中国农业碳排放效率测度、空间溢出与影响因素

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吴昊玥^{1, 2,},
黄瀚蛟³,
何宇¹,
陈文宽^1,,
1.四川农业大学管理学院　成都　611130
2.赫尔辛基大学农业与林业学院　赫尔辛基　00014
3.西北农林科技大学林学院　杨凌　712100
基金项目:国家自然科学基金项目(71704127)和四川省社会科学研究“十三五”规划项目(SC18TJ018)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@sicau.stu.edu.cn

通讯作者:陈文宽, 主要研究方向为资源配置与可持续利用。E-mail: 11454@sicau.edu.cn

中图分类号:F323

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出版历程

收稿日期:2021-04-05
录用日期:2021-06-03
网络出版日期:2021-07-26
刊出日期:2021-10-01

Measurement, spatial spillover and influencing factors of agricultural carbon emissions efficiency in China

WU Haoyue^{1, 2,},
HUANG Hanjiao³,
HE Yu¹,
CHEN Wenkuan^1,,
1. College of Management, Sichuan Agricultural University, Chengdu 611130, China
2. Faculty of Agriculture and Forestry, University of Helsinki, Helsinki 00014, Finlan
3. College of Forestry, Northwest A&F University, Yangling 712100, China
Funds:This study was supported by the National Natural Science Foundation of China (71704127) and Sichuan Provincial Social Science Research “13th Five-Year Plan” Project (SC18TJ018)

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Corresponding author:E-mail: 11454@sicau.edu.cn

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摘要
摘要:准确测度农业碳排放效率并分析关键影响因素, 可为加快实现农业减排增效提供理论参考。已有研究未将碳排放与其他要素的共同作用进行分离, 研究实质为碳排放约束下的农业生产效率, 而非农业碳排放效率。为完善既有测算思路, 本文在农业全要素框架下搭建碳排放效率的理论模型, 基于GB-US-SBM模型测算2000—2019年间中国30省(市、自治区)的农业碳排放松弛量, 根据碳排放松弛量与实际值计算农业碳排放效率。在此基础上, 从产业、要素、环境3个方面出发, 采用空间杜宾模型探讨农业碳排放效率的影响因素与溢出效应。结果表明: 研究期内中国农业碳排放效率均值为0.778, 具有较大减排潜力。省级层面上, 仅内蒙古和青海两地的农业碳排放效率达1.000, 其余地区均存在不同规模的减排空间; 根据总量与效率的双重特征, 将30省(市、自治区)分为高排高效区、低排高效区、高排低效区和低排低效区。中国农业碳排放效率全局Moran’s I显著大于0 (P<0.01), 说明效率整体存在空间自相关性。空间杜宾模型结果显示, 农业碳排放效率具有显著的正向溢出效应, 表明邻近地区间的效率呈良性互动的演进特征。就直接效应而言, 本省的农业产业结构、农业投资强度、财政支农力度和受灾程度对本省农业碳排放效率存在负向影响, 有效灌溉率和城镇化率则表现为正向作用。从溢出效应来看, 邻近地区的受灾程度将负向影响本省农业碳排放效率, 而城镇化率则呈积极影响。研究结果可为我国分区域、分类别推进低碳农业发展提供理论依据。
关键词:农业碳排放效率/
全局参比/
GB-US-SBM模型/
空间杜宾模型/
空间溢出
Abstract:The efficiency of agricultural carbon emissions is a bridge between crop production and emission reduction, acting as a critical indicator of the potential for emission mitigation in agricultural production. In previous estimations, the outcomes yield the input-output efficiency of agriculture under the carbon emission constraint, rather than the efficiency of agricultural carbon emission, due to failing to separate the contribution of carbon emissions from other factors. To optimize the existing idea and understand the efficiency more precisely, a theoretical framework and a corresponding equation were developed for analysis in this study. In agricultural production, given the input factors, the efficiency of agricultural carbon emissions under the prerequisite of no desirable output was defined as the ratio of the minimum possible emissions to the actual emissons. On this basis, the GB-US-SBM model was employed to calculate the slack of emissions in 30 Chinese provinces from 2000 to 2019, reflecting the distance between the actual emission and production frontier. Then, the efficiency was estimated based on the slacks and actual emissions. Finally, the influencing factors and spillover effects of agriculural carbon emissions efficiency were explored using the spatial Durbin model. Results showed that: (1) From 2000 to 2019, the average agricultural carbon emissions efficiency was 0.778 in China, indicating considerable potential for emission reduction. At the provincial level, only Inner Mongolia and Qinghai had an efficiency of 1.000, while the rest of the provinces had different spaces for emission mitigation. (2) According to the emissions quantity and efficiency, the 30 provinces were divided into four groups. The five provinces, Henan, Hebei, Shandong, Heilongjiang, and Guangxi, belonged to a group of high emissions with high efficiency. The group of low emissions with high efficiency accounted for the majority, including 12 provinces, such as Inner Mongolia and Gansu. The group with high emissions and low efficiency covered seven provinces, such as Hunan and Hubei. Six provinces, including Zhejiang and Fujian, were classified as low emissions with low efficiency. (3) The global Moran’s index was significantly greater than 0, with a P-value under 0.01, verifying that there was a positive spatial autocorrelation in the provinces. The spatial econometric regression showed that efficiency had a significant positive spatial spillover effect, suggesting that an interactive evolution existed among close provinces. Specifically, four factors—industry structure, investment intensity, financial support for agriculture, and the degree of disaster, harmed the agricultural carbon emissions efficiency directly. By contrast, the irrigation effectiveness and urbanization indicated significant positive effects. In terms of spillover effects, the intensity of a disaster in a province negatively affected the efficiency of agricultural carbon emissions in neighboring provinces, while the urbanization rate exhibited a positive effect. Hence, it was essential to pay attention to the key factors that influence efficiency. Making full use of spillover effects could also help in achieving regional agricultural low-carbon transition. Additionally, local solutions should be addressed, owing to the regional characteristics of efficiency. This study results could provide a theoretical basis for the development of low-carbon agriculture in China.
Key words:Agricultural carbon emissions efficiency/
Global benchmark/
GB-US-SBM/
Spatial Durbin model/
Spatial spillover

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图1基于非期望产出思想的农业碳排放效率理论模型
X: 投入; P(X): 生产可能性的集合; Y_g: 期望产出; Y_b: 非期望产出; g=(?Y_b/X, Y_g/X): 预期产出方向; A(Y_b0/X, Y_g0/X): 某一时刻的产出决策点; B(Y_b1/X, Y_g1/X): A在生产前沿上的投影点。X is the input; P(X) is the production possibilities set; Y_g is the desirable output; Y_b is the undesirable output; g=(?Y_b/X, Y_g/X) denotes the expected output direction; A(Y_b0/X, Y_g0/X) is the decision point of output; B(Y_b1/X, Y_g1/X) is the projection point of point A on the production frontier.
Figure1.Theoretical model of agricultural carbon emissions efficiency under the perspective of undesirable output

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图2基于农业碳排放总量与效率双重维度的省域类别划分
1: 湖南; 2: 江西; 3: 湖北; 4: 安徽; 5: 福建; 6: 浙江; 7: 广东; 8: 江苏; 9: 宁夏; 10: 陕西; 11: 山西; 12: 云南; 13: 四川; 14: 天津; 15: 辽宁; 16: 重庆; 17: 上海; 18: 海南; 19: 贵州; 20: 甘肃; 21: 山东; 22: 河北; 23: 广西; 24: 黑龙江; 25: 河南; 26: 北京; 27: 吉林; 28: 新疆; 29: 内蒙古; 30: 青海。1: Hunan; 2: Jiangxi; 3: Hubei; 4: Anhui; 5: Fujian; 6: Zhejiang; 7: Guangdong; 8: Jiangsu; 9: Ningxia; 10: Shaanxi; 11: Shanxi; 12: Yunnan; 13: Sichuan; 14: Tianjin; 15: Liaoning; 16: Chongqing; 17: Shanghai; 18: Hainan; 19: Guizhou; 20: Gansu; 21: Shandong; 22: Hebei; 23: Guangxi; 24: Heilongjiang; 25: Henan; 26: Beijing; 27: Jilin; 28: Xinjiang; 29: Inner Mongolia; 30: Qinghai.
Figure2.Classification of the 30 provinces (cities, autonomous regions) of China based on the dual dimension of quantity and efficiency of agricultural carbon emissions

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表1全要素框架下的农业生产投入产出指标体系
Table1.Input-output indicators of agricultural production under the total-factor framework

指标类型 Type of indicator			具体指标 Specific indicators	单位 Unit
投入 Inputs		劳动力 Labor	农业从业人数 Quantity of agricultural employees	persons
		土地 Land	耕地面积 Cropland area	hm²
		机械 Machine	农业机械总动力 Total power of agricultural machinery	kW
		灌溉 Irrigation	农业灌溉用水总量 Total water used for irrigation	m³
		肥料 Fertilizer	化肥施用量 Total fertilizer application	t
产出 Output	期望 Desirable	经济产出 Economic output	农业总产值 Total agricultural output	CNY
	期望 Desirable	生态产出 Ecologic output	农业碳吸收量 Agricultural carbon sequestration	t
	非期望 Undesirable	环境代价 Environmental cost	农业碳排放量 Agricultural carbon emissions	t

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表2农业碳排放效率影响因素变量说明及描述性统计分析
Table2.Description and statistical analysis of factors influencing the efficiency of agricultural carbon emissions

变量 Variable		符号 Symbol	计算方法 Calculation	均值 Mean	标准差 Standard deviation	最小值 Minimum	最大值 Maximum
产业 Industry	农业产业结构 Agro-industrial structure	ind	农业总产值/农林牧渔总产值 Total agricultural output value / total agricultural, forestry, livestock husbandry and fishery output value	0.524	0.086	0.339	0.740
	作物种植结构 Crop structure	cro	粮食种植面积/作物播种面积 Grain sown area / crop sown area	0.659	0.130	0.354	0.971
	产业集聚程度 Degree of industrial agglomeration	agg	参考文献[32] Reference [32]	1.191	0.635	0.043	4.209
要素 Factor	耕地规模化程度 Scale of cropland	lan	耕地面积/农业从业人数 Cropland area/quantity of agricultural employees (hm²?capita^?1)	0.499	0.386	0.104	2.812
	农业投资强度 Agricultural investment intensity	cap	农业固定资产投资/耕地面积 Investment in agricultural fixed assets / cropland area (10⁴ ￥?hm^?2)	0.241	0.414	0.001	3.618
	农技人员密度 Density of agricultural technicians	tec	农业技术人员/耕地面积 Agricultural technicians / cropland area (persons?hm^?2)	0.008	0.004	0.002	0.025
	有效灌溉率 Effective irrigation rate	irr	有效灌溉面积/耕地面积 Effective irrigated area / cropland area	0.561	0.227	0.203	1.000
环境 Environment	农业受灾程度 Extent of agricultural disaster	dis	受灾面积/农作物播种面积 Agricultural disaster area / crop sown area	0.238	0.162	0.002	0.936
	城镇化率 Urbanization rate		常住人口城镇化率 Urbanization rate of resident population	0.448	0.273	0.100	0.901
	财政支农力度 Financial support for agriculture	fis	农林水务支出/财政总支出 Agricultural financial expenditure / total financial expenditure	0.089	0.041	0.012	0.190

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表32000—2019年中国30省(市、自治区)主要年份农业碳排放效率
Table3.Efficiencies of agricultural carbon emissions of 30 provinces (cities, autonomous regions) in China in major years of 2000 to 2019

省(市) Province (city, autonomous region)	2000	2005	2010	2015	2019	年均增长率 Annual growth rate (%)	均值 Mean
湖南 Hunan	0.323	0.312	0.315	0.324	0.346	0.37	0.326
江西 Jiangxi	0.432	0.346	0.338	0.360	1.000	4.52	0.389
湖北 Hubei	0.623	0.473	0.533	0.560	0.519	?0.95	0.539
安徽 Anhui	0.496	0.538	0.567	0.570	0.507	0.11	0.540
福建 Fujian	0.457	0.488	0.562	0.624	0.814	3.08	0.569
浙江 Zhejiang	0.448	0.382	0.476	0.584	1.000	4.32	0.569
广东 Guangdong	0.498	0.538	0.553	0.632	1.000	3.74	0.604
江苏 Jiangsu	0.476	0.490	0.519	0.792	1.000	3.98	0.633
宁夏 Ningxia	0.719	0.715	0.704	0.712	0.733	0.11	0.719
陕西 Shaanxi	0.744	0.771	0.740	0.626	0.868	0.81	0.721
山西 Shanxi	0.742	0.755	0.727	0.747	0.799	0.39	0.758
云南 Yunnan	0.776	0.737	0.706	0.729	0.838	0.40	0.768
四川 Sichuan	1.000	0.751	0.724	0.742	0.811	?1.10	0.773
天津 Tianjin	0.707	0.777	0.701	0.748	1.000	1.84	0.788
辽宁 Liaoning	0.644	0.798	0.711	0.831	1.000	2.34	0.805
重庆 Chongqing	1.000	0.975	0.886	0.751	1.000	0.00	0.863
上海 Shanghai	0.696	0.923	1.000	0.788	0.848	1.04	0.873
海南 Hainan	0.834	0.748	0.685	0.829	1.000	0.96	0.877
贵州 Guizhou	1.000	0.947	0.789	0.828	0.959	?0.22	0.881
甘肃 Gansu	0.996	0.969	0.850	0.797	1.000	0.02	0.890
山东 Shandong	0.958	0.827	0.852	0.916	1.000	0.23	0.891
河北 Hebei	0.927	0.795	0.880	0.933	1.000	0.40	0.911
广西 Guangxi	0.625	1.000	0.887	0.907	1.000	2.50	0.915
黑龙江 Heilongjiang	0.828	0.880	0.920	1.000	1.000	1.00	0.924
河南 Henan	0.937	0.958	0.924	0.933	1.000	0.34	0.942
北京 Beijing	0.898	0.875	0.977	1.000	1.000	0.57	0.950
吉林 Jilin	0.840	1.000	0.901	0.970	1.000	0.92	0.957
新疆 Xinjiang	1.000	0.936	0.965	1.000	1.000	0.00	0.977
内蒙古 Inner Mongolia	1.000	1.000	1.000	1.000	1.000	0.00	1.000
青海 Qinghai	1.000	1.000	1.000	1.000	1.000	0.00	1.000
全国 Nationalwide	0.754	0.757	0.746	0.774	0.901	0.94	0.778

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表42000—2019年中国农业碳排放效率Moran’s I测算结果
Table4.Moran’s I of agricultural carbon emissions efficiency in China from 2000 to 2019

年份 Year	Moran’s I	z	P	年份 Year	Moran’s I	z	P
2000	0.359	4.069	0.000	2010	0.225	2.710	0.003
2001	0.337	3.859	0.000	2011	0.272	3.211	0.001
2002	0.393	4.459	0.000	2012	0.245	2.952	0.002
2003	0.406	4.584	0.000	2013	0.259	3.067	0.001
2004	0.315	3.648	0.000	2014	0.200	2.456	0.007
2005	0.304	3.524	0.000	2015	0.290	3.425	0.000
2006	0.298	3.454	0.000	2016	0.322	3.737	0.000
2007	0.225	2.699	0.003	2017	0.303	3.561	0.000
2008	0.213	2.566	0.005	2018	0.257	3.146	0.001
2009	0.189	2.322	0.010	2019	0.041	0.842	0.200

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表5农业碳排放效率影响因素的空间计量模型估计结果
Table5.Estimation of influencing factors of the agricultural carbon emissions efficiency based on spatial econometric model

变量 Variable	SDM (W₀)		SDM (W₁)		SDM (W₂)
变量 Variable	系数 Coefficient	z-statistics	系数 Coefficient	z-statistics	系数 Coefficient	z-statistics
Ind	?0.986^***	?3.06	?0.718^**	?2.14	?1.385^***	?2.97
cro	?0.237	?1.15	?0.212	?1.03	?0.126	?0.65
agg	?0.006	?0.27	0.016	0.78	0.001	0.05
ln(lan)	0.113	1.53	0.019	1.00	0.003	0.16
ln(cap)	?0.049^***	?2.92	?0.015	?0.47	?0.084	?0.65
ln(tec)	?0.037	?1.01	?0.040	?1.02	?0.025	?0.64
irr	0.297^**	2.09	0.268^**	2.06	0.248^*	1.80
dis	?0.070^**	?2.14	?0.082^***	?2.66	?0.066^**	?2.13
urb	0.113^*	1.77	0.109^*	1.78	0.067	1.09
fis	?0.753^***	?3.01	?0.393	?1.50	?0.840^***	?3.52
W×ind	0.460^*	1.92	0.368^*	1.83	0.341	1.33
W×cro	0.783^*	1.80	0.626^*	1.64	1.055	1.42
W×agg	?0.022	?0.39	?0.048	?1.07	?0.122	?0.80
W×ln(lan)	0.040	0.93	?0.038^*	?1.77	?0.055^**	?2.28
W×ln(cap)	0.010	0.58	0.111	1.46	0.113	1.52
W×ln(tec)	0.087	0.94	0.018	0.33	?0.048	?0.24
W×irr	?0.257	?1.52	?0.178	?1.45	?0.370	?1.14
W×dis	?0.079	?1.63	?0.018	?0.41	?0.220^**	?2.38
W×urb	0.234^*	1.77	0.173^*	1.75	0.419^**	2.26
W×fis	0.503	1.55	?0.082	?0.30	0.617	1.52
ρ	0.185^**	2.10	0.254^***	4.44	0.320^***	3.22
Hausman	33.65^***		32.32^***		37.25^***
Wald-lag	63.28^***		56.00^***		43.71^***
Wald-err	58.88^***		48.91^***		40.04^***
LR-lag	60.24^***		53.51^***		42.50^***
LR-err	58.41^***		49.07^***		41.04^***
R²	0.2592		0.2530		0.2449
Log-pseudolikelihood	692.7242		697.6138		690.8701
observations	600		600		600
、和*分别表示估计系数通过P<10%、P<5%和P<1%显著性水平下的z检验。各变量的意义见表2。, and * indicate that the estimated coefficients pass the z-test at P<10%, P<5%, and P<1% levels of significance, respectively. The meaning of each variable is shown in the table 2.

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表6农业碳排放效率影响因素的总效应、直接效应及溢出效应
Table6.Total, direct and spillover effects of influencing factors of agricultural carbon emissions efficiency

变量 Variable	总效应 Total effect		直接效应 Direct effect		溢出效应 Spillover effect
变量 Variable	系数 Coefficient	z-statistics	系数 Coefficient	z-statistics	系数 Coefficient	z-statistics
ind	?0.656^**	?2.25	?1.093^***	?3.06	0.437^*	1.83
cro	0.749	1.47	?0.219	?1.12	0.969^*	1.73
agg	?0.035	?0.48	?0.005	?0.24	?0.030	?0.43
ln(lan)	0.188^*	1.68	0.116^*	1.68	0.072	1.29
ln(cap)	?0.049^**	?2.54	?0.058^***	?3.07	0.008	0.52
ln(tec)	0.065	0.49	?0.032	?0.83	0.096	0.85
irr	0.094	0.34	0.290^**	2.11	?0.196	?0.85
dis	?0.188^***	?2.95	?0.075^**	?2.38	?0.113^**	?1.96
urb	0.441^***	2.94	0.127^**	2.03	0.314^**	2.08
fis	?0.334	?0.95	?0.738^***	?2.99	0.404	1.15
、和*分别表示估计系数通过P<10%、P<5%和P<1%显著性水平下的z检验。各变量的意义见表2。, and * indicate that the estimated coefficients pass the z-test at P<10%, P<5%, and P<1% levels of significance, respectively. The meaning of each variable is shown in the table 2.

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参考文献(32)

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