years-science and technology develop rapidly. (2022-08-19)[2023-05-10]. http://www.kjs.moa.gov.cn/gzdt/202208/t20220819_6407317.htm. (in Chinese)
[3]	关于印发《减污降碳协同增效实施方案》的通知. (2022-06-10) [2023-05-10]. http://www.gov.cn/zhengce/zhengceku/2022-06/17/content_5696364.htm.
	Notice on the issuance of the implementation plan for pollution reduction and carbon reduction synergy. (2022-06-10)[2023-05-10]. http://www.gov.cn/zhengce/zhengceku/2022-06/17/content_5696364.htm. (in Chinese)
[4]	国务院关于印发“十四五”推进农业农村现代化规划的通知. (2021-11-12)[2023-05-09]. http://www.gov.cn/zhengce/content/2022-02/11/content_5673082.htm.
	Notice of the state council on the issuance of the 14th five-year plan for promoting agricultural and rural modernization. (2021-11-12) [2023-05-09]. http://www.gov.cn/zhengce/content/2022-02/11/content_5673082.htm. (in Chinese)
[5]	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. Agriculture, Ecosystems & Environment, 2002, 91(1/2/3): 217-232. doi: 10.1016/S0167-8809(01)00233-X
[6]	董红敏, 李玉娥, 陶秀萍, 彭小培, 李娜, 朱志平. 中国农业源温室气体排放与减排技术对策. 农业工程学报, 2008, 24(10): 269-273.
	DONG H M, LI Y E, TAO X P, PENG X P, LI N, ZHU Z P. China greenhouse gas emissions from agricultural activities and its mitigation strategy. Transactions of the Chinese Society of Agricultural Engineering, 2008, 24(10): 269-273. (in Chinese)
[7]	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. 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. China Population, Resources and Environment, 2011, 21(8): 80-86. (in Chinese)
[8]	李俊杰. 民族地区农地利用碳排放测算及影响因素研究. 中国人口·资源与环境, 2012, 22(9): 42-47.
	LI J J. Research on characteristics and driving factors of agricultural land carbon emission in provinces of minorities in China. China Population, Resources and Environment, 2012, 22(9): 42-47. (in Chinese)
[9]	刘月仙, 刘娟, 吴文良. 北京地区畜禽温室气体排放的时空变化分析. 中国生态农业学报, 2013, 21(7): 891-897.
	LIU Y X, LIU J, WU W L. Spatiotemporal dynamics of greenhouse gases emissions from livestock and poultry in Beijing area during 1978—2009. Chinese Journal of Eco-Agriculture, 2013, 21(7): 891-897. (in Chinese) doi: 10.3724/SP.J.1011.2013.00891
[10]	邵桂兰, 孔海峥, 于谨凯, 李晨. 基于LMDI法的我国海洋渔业碳排放驱动因素分解研究. 农业技术经济, 2015(6): 119-128.
	SHAO G L, KONG H Z, YU J K, LI C. Study on decomposition of driving factors of carbon emissions from marine fisheries in China based on LMDI method. Journal of Agrotechnical Economics, 2015(6): 119-128. (in Chinese)
[11]	GROOT L. Carbon lorenz curves. Resource and Energy Economics, 2010, 32(1): 45-64. doi: 10.1016/j.reseneeco.2009.07.001
[12]	田云, 张俊飚, 李波. 基于投入角度的农业碳排放时空特征及因素分解研究: 以湖北省为例. 农业现代化研究, 2011, 32(6): 752-755.
	TIAN Y, ZHANG J B, LI B. Research on spatial-temporal characteristics and factor decomposition of agricultural carbon emission based on input angle—taking Hubei Province for example. Research of Agricultural Modernization, 2011, 32(6): 752-755. (in Chinese)
[13]	闵继胜, 胡浩. 中国农业生产温室气体排放量的测算. 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. China Population, Resources and Environment, 2012, 22(7): 21-27. (in Chinese)
[14]	戴小文. 中国农业隐含碳排放核算与分析: 兼与一般碳排放核算方法的对比. 财经科学, 2014(12): 127-136.
	DAI X W. Calculation and analysis of embodied CO₂ emission in China’s agriculture department—a comparison with the general CO₂ emission calculate method. Finance & Economics, 2014(12): 127-136. (in Chinese)
[15]	章胜勇, 尹朝静, 贺亚亚, 肖小勇. 中国农业碳排放的空间分异与动态演进: 基于空间和非参数估计方法的实证研究. 中国环境科学, 2020, 40(3): 1356-1363.
	ZHANG S Y, YIN C J, HE Y Y, XIAO X Y. Spatial differentiation and dynamic evolution of agricultural carbon emission in China— empirical research based on spatial and non-parametric estimation methods. China Environmental Science, 2020, 40(3): 1356-1363. (in Chinese)
[16]	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. 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. Chinese Rural Economy, 2022(3): 104-127. (in Chinese)
[17]	陈胜涛, 张开华, 张岳武. 农业碳排放绩效的测量与脱钩效应. 统计与决策, 2021, 37(22): 85-88.
	CHEN S T, ZHANG K H, ZHANG Y W. Measurement and decoupling effect of agricultural carbon emission performance. Statistics & Decision, 2021, 37(22): 85-88. (in Chinese)
[18]	SUN J W. The decrease of CO₂ emission intensity is decarbonization at national and global levels. Energy Policy, 2005, 33(8): 975-978. doi: 10.1016/j.enpol.2003.10.023
[19]	RAMANATHAN R. Combining indicators of energy consumption and CO₂ emissions: a cross-country comparison. International Journal of Global Energy Issues, 2002, 17(3): 214. doi: 10.1504/IJGEI.2002.000941
[20]	邵帅, 范美婷, 杨莉莉. 经济结构调整、绿色技术进步与中国低碳转型发展: 基于总体技术前沿和空间溢出效应视角的经验考察. 管理世界, 2022, 38(2): 46-69, 4.
	SHAO S, FAN M T, YANG L L. Economic restructuring, green technical progress, and low-carbon transition development in China: an empirical investigation based on the overall technology frontier and spatial spillover effect. Journal of Management World, 2022, 38(2): 46-69, 4. (in Chinese)
[21]	朱洪革, 曹博, 赵文铖. 中国农业全要素碳排放绩效时序演进及空间收敛特征. 统计与决策, 2022, 38(9): 63-68.
	ZHU H G, CAO B, ZHAO W C. Temporal evolution and spatial convergence characteristics of China’s agricultural total factor carbon emission performance. Statistics & Decision, 2022, 38(9): 63-68. (in Chinese)
[22]	周泽炯, 张葆俊, 江珂鑫, 朱家明. 绿色发展下的乡村碳排放绩效评价及随机性收敛研究: 基于SE-SBM与面板单位根检验. 哈尔滨师范大学自然科学学报, 2023, 39(1): 20-29.
	ZHOU Z J, ZHANG B J, JIANG K X, ZHU J M. Evaluation of rural carbon emission performance under green development and stochastic convergence study—based on SE-SBM with panel unit root test. Natural Science Journal of Harbin Normal University, 2023, 39(1): 20-29. (in Chinese)
[23]	王方怡, 洪志猛, 康智明, 兰思仁, 陈世品, 叶玉珍. 福建省农业碳排放时空变化及其驱动因素. 福建农业学报, 2019, 34(1): 124-134.
	WANG F Y, HONG Z M, KANG Z M, LAN S R, CHEN S P, YE Y Z. Spatial-temporal variations and driving forces of agricultural carbon emissions in Fujian. Fujian Journal of Agricultural Sciences, 2019, 34(1): 124-134. (in Chinese)
[24]	WANG M X, DAI A G, SHEN R X, HELMUTSCHITZ, HEINZRENNENBERG, WOLFGANGSEILER, WU H B. ch₄ emission from a Chinese rice paddy field. Acta Meteorologica Sinica, 1990, 4(3): 265-275.
[25]	MATTHEWS E, FUNG I, LERNER J. Methane emission from rice cultivation: geographic and seasonal distribution of cultivated areas and emissions. Global Biogeochemical Cycles, 1991, 5(1): 3-24. doi: 10.1029/90GB02311
[26]	CAO M. Methane emissions from China’s paddyland. Agriculture, Ecosystems & Environment, 1995, 55(2): 129-137. doi: 10.1016/0167-8809(95)00613-W
[27]	OECD. Indicators to Measure Decoupling of Environmental Pressures from Economic Growth. Paris: OECD, 2002.
[28]	徐卫涛, 张俊飚, 李树明, 孙笑男. 我国循环农业中的化肥施用与粮食生产脱钩研究. 农业现代化研究, 2010, 31(2): 200-203.
	XU W T, ZHANG J B, LI S M, SUN X N. Decoupling research on relationship between chemical fertilizer usage and grain production of recycling agriculture in China. Research of Agricultural Modernization, 2010, 31(2): 200-203. (in Chinese)
[29]	王崇梅. 中国经济增长与能源消耗脱钩分析. 中国人口·资源与环境, 2010, 20(3): 35-37.
	WANG C M. Decoupling analysis of China economic growth and energy consumption. China Population, Resources and Environment, 2010, 20(3): 35-37. (in Chinese)
[30]	李期, 郑明贵. 中国能源消耗与经济增长关系研究: 基于1996—2017年数据. 江西理工大学学报, 2019, 40(4): 57-61.
	LI Q, ZHENG M G. Study on the relationship between energy consumption and economic growth in china—based on the data from 1996 to 2017. Journal of Jiangxi University of Science and Technology, 2019, 40(4): 57-61. (in Chinese)
[31]	陈浩, 曾娟. 武汉市经济发展与能源消耗的脱钩分析. 华中农业大学学报(社会科学版), 2011(6): 90-95.
	CHEN H, ZENG J. Study on decoupling between economic development and energy consumption of Wuhan. Journal of Huazhong Agricultural University (Social Sciences Edition), 2011(6): 90-95. (in Chinese)
[32]	彭佳雯, 黄贤金, 钟太洋, 赵雲泰. 中国经济增长与能源碳排放的脱钩研究. 资源科学, 2011, 33(4): 626-633.
	PENG J W, HUANG X J, ZHONG T Y, ZHAO Y T. Decoupling analysis of economic growth and energy carbon emissions in China. Resources Science, 2011, 33(4): 626-633. (in Chinese)
[33]	徐玥, 王辉, 韩秋凤. 徐州市农业碳排放时空特征与脱钩效应. 水土保持通报. https://doi.org/10.13961/j.cnki.stbctb.20230526.004.
	XU Y, WANG H, HAN Q F. Temporal and spatial characteristics and decoupling effect of agricultural carbon emissions in Xuzhou City. Bulletin of Soil and Water Conservation. https://doi.org/10.13961/j.cnki.stbctb.20230526.004. (in Chinese)
[34]	孙耀华, 李忠民. 中国各省区经济发展与碳排放脱钩关系研究. 中国人口·资源与环境, 2011, 21(5): 87-92.
	SUN Y H, LI Z M. Analysing on the decoupling relationship between carbon dioxide emissions and economic growth of each province in China. China Population, Resources and Environment, 2011, 21(5): 87-92. (in Chinese)
[35]	SHESTALOVA V. Sequential malmquist indices of productivity growth: an application to OECD industrial activities. Journal of Productivity Analysis, 2003, 19(2): 211-226. doi: 10.1023/A:1022857501478
[36]	程开明, 李泗娥. 全要素生产率指数:演变、比较及展望. 统计学报, 2022, 3(1): 11-26.
	CHENG K M, LI S E. Total factor productivity indices: evolution, comparison and prospect. Journal of Statistics, 2022, 3(1): 11-26. (in Chinese)
[37]	GROSSMAN G M, KRUEGER A B. Economic growth and the environment. The Quarterly Journal of Economics, 1995, 110(2): 353-377. doi: 10.2307/2118443
[38]	伍国勇, 刘金丹, 杨丽莎. 中国农业碳排放强度动态演进及碳补偿潜力. 中国人口·资源与环境, 2021, 31(10): 69-78.
	WU G Y, LIU J D, YANG L S. Dynamic evolution of China’s agricultural carbon emission intensity and carbon offset potential. China Population, Resources and Environment, 2021, 31(10): 69-78. (in Chinese)
[39]	陈海韵. 环保“费改税”对环境保护效果的影响研究: 基于G省案例分析[D]. 广州: 华南理工大学, 2019.
	CHEN H Y. Study on the effect of environmental protection “fee to tax”[D]. Guangzhou: South China University of Technology, 2019. (in Chinese)
[40]	薛钢, 明海蓉, 刘彦龙. 环境保护税减排治污的“倒U”效应: 基于区域征收强度的测算. 税收经济研究, 2020, 25(3): 25-34.
	XUE G, MING H R, LIU Y L. Inverted U-shaped effect of environmental protection tax on emission reduction and pollution control—based on measurement of the intensity of regional collection. Tax and Economic Research, 2020, 25(3): 25-34. (in Chinese)
[41]	国务院关于印发全国农业现代化规划(2016—2020年)的通知.(2016-10-20) [2023-05-02]. http://www.gov.cn/zhengce/content/2016-10/20/content_5122217.htm.
	Notice of the state council on issuing the national agricultural modernization plan (2016-2020). (2016-10-20)[2023-05-02]. http://www.gov.cn/zhengce/content/2016-10/20/content_5122217.htm. (in Chinese)
[42]	中华人民共和国土壤污染防治法. (2018-08-31)[2023-05-02] https://www.mee.gov.cn/ywgz/fgbz/fl/201809/t20180907_549845.shtml.
	Law of the people's republic of China on the prevention and control of soil pollution. (2018-08-31)[2023-05-02]. https://www.mee.gov.cn/ywgz/fgbz/fl/201809/t20180907_549845.shtml. (in Chinese)

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

类别 Category	碳源 Carbon source	排放系数 Emission coefficient	排放系数来源 Source of emission coefficient
农用物资 Agricultural material	化肥 Chemical fertilizer (kg CE·kg^-1)	0.896	美国橡树岭国家实验室 Oak Ridge National Laboratory
	农药 Pesticide (kg CE·kg^-1)	4.934	美国橡树岭国家实验室 Oak Ridge National Laboratory
	农膜 Agricultural film (kg CE·kg^-1)	5.180	南京农业大学农业资源与生态环境研究所 Institute of Resource, Ecosystem and Environment of Agriculture, Nanjing Agricultural University
	农业机械 Agricultural machinery (kW·h)	0.180	West et al^[5]
农业灌溉 Agricultural irrigation	灌溉 Irrigate (kg CE·hm^-2)	266.480	West et al^[5]
农业种植 Agricultural cultivation	稻谷种植 Rice planting (g CE·m^-2·d^-1)	3.136	WANG^[24], MATTHEWS^[25], CAO^[26]et al
农业种植 Agricultural cultivation	复种 Multiple cropping (kg CE·hm^-2)	312.600	中国农业大学生物学院 College of Biological Sciences, China Agricultural University
畜牧养殖 Animal husbandry	猪 Per pig (kg CE·a^-1)	34.091	联合国政府间气候变化专门委员会 IPCC
	牛 Per cattle (kg CE·a^-1)	415.910	联合国政府间气候变化专门委员会 IPCC
	羊 Per sheep (kg CE·a^-1)	35.182	联合国政府间气候变化专门委员会 IPCC
农业能源消耗 Agricultural energy consumption	煤炭 Coal (kg CE·kg^-1)	0.757	联合国政府间气候变化专门委员会 IPCC
	汽油 Gasoline (kg CE·kg^-1)	0.552	联合国政府间气候变化专门委员会 IPCC
	柴油 Diesel oil (kg CE·kg^-1)	0.593	联合国政府间气候变化专门委员会 IPCC
	电力 Electricity(kg CE·kg^-1)	1.773	联合国政府间气候变化专门委员会 IPCC

类别 Category	状态 State	环境压力 EP	经济增长 DF	弹性 e
负脱钩 Negative decoupling	扩张负脱钩 Expansion negative decoupling	>0	>0	e>1.2
	强负脱钩 Strong negative decoupling	>0	<0	e<0
	弱负脱钩 Weak negative decoupling	<0	<0	0≤e<0.8
脱钩 Decoupling	弱脱钩 Weak decoupling	>0	>0	0≤e<0.8
	强脱钩 Strong decoupling	<0	>0	e<0
	衰退脱钩 Recessive decoupling	<0	<0	e>1.2
连接 Coupling	扩张连接 Expansion coupling	>0	>0	0.8≤e≤1.2
连接 Coupling	衰退连接 Recessive coupling	<0	<0	0.8≤e≤1.2

一级指标 Level 1 indicator	二级指标 Level 2 indicator	变量 Variable
投入 Input	劳动力要素 Labor element	第一产业就业人员数 Number of employees in primary industry (×10⁴)
	资本要素 Capital element	第一产业固定资产投资额 Primary industry fixed asset investment (×10⁸ yuan)
	土地要素 Land element	农作物总播种面积 Total planting area of crops (×10³hm²)
	其他要素 Other elements	农药使用量 Pesticide usage (t)
		农用薄膜使用量 Agricultural film usage (t)
		农业机械总动力 Total power of agricultural machinery (×10⁴kW·h)
期望产出 Desirable output	农业总产值 Gross value of agricultural output (×10⁸ yuan)	-
非期望产出 Undesirable output	农业碳排放 Agricultural carbon emissions (×10⁴ t)	-

年份 Year	经度 CenterX (°E)	纬度 CenterY (°N)	短轴 XStdDist (km)	长轴 YStdDist (km)	方位角 Rotation (°)	周长 Shape-Leng (km)	面积 Shape-Area (×10⁴km²)
2007	113.670	34.711	856.581	1124.487	29.996	6252.097	302.585
2008	113.592	34.641	868.014	1127.473	29.562	6295.434	307.439
2009	113.528	34.692	877.827	1131.104	29.688	6336.250	311.916
2010	113.510	34.745	882.632	1134.134	30.048	6360.416	314.463
2011	113.471	34.779	888.164	1138.055	30.322	6389.686	317.528
2012	113.395	34.811	908.207	1138.908	30.456	6451.544	324.937
2013	113.358	34.860	915.593	1140.389	30.933	6478.282	328.006
2014	113.256	34.926	938.222	1140.385	31.412	6545.494	336.112
2015	113.236	34.957	941.804	1142.787	31.613	6564.069	338.105
2016	113.238	34.992	941.149	1145.643	31.966	6571.505	338.715
2017	113.453	35.169	925.936	1158.203	32.354	6567.766	336.893
2018	113.431	35.200	932.339	1158.368	32.892	6587.262	339.271
2019	113.405	35.227	936.561	1161.511	33.087	6610.151	341.732
2020	113.388	35.215	934.996	1163.445	33.206	6611.900	341.729

年份 Year	农业碳排放总量 Total agricultural carbon emissions, C (×10⁴ t)	农业碳排放指数 Agricultural carbon emission index, C_n	农业总产值 Total agricultural output value, AGRI (×10⁸ yuan)	农业经济增长指数 Agricultural economic growth index, AGRI_n	脱钩指数 Decoupling index, D_n
2007	8286.715	1.000	24658.091	1.000	1.000
2008	8400.551	1.014	28044.152	1.137	0.891
2009	8708.147	1.051	30611.073	1.241	0.846
2010	8889.450	1.073	36941.111	1.498	0.716
2011	9053.173	1.092	41988.638	1.703	0.642
2012	9206.634	1.111	46940.458	1.904	0.584
2013	9355.943	1.129	51497.369	2.088	0.541
2014	9505.084	1.147	54771.600	2.221	0.516
2015	9588.401	1.157	57635.797	2.337	0.495
2016	9612.699	1.160	59287.782	2.404	0.482
2017	9680.836	1.168	58059.758	2.355	0.496
2018	9502.133	1.147	61452.595	2.492	0.460
2019	9300.162	1.122	66066.451	2.679	0.419
2020	9253.597	1.117	71748.100	2.910	0.384

Spatial-Temporal Evolution, Decoupling Effect and Performance Evaluation of China’s Agricultural Carbon Emissions

RichHTML

PDF

Abstract

Cite this article

share this article

Figures/Tables 13

References 42

Related Articles 0

Metrics

Comments

Recommended 0

年份 Year	ΔC/C	ΔAGRI/AGRI	弹性值 e	碳排放特征 Carbon emission characteristics
2007	0.013	0.133	0.098	弱脱钩 Weak decoupling
2008	0.014	0.121	0.112	弱脱钩 Weak decoupling
2009	0.035	0.084	0.421	弱脱钩 Weak decoupling
2010	0.020	0.171	0.119	弱脱钩 Weak decoupling
2011	0.018	0.120	0.150	弱脱钩 Weak decoupling
2012	0.017	0.105	0.158	弱脱钩 Weak decoupling
2013	0.016	0.088	0.180	弱脱钩 Weak decoupling
2014	0.016	0.060	0.262	弱脱钩 Weak decoupling
2015	0.009	0.050	0.175	弱脱钩 Weak decoupling
2016	0.003	0.028	0.091	弱脱钩 Weak decoupling
2017	0.007	-0.021	-0.333	强负脱钩 Expansion negative decoupling
2018	-0.019	0.055	-0.341	强脱钩 Strong decoupling
2019	-0.022	0.070	-0.311	强脱钩 Strong decoupling
2020	-0.005	0.079	-0.064	强脱钩 Strong decoupling

年份 Year	农业碳排放绩效 AMCPI	技术效率变化 EC	生产技术变化 TC
2007-2008	1.017	0.922	1.068
2008-2009	1.006	1.044	0.995
2009-2010	1.176	1.052	1.185
2010-2011	1.117	1.040	1.125
2011-2012	1.027	0.999	1.120
2012-2013	1.027	1.003	1.103
2013-2014	1.011	0.990	1.072
2014-2015	1.008	0.933	1.093
2015-2016	1.020	1.094	1.012
2016-2017	0.997	1.087	0.927
2017-2018	1.035	1.029	1.118
2018-2019	1.136	1.115	1.349
2019-2020	1.045	1.011	1.073

年份 Year	北部沿海 North coast	大西南 Great Southwest	长江中游 Middle reaches of the Yangtze River	黄河中游 Middle reaches of the Yellow River	南部沿海 Southern coast	东部沿海 Eastern coastal	大西北 Great Northwest
2007-2008	0.917	0.837	0.885	0.901	0.858	0.954	0.912
2008-2009	0.941	0.935	0.907	0.919	1.035	0.908	0.890
2009-2010	1.069	1.034	1.042	1.036	1.003	1.033	1.152
2010-2011	0.990	1.056	1.106	1.043	1.004	1.064	0.983
2011-2012	0.962	1.041	0.919	1.010	0.996	0.993	1.007
2012-2013	1.055	0.979	1.057	0.989	0.997	0.948	0.931
2013-2014	0.962	1.019	0.978	0.994	1.014	0.928	0.836
2014-2015	1.033	0.965	0.935	0.917	0.878	0.974	0.952
2015-2016	1.005	1.039	1.070	0.914	1.157	1.003	1.023
2016-2017	0.912	0.987	0.874	0.989	0.969	1.024	1.070
2017-2018	1.079	0.942	0.998	1.045	1.002	1.135	1.112
2018-2019	1.022	1.139	1.147	1.095	1.000	1.036	1.133
2019-2020	1.062	1.046	1.103	1.174	1.114	1.031	1.054
均值 Mean	1.001	1.001	1.002	1.002	1.002	1.002	1.004
排名 Order	2	1	3	4	5	6	7