中国粮食生产用水绿色效率与回弹效应研究——基于三阶段超效率SBM-Malmquist模型

doi:10.12118/j.issn.1000-6060.2023.615

摘要/Abstract

摘要：

提高粮食生产用水绿色效率是降低农业用水规模、实现农业绿色发展的有效途径之一。基于中国31个省（市、自治区）2010—2020年面板数据，以粮食生产水足迹作为投入指标，粮食生产灰水足迹作为非期望产出，采用三阶段超效率SBM-Malmquist指数模型，对中国粮食生产用水绿色效率进行评价，在此基础上，测算技术进步条件下粮食生产用水的回弹效应。结果表明：（1）种植结构、灌溉技术和氮肥施用量等因素对粮食生产用水效率影响显著；不同粮食生产水足迹、灰水足迹存在显著差异；灰水足迹平均增长速度排名为：中部>东部>西部。（2）外部环境对中国粮食生产用水绿色效率具有显著影响；剔除环境因素后，综合技术效率均值下降0.147，纯技术效率均值和规模效率均值分别下降0.018、0.250；粮食生产用水全要素生产率整体处于优化阶段，其主要依赖于技术进步变化。（3）技术作为提高农业水资源利用效率的主导因素，很大程度上影响了中国粮食生产用水回弹效应的变化节奏。除东部地区2017年粮食生产用水未产生回弹效应外，其他年份均存在回弹效应，农业节水技术地区发展不平衡特征明显。建议加强农业水资源管理，深挖用水效率提升潜力，优化粮食生产要素投入结构，推动节水技术推广与应用，从而减轻水环境污染，实现农业绿色可持续发展。

关键词: 绿色效率, 水足迹, 三阶段超效率SBM-Malmquist, 非期望产出, 回弹效应

Abstract:

Improving the green efficiency of water use for grain production is one of the effective ways to reduce the scale of agricultural water use and realize the green development of agriculture. Based on the panel data of 31 provinces (municipalities and autonomous regions) in China from 2010 to 2020, the green efficiency of water use for grain production is evaluated by using the water footprint of grain production as an input indicator and the gray water footprint of grain production as a non-desired output, using the three-stage super-efficiency SBM-Malmquist index model, based on which the rebound of water use for grain production under the condition of technological progress is measured. The study found that: (1) Factors such as planting structure, irrigation technology and nitrogen fertilizer application have a significant impact on the water use efficiency of grain production; there are significant differences in the water footprint and gray water footprint of different grains; the average growth rate of the gray water footprint is ranked as follows: central China>eastern China>western China. (2) The external environment has a significant impact on the green efficiency of water use for grain production in China; after removing the environmental factors, the mean value of comprehensive technical efficiency decreases by 0.147, the mean value of pure technical efficiency and the mean value of scale efficiency decreases by 0.018 and 0.250, respectively; the green total factor productivity of water use for grain production as a whole is in the optimization stage, which is mainly dependent on the changes of technological progress. (3) Technology, as the dominant factor in improving agricultural water use efficiency, largely influences the pace of change in the rebound effect of water use for grain production in China. In addition to the eastern region in 2017, the rebound effect of water for grain production did not occur, the rebound effect exists in all other years, and the regional development of agricultural water conservation technology is characterized by an obvious imbalance. It is recommended to strengthen the management of agricultural water resources, deepen the potential of water use efficiency improvement, optimize the input structure of food production factors, and promote the up-lift and application of water-saving technologies, so as to mitigate water environmental pollution and realize the green and sustainable development of agriculture.

Key words: green efficiency, water footprint, three-stage super-efficiency SBM-Malmquist, undesired output, rebound effect

罗静怡, 东梅. 中国粮食生产用水绿色效率与回弹效应研究——基于三阶段超效率SBM-Malmquist模型[J]. 干旱区地理, 2024, 47(9): 1508-1517.

LUO Jingyi, DONG Mei. Green efficiency and rebound effect of water for grain production in China: Based on the three-stage super-efficiency SBM-Malmquist model[J]. Arid Land Geography, 2024, 47(9): 1508-1517.

图/表 9

表1

水足迹、灰水足迹计算指标"

名称	公式	变量解释与单位	意义	参考来源
参考作物蒸散量	$E T 0 = 0.408 Δ R n - G + γ 900 T + 273 (e a - e b) Δ + γ (1 + 0.34 U 2)$	ET₀为参考作物蒸散量（mm·d^-1）；R_n为净辐射；G为土壤热通量（MJ·m^-2·d^-1）； T为日平均气温（^oC）；U₂为风速（m·s^-1）；e_a为饱和水汽压（kPa）；e_b为实际水汽压（kPa）；Δ为温度曲线斜率（kPa·K^-1）； γ为干湿度常数（kPa·K^-1）。	在判断气候干旱程度，评价植被耗水量等方面不可或缺。	唐书玥等^[14] 傅迎豪等^[15]
粮食作物蒸散量	$E T c = K c × E T 0$	ET_c为不同粮食作物的蒸散量（mm·d^-1）；K_c为各粮食作物系数。	根据不同粮食系数对参考作物蒸散量进行调整的结果。	唐书玥等^[14] 傅迎豪等^[15]
粮食全生育期需水量	$C W R = 10 K c ∑ i = 1 n E T 0$	CWR为粮食在单位面积、全生育期内的需水量（m³·hm^-2）；n为全生育期天数（d）。	粮食全生育期内蒸散量的总和。	唐书玥等^[14] 陈红等^[13]
粮食生产水足迹	$T V W = C W R × T C Y C Y$	TVW为粮食生产水足迹（10⁸ m³）；TCY为各地区粮食总产量（kg·hm^-2）；CY为单位面积粮食产量（kg·hm^-2）。	区域内一定时期粮食生产过程中吸收和利用的水资源总量。	陈红等^[13]
粮食生产灰水足迹	$C W F g r e y = (α × A R) (C m a x - C n a t)$	CWF_grey为粮食生产灰水足迹（10⁸ m³）； α为氮肥淋溶率；AR为氮肥的施用量（kg·hm^-2）；C_max为达到水质标准下污染物最高浓度（mg·L^-1）；C_nat为水体自然容许浓度（mg·L^-1）。	衡量水污染的指标。氮肥是农业生产用水中最主要的污染物。	陈红等^[13]

表1

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图3

表3

表4

表5

表6

参考文献 27

[1]	陈杰, 许朗. 基于面板三阶段DEA-Malmquist模型的中国农业绿色水资源利用效率研究[J]. 地理科学, 2023, 43(4): 709-718. doi: 10.13249/j.cnki.sgs.2023.04.014
	[Chen Jie, Xu Lang. Utilization efficiency of Chinese agricultural green water resources based on panel three-stage DEA-Malmquist model[J]. Scientia Geographica Sinica, 2023, 43(4): 709-718.] doi: 10.13249/j.cnki.sgs.2023.04.014
[2]	孙才志, 姜坤, 赵良仕. 中国水资源绿色效率测度及空间格局研究[J]. 自然资源学报, 2017, 32(12): 1999-2011. doi: 10.11849/zrzyxb.20161076
	[Sun Caizhi, Jiang Kun, Zhao Liangshi. Measurement of green efficiency of water utilization and its spatial pattern in China[J]. Journal of Natural Resources, 2017, 32(12): 1999-2011.] doi: 10.11849/zrzyxb.20161076
[3]	褚家琦, 蒋志辉. 新疆农业水资源绿色效率时空演变及影响因素研究[J]. 干旱区地理, 2024, 47(7): 1231-1241. doi: 10.12118/j.issn.1000-6060.2023.577
	[Chu Jiaqi, Jiang Zhihui. Spatiotemporal evolution and influencing factors of green efficiency of agricultural water resources in Xinjiang[J]. Arid Land Geography, 2024, 47(7): 1231-1241.] doi: 10.12118/j.issn.1000-6060.2023.577
[4]	曹姗姗, 何昭丽, 徐力璇, 等. 区域旅游发展对水资源绿色效率的影响及其时空分异[J]. 经济地理, 2023, 43(7): 215-224. doi: 10.15957/j.cnki.jjdl.2023.07.021
	[Cao Shanshan, He Zhaoli, Xu Lixuan, et al. The impact of tourism development on green efficiency of water resources in China and its spatial-temporal differentiation[J]. Economic Geography, 2023, 43(7): 215-224.] doi: 10.15957/j.cnki.jjdl.2023.07.021
[5]	王纪凯, 张峰, 油建盛, 等. 黄河流域工业绿色水资源效率空间网络关联特征[J]. 地理科学, 2023, 43(6): 1032-1042. doi: 10.13249/j.cnki.sgs.2023.06.010
	[Wang Jikai, Zhang Feng, You Jiansheng, et al. Correlation characteristics of spatial network of industrial green water resources efficiency in the Yellow River Basin[J]. Scientia Geographica Sinica, 2023, 43(6): 1032-1042.] doi: 10.13249/j.cnki.sgs.2023.06.010
[6]	Souissi A, Mtimet N, McCann L, et al. Determinants of food consumption water footprint in the Mena region: The case of Tunisia[J]. Sustainability, 2022, 14(3): 1539, doi: 10.3390/su14031539.
[7]	Silva D L R, Rivero-Camacho C, Rusu D. Methodology for improving the sustainability of industrial buildings via matrix of combinations water and carbon footprint assessment[J]. Sustainability, 2022, 14(22): 15297, doi: 10.3390/su142215297.
[8]	Karandish F, Hoekstra A Y. Informing national food and water security policy through water footprint assessment: The case of Iran[J]. Water, 2017, 9(11): 831, doi: 10.3390/w9110831.
[9]	Muratoglu A. Grey water footprint of agricultural production: An assessment based on nitrogen surplus and high-resolution leaching runoff fractions in Turkey[J]. Science of the Total Environment, 2020, 742: 140553, doi: 10.1016/j.scitotenv.2020.140553.
[10]	胡东兰, 申颢, 刘自敏. 中国城市能源回弹效应的时空演变与形成机制研究[J]. 中国软科学, 2019(11): 96-108.
	[Hu Donglan, Shen Hao, Liu Zimin. Study on the spatial-temporal evolution and formation mechanism energy rebound effect in Chinese cities[J]. China Soft Science, 2019(11): 96-108.]
[11]	Nesta H, Martin D, Lekha W. Farm level effects of on-farm irrigation infrastructure programs in the southern Murray-Darling Basin[J]. Australian Economic Review, 2020, 53(4): 494-516.
[12]	Wheeler S A, Carmody E, Grafton Q, et al. The rebound effect on water extraction from subsidising irrigation infrastructure in Australia[J]. Resources, Conservation and Recycling, 2020, 159: 104755, doi: 10.1016/j.resconrec.2020.104755.
[13]	陈红, 王浩坤, 秦帅. 水足迹视角下黑龙江粮食生产用水绿色效率研究——基于三阶段SBM-Malmquist指数分析法[J]. 长江流域资源与环境, 2020, 29(12): 2790-2804.
	[Chen Hong, Wang Haokun, Qin Shuai. Study on green efficiency of grain water in Heilongjiang Province from the perspective of water footprint: Based on three-stage SBM-Malmquist index analysis method[J]. Resources and Environment in the Yangtze Basin, 2020, 29(12): 2790-2804.]
[14]	唐书玥, 任思琪, 李子怡, 等. 我国不同参考作物蒸散经验模型适用性分析及参数修正[J]. 中国农业大学学报, 2023, 28(10): 1-14.
	[Tang Shuyue, Ren Siqi, Li Ziyi, et al. Applicability analysis of the empirical models for evapotranspiration of different reference crops in China and their parameters corrections[J]. Journal of China Agricultural University, 2023, 28(10): 1-14.]
[15]	傅迎豪, 申晓晶, 李王成, 等. 基于Hargreaves-Samani回归修正的作物参考蒸散发计算适用性研究[J]. 干旱区地理, 2022, 45(6): 1752-1760. doi: 10.12118/j.issn.1000-6060.2022.133
	[Fu Yinghao, Shen Xiaojing, Li Wangcheng, et al. Applicability of reference crop evapotranspiration calculation based on Hargreaves-Samani regression correction[J]. Arid Land Geography, 2022, 45(6): 1752-1760.] doi: 10.12118/j.issn.1000-6060.2022.133
[16]	Hoekstra A Y, Chapagain A K, Aldaya M M, et al. The water footprint assessment manual: Setting the global standard[M]. London: Routledge, 2011.
[17]	李长红, 许海涛, 孙联合. 夏玉米形态指标、氮肥利用及抗倒特性对密度与氮肥耦合效应的响应[J]. 江苏农业科学, 2022, 50(11): 63-70.
	[Li Changhong, Xu Haitao, Sun Lianhe. Response of morphological index, nitrogen utilization and lodging resistance characteristics of summer maize to coupling effect of density and nitrogen fertilizer[J]. Jiangsu Agricultural Sciences, 2022, 50(11): 63-70.]
[18]	张仔罗, 文雯, 曹硕, 等. 滴灌灌溉量和频次对小麦—青贮玉米复播体系蒸发蒸腾量和作物系数的影响[J]. 江苏农业科学, 2019, 47(13): 104-109.
	[Zhang Ziluo, Wen Wen, Cao Shuo, et al. Influences of drip irrigation amount and frequency on evapotranspiration and crop coefficient of wheat-silage corn double cropping system[J]. Jiangsu Agricultural Sciences, 2019, 47(13): 104-109.]
[19]	张炜, 覃求. 水足迹视角下陕西省种植业水资源利用评价及经济发展脱钩分析[J]. 中国生态农业学报, 2019, 27(1): 153-162.
	[Zhang Wei, Qin Qiu. Decoupling analysis on water resources utilization of planting industry and economic development in Shaanxi Province from the perspective of water footprint[J]. Chinese Journal of Eco-Agriculture, 2019, 27(1): 153-162.]
[20]	包菡, 傅建伟, 张雷, 等. 内蒙古典型农田地表径流氮磷流失特征[J]. 中国农技推广, 2021, 37(11): 67-69.
	[Bao Han, Fu Jianwei, Zhang Lei, et al. Characteristics of nitrogen and phosphorus loss from surface runoff in typical agricultural fields in Inner Mongolia[J]. China Agricultural Technology Extension, 2021, 37(11): 67-69.]
[21]	Tone K. A slacks-based measure of efficiency in data envelopment analysis[J]. European Journal of Operational Research, 2001, 130(3): 498-509.
[22]	吉雪强, 尚杰. 基于三阶段SBM模型的中国农业生态效率研究[J]. 中国农业资源与区划, 2021, 42(7): 210-217.
	[Ji Xueqiang, Shang Jie. A study on China’s agricultural ecological efficiency based on the third stage SBM model[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2021, 42(7): 210-217.]
[23]	Fare R, Grosskopf S, Knox L C A. Production frontiers: Frontmatter[M]. Cambridge: Cambridge University Press, 1993.
[24]	刘自敏, 朱朋虎, 邓明艳, 等. 能源效率内生化、回弹效应非对称性与减排潜力估算——基于中国城市电力数据的实证分析[J]. 南京财经大学学报, 2022(1): 64-74.
	[Liu Zimin, Zhu Penghu, Deng Mingyan, et al. Endogenous energy efficiency, rebound effect asymmetry, and emission reduction potential estimation: An empirical analysis based on city power data in China[J]. Journal of Nanjing University of Finance and Economics, 2022(1): 64-74.]
[25]	崔海洋, 卓雯君, 虞虎, 等. 基于三阶段DEA模型的农业生产效率及其时空特征研究——以长江经济带为例[J]. 中国生态农业学报, 2021, 29(7): 1243-1252.
	[Cui Haiyang, Zhuo Wenjun, Yu Hu, et al. Calculation of agricultural production efficiency based on a three-stage data envelopment analysis model and analysis of the spatial-temporal characteristics: An example from the Yangtze River Economic Belt[J]. Chinese Journal of Eco-Agriculture, 2021, 29(7): 1243-1252.]
[26]	李强, 魏巍, 徐康宁. 技术进步和结构调整对能源消费回弹效应的估算[J]. 中国人口·资源与环境, 2014, 24(10): 64-67.
	[Li Qiang, Wei Wei, Xu Kangning. Estimation of technological progress and structural readjustment on the energy consumption rebound effect[J]. China Population, Resources and Environment, 2014, 24(10): 64-67.]
[27]	徐瑞璠, 赵敏娟, 高建中. 中国农业灰水足迹测度及时空分异研究[J]. 中国农业资源与区划, 2023, 44(10): 52-66.
	[Xu Ruifen, Zhao Minjuan, Gao Jianzhong. Measurement and spatial-temporal variation of agricultural grey water footprint in China[J]. Chinese Journal Agricultural Resources and Regional Planning, 2023, 44(10): 52-66.]

变量类型	名称	单位	均值	标准差
投入变量	粮食生产水足迹	10⁸ m³	1559.216	58.629
	粮食播种面积	hm²	2972023.846	2537339.253
	机械总动力	10⁴ kW	3288.996	2913.746
	化肥施用量	10⁴ t	185.298	146.572
期望产出	粮食生产总值	10⁸元	435.752	386.690
非期望产出	粮食生产灰水足迹	10⁸ m³	9.398	8.014
环境变量	地区GDP	10⁸元	23788.350	20144.313
	人均水资源拥有量	m³·人^-1	6577.653	24157.476
	粮食产值占地区GDP比重	%	2.296	2.025

变量	地区GDP	人均水资源拥有量	粮食生产总值占地区GDP比重	γ	Log值	LR检验
粮食生产水足迹	0.00^***	0.00^***	-6.21^***	0.41	-1867.96	17.55
粮食播种面积	-14.29^***	7.81^**	-55822.69^***	0.84	-4933.68	256.65
机械总动力	-0.02^**	0.034^**	-78.82^***	0.88	-2908.28	288.72
化肥施用量	-0.00^***	0.00^***	-5.23^***	0.87	-1896.59	265.26

地区	省市	综合技术效率		纯技术效率		规模效率
地区	省市	一阶段	三阶段	一阶段	三阶段	一阶段	三阶段
东部地区	北京	0.468	0.081	0.932	0.535	0.514	0.153
	天津	0.567	0.118	0.593	0.543	0.957	0.216
	河北	0.476	0.482	0.505	0.547	0.947	0.875
	辽宁	0.782	0.704	0.816	0.802	0.961	0.843
	上海	1.000	0.162	1.000	0.540	1.000	0.302
	江苏	0.837	0.970	0.978	0.979	0.859	0.989
	浙江	0.623	0.392	0.694	0.627	0.911	0.602
	福建	0.563	0.279	0.604	0.568	0.937	0.488
	山东	0.500	0.549	0.584	0.609	0.894	0.908
	广东	0.582	0.415	0.621	0.584	0.942	0.709
	海南	0.425	0.104	0.442	0.527	0.960	0.198
中部地区	山西	0.541	0.385	0.566	0.585	0.964	0.645
	内蒙古	0.634	0.566	0.656	0.680	0.964	0.830
	吉林	0.725	0.662	0.758	0.747	0.956	0.880
	黑龙江	0.961	1.000	1.000	1.000	0.961	1.000
	安徽	0.612	0.646	0.658	0.703	0.935	0.916
	江西	0.719	0.568	0.738	0.710	0.974	0.797
	河南	0.497	0.593	0.624	0.640	0.854	0.938
	湖北	0.635	0.612	0.671	0.702	0.947	0.870
	湖南	0.673	0.705	0.754	0.780	0.914	0.894
西部地区	广西	0.509	0.426	0.540	0.586	0.945	0.725
	重庆	0.615	0.337	0.649	0.614	0.950	0.547
	四川	0.652	0.627	0.692	0.716	0.948	0.869
	贵州	0.467	0.306	0.498	0.551	0.941	0.549
	云南	0.434	0.351	0.456	0.513	0.952	0.685
	西藏	1.000	1.000	1.000	1.000	1.000	1.000
	陕西	0.433	0.327	0.459	0.515	0.945	0.632
	甘肃	0.512	0.327	0.532	0.565	0.963	0.575
	青海	0.370	0.046	0.620	0.578	0.596	0.080
	宁夏	0.516	0.158	0.543	0.545	0.949	0.291
	新疆	0.482	0.361	0.511	0.556	0.942	0.644
	最大值	1.000	1.000	1.000	1.000	1.000	1.000
	最小值	0.370	0.046	0.442	0.513	0.514	0.080
	平均值	0.607	0.460	0.668	0.650	0.919	0.666

地区	省市	EC	TC	PEC	SEC	ML
东部地区	北京	1.515	1.154	1.007	1.540	1.806
	天津	0.993	1.134	1.003	0.979	1.046
	河北	0.978	1.166	0.988	0.984	1.088
	辽宁	1.030	1.252	0.993	0.986	1.200
	上海	1.128	1.478	1.004	1.123	1.148
	江苏	1.016	1.350	1.007	1.002	1.349
	浙江	1.055	1.368	1.024	0.969	1.116
	福建	0.997	1.221	1.021	0.969	1.085
	山东	0.967	1.156	0.952	1.020	1.059
	广东	0.986	1.204	1.013	0.970	1.145
	海南	0.969	1.156	1.024	0.950	1.091
中部地区	山西	0.989	1.141	0.988	0.973	1.027
	内蒙古	0.991	1.184	1.001	0.990	1.168
	吉林	0.968	1.172	0.979	0.983	1.102
	黑龙江	1.000	1.259	1.000	1.000	1.259
	安徽	0.981	1.155	0.988	0.990	1.100
	江西	1.010	1.165	1.023	0.985	1.164
	河南	0.974	1.152	0.974	1.020	1.051
	湖北	0.982	1.179	1.000	0.981	1.122
	湖南	0.965	1.184	0.971	0.984	1.061
西部地区	广西	0.990	1.178	1.015	0.974	1.138
	重庆	0.975	1.162	1.007	0.964	1.072
	四川	0.980	1.154	0.989	0.982	1.067
	贵州	0.964	1.134	0.992	0.956	1.012
	云南	0.988	1.135	1.009	0.979	1.092
	西藏	1.000	1.142	1.000	1.000	1.142
	陕西	0.974	1.134	0.995	0.970	1.046
	甘肃	0.974	1.136	0.998	0.969	1.053
	青海	0.988	1.125	1.014	0.975	1.082
	宁夏	0.975	1.133	1.012	0.962	1.058
	新疆	0.980	1.120	0.995	0.976	1.064
	最大值	1.515	1.478	1.024	1.540	1.806
	最小值	0.964	1.120	0.952	0.950	1.012
	平均值	1.009	1.187	1.000	1.003	1.129

地区	年份	粮食生产用水强度	增加量	技术贡献率	节约量	水资源回弹效应
东部地区	2012	1.326	3.197	2.996	22.181	2.269
	2017	1.403	-13.295	2.523	-52.322	-20.866
	2020	1.144	6.438	1.964	20.506	1.519
中部地区	2012	1.011	8.472	0.983	77.950	0.985
	2017	1.484	-40.627	1.207	-273.182	1.309
	2020	0.902	9.967	0.497	65.366	0.395
西部地区	2012	1.077	3.228	1.171	31.957	1.132
	2017	1.233	-11.031	1.580	-50.620	2.093
	2020	0.883	1.408	0.785	19.459	0.659
全国平均		1.163	-3.583	1.523	-15.412	-1.167