湖北省农业碳排放效率时空差异及影响因素

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田云^,, 王梦晨中南财经政法大学工商管理学院,武汉 430073

Research on Spatial and Temporal Difference of Agricultural Carbon Emission Efficiency and Its Influencing Factors in Hubei Province

TIAN Yun^,, WANG MengChenSchool of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073

责任编辑: 李云霞
收稿日期:2020-05-4接受日期:2020-07-21网络出版日期:2020-12-16

基金资助:

国家自然科学基金.71903197
中南财经政法大学中央高校基本科研业务费专项资金资助项目.2722020JCT020

Received:2020-05-4Accepted:2020-07-21Online:2020-12-16
作者简介 About authors
田云,Tel：13554101207;E-mail： tianyun1986@163.com

摘要
【目的】传统农业大省湖北省对化肥、农药等农用物资的依赖度过高,客观上导致其农业生产相对高碳。本研究目的在于厘清其农业碳排放效率及影响因素,为湖北省农业低碳生产的切实推进提供参考依据与政策启示。【方法】利用DEA-Malmquist分解法对湖北省农业碳排放效率进行有效测度并分析其时空差异特征;在此基础上运用Tobit模型探究影响其碳排放效率变化的关键因素。【结果】2011年以来湖北省农业碳排放效率虽年际间存在一定波动,但总体处于增长态势,年平均增速为2.9%;从驱动源泉来看其提升主要依赖于前沿技术进步而非技术效率改善,进一步对技术效率分解可知,纯技术效率恶化趋势较为明显而规模效率得到了轻微改善。湖北省各市（州）农业碳排放效率差异明显,其中以武汉最高,达到了1.584,而荆门最低,仅为0.803;结合数值差异可将15个地区划分为高速增长、低速增长以及下降等3个不同组别;前沿技术进步在推进各地区农业碳排放效率提升上发挥了更为明显的作用,而技术效率改善所起作用相对较小,分解技术效率可知,纯技术效率与规模效率的作用方向因地而异,但后者作用力度要略大于前者。农村经济发展水平、城镇化水平、农村用电量均对湖北省农业碳排放效率产生了显著的正向影响,即在其他条件维持不变的前提下,农民人均纯收入越高、或者城镇化水平越高、或者农村用电量越大,农业碳排放效率越高;而农业产业结构所处情形正好相反,具体表现为,种植业产值比重越高越不利于农业碳排放效率的提升。【结论】湖北省农业碳排放效率总体处于上升态势,但伴随着年际波动各市（州）碳排放效率存在较大差异,无论是湖北省还是各市（州）其农业碳排放效率的提升都更多地依赖于前沿技术进步而非技术效率的改善,这也要求我们在推进湖北农业低碳生产过程中不仅要注重新技术的研发,更需强化对各类技术的合理运用。考虑到农村经济发展、城镇化水平、农村用电量以及农业产业结构均对农业碳排放效率产生了显著影响的现实境况,实践中可以通过繁荣农村经济发展、提升城镇化水平、保障农村用电需求、优化农业产业结构、完善法制建设与制度保障等手段来切实确保农业碳排放效率得到提高。
关键词： 农业碳排放;碳排放效率;时空差异;影响因素;湖北省

Abstract

【Objective】As a traditional agricultural province, Hubei Province has a heavy dependence on agricultural inputs such as chemical fertilizers and pesticides, which has objectively led to its relatively high carbon emissions in the agricultural production. Clarifying its agricultural carbon emission efficiency and influencing factors could provide necessary references and policy implications for the practical promotion of agricultural low-carbon production in Hubei Province. 【Method】The DEA-Malmquist decomposition method was employed to effectively measure the agricultural carbon emission efficiency of Hubei Province, and its temporal and spatial characteristics were analyzed. On this basis, Tobit model was adopted to explore the key factors affecting the change of its carbon emission efficiency. 【Result】Since 2011, the agricultural carbon emission efficiency of Hubei has been overall increasing but with certain interannual fluctuations, and the average annual growth rate was 2.9%. From the perspective of driving sources, its enhancement mainly depended on the progress of frontier technology rather than technical efficiency improvement. Further decomposition of technical efficiency showed that the pure technical efficiency had an obvious trend of deterioration, while the scale efficiency has been slightly improved. There were apparent differences in agricultural carbon emission efficiency among cities and prefectures in Hubei Province, among which Wuhan had the highest of 1.584 while Jingmen has the lowest of 0.803. Among to numerical differences, 15 regions could be divided into three different groups: high-speed growth, low-speed growth and decline. Frontier technological progress played a more obvious role in promoting the agricultural carbon emission efficiency for all regions, while the improvement of technical efficiency played a relatively small role. By decomposing the technical efficiency, it could be found that the influencing direction of pure technical efficiency and scale efficiency varied by regions, but the latter had a slightly greater effect than the former. Rural economic development level, urbanization level and rural electricity consumption had a significant and positive impact on agricultural carbon emission efficiency of Hubei Province, that is, under the premise that other conditions remained unchanged, the higher farmers’ net income per capita, the higher the urbanization level, or the greater the rural electricity consumption, the higher the agricultural carbon emission efficiency. However, the situation of agricultural industrial structure was exactly the opposite. Specifically, the higher proportion of the output value of planting industry was not conducive to the improvement of agricultural carbon emission efficiency. 【Conclusion】The agricultural carbon emission efficiency in Hubei Province was generally on the rise but with interannual fluctuations, and there were great differences among cities and prefectures. Whether it was Hubei Province or cities and prefectures, the enhancement of agricultural carbon emission efficiency depended more on frontier technological progress than technical efficiency improvement, which also required us not only to pay attention to the research and development of new technologies but to strengthen the rational use of various technologies in the process of promoting agricultural low-carbon production in Hubei Province. Considering the realistic situation that rural economic development, urbanization level, rural electricity consumption and agricultural industrial structure all had a significant impact on agricultural carbon emission efficiency, in practice, the enhancement of agricultural carbon emission efficiency could be effectively ensured by means of prospering rural economic development, improving urbanization level, ensuring rural electricity demand, optimizing agricultural industrial structure, improving the legal system construction and institutional support, etc.

Keywords：agricultural carbon emissions;carbon emission efficiency;temporal and spatial differences;influencing factors;Hubei Province

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本文引用格式
田云, 王梦晨. 湖北省农业碳排放效率时空差异及影响因素[J]. 中国农业科学, 2020, 53(24): 5063-5072 doi:10.3864/j.issn.0578-1752.2020.24.009
TIAN Yun, WANG MengChen. Research on Spatial and Temporal Difference of Agricultural Carbon Emission Efficiency and Its Influencing Factors in Hubei Province[J]. Scientia Acricultura Sinica, 2020, 53(24): 5063-5072 doi:10.3864/j.issn.0578-1752.2020.24.009

开放科学（资源服务）标识码（OSID）：

0 引言

【研究意义】近年来,气候变化问题已引起了世界各国以及社会各界的广泛关注,并成为了众多****探讨的焦点。其中,联合国政府间气候变化专门委员会（IPCC）发布的第四次评估报告显示,在未来100年内全球地表温度有可能提升1.8—4.0℃,为此抑制全球气候变暖已显得刻不容缓。虽然引起气温上升的原因是多方面的,但人类活动所导致的碳排放在其中扮演极为重要的角色。诚然工业和服务业是产生碳排放的主要源泉,但快速发展的农业所起的助推作用也不容忽视。而作为我国传统农业大省,湖北虽为我国提供了大量的商品粮、棉、油、肉以及水产品,但由于自身对化肥、农药等农用物资的依赖程度过高,客观上也导致了其农业生产相对高碳。为了更好地践行绿色发展战略,加快推进湖北农业低碳发展已迫在眉睫,而厘清其农业碳排放现状及效率特征是实现这一目标的重要前提。【前人研究进展】越来越多的****开始围绕农业碳排放问题展开探究,并涉及到了方方面面。其中,早期主要偏重于农业碳排放测算、时空特征分析以及驱动因素探究。李波等^[1]、闵继胜等^[2]、TIAN等^[3] 的综合研究表明,自20世纪90年代以来,我国农业碳排放量总体处于增长态势而且地区差异明显,农业经济发展是导致其数量变化的关键性因素。而后,随着研究的逐步深入,****们开始聚焦于农业碳排放的效率测度与影响因素探讨,且多立足于国家视角^[4,5,6]。研究发现,虽然中国农业碳排放效率总体有较大提高,但区域间存在明显的非均衡性,其变化主要受产业集聚、城镇化水平等因素影响。除此之外,还有不少****围绕农业碳排放与经济发展的互动关系^[7,8]、气候变化与低碳农业生产关系^[9]、农业碳减排潜力评估与减排成本测度^[10,11,12]、农业低碳技术与生产行为^[13,14,15]等问题展开深度剖析。与此同时,虽然也有不少****围绕湖北农业碳排放问题展开探究,但主要聚焦于农业碳排放现状测度与驱动因素分解^[16]、农业产业化与农村碳排放关系^[17]、技术进步与生猪养殖温室气体排放^[18]、农业碳排放与农业经济的相互关联^[19]等几个领域,而较少涉及其他方面。【本研究切入点】从现有文献来看,农业碳排放效率及其影响因素的研究主要集中于国家层面,多以31个省区作为考察对象,而鲜有****围绕湖北省及各市（州）农业碳效率问题展开深度探讨。所谓农业碳排放效率,即指碳排放约束下的农业生产率水平,在此将农业碳排放作为了非期望产出。【拟解决的关键问题】本文将以湖北省作为研究对象,首先利用DEA-Malmquist分解法完成对其农业碳排放效率的测度并分析其时空差异特征;而后运用Tobit模型探究影响其碳排放效率变化的关键因素。相关研究结论的获取能够为湖北农业低碳生产的切实推进提供必要的参考依据与政策启示。

1 研究方法

1.1 DEA-Malmquist指数分解法与变量选取

本文所要探讨的农业碳排放效率属于传统农业生产率的特殊表现形式,唯一的区别在于产出指标还兼顾了非期望产出。由于存在非期望产出,使得Shephard距离函数无法完成对碳排放生产率的准确测度。为此,实际分析中将构造基于SBM方向距离函数的Malmquist-Luenberger生产率指数对湖北省农业碳排放效率进行测度。具体分析中,将基于跨期动态概念,参照Malmquist指数几何平均值思路,构建从t到t+1基于乘除结构和相邻参比的SBM方向性距离函数的全要素生产率指数^[21],并定义为碳排放生产效率（CTFP）指数：

（1）$CTFP=\left[ \frac{\vec{D}_{C}^{t}({{x}^{t+1}},{{y}^{t+1}},{{z}^{t+1}})}{\vec{D}_{C}^{t}({{x}^{t}},{{y}^{t}},{{z}^{t}})}\times \frac{\vec{D}_{C}^{t+1}({{x}^{t+1}},{{y}^{t+1}},{{z}^{t+1}})}{\vec{D}_{C}^{t+1}({{x}^{t}},{{y}^{t}},{{z}^{t}})} \right]$

（2）$CEFF=\frac{\vec{D}_{C}^{t}({{x}^{t+1}},{{y}^{t+1}},{{z}^{t+1}})}{\vec{D}_{C}^{t}({{x}^{t}},{{y}^{t}},{{z}^{t}})}$

（3）$CTECH={{\left[ \frac{\vec{D}_{C}^{t}({{x}^{t+1}},{{y}^{t+1}},{{z}^{t+1}})}{\vec{D}_{C}^{t+1}({{x}^{t+1}},{{y}^{t+1}},{{z}^{t+1}})}\times \frac{\vec{D}_{C}^{t}({{x}^{t}},{{y}^{t}},{{z}^{t}})}{\vec{D}_{C}^{t+1}({{x}^{t}},{{y}^{t}},{{z}^{t}})} \right]}^{\frac{1}{2}}}$

（4）CTFP=CEFF×CTECH

式中,CTFP代表第t期到t+1期农业碳排放效率的变化率,若CTFP>1时,表明农业碳排放效率有所增长;反之,则下降。进一步,CTFP可分解为技术效率（CEFF）和前沿技术进步（CTECH）,其中CEFF表示第t期到t+1期技术效率的变化率,CEFF>1则揭示技术效率提高,反之则下降;CTECH表示第t期到t+1期的技术水平变化,CTECH>1表明技术有所进步,反之即为恶化。需要说明的是,技术效率可以进一步分解为纯技术效率（CPECH）和规模效率（CSECH）。

选择合适的投入与产出变量对于农业碳排放效率测度至关重要,为了确保变量选择的科学性,在此对已有相关研究进行回顾与梳理。其中,关于投入变量的选择,农业劳动力、农业机械总动力、耕地面积与役畜投入得到了较多****^[20,21]的认可;而关于产出指标的确定,农业总产值一般被当作期望产出,而农业面源污染、农业碳排放等则通常作为非期望产出^[22,23,24]。本文将在参照已有研究成果的基础上,结合数据的可获取性,选取农业投入产出变量具体如下：

农业投入变量包含农业劳动力、土地、农业机械动力、灌溉以及役畜投入等5个方面。其中,农业劳动力反映了人力资本层面的投入,具体将以湖北省各市（州）第一产业年末从业人数作为其衡量标准,单位为万人;土地是农业生产活动得以开展的物质基础,具体以各市（州）年末耕地面积作为衡量指标,单位为khm²;农业机械的广泛使用是促进农业生产力提升的重要手段,故在此也将其作为投入指标,具体以各市（州）历年农业机械总动力作为衡量依据,单位为万kW;随着农田水利等基础设施建设的日趋完善,灌溉在农作物种植过程中扮演着越发重要角色,因此我们不能忽视灌溉投入对农业生产的影响,在此以各市（州）历年有效灌溉面积作为衡量灌溉投入的具体指标,单位为khm²;除此之外,役畜虽然在农业生产中所起作用越来越小,但也不可忽视其存在,仍有必要将其作为投入指标之一,具体以牛（非肉牛、奶牛）、马、驴、骡等的数量作为替代变量,单位为万头。农业产出指标包含两个方面,即期望产出农业总产值,以各市（州）历年农业总产值作为衡量标准,单位为亿元,以及非期望产出农业碳排放量,以各市（州）历年实际农业碳排放数量为准,单位为万t。

1.2 Tobit模型

在厘清湖北省及各市（州）农业碳排放效率之后,为了确保对策建议的针对性,有必要围绕其影响因素展开探讨。具体而言,以历年各地区农业碳排放效率值为因变量,各影响因素为解释变量构建线性回归方程,而后通过自变量系数判断各影响因素对效率值的作用方向及程度。由于DEA方法测算出的效率值一般大于0,直接运用普通最小二乘法实施回归可能会导致参数估计值出现偏向0的情形,为此本文将借鉴一些****^[21,25]的已有做法,运用因变量受限制的Tobit模型展开实证分析。其模型基本形式如下：

（5）$\left\{ \begin{matrix} & {{y}_{i}}={{\beta }^{T}}{{x}_{i}}+{{e}_{i}},若{{\beta }^{T}}+{{e}_{i}}={{y}_{0}} \\ & \text{ }0,其他 \\ \end{matrix} \right.$

e_i~N(0, σ²), i=1,2,3,K,……n

式（5）中,e_i为受限因变量,x_i为解释变量,β为回归系数。在此基础上,结合本文研究目的,可构建以农业碳排放效率（CTFP）为被解释变量的Tobit模型如下：

（6）$CTFP={{\beta }_{0}}+{{\beta }_{1}}x_{i}^{t}+{{\beta }_{2}}x_{i}^{t}+\cdots \cdots +{{\beta }_{n}}x_{i}^{t}+\mu _{i}^{t}$

式（6）中的CTFP为农业碳排放效率值,i表示各市（州）,t表示年份,β₀为常数项,β₁、β₂、β_n分别为各个解释变量的回归系数,μ为随机扰动项。

1.3 数据来源及处理

耕地面积、第一产业从业人员、役畜数量、农用机械总动力、有效灌溉面积、农业总产值等投入产出指标的原始数据出自《湖北统计年鉴》《湖北农村统计年鉴》以及各市（州）年鉴。同时,考虑到以实价计算的农林牧业总产值不能进行纵向对比,实际分析中将基于2010年不变价对各市（州）历年农业总产值进行调整。至于各市（州）农业碳排放量,由于目前官方尚无统计,且已有研究成果也较少涉及,为此本文将对其进行有效测度。纵览现有文献可知,一般从农用物资投入、水稻种植以及畜禽养殖等3个维度完成对农业碳排放的测算^[26,27],本文也将参照这一做法;但受限于数据的可获取性,在具体指标的选择上有所区别。其中,农用物资投入包含化肥、农药、农膜以及农用柴油,并以各自实际使用量为准;水稻则结合其生长周期的不同分为早稻、中稻和晚稻,均以实际播种面积为准;畜禽养殖主要涉及猪、牛、羊等三大主要牲畜,且考虑到各自饲养周期存在差异,在此参照胡向东等^[28]的计算方法对其年均饲养量进行调整。具体测算方法及各自所对应的碳排放系数出自IPCC以及闵继胜等^[2]、TIAN等^[3]的相关研究,限于篇幅,在此不做过多介绍。另外,鉴于各类基础数据缺失较为严重,鄂州与神农架均不在本次研究的考察范围之内。各市（州）农业投入、产出变量的一般描述性分析如表1所示。

Table 1
表1
表1农业投入、产出指标的一般描述性统计结果
Table 1General descriptive statistical results of agricultural input and output indicators

指标 Indicators		变量 Variable	单位 Unit	极小值 Minimum	极大值 Max	均值 Mean	标准差 St. deviation
产出指标 Output indicator	期望产出 Expected output	农业总产值 Gross agricultural output value	×10⁸ yuan	31.86	352.19	141.75	83.32
产出指标 Output indicator	非期望产出 Unexpected output	农业碳排放量 Agricultural carbon emissions	×10⁴t	25.55	212.07	98.19	60.10
投入指标Input index		劳动力Labor force	×10⁴	7.81	134.35	62.09	34.82
		土地Land	khm²	70.22	470.34	225.3	120.87
		农用机械Agricultural machinery	×10⁴kW	72.34	646.07	264.65	149.90
		灌溉Irrigation	khm²	30.38	423.26	151.1	101.23
		役畜Beast of burden	×10⁴	0.31	108.19	29.05	30.31

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2 结果

2.1 湖北省农业碳排放效率时序比较

基于前文所构建公式,利用MAX-DEA软件测度湖北省农业碳排放效率如表2所示。从中不难发现,2011年以来湖北省农业碳排放效率虽年际间存在一定波动但总体处于增长态势,年均增速为2.9%。从驱动源泉来看,农业碳排放综合效率的提升主要依赖于前沿技术进步,其年均贡献率达到了3.6%;农业技术效率则处于恶化状态,年均递减0.3%,进一步分解可知,纯技术效率恶化趋势较为明显,年均递减1.1%,而规模效率得到了轻微改善,年均递增0.7%。

Table 2
表2
表2湖北省农业碳排放效率变化情况
Table 2Changes in agricultural carbon emission efficiency in Hubei Province

年份 Year	前沿技术进步 CTECH		技术效率 CEFF		纯技术效率 CPECH		规模效率 CSECH		综合效率 CTFP
年份 Year	年际值 Interannual	累计值 Cumulative	年际值 Interannual	累计值 Cumulative	年际值 Interannual	累计值 Cumulative	年际值 Interannual	累计值 Cumulative	年际值 Interannual	累计值 Cumulative
2011	1.024	1.024	0.974	0.974	0.991	0.991	0.983	0.983	0.998	0.998
2012	1.007	1.031	0.951	0.926	0.944	0.936	1.008	0.991	0.958	0.956
2013	1.220	1.258	0.875	0.809	0.954	0.892	0.917	0.909	1.067	1.020
2014	1.070	1.339	0.989	0.791	1.016	0.903	0.974	0.884	1.059	1.083
2015	1.133	1.497	1.165	0.919	1.062	0.961	1.097	0.968	1.320	1.428
2016	0.769	1.118	1.070	1.021	1.030	0.996	1.039	1.017	0.822	1.152
2017	1.047	1.280	0.953	0.976	0.926	0.923	1.029	1.048	0.998	1.222
平均Average	1.036	–	0.997	–	0.989	–	1.007	–	1.029	–

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具体到各个年份,2013年、2014年和2015年的农业碳排放综合效率值高于1.0,表明在这3年中湖北省农业低碳发展态势较好;而其他各年综合效率值均在1.0以下,揭示其农业低碳生产面临一定挑战。其中,以2015年综合生产率指数最高,为1.320,表明该年湖北农业碳排放效率较2014年提升了32.0%;与此对应2016年的综合效率值最低,仅为0.822,即较2015年降低了17.8%。进一步剖析历年农业碳排放效率的增长源泉可知,综合效率值高于1.0的3个年份中,2013和2014年碳排放效率的提升完全归功于前沿技术的进步,此时农业技术效率甚至处于下降态势,但所导致的损失要明显小于技术进步带来的贡献;2015年则得益于前沿技术进步与农业技术效率提升的双重贡献,且后者所发挥作用要略大于前者。综合效率值低于1.0的4个年份中,2011年、2012年和2017年碳排放效率的下降完全归结于农业技术效率的恶化,前沿技术虽处于进步状态但却无法弥补技术效率恶化所带来的巨大损失;2016年情形正好相反,其综合效率的下降完全源于前沿技术的大幅衰退,此时农业技术效率虽得到一定提升但却难以抵消技术退化所导致的损失。对各年农业技术效率进行分解可知,纯技术效率与规模效率的作用方向因年而异,作用力度各年情形虽也不尽相同但后者要略大于前者。

2.2 湖北省农业碳排放效率区域比较

接下来,测算湖北省15个市（州）农业碳排放效率如表3所示。从中不难发现,有 8个地区农业碳排放综合效率均值高于1.0,且以武汉最高,达到了1.584;与此对应,其他7个地区综合效率均值低于1.0,且以荆门最低,仅为0.803。结合农业碳排放效率数值差异可将15个市（州）划分为3个组别,即高速增长、低速增长以及下降组。其中,“高速组”包含武汉、宜昌、孝感、随州等4地,其农业碳排放综合效率均在1.10以上。独特的产业结构特点是促使各地效率较高的关键动因,比如武汉经济作物种植比重较高客观提升了其经济产出水平,各地畜牧业占比相对较小或者水稻种植规模偏低也在一定程度上减少了碳排放量。“低速组”包含襄阳、黄冈、仙桃、天门等4地,其农业碳排放综合效率均在1.05以下。黄冈虽为农业大市,但受制于平原面积较少,致使其农业附加值相对较低;襄阳农业产出水平总体尚可,但过高的农用物资投入导致其碳排放量居高不下;仙桃、天门均为省直管县级市,总体处于高排放、高产出状态。“下降组”包含黄石、十堰、荆门、荆州、咸宁、恩施、潜江等6市1州,其农业碳排放综合效率均在1.0以下。其中十堰、恩施以山区地形为主,农业产出水平较低且碳排放处于较高水平,由此导致碳排放效率较低;余下4地主要坐落于平原地区,农作物以水稻种植为主,经济价值较为一般却引发了较高的碳排放量,客观上导致各自碳排放效率较低。

Table 3
表3
表3各市（州）农业碳排放效率及其增长源泉比较
Table 3Comparison of agricultural carbon emission efficiency and growth sources among cities and prefectures

地区 Region	前沿技术进步 CTECH	技术效率 CEFF	纯技术效率 CPECH	规模效率 CSECH	综合效率 CTFP	排名 Rank	增长类型 Growth type
武汉Wuhan	1.398	1.133	1.000	1.133	1.584	1	高速High speed
黄石Huangshi	1.012	0.984	0.987	0.996	0.995	9	下降Decline
十堰Shiyan	0.827	1.000	1.000	1.000	0.827	14	下降Decline
宜昌Yichang	1.191	1.000	1.000	1.000	1.191	2	高速High speed
襄阳Xiangyang	1.007	1.000	1.000	1.000	1.007	7	低速Low speed
荆门Jingmen	0.803	1.000	1.000	1.000	0.803	15	下降Decline
孝感Xiaogan	1.068	1.043	1.017	1.025	1.113	3	高速High speed
荆州Jingzhou	0.977	1.004	1.000	1.004	0.982	11	下降Decline
黄冈Huanggang	1.051	0.989	1.000	0.989	1.040	5	低速Low speed
咸宁Xianning	1.036	0.946	0.947	0.999	0.979	12	下降Decline
随州Suizhou	1.106	1.000	1.000	1.000	1.106	4	高速High speed
恩施Enshi	1.014	0.880	0.886	0.994	0.893	13	下降Decline
仙桃Xiantao	1.027	0.979	0.998	0.981	1.006	8	低速Low speed
潜江Qianjiang	1.008	0.978	0.994	0.984	0.986	10	下降Decline
天门Tianmen	1.043	0.980	0.995	0.985	1.022	6	低速Low speed

^{各市（州）农业碳排放效率取其2011—2017年的均值}
^{The agricultural carbon emission efficiency of each city and prefectures takes its average value from 2011 to 2017}

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从各市（州）农业碳排放效率的增长源泉来看,8个综合效率值高于1.0的地区中,武汉和孝感碳排放效率的提升源于前沿技术进步与农业技术效率改善的双重贡献,且前者作用更为明显;其他6地农业碳排放效率的改善完全依赖于前沿技术进步,其农业碳排放效率或处于不变状态（宜昌、襄阳、随州）、或呈现恶化状态（黄冈、仙桃、天门）。对上述各地技术效率进行分解可知,纯技术效率以不变和恶化状态为主而规模效率三类情形（即改善、不变、恶化）分布较为均衡。7个综合效率值低于1.0的地区中,黄石、咸宁、恩施、潜江等4地的碳排放效率下降主要归结于农业技术效率的大幅恶化,各自前沿技术虽处于进步状态但其幅度有限,无法避免技术效率恶化所带来的效率损失;十堰、荆门、荆州等3地则完全源于前沿技术的退化,各自农业技术效率处于不变或者改进状态。分解7地农业技术效率可知,除荆州之外,其他各地区纯技术效率与规模效率要么同时维持不变、要么均为恶化状态。总体而言,前沿技术进步在推进各地区农业碳排放效率提升上发挥了更为明显的作用,而技术效率改善所起作用相对较小;对技术效率进行分解可知,纯技术效率与规模效率的作用方向因地而异,但后者作用力度要略大于前者。

2.3 湖北省农业碳排放效率影响因素分析

2.3.1 变量确定分析农业碳排放效率影响因素的关键在于变量的确定,其中被解释变量即为农业碳排放效率值,而解释变量则需在参考已有研究成果的基础上科学选择。近年来,有不少****围绕这一选题展开研究,不过各自在解释变量的选择上却略有差异。纵览文献可知,产业结构、经济水平、城镇化水平、自然灾害等变量得到了广泛认同^[29,30,31];除此之外,还涉及到农村用电量^[22]、种植规模^[32]等变量。基于湖北农业生产的现状特征,并考虑到数据的可获取性,本文拟选取农村经济发展水平、耕地规模、农业产业结构、城镇化水平与农村用电量等5个变量作为潜在的解释变量,并提出如下研究假设。

农村经济发展水平。经济发展水平一定程度上决定农业发展高度、反映农民日常生活水准,因此它是呈现农业农村发展水平的重要指标。一般而言,经济水平越高的地区,农业生产会倾向于资本密集型而非劳动力密集型,农用物资投入量相对较高,客观上会导致温室气体排放绝对数量的增加;但同时,农资投入的增加会有助于农业现代化步伐的加快,进而使得农业产出水平得到提升。因此,农村经济发展水平能否促进农业碳排放效率提升将取决于碳增量与农业产出水平各自的作用力大小。在此,以农民人均纯收入作为农村经济发展的替代指标,单位为元,该变量作用方向难以确定,亟待实证检验。

耕地规模。耕地规模能较为客观地反映种植业发展态势,规模增加,农作物播种面积就有可能扩大,反之则可能减少。湖北素有“鱼米之乡”美誉,且江汉平原是我国重要的商品粮生产基地并以水稻种植为主,而耕地规模变化一般会对水稻生产产生直接影响。众所周知,除了一般农用物资投入所引发的碳排放外,水稻在其生长过程中还会产生大量甲烷,由此导致其碳排放量处于较高水平,进而对农业碳排放效率产生影响。在此,我们以实际耕地面积作为衡量耕地规模的指标,单位为khm²,并假定耕地规模越大农业碳排放效率越低。

农业产业结构。农业由种植业、林业、畜牧业和渔业等四大产业部门构成,由于各自产业特征存在差异,其碳排放水平必然会有所区别,为此产业结构变化在一定程度上可能也会对农业碳排放效率产生影响。结合湖北农业碳排放特征可知,由于农用物资投入量较大且粮食作物生产以水稻为主,导致种植业是碳排放的主要源头;且与其他产业相比,种植业单位产值所引发的碳排放量明显更高。为此,我们不能忽视农业产业结构对碳排放效率的影响,在此以种植业产值所占农业总产值比重作为衡量指标,并假定其比重越高农业碳排放性效率越低。

城镇化水平。近年来,随着社会经济水平的不断提升,我国城市化进程明显加快,一定程度上也对农业生产部门产生了影响。一方面,城镇化水平的提升意味着二、三产业的不断发展,而制造业、服务业水平的不断进步会反作用于农业生产,促进其机械化、现代化步伐的快速推进,由此也会导致农用能源使用量的增加,进而加剧温室气体排放;但另一方面,城镇化水平的提升必然会导致城市规模的扩大,在这过程中农业用地会受到一定冲击,种植业生产由此受到影响,客观上也会导致碳排放量的减少。为此,有必要将城镇化水平（即城镇人口占总人口的比重）作为解释变量之一,其作用方向目前较难确定,亟待实证检验。

农村用电量。农业农村发展离不开电力的有力支持,如农业灌溉活动在很大程度上就会对电能产生依赖;另外,随着农业现代化步伐的加快,电能也正逐步取代以柴油为代表的农用能源,成为农业生产的重要动力支撑。事实上,虽然电能（尤其是火电）在产生过程中也会导致温室气体排放,但其作用强度可能略低于农用能源的大量使用。考虑到目前有关各地农业用电的统计资料较为缺乏,本文选择农村用电量近似替代,单位为kW·h。之所以如此考虑,是基于当前各个农村家庭的生活用电已趋于稳定而农村用电量的变化主要归结于农业生产活动这一现实情况。在此假定农村用电量越大农业碳排放效率越高。

2.3.2 实证分析 Hausman检验表明对农业碳排放效率的回归分析应选择固定效应的面板数据Tobit模型。在此,利用Stata软件进行估算,回归结果如表4所示。由此可知,农村经济发展水平、农业产业结构、城镇化水平、农村用电量均对农业碳排放性效率产生了显著影响,仅有耕地规模未通过显著性检验。

Table 4
表4
表4农业碳排放效率影响因素的实证分析结果
Table 4Empirical analysis results on influencing factors of agricultural carbon emission efficiency

变量Variable	系数Coefficient	标准差St. deviation	检验值Inspection value
农村经济发展水平Rural economic development level	0.0078^**	0.7270	2.43
耕地规模Cultivated land scale	-0.0484	0.1016	-0.48
农业产业结构Agricultural industrial structure	-0.7449^**	0.3618	-2.06
城镇化水平Urbanization level	0.0136^***	0.1681	3.15
农村用电量Rural electricity consumption	0.0025^*	0.4502	1.32
常数项Constant term	1.3705^***	0.2778	4.94

^*、^**、^***分别表示在10%、5%、1%的水平下显著
^*, ^**, ^*** denote statistical significance at 10%, 5%, and 1%, respectively

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具体而言,农村经济发展水平对农业碳排放效率具有显著的正向影响,即在其他条件保持不变的情况下,农民人均纯收入越高,农业碳排放效率越高。可能的解释是,农民收入的提高意味着更多资本投入到农业生产中,由此利于其生产方式改革,加之历年“中央一号”文件均鼓励先进农业生产技术的使用,客观上也有助于农业生产率水平的提升,进而使得农业碳排放效率也得到一定改善。农业产业结构与农业碳排放效率呈现显著的负相关,即在其他条件不变的情况下,种植业产值比重越高越不利于农业碳排放效率的提升。测算结果表明,种植业是各市（州）农业碳排放的主要源头,占比多在80%以上;在既定产出规模约束下,种植业比重的提升显然会导致碳排放量的边际递增,进而导致碳排放效率下降。城镇化水平对农业碳排放效率具有显著的正向影响,即倘若其他条件不变,提升城镇化水平能促进农业碳排放效率的改善。通常而言,城镇化进程的加快必然意味着建设用地的增加,一定程度上会导致农业用地规模的缩减,进而对种植业产生较大影响,客观上也会促使农业碳排放量的减少与碳排放效率的提升。农村用电量与农业碳排放效率呈现显著的正相关关系,即如果其他条件维持不变,农村用电量越大农业碳排放效率越高。虽然电能耗费也会导致碳排放的产生,但其作用强度要低于能源使用,在单位产出一定时,若以电能取代能源（主要为柴油）消耗,将会极大减少碳排放量,进而促使农业碳排放效率的提升。

3 讨论

本文以湖北省作为研究对象,围绕其农业碳排放效率及影响因素展开探讨,这是对现有湖北农业碳排放问题研究体系的有力补充,在一定程度上弥补了现有研究的不足,同时也能为湖北农业低碳生产提供必要的参考依据。研究结果主要揭示两点：一是湖北省农业碳排放效率时空差异明显,其提升主要依赖于前沿技术进步;二是农村经济发展水平、城镇化水平、农村用电量以及农业产业结构是导致湖北碳排放效率变化的关键性因素。这也要求我们在推进湖北农业低碳生产过程中不仅要注重新技术的研发,更需要强化对各类技术的合理运用,以保障技术效率得到提升。同时,还可通过以下举措切实推进湖北省农业低碳生产：一是加大对农业农村的有效支持,进一步促进农村经济繁荣发展;二是制定农业低碳发展战略规划,积极优化农业产业结构;三是各地加快城市化进程,着力实现城乡一体化发展;四是逐步完善农业低碳生产相关立法,强化制度支持与政策保障。

与已有研究成果^[20,24,31]相比,本文所呈现出来的见解同中有异：从共同点来看,农业碳排放效率总体处于波动上升态势且各地区水平差异明显;农村经济发展水平越高、或者种植业占比越低,越有利于农业碳排放效率的提升。从差异因素来看,农业开放度变量在不少研究中得到运用且通过了显著性检验,但本文却未曾涉及,原因在于其他研究多立足于省域层面进行考察,而本文仅着眼于市级维度,相关数据较为缺乏;城镇化水平在不少研究中并未通过显著性检验,而于本文却表现出了正向作用,这主要是由于城镇化测度方法的不同以及各地实际情况的差异所致。

当然,本研究也存在一定欠缺,主要表现在：第一,由于市级层面的数据极难获取,所采用的是广义农业指标,而农业碳排放测算主要考察的是种植业和畜牧业,渔业和林业涉及较少,由此可能导致农业投入指标与农业碳排放之间存在不匹配问题,进而使得农业碳排放效率指数分解结果存在一定偏差。第二,本文测度农业碳排放效率涉及年限较短,不利于其长期演变轨迹的深度剖析;由于现有年鉴难以搜集到湖北省各市（州）2010年之前的相关原始数据,我们只能通过缩短考察周期来保证研究的顺利开展。而在今后,亟需拓展数据来源渠道以确保分析结果更加客观合理。

4 结论

4.1 湖北省农业碳排放效率总体处于上升态势但伴随着年际波动,各市（州）碳排放效率存在较大差异,无论是湖北省还是各市（州）其农业碳排放效率的提升都更多地依赖于前沿技术进步而非技术效率的改善,这也要求我们在推进湖北农业低碳生产过程中不仅要注重新技术的研发,更需强化对各类技术的合理运用。

4.2 考虑到农村经济发展、城镇化水平、农村用电量以及农业产业结构均对农业碳排放效率产生了显著影响的现实境况,实践中我们可以通过繁荣农村经济发展、提升城镇化水平、保障农村用电需求、优化农业产业结构、完善法制建设与制度保障等手段来切实确保农业碳排放效率得到提高。

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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(10):269-273.

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Agriculture is one of the important greenhouse gas (GHG) emission sources. Based on People’s Republic of China Initinal National Communications on Climate Change, the GHG emission from agricultural sources contributed to 17% of China’s total greenhouse gas emissions in 1994. The methane emission from agricultural activities amounted for 50.15% of China’s total methane, the nitrous oxide emission from agricultural sources accounted for 92.47% of China’s total. Analysis of published papers and documents suggested that following technologies have the potential to mitigate GHG emission from agricultural activities. Improvement of ruminant nutrient is estimated to reduce CH4 emission from individual yellow cattle by 16%～30%; Intermittent irrigation of rice paddy field could reduce CH4 emissions flux by 30% in comparison with flush irrigation; Maximum GHG emission reduction per household biogas digesters could be 2.0～4.1 t CO2 equivalent per year. Application of release-controlled nitrogen fertilizer to crop land reduced N2O emission flux by 50%～70% against regular fertilizers. It is suggested that demonstration and assessment of identified mitigation technology are definitely necessary.

DONG H

, LI Y

, TAO X

, PENG X

, LI

, ZHU Z

. China greenhouse gas emissions from agricultural activities and its mitigation strategy
Transactions of the Chinese Society of Agricultural Engineering, 2008(10):269-273. (in Chinese)

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田云, 张银岭. 中国农业碳减排成效评估、目标重构与路径优化研究
干旱区资源与环境, 2019,33(12):1-7.

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TIAN

, ZHANG Y

. Research on effectiveness evaluation, target reconstruction and path optimization of agricultural carbon emission reduction in China
Journal of Arid Land Resources and Environment, 2019,33(12):1-7. (in Chinese)

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胡向东, 王济民. 中国畜禽温室气体排放量估算
农业工程学报, 2010,26(10):247-252.

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To preliminarily explore livestock greenhouse gas emissions and trends, and assess the amount of greenhouse gases emitted by China’s livestock industries, using the calculation methodology and livestock industry gas emission parameters published by the In

HU X

, WANG J

. Estimation of livestock greenhouse gases discharge in China
Transactions of the Chinese Society of Agricultural Engineering, 2010,26(10):247-252. (in Chinese)

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刘其涛. 中国农业碳排放效率的区域差异——基于Malmquist- Luenberger指数的实证分析
江苏农业科学, 2015,43(9):497-501.

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LIU Q

. Regional difference of China’s agricultural carbon emissions efficiency—Based on empirical analysis of Malmquist-Luenberger index

Jiangsu Agricultural Sciences,

2015,43(9):497-501. (in Chinese)

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张俊飚, 程琳琳, 何可. 中国农业低碳经济效率的时空差异及影响因素研究——基于“碳投入”视角
环境经济研究, 2017,2(2):36-51.

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ZHANG J

, CHENG L

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. The difference of China’s agricultural low-carbon economic efficiency in spatial and temporal and its influencing factors: A perspective of carbon input
Journal of Environmental Economics, 2017,2(2):36-51. (in Chinese)

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吴贤荣, 张俊飚, 田云, 李鹏. 中国省域农业碳排放:测算、效率变动及影响因素研究——基于DEA-Malmquist指数分解方法与Tobit模型运用
资源科学, 2014,36(1):129-138.

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WU X

, ZHANG J

, TIAN

, LI

. Provincial agricultural carbon emissions in China: Calculation, performance change and influencing factors
Resources Science, 2014,36(1):129-138. (in Chinese)

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马大来. 中国农业能源碳排放效率的空间异质性及其影响因素——基于空间面板数据模型的实证研究
资源开发与市场, 2018,34(12):1693-1700, 1765.

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MA D

. Spatial heterogeneity and influencing factors of agricultural energy carbon emission efficiency in China—An empirical research of spatial panel data model
Resource Development & Market, 2018,34(12):1693-1700, 1765. (in Chinese)

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