Super-efficiency Model Research Articles

EFFICIENCY MEASUREMENT OF ARTIFICIAL INTELLIGENCE: A RESEARCH ON COMPANIES IN TÜRKİYE

The use of technology is increasing due to Industry 4.0. Both countries and organizations have had to invest in the field of artificial intelligence (AI) to compete with their rivals in global competitive conditions and to adapt to the ever-changing world. An organization or a country needs to evaluate its performance to ensure its sustainability constantly. The Data Envelopment Analysis (DEA) method is widely used in performance evaluation. This study aimed to evaluate Türkiye AI performance for the nine years between 2014 and 2022. In the research, years were included in the analysis as the decision-making unit. Two input and two output variables were used in the analyses. The study was carried out by using the input-oriented CCR DEA model and its super-efficiency model. According to the results of the analysis, efficient/inefficient decision-making units were determined. Several potential improvement suggestions have been put forward for inefficient decision-making units.

Efficient optimization of the multi-response problem in the taguchi method through advanced data envelopment analysis formulations integration

This study introduces an innovative strategy to address the multi-response challenge inherent in the Taguchi Method, frequently encountered in the enhancement of production processes and productivity indices. By integrating the Taguchi Method with Data Envelopment Analysis (DEA), experimental trials are transformed into Decision Making Units (DMUs), with response variables categorized as inputs and outputs. Advanced DEA models, including simultaneous DEA and super-efficiency models, are employed to determine the DMUs’ relative and cross efficiencies, thus retaining the advantages of the Taguchi Design of Experiments (DoE) while offering an optimal solution for enhancing multiple quality responses. The superiority of this approach is validated by the Addictive Model for Factor Effects, demonstrating enhanced outcomes and reduced computational effort compared to existing empirical and scholarly solutions. These findings have significant implications for advancing manufacturing practices that require multi-response optimization, marking a notable contribution to the field.

Urban eco-efficiency of China: Spatial evolution, network characteristics, and influencing factors

Urban eco-efficiency (EE) refers to the ability of a city to balance economic growth with environmental protection through the optimal use of resources and environmental management, reflecting the city’s performance in sustainable development. Quantitative EE analysis is a crucial evaluation criterion for measuring the process of sustainable development (SD). To assess the current state of sustainable development in China, this study employs the slacks-based super efficiency model (Super-SBM) to measure the the urban eco-efficiency in China (CUEE) of 284 cities. The study utilizes Kernel Density Estimation (KDE) and Spatial Autocorrelation Analysis (SAA) to investigate the fundamental patterns of CUEE in terms of inter-city differences and relationships. Complex Network Analysis (CNA) and Link Prediction methods are applied to examine the characteristics and future trends of the CUEE network. Additionally, Geodetector and Geographically Weighted Regression (GWR) are used to analyze the influencing factors of CUEE. The findings are as follows: Overall CUEE is relatively low but shows a steady increase. The spatial association network of CUEE is becoming more complex, with increasing association strengths. The Chengdu-Chongqing Economic Circle (CCEC) is expected to further enhance its spatial association strength and may emerge as a new core area. The effectiveness of the interactions among selected factors is greater than that of individual factors. Degree centrality (X1) is identified as a core explanatory factor influencing CUEE, showing a positive correlation with CUEE. Scientific expenditure (X5) is also positively correlated with CUEE. Local fiscal budget expenditure (X3), industrial output value of foreign-invested enterprises (X7), and total cargo transportation volume (X9) are negatively correlated with CUEE. The analytical framework and research conclusions of this study offer valuable insights into the development of urban ecological efficiency in China and provide constructive recommendations for promoting sustainable urban development.

Efficiency evaluation and influencing factors of Industrial Internet enabling manufacturing industry

Abstract The industrial Internet is an important support for the transformation and upgrading of the manufacturing industry. In the present work, a non-oriented SBM-DEA super-efficiency model is constructed. It takes Jiangsu Province in China as an example to estimate the efficiency of industrial Internet enabling manufacturing transformation and upgrading and further analyzes the influencing factors of enabling efficiency by using the Tobit model. It shows that the efficiency level of the transformation of the industrial Internet-enabling manufacturing industry varies greatly from place to place, the distribution is unstable, and there is a big contrast with the level of economic development in different places. The number of supporting service institutions, policy support, and the proportion of secondary industry have a significant positive influence on the enabling efficiency of the industrial Internet, while the degree of opening to the outside world has a significant inhibitory effect on the comprehensive efficiency of the industrial Internet.

Two-stage super-efficiency model for measuring efficiency of education in South-East Asia

Two-stage super-efficiency model for measuring efficiency of education in South-East Asia

The diffusion path and influencing factors of shrimp farming technology

Aquatic products have played an increasingly important role in residents’ diets, with improved production capacity and living standards in recent years. Accelerating the organic diffusion of aquaculture technology is an effective way to increase production. Taking the example of South American white shrimp aquaculture, this article combines carbon emissions and aquaculture cost benefits. By using data from South American white shrimp aquaculture in Hebei, Shandong, and Jiangsu Province in China from 2016 to 2021, the article innovatively considers aquaculture cycle carbon emissions as non-expected output, constructs a non-expected SBM-DEA super-efficiency model to evaluate the comprehensive efficiency of two aquaculture technologies, and analyzes the influencing factors of the diffusion of shrimp culture technology through case studies. The research results show that the comprehensive efficiency of factory-based aquaculture technology is generally better than pond-based aquaculture, but carbon emissions are higher in the factory model. The main factors affecting the diffusion of factory-based technology are policy support, social networks, farmers’ own situations, and technological attributes.

EFFICIENCY ASSESSMENT OF SOME DANUBE PORTS BY USING DEA WINDOW ANALYSIS

The aim of this paper is to empirically assess the efficiency / inefficiency of five Danube ports in five neighbouring countries and to observe how it evolves over an eight-year period ranging from 2014 to 2021. The five ports are located on the lower course of the Danube River and are the most important in their states: Smederevo (Serbia), Ruse (Bulgaria), Galați (Romania), Giurgiulești (Moldova), and Izmail (Ukraine). The study uses a mixed-method, namely the Data Envelopment Analysis (DEA) window analysis method and Andersen and Petersenʼs super-efficiency model to evaluate the efficiency of these ports over time. Port efficiency analysis is based on a single output, i.e. the cargo throughput, and four inputs: the total port area, the total area of warehouses, the quay length, and the number of cranes. It was determined that none of the five ports analysed reaches the maximum efficiency of 1.000, their average efficiency being quite low, only 0.631. The highest average efficiency is recorded by the Serbian port of Smederevo, with 0.768, this port being found to make good use of its resources for production. On the other extreme, the Bulgarian port of Ruse was found to be the least efficient port, obtaining the lowest average efficiency over an eight-year period, with only 0.360. The study tries to capture the causes of the inefficiency of selected ports and propose some measures to improve their efficiency.

The spillover effects of China’s carbon trading policy on coordinated emission efficiency

The significance of carbon trading policy (CTP) for China’s carbon reduction goals cannot be overstated. Examining the practical impacts and inherent features of this policy is crucial for fostering its healthy development and effectiveness. This study utilizes the directional SBM super-efficiency model to calculate the combined emission efficiency (CEE) of greenhouse gases and atmospheric pollutants across 30 provinces and cities in China from 2005 to 2020. Through spatiotemporal analysis of the CEE evolution using hotspot analysis, it is evident that hotspots gradually shift towards the southeast coastal areas over time, while cold spots shift towards the northwest. Additionally, employing the differences-in-differences (DID) model and conducting robustness tests, the study finds that the CTP significantly enhances the CEE development. Spatial econometric analysis reveals that the CEE primarily follows a distribution pattern characterized by low-low (LL) and high-high (HH) regions, with positive spatial spillover effects. However, due to the incomplete state of early green development in China, the CTP temporarily exhibits negative spatial spillover effects. Finally, considering the current state of China's carbon trading policy, corresponding policy recommendations are proposed in this paper.

Stability analysis and enhancement of super-efficiency model based on space distance

The traditional super-efficiency data Enveloping analysis (DEA) model can further distinguish the efficiency of efficient DMU. However, this distinction is unstable when there are perturbations in the efficient DMU inputs and outputs. The spatial distance can reflect the spatial variation of DMU on the envelope surface. We investigate the stability of the modified VRS super-efficiency model in the presence of data perturbations in efficiency DMUs and calculate its stability with spatial distances, providing a necessary and sufficient condition for such perturbations to affect the results of calculations of other efficient DMU super-efficiencies. A new super-efficiency model is proposed, which combines spatial distance to increase the constraint on projection point. Numerical examples are used to illustrate the model. On this basis, a spatial distance model for calculating inefficient DMU efficiency is further developed.

Unleashing China's coal conservation potentials by analyzing efficiency of energy intensive industries: A Logarithm Mean Divisia Index (LMDI) model

Considering China's ambitions for carbon peaking as of 2030 to ensure environmental protection and energy security, the present study is intended to explore sustainable pathways to reduce coal consumption by enhancing energy efficiency. The current article estimates coal consumption efficiency and radial super-efficiency by applying DEA-CCR and radial super-efficiency models. In the second stage, the Logarithm Mean Divisia Index (LMDI) and DEA-Malmquist models were used to explore the components of coal consumption in China's six key energy-intensive industries from 2000 to 2020. Findings indicate that (1) there is a substantial coal consumption efficiency gap among these industries, and they are working well below the production frontier. (2) Findings of DEA-Malmquist indicate that technological changes positively contributed to total productivity changes, while technical efficiency negatively impacted coal consumption growth. (3) The results of the LMDI model reveal that industrial output growth and structural changes are the key factors accelerating coal consumption. In contrast, the coal intensity had deaccelerated the coal consumption in the energy intensive industries. The current study provides several policy proposals to enhance coal conservation and consumption efficiency to achieve the aspiring goals of sustainable development.

A Novel Slacks-Based Interval DEA Model and Application

This paper proposes a novel slacks-based interval DEA approach that computes interval targets, slacks, and crisp inefficiency scores. It uses interval arithmetic and requires solving a mixed-integer linear program. The corresponding super-efficiency formulation to discriminate among the efficient units is also presented. We also provide a case study of its application to sustainable tourism in the Mediterranean region, assessing the sustainable tourism efficiency of twelve Mediterranean regions to validate the proposed approach. The inputs and outputs cover the three sustainability dimensions and include GHG emissions as an undesirable output. Three regions were found to be inefficient, and the corresponding inputs and output improvements were computed. A total rank of the regions was also obtained using the super-efficiency model.

Empirical analysis of factors influencing industrial eco-efficiency in the Yellow River Basin from a social embeddedness perspective

Measuring industrial eco-efficiency (IEE) is essential to improve environmental quality and industrial restructuring. However, most studies ignore the influence of embeddedness on industrial eco-efficiency and lack analysis of the pathways of influence factors. Therefore, this study assesses industrial eco-efficiency in the Yellow River Basin (YRB) using a super-efficiency model slacks-based measure (Super-SBM) that considers non-desired outputs, outlines the social embeddedness of IEE, and empirically analyzes the driving mechanism of IEE from the perspective of embeddedness by constructing hierarchical linear modeling (HLM) to address the pathways of action of the influencing factors of industrial eco-efficiency. The results showed that 50.79% of the overall differences in IEE in the YRB were caused by social embeddedness. Economic development level, industrial agglomeration, and environmental regulation (ER) are significant direct influencing factors. Increasing cognitive and cultural embeddedness will enhance the positive relationship between economic development level and IEE. Political and relational embeddedness significantly moderates the positive relationship between industrial agglomeration and eco-efficiency. Cultural embeddedness can significantly and directly affect industrial eco-efficiency and weaken the positive relationship between ERs and industrial eco-efficiency. Therefore, improving IEE should consider both fundamental and embedded factors. Our findings are conducive to promoting high-quality development in the YRB and support the government in formulating differentiated policies. In addition, this paper tries to establish an empirical analysis method suitable for social embeddedness theory, and the empirical results help to improve the situation due to the lack of empirical analysis of social embeddedness theory.

Evaluating Company Efficiency in Malaysian Stock Markets: Insights from DEA and Super Efficiency Models

Evaluating Company Efficiency in Malaysian Stock Markets: Insights from DEA and Super Efficiency Models

Spatial-temporal differences and convergence analysis of residential building carbon emission efficiency in China.

Data indicate that carbon dioxide emissions from residential buildings in China constitute 60% of the country's total, making carbon reduction efforts in residential construction crucial for achieving dual carbon goals. From the perspective of eight major economic regions, this paper selects energy consumption, per capita residential area, and residential population as input indicators, per capita disposable income as the output indicator, and carbon dioxide emissions as the undesired output indicator. It employs the super-efficiency model based on the directional distance (super-DDF) function and the Malmquist-Luenberger (ML) index to measure the static and dynamic carbon emission efficiencies of residential buildings (RBCEE) during their operational phase from 2010 to 2020. After analyzing the differences and equity in RBCEE among regions using the Theil index and Gini coefficient, the σ-convergence, absolute β-convergence, and conditional β-convergence methods are utilized to explore the changing trends of RBCEE across the eight major economic regions. Results show that the static RBCEE in China is at a medium level; dynamic efficiency has improved across all eight regions, though at varying rates; overall, RBCEE exhibits poor equity and significant differences, with intra-group differences being a major cause. In terms of convergence, all eight economic regions display significant absolute β-convergence and conditional β-convergence. Finally, based on the research findings, this paper proposes corresponding emission reduction recommendations for the eight major economic regions.

Is multi-pronged better? Research on the driving effect of the combination of environmental regulation in mining cities

This paper chooses 98 mining cities in China from 2007 to 2021 as research samples. It investigates the policy practice of green development in mining cities according to the point of view of policy combination. We establish a super-efficiency model considering undesirable output to measure the green development level of mining cities and further construct a dynamic panel model based on the Gaussian mixture model (GMM) to test the transmission mechanism and heterogeneous effect of different types of environmental regulation to promote the green development of mining cities. The findings demonstrate that environmental regulations about command control, market incentives, and public participation can all help mining cities develop more sustainably. Additionally, there is a nonlinear relationship of an inverted "U" shape between the degree of green development in mining cities and environmental regulations related to command control. Combining policies can encourage the green development of mining cities, but there are crowding-out and substitution effects when environmental regulations are combined with market forces. Environmental regulation promotes urban green development by improving the energy consumption structure of mining cities. In this process, the level of digital development positively regulates the impact of environmental regulation on urban green development. Therefore, the government should choose environmental regulation tools and combinations according to local needs to achieve a multi-pronged effect of energy structure improvement and urban green development and use digital technology to support the green development of mining cities.

A multi-attribute group decision-making method based on data envelopment analysis under parallel expert evaluation system

In traditional multi-attribute group decision-making (MAGDM) methods, the aggregation of expert evaluation information, the normalization of attribute scales, and the determination of attribute prior weights can lead to information loss and inconsistent results. In addition, these methods cannot identify and improve the shortcomings of non-optimal alternatives. Considering these, this paper proposes a MAGDM method based on data envelopment analysis (DEA) by setting up a parallel expert evaluation system, thus integrating the initial opinions of all experts to rank the alternatives from a global perspective. First, we construct the multi-expert evaluation process as a parallel system from the global perspective so that the decision-making process is based on the initial expert evaluation information. Then, a new directional distance function model based on endogenous projection directions is proposed to measure the alternatives under each expert subsystem, eliminating data processing and artificial weight assignment. The non-optimal alternatives can be improved to an efficient state by adjusting the attribute levels to target values. Next, we propose a DDF super-efficiency model to perform a full ranking of the alternatives. Finally, a case study is provided to demonstrate the rationality and effectiveness of the proposed method.

Economic growth factors of industrial and commercial enterprises in coastal cities based on the model of unexpected super efficiency

In order to improve the economic growth efficiency of industrial and commercial enterprises in coastal cities and realize the GDP growth of coastal cities, this paper studies the economic growth factors of industrial and commercial enterprises in coastal cities based on the unexpected super efficiency model. Based on the research and analysis of the previous economic growth theories, this paper finds out the main factors that affect the economic growth of industrial and commercial enterprises in coastal cities, and uses the advanced econometric method to establish the relevant test model to analyze the correlation between the time series of economic growth factors and the time series of coastal cities, so as to realize the economic growth factors of industrial and commercial enterprises in coastal cities Element study. The empirical results show that the main factors affecting the economic growth of industrial and commercial enterprises in coastal cities are capital and labor force, with labor force as the main body; Technical and institutional factors also contribute to the GDP of industrial and commercial enterprises in coastal cities, but the impact is not significant and needs further improvement. In general, these factors can promote the economic growth of industrial and commercial enterprises in coastal cities. The time series and time series of each factor variable are first-order non-stationary series with long-term cointegration relationship.

Supply Chain Finance and Industrial Efficiency: Evidence From ICT Industry

This study explores the impact of supply chain finance on industrial efficiency and the internal mechanism. The super-efficiency model and quantitative analysis method were used based on the time series data of the ICT industry in Shenzhen from 2005 to 2020. In order to better investigate the impact of supply chain finance on industrial efficiency, this study categorized industrial efficiency into operational efficiency and innovation efficiency to reflect the durability and virtuousness of the industry development. Results indicate that supply chain finance has an inverted U-shaped nonlinear effect on industrial efficiency. Even though the long-term effect on operational efficiency is positive, the effect on innovation efficiency reveals an inverted U-shape, mainly due to the mediating effect of industrial concentration. Theoretically, this study sheds light on the intricate effects of supply chain finance at the industrial level, and also provides valuable insights into the potential inhibition mechanism, which enriches and deepens the related research on the supply chain finance from the nonlinear perspective, make the research findings more consistent with the fact of industrial development and avoid the linear stereotypes of the existing theory consensus. Practically, it is conductive to an objective understanding for managers and policymakers, so that supply chain finance can facilitate both operational and innovation efficiencies and realize the effective integration of supply chain finance and industrial development.

Dynamics of green economic development in countries joining the belt and road initiative: Is it driven by green investment transformation?

Green investment transformation is the main interval difference of the Belt and Road Initiative, however, its actual effects are still uncertain. Finding out its utility, mechanism and improvement on green economy may accelerate green development. In this paper, Slack Based Measure model is used to obtain original results, Super-Efficiency model is used to sort effective decision-making units for stronger efficient frontier and Minimum Distance to Strong Efficient Frontier model is used to narrow gaps between non-effective ones and real situations. Then Global Malmquist-Lunberger index makes results dynamisation. Samples of 51 countries joining the Belt and Road Initiative from 2008 to 2022 are used in above-mentioned models to portray green economic dynamics. Then utility, mechanism and improvement of green investment transformation on green economy are studied through econometric model. The findings show that (i) Green Belt and Road Initiative makes green economic development more stable. The standard deviation decreases by 96.53% and mean maintains in 1.5. However, disconnection between a 36.36% increase of pure technological change and a 24.83% decline of its scale effect inhibits technical advance. (ii) Share of countries obtaining green scale revenues reaches 56.86%, which realizes a 52.64% increase. Furthermore, gaps between countries with positive status and those with negative status narrow 68.42%. Positive group accounts for 50% in developed countries and 46.15% in developing countries. (iii) Performance of Green Belt and Road Initiative is better than the Belt and Road Initiative. A one standard deviation increase in green investment transformation increases green economic development by 0.2705 (0.2105), which is a 18.57% (14.45%) increase relative to average green economic development of 1.457. Former’s strengths also reflect in different quantiles, lagging effect and heterogeneity analysis. (iv) Green investment transformation of Green Belt and Road Initiative broadens more reliable mechanism (Promote Innovation - Strict Regulation - Ensure Commercialization) based on original one (Improve Efficiency - Decrease Costs - Increase Revenues) of the Belt and Road Initiative. (v) For the green investment transformation with government subsidies, property rights protection, investment environment stability and exchange cooperation, magnitudes of its effects on green economic development have significant increases by 25.03%, 31.77%, 8.01% and 10.12% respectively. The findings not only help understand green economic status, but also support some policy insights for achieving green economy by discovering utility, mechanism and improvement of green investment transformation.

Measuring the Construction Efficiency of Zero-Waste City Clusters Based on an Undesirable Super-Efficiency Model and Kernel Density Estimation Method

The global total amount of generated solid waste is currently on a rapid growth trend. China, as the largest developing country, promulgated its Pilot Work Plan for the Construction of Zero-Waste Cities led by the new development concept in 2018 after recognizing the inadequacy and urgency of solid waste management, and the lack of valuable experience and benchmark cities for the construction of zero-waste cities. This study uses the undesirable super-efficiency model and kernel density estimation method to measure the efficiency of zero-waste city construction in 16 prefecture-level cities in Shandong Province and analyze their spatial and temporal differences. Three major problems were found, namely, low regional coordination, the rigid policies of some local governments, and the unbalanced development of scale efficiency and pure technical efficiency. Results show that the zero-waste city construction efficiency as a whole shows a declining and then fluctuating growth trend, and that low-scale efficiency is the main reason behind the decrease in construction efficiency. Suggestions are then provided considering three aspects: improving regional synergy; improving government quality and capacity, and strengthening government supervision and revitalizing the market; and introducing social capital for environmental pollution treatment. These suggestions ultimately help improve the level of zero-waste city construction.