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

摘要/Abstract

摘要：

【目的】科学分析我国农业碳排放的时序特征、空间格局、演变模式、脱钩关系和绩效评估等问题，为助力我国实现“双碳”目标、加强建设农业强国提供依据。【方法】构建我国农业碳排放和农业碳排放绩效评估的指标体系，基于2007—2020年我国省域农业碳排放的系统测度指数，采用核密度估计和标准化椭圆可视化分析农业碳排放的区域分布特征和时空演化趋势，选用Tapio模型考察农业碳排放与经济增长之间的脱钩关系，构建非期望产出的超效率SBM模型报告我国和七大经济区的农业碳排放绩效及分解效率。【结果】2007—2020年，我国农业碳排放整体呈现先上升后下降的“倒U型”曲线，区位差异明显，等级分布稳定。东部地区减排效果最优，中部地区出现“两极化”分布，西部地区减排压力较大。空间格局整体以东北-西南方向为主导，并向东北和西北方向趋向分散化。我国农业碳排放和农业经济发展之间已保持在弱脱钩水平并向强脱钩水平突破，可划分为平稳期（2007—2016年）和突破期（2017—2020年）两个阶段。农业碳排放绩效呈现出“迅速上升-缓慢下降-平稳改善”趋势，其中大西北经济区和北部沿海经济区分别居于首位和末位，农业生产技术变化（TC）相较于技术效率变化（EC）贡献更为突出。【结论】以2017年为拐点，我国农业碳排放整体呈现下降趋势，农业经济发展整体上逐渐摆脱对农业碳排放的依赖。各区块与各省份农业基础各异、减排目标不同，需因地制宜合理规划农业比较优势产业的规模和内部结构，合理选择区域内产业的资源禀赋生产特征，同时重视技术迭代与更新在农业经济发展与节能减排之中的推动作用，兼顾地区生态效益与经济效益。

关键词: 农业碳排放, 时空演化, 脱钩效应, 绩效评估

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

孟庆雷, 殷宇翔, 王煜昊. 我国农业碳排放的时空演化、脱钩效应及绩效评估[J]. 中国农业科学, 2023, 56(20): 4049-4066.

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.

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链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2023.20.010

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

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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