super efficiency
Recently Published Documents

In recent years, the adoption of the precise approach for coordinating socio-economic growth and environmental protection to attain sustainable development has become an urgent challenge to be addressed in China. The current development of environmental governance and the efficiency of government financial expenditure on environmental protection (EPEE) deserves enormous attention. With the aid of the provincial panel data, this study emphasized on the use of the SBM super-efficiency model based on unexpected output to quantify the efficiency of China’s government expenditure on environmental protection from 2013-2018 and constructed the Ordinary Least Squares (OLS) regression model to investigate the influencing factors of the government expenditure on environmental protection. The results of the efficiency measurement portray that the aggregate EPEE in China is unsatisfactory. The efficiency value for the western region is greater than that of the central and eastern regions, while the values obtained from the eastern region are above that of the central region. Population size and urbanization level have negative implications on the efficiency value, while the regional economic development level positively promotes the efficiency of the government expenditure on environmental protection. Finally, the corresponding countermeasures and suggestions for policy implementation are outlined accordingly.

According to the situation of real estate industry in different provinces and cities, the benefit is different, and the benefit of real estate industry in some regions is low. Based on the statistical data of real estate industry in various regions, this paper selects nine indicators, such as the number of enterprises, the average number of employees, the land purchase area (10000 square meters) in this year, to evaluate and analyze the benefit of real estate industry in 31 regions of China by using two-stage super efficiency model. The results show that the input-output of the real estate industry in 17 regions is effective, and the remaining 14 regions are DEA ineffective. The reasons for the inefficiency of the input-output are analyzed, and some suggestions to improve the efficiency are given.

The aim of this study is to develop a novel decision support system, which has never been developed yet, in order to optimize machining parameters. We combine the three distinct methods: experimental design and analysis, fuzzy data envelopment analysis (DEA) and fuzzy analytical hierarchy process (AHP). Firstly, a full factorial experiment including four factors and three levels is carried out. We take into account cutting speed, feed rate, depth of cut and number of cutting tool inserts as factors. The following three outputs are selected: Material Removal Rate, Machining Time and Surface Roughness. Secondly, a total of 23 experiments are determined as efficient decision-making units using fuzzy DEA with super efficiency method. Finally, a fuzzy AHP approach is conducted to rank the efficient experiments among each other. In conclusion, the results show that the Fuzzy DEA-Fuzzy AHP and the Fuzzy DEA with Super Efficiency generate clearly different rankings of experiments and Fuzzy DEA-Fuzzy AHP Approach has outperformed Fuzzy DEA with Super Efficiency Approach. The results highlight the importance of taking into account the expert opinions in the decision-making processes.

This paper entails a systematic approach for measuring the Super Efficiency Scores of a set of rival firms. This evaluation process is dependent on the location of the worst Decision-Making Unit retained by the technology set. Unlike antecedent researches, the worst point is selected from a predefined neighbourhood with an application of a linear model. Finally, the new Super Efficiency model measures the Efficiency score while embedding the worst point within the direction vector. This two-stage model is akin to the standard form of a Directional Distance Function and does not end up with problems of infeasibility, negative data or zero data. In other words, the method is found robust to classify the Decision Making Units into the Super-Efficient, Strongly Efficient, Weakly Efficient and Inefficient groups. Two cases once addressed by Seiford and Zhu (1997) and Byrnes et al. (1984) are illustrated here to explore the functionality of the model in comparison to a few renowned ones.

With the expansion of China's securities market scale, securities companies as an important participant in the securities market have also developed rapidly. Under the influence of the global epidemic, the transaction volume of securities companies continued to decline, the profit prospects of securities firms increased, liquidity continued to be loose, and the overall policy budget was surplus. Through the efficiency analysis of securities companies, we can understand the resource utilization and industry conditions of securities companies, find out the gaps and deficiencies, and then improve, and improve the operating efficiency of the industry. In order to evaluate the operating efficiency of securities companies more scientific and objective, this paper adopts DEA-Malmquist index and super-efficiency DEA method, based on the financial statement data disclosed by 25 securities companies in 2016~2020, establishes an evaluation index system, analyzes the operating efficiency of securities companies, calculates and analyzes the operating efficiency of securities companies and reveals their internal reasons. The research results show that (1) steady improvement of operating productivity; (2) technical progress is the main factor; operating efficiency of (3) with different securities companies, scale efficiency weakly hinders technical efficiency; (4) Large securities companies should pay attention to control scale, develop innovative business, strengthen cost management, risk control and risk response ability.