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

Abstract

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

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.

Figures/Tables 9

Tab. 1

Calculation indices of water footprint and grey water footprint"

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]

Tab. 1

Tab. 2

Fig. 1

Fig. 2

Fig. 3

Tab. 3

Tab. 4

Tab. 5

Tab. 6

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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

Green efficiency and rebound effect of water for grain production in China: Based on the three-stage super-efficiency SBM-Malmquist model

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