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      Receiving Robust Analysis of Spatial and Temporary Variation of Agricultural Water Use Efficiency While Considering Environmental Factors: On the Evaluation of Data Envelopment Analysis Technique

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      Sustainability

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          Abstract

          With accelerated urbanisation, continued growth in water demand and the external pressure of water demand from the South–North Water Transfer Project, agricultural water use in Jiangsu is facing a critical situation. Therefore, it is important to explore the spatial and temporal variation in agricultural water use efficiency in order to clarify the pathway for improving agricultural water use efficiency. Firstly, the Super-Slacks-Based Measure (SBM) model was utilized to measure agricultural water use efficiency in Jiangsu Province, China, from 2011 to 2020, and secondly, a fixed-effects model was used to investigate agricultural water use efficiency and the factors influencing it in 13 prefectures in Jiangsu Province in both time and space. The results show that (1) the overall value of agricultural water use efficiency in Jiangsu Province is below 1, which means that agricultural water use efficiency in Jiangsu Province is low and far from the effective boundary, and there is more room for improvement in agricultural water use efficiency; (2) a total of 92% of prefectures in Jiangsu Province have input redundancy, which seriously inhibits the progress of agricultural water use efficiency in Jiangsu Province, among which the redundancy of total agricultural machinery power and agricultural water use is the highest; (3) Regarding total factor productivity and its decomposition index for agricultural use in Jiangsu Province, in the time dimension, the number of professional and technical personnel inputs has a positive impact on agricultural water use efficiency. In the spatial dimension, the number of professional and technical personnel inputs, industrial structure and arable land area have a positive impact on improving regional agricultural water use efficiency, among which the industrial structure has a smaller contribution to agricultural water use efficiency.

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          Most cited references27

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          Measuring the efficiency of decision making units

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            A Procedure for Ranking Efficient Units in Data Envelopment Analysis

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              Evaluation and difference analysis of regional energy efficiency in China under the carbon neutrality targets: Insights from DEA and Theil models.

              In the context of carbon neutrality, the paper uses Super Efficiency Data Envelopment Analysis (DEA) to evaluate the level of regional energy efficiency in China, then selects Theil index to analyze the difference and change of regional energy efficiency. The results are as follows: (1) The overall level of energy efficiency in China is relatively low, and the average efficiency level from 2007 to 2019 is 0.568. Among them, Beijing is the highest, at 1.261, and Xinjiang is the lowest, at only 0.205. The energy efficiency of the eastern region is the highest in China: the average is 0.812. The central region is lower than the national average, with the value of 0.534. The lowest energy efficiency is in the western region, which is only 0.349. It can be seen that there are significant differences in regional energy efficiency, and from west to east has increased. (2) The Theil coefficient increased before 2012 and began to show a downward trend in 2013. The difference in the eastern region maintained the smallest downward trend among the three regions. The difference in the central region fluctuated and increased before 2011, and dropped sharply after 2012. In the western region, the coefficient is relatively stable and has not changed significantly. (3) Differences within and between regions in China show an overall downward trend. From the perspective of contribution rate, the difference between regions contributes the most to the overall difference.
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                Author and article information

                Journal
                SUSTDE
                Sustainability
                Sustainability
                2071-1050
                March 2023
                February 21 2023
                : 15
                : 5
                : 3926
                Article
                10.3390/su15053926
                597208b9-ca1a-4f77-add1-6b7dade8d8f3
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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