新疆农业水资源绿色效率时空演变及影响因素研究

doi:10.12118/j.issn.1000-6060.2023.577

Abstract

Abstract:

An index system was developed by measuring variables such as agricultural surface source pollution and agricultural net carbon sinks. Super-efficiency SBM and Malmquist index methods were used to calculate the green efficiency values of agricultural water resources and their decomposition index values in 14 administrative regions of Xinjiang, China, from 2011 to 2020. Standard deviation ellipses were drawn to analyze the movement of centers of efficiency, and a Markov chain probability matrix was constructed to analyze their spatial pattern of changes. The Tobit model was also used to analyze its impact on the green efficiency of agricultural water resources in Xinjiang in terms of cropping structure, agricultural economy, water supply structure, water conservation facilities, urbanization level, resource endowment, and economic development level. The results show the following: (1) The average value of green efficiency of agricultural water resources during the study period in each administrative region of Xinjiang is 0.865; there are obvious differences in the level, improvement effect, and speed of the green efficiency of agricultural water resources in each region. (2) The coordinates of the center of gravity during the study period always moved within Bayinbuluk Town, Hejing County, Bayingol Mongolian Autonomous Prefecture, first to the northeast and then to the southwest, and eventually turned back to the northeast; there is a probability of transferring to each other or across types between lower, higher, and high green efficiency types of the regions, and it is difficult for inefficient regions to grow rapidly or across types. (3) The levels of economic development, agricultural economy, and water supply structure have a significant positive impact on the green efficiency of agricultural water resources, and resource endowment and water conservation facilities have a significant negative impact on the efficiency of agricultural water resources. The results of this study are of great practical significance for the sustainable use of agricultural water resources and the construction of green and efficient agriculture in Xinjiang.

Key words: agricultural green water resource efficiency, super-efficient SBM model, Malmquist index model, spatial evolution, Markov chain, Tobit model

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.

Figures/Tables 8

Tab. 1

Tab. 2

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Tab. 3

Tab. 4

References 29

[1]	李秀花, 吴纯渊. 中国西北五省区水资源利用的协调性分析[J]. 干旱区地理, 2022, 45(1): 9-16.
	[Li Xiuhua, Wu Chunyuan. Coordination of water resources utilization in the five provinces of northwest China[J]. Arid Land Geography, 2022, 45(1): 9-16.]
[2]	李可柏, 陶军, 卢慧. 中国水资源利用效率评价及空间集聚分析[J]. 人民黄河, 2023, 45(9): 96-103.
	[Li Kebai, Tao Jun, Lu Hui. Spatial agglomeration analysis of inter-provincial water resources utilization efficiency in China[J]. Yellow River, 2023, 45(9): 96-103.]
[3]	阿哈提·克力木, 曾俣杰, 刘德地. 干旱区水资源供需平衡分析方法研究——以新疆哈密市伊州区为例[J]. 中国农村水利水电, 2023(6): 21-26, 35. doi: 10.12396/znsd.221458
	[Kelimu Ahati, Zeng Yujie, Liu Dedi. An analysis of the balance between water demand and supply in arid areas: A case study of the Yizhou District of Hami City of Xinjiang Uygur Automons Region[J]. China Rural Water and Hydropower, 2023(6): 21-26, 35.]
[4]	苗峻瑜. 沿黄九省工业水资源效率及其影响因素的时空异质性[J]. 中国沙漠, 2022, 42(6): 142-152. doi: 10.7522/j.issn.1000-694X.2022.00066
	[Miao Junyu. Spatial and temporal heterogeneity of industrial water resources efficiency and its influencing factors in nine provinces along the Yellow River[J]. Journal of Desert Research, 2022, 42(6): 142-152.] doi: 10.7522/j.issn.1000-694X.2022.00066
[5]	郝铭, 段琳琼, 陈常优, 等. 黄河流域与长江经济带农业水资源利用效率与影响因素的差异性研究[J]. 水土保持通报, 2022, 42(4): 267-277.
	[Hao Ming, Duan Linqiong, Chen Changyou, et al. Differences in agricultural water use efficiency and its influencing factors between Yellow River Basin and Yangtze River Economic Belt[J]. Bulletin of Soil and Water Conservation, 2022, 42(4): 267-277.]
[6]	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 spatio-temporal differentiation[J]. Economic Geography, 2023, 43(7): 215-224.] doi: 10.15957/j.cnki.jjdl.2023.07.021
[7]	邓正华, 邓丽萍, 何姣, 等. 基于三阶段超效率SBM-Malmquist指数分解的洞庭湖区农业水资源绿色效率研究[J]. 生态经济, 2023, 39(8): 106-111, 186.
	[Deng Zhenghua, Deng Liping, He Jiao, et al. Study on green efficiency of agricultural water resources in Dongting Lake area based on three-stage super-efficiency SBM-Malmquist index decomposition[J]. Ecological Economy, 2023, 39(8): 106-111, 186.]
[8]	苏喜军, 纪德红, 何慧爽. 黄淮海平原农业水资源绿色效率时空差异与影响因素研究[J]. 生态经济, 2021, 37(3): 106-111.
	[Su Xijun, Ji Dehong, He Huishuang. Study on spatial and temporal differences and affecting factors of agricultural water resources green efficiency in Huang-Huai-Hai Plain[J]. Ecological Economy, 2021, 37(3): 106-111.]
[9]	刘华军, 乔列成, 孙淑惠. 黄河流域用水效率的空间格局及动态演进[J]. 资源科学, 2020, 42(1): 57-68. doi: 10.18402/resci.2020.01.06
	[Liu Huajun, Qiao Liecheng, Sun Shuhui. Spatial distribution and dynamic change of water use efficiency in the Yellow River Basin[J]. Resources Science, 2020, 42(1): 57-68.] doi: 10.18402/resci.2020.01.06
[10]	刘涛, 崔永正, 李继霞. 基于水污染视角的中国农业生态用水效率及其影响因素[J]. 水土保持研究, 2021, 28(5): 301-307.
	[Liu Tao, Cui Yongzheng, Li Jixia. Ecological use efficiency of agricultural water and its influencing factors in China based on the perspective of water pollution[J]. Research of Soil and Water Conservation, 2021, 28(5): 301-307.]
[11]	黄程琪. 新疆农业水资源利用效率及影响因素分析[D]. 石河子: 石河子大学, 2019.
	[Huang Chengqi. Analysis on utilization efficiency and influencing factors of agricultural water resources in Xinjiang[D]. Shihezi: Shihezi University, 2019.]
[12]	杜书, 梁彦庆, 张梦, 等. 河北省农业水资源利用效率时空分异特征及影响因素研究[J]. 节水灌溉, 2021(6): 74-80.
	[Du Shu, Liang Yanqing, Zhang Meng, et al. A study on spatio-temporal differentiation characteristic and influencing factors of agricultural water resource utilization efficiency in Hebei Province[J]. Water Saving Irrigation, 2021(6): 74-80.]
[13]	Tone K. A slacks-based measure of super-efficiency in data envelopment analysis[J]. European Journal of Operational Research, 2002, 143(1): 32-41.
[14]	鹿晨昱, 黄萍, 张彤, 等. 甘肃省绿色发展效率时空演化与驱动因素研究[J]. 干旱区地理, 2023, 46(2): 305-315.
	[Lu Chenyu, Huang Ping, Zhang Tong, et al. Spatiotemporal evolution and driving factors of the green development efficiency in Gansu Province[J]. Arid Land Geography, 2023, 46(2): 305-315.]
[15]	Pastor J T, Lovell C A K. A global Malmquist productivity index[J]. Economics Letters, 2005, 88(2): 266-271.
[16]	陈雯. 齐次马尔科夫链的相关研究及其应用[D]. 成都: 四川师范大学, 2021.
	[Chen Wen. Related research and application of homogeneous Markov Chain[D]. Chengdu: Sichuan Normal University, 2021.]
[17]	顾世祥, 朱赟, 李亚龙, 等. 基于DEA和Malmquist指数的农业水资源利用效率分析——以滇中受水区为例[J]. 中国农村水利水电, 2021(8): 98-102.
	[Gu Shixiang, Zhu Yun, Li Yalong, et al. An analysis of the agricultural water resources utilization efficiency based on DEA and Malmquist index: Taking the water receiving area in central Yunnan as an example[J]. China Rural Water and Hydropower, 2021(8): 98-102.]
[18]	李波, 张俊飚, 李海鹏. 中国农业碳排放时空特征及影响因素分解[J]. 中国人口·资源与环境, 2011, 21(8): 80-86.
	[Li Bo, Zhang Junbiao, Li Haipeng. Research on spatial-temporal characteristics and affecting factors decomposition of agricultural carbon emission in China[J]. China Population, Resources and Environment, 2011, 21(8): 80-86.]
[19]	吴嘉莘, 杨红娟. 农业净碳汇测算方法研究综述[J]. 农业经济, 2020(10): 29-31.
	[Wu Jiaxin, Yang Hongjuan. A review of research on methodologies for measuring net carbon sinks in agriculture[J]. Agricultural Economy, 2020(10): 29-31.]
[20]	田云, 张俊飚. 中国农业生产净碳效应分异研究[J]. 自然资源学报, 2013, 28(8): 1298-1309. doi: 10.11849/zrzyxb.2013.08.003
	[Tian Yun, Zhang Junbiao. Regional differentiation research on net carbon effect of agricultural production in China[J]. Journal of Natural Resources, 2013, 28(8): 1298-1309.] doi: 10.11849/zrzyxb.2013.08.003
[21]	白雪冰, 胡浩, 周应恒, 等. 中国畜牧业碳排放的时空演进及其影响因素分析[J]. 中国农业大学学报, 2023, 28(9): 260-274.
	[Bai Xuebing, Hu Hao, Zhou Yingheng, et al. Study on space-time evolution and influence factors of carbon missions in China’s animal husbandry[J]. Journal of China Agricultural University, 2023, 28(9): 260-274.]
[22]	夏文浩, 潘生亮, 霍瑜, 等. 新疆农业面源污染的时空分异及动态演进——基于特色畜牧视角的再分析[J]. 资源开发与市场, 2022, 38(10): 1190-1199.
	[Xia Wenhao, Pan Shengliang, Huo Yu, et al. Spatial-temporal differentiation and dynamic evolution of agricultural non-point source pollution in Xinjiang: Re-analysis based on the perspective of characteristic animal husbandry[J]. Resource Development & Market, 2022, 38(10): 1190-1199.]
[23]	赖斯芸, 杜鹏飞, 陈吉宁. 基于单元分析的非点源污染调查评估方法[J]. 清华大学学报(自然科学版), 2004(9): 1184-1187.
	[Lai Siyun, Du Pengfei, Chen Jining. Evaluation of non-point source pollution based on unit analysis[J]. Journal of Tsinghua University (Science and Technology Edition), 2004(9): 1184-1187.]
[24]	陈敏鹏, 陈吉宁, 赖斯芸. 中国农业和农村污染的清单分析与空间特征识别[J]. 中国环境科学, 2006, 26(6): 751-755.
	[Chen Minpeng, Chen Jining, Lai Siyun. Inventory analysis and spatial distribution of Chinese agricultural and rural pollution[J]. China Environmental Science, 2006, 26(6): 751-755.]
[25]	龚世飞, 丁武汉, 肖能武, 等. 丹江口水库核心水源区典型流域农业面源污染特征[J]. 农业环境科学学报, 2019, 38(12): 2816-2825.
	[Gong Shifei, Ding Wuhan, Xiao Nengwu, et al. Characteristics of surface runoff and agricultural non-point source pollution in the core water source area of the Danjiangkou Reservoir[J]. Journal of Agro-Environment Science, 2019, 38(12): 2816-2825.]
[26]	葛继红. 江苏省农业面源污染及治理的经济学研究——以化肥污染与配方肥技术推广政策为例[D]. 南京: 南京农业大学, 2011.
	[Ge Jihong. The economic analysis on the control of agricultural non-point source pollution in Jiangsu Province: Taking fertilizer pollution and promotional policy of formula fertilizer technology as an example[D]. Nanjing: Nanjing Agricultural University.]
[27]	袁菊红, 陈拉, 胡绵好. “中四角”绿色水资源利用效率时空分异及其影响因素——基于非期望产出SBM-DEA和地理探测器模型[J]. 生态经济, 2023, 39(9): 138-147.
	[Yuan Juhong, Chen La, Hu Mianhao. Spatial-temporal differentiation of green water utilization efficiency and its influencing factors in “Four-City Area in Middle China”: Based on SBM-DEA model with undesired outputs and geographical detector model[J]. Ecological Economy, 2023, 39(9): 138-147.]
[28]	张玲玲, 丁雪丽, 沈莹, 等. 中国农业用水效率空间异质性及其影响因素分析[J]. 长江流域资源与环境, 2019, 28(4): 817-828.
	[Zhang Lingling, Ding Xueli, Shen Ying, et al. Spatial heterogeneity and influencing factors of agricultural water use efficiency in China[J]. Resources and Environment in the Yangtze Basin, 2019, 28(4): 817-828.]
[29]	李青松, 张凤太, 苏维词, 等. 长江经济带农业用水绿色效率测度及影响因素分析——基于超效率EBM-Geodetector模型[J]. 中国农业资源与区划, 2022, 43(5): 40-52.
	[Li Qingsong, Zhang Fengtai, Su Weici, et al. Measurement of green efficiency of agricultural water in the Yangtze River Economic Belt and analysis of influencing factors: Based on the super-efficiency EBM-Geodetector model[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2022, 43(5): 40-52.]

准则层	指标层	计算方式
种植结构	粮食播种面积占比/%	粮食播种面积/农作物播种面积
农业经济	第一产业增加值占比/%	第一产业增加值占比
供水结构	地下水供水量占比/%	地下水供水量/供水总量
节水设施	人均节水灌溉面积/10³hm²·人^-1	节水灌溉面积/乡村人口
城镇化水平	城镇人口数量占比/%	城镇人口/总人口
资源禀赋	人均水资源量/10⁴m³·人^-1	水资源总量/总人口
经济发展水平	人均GDP/10⁴元·人^-1	生产总值/总人口

地区		2011年	2012年	2013年	2014年	2015年	2016年	2017年	2018年	2019年	2020年	平均值
北疆	乌鲁木齐市	0.950	1.001	1.008	0.967	1.002	1.001	0.961	1.015	1.017	1.019	0.994
	克拉玛依市	1.000	1.030	1.012	1.017	1.001	1.000	0.926	0.911	1.037	1.008	0.994
	昌吉州	0.899	1.014	1.019	1.026	0.756	0.736	1.001	1.001	1.045	1.020	0.952
	伊犁州直	0.616	0.684	0.793	1.007	1.016	1.012	1.073	0.837	1.008	1.014	0.906
	塔城地区	1.027	1.003	0.848	1.071	1.017	1.002	1.035	0.935	1.002	1.004	0.994
	阿勒泰地区	0.578	0.585	0.585	0.582	0.576	0.558	0.546	0.554	0.576	0.588	0.573
	博州	1.001	1.004	1.014	1.018	1.020	0.919	1.002	0.920	0.960	0.951	0.981
	平均值	0.868	0.903	0.897	0.955	0.913	0.890	0.935	0.882	0.949	0.943	0.914
南疆	克州	0.711	0.733	0.733	0.743	0.799	1.001	0.729	0.703	0.720	0.684	0.755
	和田地区	1.000	0.622	0.618	0.621	0.616	0.592	0.659	0.637	0.637	0.591	0.659
	喀什地区	1.013	0.813	0.654	1.040	0.968	1.012	1.056	0.887	1.015	1.021	0.948
	巴州	0.900	1.016	1.007	1.035	1.000	1.013	1.057	0.939	0.960	1.025	0.995
	阿克苏地区	0.617	0.661	0.636	0.771	0.804	0.818	1.044	0.905	0.901	1.013	0.817
	平均值	0.848	0.769	0.729	0.842	0.837	0.887	0.909	0.814	0.846	0.867	0.835
东疆	吐鲁番市	0.944	1.005	0.957	1.027	1.003	1.007	0.959	1.023	1.050	1.015	0.999
	哈密市	0.849	0.932	0.921	0.899	0.876	0.879	0.931	0.906	1.002	1.001	0.920
	平均值	0.896	0.968	0.939	0.963	0.940	0.943	0.945	0.964	1.026	1.008	0.959
平均		0.865	0.864	0.843	0.916	0.890	0.896	0.927	0.869	0.924	0.925	0.865

年份	效率类型	低	较低	较高	高
2011—2015	低	0.750	0.188	0.000	0.063
	较低	0.077	0.462	0.231	0.231
	较高	0.077	0.077	0.385	0.462
	高	0.000	0.143	0.500	0.357
2016—2020	低	0.733	0.067	0.133	0.067
	较低	0.000	0.438	0.250	0.313
	较高	0.077	0.231	0.308	0.385
	高	0.083	0.333	0.083	0.500
2011—2020	低	0.844	0.094	0.031	0.031
	较低	0.029	0.412	0.235	0.324
	较高	0.063	0.219	0.375	0.344
	高	0.036	0.214	0.357	0.393

解释变量	系数	标准误	T统计量	P值
经济发展水平	0.028	0.009	3.29	0.001^***
资源禀赋	-0.101	0.050	-1.99	0.048^**
农业经济比重	0.462	0.195	2.37	0.019^**
供水结构	0.194	0.109	1.77	0.078^*
节水设施	-0.187	0.095	-1.97	0.051^*
城镇化水平	0.031	0.102	0.31	0.760
种植结构	-0.030	0.120	-0.25	0.801
常数项	0.663	0.091	7.27	0.000^***

Spatiotemporal evolution and influencing factors of green efficiency of agricultural water resources in Xinjiang

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 29

Related Articles 14

Metrics

Comments

Recommended 0

[1]	LIN Qiuping, LI Songrui, YANG Shangguang, WANG Yunyun. Spatiotemporal evolution, influencing factors, and development strategies of logistics enterprise location in Urumqi City [J]. Arid Land Geography, 2024, 47(7): 1252-1262.
[2]	LI Junjia, ZHAO Meifeng. Spatial evolution and influencing mechanism of high-quality development in ethnic minority areas of China [J]. Arid Land Geography, 2024, 47(3): 496-505.
[3]	BAI Yang,HU Jingxuan,CHEN Chunyan,LU Wen. Regional differences and influencing factors of efficiency of tourism aid for Xinjiang: Based on three-stage DEA and Tobit model [J]. Arid Land Geography, 2023, 46(8): 1366-1375.
[4]	MU Jiawei, QIAO Baorong, YU Guoxin. Spatial and temporal patterns of agricultural low-carbon productivity and its influence effects in the counties of Tarim River Basin, Xinjiang [J]. Arid Land Geography, 2023, 46(6): 968-981.
[5]	YANG Yang, QIAO Jiajun, WANG Wei, ZHANG Ershen. Impact of poverty alleviation resource allocation on income of rural low-income farmer households: A case of 18 villages in Qinghai Province [J]. Arid Land Geography, 2023, 46(2): 325-336.
[6]	NI Honghong, MA Qiang, BU Yuankun, YANG Xiaoxuan, LI Weizhong. Spatiotemporal distribution pattern evolution and influencing factors of forestry enterprises in Shaanxi Province [J]. Arid Land Geography, 2023, 46(12): 2098-2110.
[7]	CHENG Jing,WANG Peng,CHEN Hongxiang,HAN Yonggui. Geographical exploration of the spatial and temporal evolution of ecological risk and its influencing factors in semi-arid regions: A case of Yanchi County in Ningxia [J]. Arid Land Geography, 2022, 45(5): 1637-1648.
[8]	ZHOU Di, ZHOU Feng-nian, ZHENG Chu-peng. Regional differences and its solidification of carbon emission in China: An equity and efficiency perspective [J]. Arid Land Geography, 2019, 42(6): 1461-1469.
[9]	KANG Jia-yu, SUO Zhi-hui, LIANG Liu-ke, MAI Zhe, FANG Ming. Temporal and spatial evolution and its affecting factors of housing affordability at urban scale in China [J]. Arid Land Geography, 2019, 42(5): 1153-1165.
[10]	ZHOU Di, ZHOU Feng-nian, ZHONG Shao-jun. “Club convergence” of per capita water resource distribution in China: Based on extended Markov chain model [J]. 干旱区地理, 2018, 41(4): 867-873.
[11]	DING Jin-mei, YANG Kui, MA Cai-hong, WEN Qi. Spatial-temporal evolution of grain production in China [J]. , 2017, 40(6): 1290-1297.
[12]	SUN Cai-zhi, WU Yong-jie, LIU Wen-xin. Application of DPSIR-PLS model to analyze water poverty in China [J]. , 2017, 40(5): 1079-1088.
[13]	MA Jing, ZHU Mei-ling, MA Yong-ren, WANG Jiao. Spatial temporal evolution analysis between the county population and animal husbandry economy in xinjiang [J]. , 2016, 39(6): 1380-1388.
[14]	MA Jing, ZHU Mei-ling, MA Yong-ren, WANG Jiao. Spatial temporal evolution analysis between the county population and animal husbandry economy in xinjiang [J]. , 2016, 39(6): 1380-1388.