Network Data Envelopment Analysis Model Research Articles

A Modified Network DEA Model for Bank Efficiency Analysis Considering Risk Factors

A Modified Network DEA Model for Bank Efficiency Analysis Considering Risk Factors

Temporal-Spatial Analysis of Chinese Railway Efficiency Under CO2 Emissions: A Malmquist–Network Data Envelopment Analysis Model

Temporal-Spatial Analysis of Chinese Railway Efficiency Under CO2 Emissions: A Malmquist–Network Data Envelopment Analysis Model

A study on the operational and competitive efficiency of National Oil Companies using two-stage network DEA model

A study on the operational and competitive efficiency of National Oil Companies using two-stage network DEA model

A novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical uncertainty: a real case study.

Organ transplantation is one of the most complicated and challenging treatments in healthcare systems. Despite the significant medical advancements, many patients die while waiting for organ transplants because of the noticeable differences between organ supply and demand. In the organ transplantation supply chain, organ allocation is the most significant decision during the organ transplantation procedure, and kidney is the most widely transplanted organ. This research presents a novel method for assessing the efficiency and ranking of qualified organ-patient pairs as decision-making units (DMUs) for kidney allocation problem in the existence of COVID-19 pandemic and uncertain medical and logistical data. To achieve this goal, two-stage network data envelopment analysis (DEA) and credibility-based chance constraint programming (CCP) are utilized to develop a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method. The main benefits of the developed method can be summarized as follows: considering internal structures in kidney allocation system, investigating both medical and logistical aspects of the problem, the capability of expanding to other network structures, and unique efficiency decomposition under uncertainty. Moreover, in order to evaluate the validity and applicability of the proposed approach, a validation algorithm utilizing a real case study and different confidence levels is used. Finally, the numerical results indicate that the developed approach outperforms the existing kidney allocation system.

Efficiency decomposition and frontier projection of two-stage network DEA under variable returns to scale

The efficiency decomposition and frontier projection of traditional two-stage network data envelopment analysis (DEA) model under variable returns to scale (VRS) are often not equivalent; which not only contradicts DEA theory, but also reduces the scientificity of the model. The main reason for this inequivalence is that there is a synergistic effect of variable scale return in two different stages. Therefore, this paper describes the production frontier of two-stage DEA under VRS for analyzing this synergistic effect, and then the efficiency evaluation pitfalls of two-stage DEA under VRS are identified. From the input orientation, output orientation, non-orientation perspectives, different two-stage network DEA models under VRS are respectively constructed to solve these evaluation pitfalls, and the equivalence relationships of their multiplier model and envelopment model are proved; and then the efficiency decomposition and frontier projection with equivalence relationship can be obtained to meet the different needs of decision-makers. Furthermore, variable intermediate element is discussed in the non-orientation model for achieving the Pareto optimality of two stages during the process of efficiency decomposition and frontier projection. By these models, the theoretical foundation of two-stage network DEA under VRS has been further improved. Finally, two examples are provided to illustrate the effectiveness of the new models.

Assessing performance of water supply operation of water companies in China: A union dynamic multi-activity network data envelopment analysis

Urban growth and the increasing urban water demand highlight the importance of performance analysis in the water supply industry. Both water supply companies (WSCs) and water supply and sewage companies (WSSCs) produce and deliver potable water for city dwellers. Water authorities use benchmarks to monitor the two types of companies’ operations. The internal operation process and heterogeneities of observations were not considered for relative performance evaluation by conventional DEA models. To overcome these limitations, this study proposes a union dynamic multi-activity network data envelopment analysis (DMANDEA) model to extend static network DEA to a more general case for dynamic efficiency assessment of the water supply operations of water companies. For the first time, the model allows for the decomposition of the water supply operation into water production (WP) and water consumption (WC) substages and performance comparisons for water companies by considering their heterogeneity simultaneously. Specifically, by using a non-radial directional distance function (DDF), the inefficiencies for substages and the entire system can be both assessed dynamically. This model is applied to a sample of 246 water companies in China in 2016–2018. The overall, period, and substages’ group efficiencies, meta efficiency, and technology gap ratio (TGR) for 179 WSCs and 67 WSSCs are all provided. The empirical results reveal that: (1) the average group efficiencies of WSCs and WSSCs are 0.4567 and 0.6611, respectively. The meta efficiencies of WSCs and WSSCs are 0.4523 and 0.4703, respectively. The TGR of WSCs (0.994) is greater than that of WSSCs (0.7067). (2) WSSCs perform better than WSCs do, which suggests that economies of scope do exist in WSSCs. However, WSCs possess the best technology for water supply operations because of their high TGR. (3) The average meta efficiency for all the observed water companies (0.4572) is not optimistic, which correlates more with inefficiency in the WP stage. The causes stem from the natural monopoly, lack of competition, and relatively lower water tariffs. The Chinese water supply industry requires diversification of ownership and operation modes, and gradual increases in water tariffs. (4) Water companies in the eastern region outperform those in other regions. Regional cooperation, such as technology sharing, the transfer of managerial experience, and information exchange, needs to be emphasized.

Airline performance measurement using bias-corrected network data envelopment analysis estimates: Evidence from a turbulent era

This study's objective is to estimate airline performance and identify the most resilient business model during periods affected by global crises, including financial crises, natural disasters, pandemics, terrorist attacks, and post-crisis periods. A balanced panel of 80 airlines, classified into full-service carriers (FSC), low-cost carriers (LCC), high perceived quality, and low perceived quality, during 2008–2021 is used in this work. The data is sourced from the ICAO Data+ and Skytrax databases. Following the recent literature on airline efficiency, we consider airlines' internal structure, which unfolds into three stages: (a) operations, (b) services, and (c) sales. Also, we employ a novel three-stage multi-parametric bias correction (MPBC) slacks-based measure (SBM) network data envelopment analysis (NDEA) approach to estimate system- and stage-specific efficiencies. It has been proven that the MPBC estimates are consistent, while the MPBC-SBM-NDEA approach addresses the limitations of conventional NDEA and SBM-NDEA models. This work highlights that airline passengers have become more price-sensitive, selecting value-for-money airline products and services after the Global Financial Crisis. Given this trend and the remainder of the study's findings, LCCs offering value-for-money products and services and operating smaller-scale networks with smaller plane capacities are less vulnerable to external shocks and are easier to rebound during post-crisis periods.

Analysing group‐ and metatechnology efficiencies and technology gap of airlines for a sustainable future

AbstractAirline performance evaluation, which involves incorporating sustainable concepts, provides stakeholders and managers with an objective basis for decision‐making. This evaluation considers not only conventional metrics but also environmental impacts, contributing to the advancement of sustainable development goals. This study incorporates a two‐stage network of airline operations, which implies that airlines utilise limited resources to meet their service capacity in the first stage and generate passenger and cargo services alongside carbon emissions in the second stage. The study proposes a union dynamic network data envelopment analysis model, which integrates group technology and meta‐technology into a unified model. This model not only solves the unreasonable technology gap ratio problem but also accounts for the technological heterogeneity (whether or not to participate in a strategic alliance) and the dynamic efficiency of different airlines in the network structure during different operation periods. A sample of 24 global airlines from 2017 to 2019 is used in the study. The empirical results show that the dynamic total meta‐efficiency of the alliance group is significantly greater than that of the nonalliance group over the 3 years. This suggests that joining a strategic alliance has a positive effect on airlines' operational efficiency while reinforcing their commitment to sustainability initiatives.

Assessing airline efficiency with a network DEA model: A Z-number approach with shared resources, undesirable outputs, and negative data

This study measures the efficiency of airlines using a novel fuzzy common weight additive network data envelopment analysis (NDEA) with shared resources, negative data, and undesirable outputs. First, an appropriate two-stage network is designed for each airline so that stages 1 and 2 are called the Production and Service stages, respectively. The proposed model adopts a top-down approach and calculates the efficiency of the system first and then estimates the efficiency of stages 1 and 2. To evaluate and predict the airlines’ efficiency considering fuzzy data and the reliability of the information, the values of input/intermediate/output variables are predicted as the Z-number and the appropriate Z-number version of NDEA (ZNDEA) models is proposed. To develop the proposed ZNDEA models and find common weights for the variables, three multi-objective ZNDEA models for the system, stage 1 and stage 2 are presented. The multi-objective common weight ZNDEA models are solved using the min-max Chebyshev goal programming technique and the final efficiencies are calculated. To illustrate the capability of the proposed approach, real-life data from Iranian airlines in 2022 are collected, and the efficiencies are analyzed.

From resources to capital: Investigating the efficiency of forest ecosystem products value realization in China

Forest resources provide a good ecological environment for human beings and offer economic well-being and benefits. Value realization of ecosystem products reflects the process from resources to capital. This study investigates the two-stage value realization efficiency of China's forest ecosystem products from 2011 to 2021. A parallel nested network Data Envelopment Analysis model is applied to assess product supply and value transformation stages. In addition, the evolution of the spatial pattern is depicted by a multilayer Standard Deviational Ellipse. The results reveal that (1) the overall value realization trend of China's forest ecosystem products shows a fluctuating growth trend, with an average annual efficiency value of 0.86. (2) The efficiency level of the value transformation stage is lower than that of the product supply stage. (3) Regional disparities persist. The efficiency values in East China and South China are higher than that in other regions. (4) The value realization capacity of China's forest ecosystem products may be closely related to national development strategies and policy orientations.

Using the hybrid undesirable network data envelopment analysis model to evaluate the efficiency of Taiwan’s social care system

In this study, a hybrid undesirable network data envelopment analysis model is developed for assessing the efficiency of social care systems. Studies investigating the performance of social care activities have mostly focused on a single social care type. To date, no study has proposed an integrated framework for assessing social care performance. To address this gap in the literature, the developed model incorporates four divisions for a comprehensive performance measurement: disability care, child and youth care, women care, and elderly care. In the model, the government’s expenditure on social care is the initial input, and the efficiency of social care activities in the aforementioned four divisions is evaluated in the second stage. Social care facilities and social care workers serve as the nonradial input and radial input, respectively. Care recipients and those affected by violence serve as the desirable output and undesirable output, respectively, in the second stage. The empirical results have several practical implications for Taiwan’s social care system.

Efficiency analysis of the innovation-driven sustainable logistics industry

The logistics industry is at the heart of socio-economic development. Disruption in the logistics and supply chain industry grounded many sectors to a halt at the height of COVID-19 with economic and environmental implications. This study examines the efficiency and interconnections among innovation, energy, infrastructure, and logistics performance as the logistics industry strives to attain a green supply chain while maintaining economic growth and environmental sustainability. To capture these relationships, a network data envelopment analysis model is designed for efficiency analysis. 36 % of the evaluated countries had below-average efficiency. Results also indicate innovation and energy sustainability as drivers of efficiency and sustainability in the logistics industry. This implies the need for innovation in logistics energy infrastructure by decarbonizing the logistics industry. Regional and national partnerships in the technological development of green energy to power the logistics industry are recommended.

Green transformation and performance synergy efficiency of china’s thermal power enterprises on the basis of the environmental tax burden

Global climate change and the collection of environmental protection taxes are accelerating the green transformation of thermal power enterprises. This study selected Chinese thermal power listed companies as samples and used a dynamic three-stage (operational, green transformation, and market performance) network DEA model to evaluate their transformation efficiency and corporate performance. This paper incorporates targeted indicators such as ESG (environment, society, governance) and stock prices into the model and conducts a comparative study on the basis of macro policies and the geographical location of the enterprise. A comparative analysis was conducted on the efficiency of enterprises before and after the adjustment of the environmental tax burden, using the environmental tax burden as an exogenous variable. Thus, the following conclusions can be drawn: there is a certain positive correlation between the collaborative efficiency of the two links of thermal power enterprises and the economic development of their respective regions. Moreover, the green transformation efficiency of most thermal power enterprises is superior to the market performance efficiency. The environmental tax burden mainly improves the overall efficiency of thermal power enterprises by improving their operational efficiency and efficiency in the green transformation stage without affecting market performance. To further improve efficiency, thermal power enterprises should actively communicate with stakeholders to strive for more financial relief.

Assessment of Iranian airlines using network cross-efficiency DEA and the regret theory

Network Data Envelopment Analysis (NDEA) has been extensively applied to evaluate the air transportation sector. NDEA provides a tool for evaluating the internal processes of Decision-Making Units (DMUs). Optimistic Network Cross-Efficiency (ONCE) has recently been extended to the basic two-stage system. However, there are still two main shortcomings that need to be addressed. First, the ONCE evaluates DMUs based only on the optimistic viewpoint, neglecting the pessimistic viewpoint. The optimistic viewpoint assumes that there is only one set of reference points, which includes the best practice DMUs. The first contribution of this study is to develop a new Pessimistic Network Cross-Efficiency (PNCE) method. This method is based on a new set of reference points, which includes the worst-performing DMUs. The PNCE is developed as an extension of the ONCE. Second, both the ONCE and newly developed PNCE methods may lead to unrealistic results because they neglect the subjective preferences of Decision Makers (DMs). These NDEA models employ the Arithmetic Mean (AM) as the cross-evaluation aggregation method, which not only underestimates the importance of self-evaluation but also overestimates the importance of peer evaluations. Consequently, ONCE and PNCE may lead to biased efficiency results. To address this drawback, the second contribution of this study is to develop a new Aggregation method based on the Regret theory and Consensus (ARC). This method aims to reflect the psychological preferences of DMs when estimating cross-evaluation weights. To achieve this goal, we obtained new optimistic and pessimistic efficiencies by utilizing the newly developed ONCE-ARC and PNCE-ARC methods. Subsequently, a Double-Frontier Network Cross-Efficiency with ARC (DFNCE-ARC) is developed as a more comprehensive NDEA. Finally, a practical application is conducted to assess the performance of a set of Iranian airlines, demonstrating the usefulness and applicability of DFNCE-ARC.

An Innovative Double-Frontier Approach to Measure Sustainability Efficiency Based on an Energy Use and Operations Management Perspective

China’s economic development has achieved great success in recent years, but the problems of energy scarcity and environmental pollution have become increasingly serious. To enhance the reliability and efficiency between energy, the environment and the economy, sustainable development is an inevitable choice. In the context of measuring sustainability efficiency, a network data envelopment analysis model is proposed to formulate the two-stage process of energy use and operations management. A double frontier is derived to optimize the available energy for sustainable development. Due to nonlinearity, previous linear methods are not directly applicable to identify the double frontier and calculate stage efficiencies for inefficient decision-making units. To address this problem, this study develops the primal-dual relationship between multiplicative and envelopment network models based on the Lagrange duality principle of parametric linear programming. The newly developed approach is used to evaluate the sustainability efficiency of 30 administrative regions in China. The results show that insufficient sustainability efficiency is a systemic problem. Different regions should take different measures to conserve energy and reduce pollutant emissions for sustainable development. To increase sustainability efficiency, regions should support energy-saving and emission-reducing technologies in production processes and strengthen their capacity for technological innovation. Compared with energy use efficiency, operations management efficiency in China has a wider range of changes. During the operations management stage, there is not much difference between the capacity and quantity of each region. Based on benchmark regions at the efficiency frontier, there is an opportunity to improve operations management in the near future. Blockchain technology can effectively improve energy allocation efficiency.

Efficiency analysis of China's urban water supply utilities using a dynamic multiactivity network DEA model

Urban water scarcity and severe consumption challenges are crucial for improving performance in the urban water supply industry. Urban Water Supply Utilities (UWSUs) perform the primary task of providing tap water to city residents, and regulators use benchmarks to monitor UWSU operations. To assess the efficiency of UWSUs in detail, this study introduces a dynamic two-stage network data envelopment analysis (DEA) model with shared inputs and shared carryover. This model decomposes UWSU operations into water production (WP) and water distribution (WD) subprocesses. By combining dynamic features with network DEA, the study extends the static network DEA to a more general case for evaluating UWSU efficiency. The study sample includes 246 UWSUs from 24 provinces in China over the period 2016–2018. The analysis covers overall efficiency, period efficiency, subprocess efficiency, and regional disparities among the sampled water utilities. The findings indicate that the average efficiency of the UWSUs was 0.643 during 2016–2018, with slightly lower efficiency in the WP subprocess. UWSUs in the eastern provinces outperformed those in the central and western provinces, which require significant improvements. To enhance the performance of China's water supply sector, accelerated market competition and infrastructure improvements are necessary.

Efficiency assessment and scenario simulation of the water-energy-food system in the Yellow river basin, China

Comprehending the efficiency of the Water-Energy-Food system within basins is crucial to enhance their resource utilization and facilitate high-quality development. This research evaluates the efficiency of the Water-Energy-Food (WEF) system in the Yellow River Basin between 2005 and 2021 using the super efficiency network Data Envelopment Analysis (DEA) model, addressing the limitation of traditional DEA models by considering both input and output within system. A multi-scenario combination is established under different development modalities, and an innovative attempt is made to introduce machine learning into efficiency prediction. The GA-LSTM (Genetic Algorithm-Long Short-Term Memory) model is constructed to forecast the efficiency values across multiple scenarios and identify the optimal scenario namely the scenario with the highest efficiency value, to carry out the spatial-temporal analysis. The results indicate that: (1) The WEF system efficiency in the Yellow River Basin exhibited a variable rising trajectory from 2005 to 2021, with the economic scale playing a significant role as an influencing element; (2) The trained GA-LSTM model can accurately predict the efficiency value and change trend of WEF system in the Yellow River Basin; (3) Under scenario 4 when water and food subsystems are maintained in the baseline scenario, while the energy subsystem is regulated in the general saving scenario, the overall average efficiency value of WEF system in the Yellow River Basin in 2022–2035 is the highest, reaching 1.28, which is the optimal enhancement path; (4) Under the optimal scenario, the WEF system efficiency in the Yellow River Basin increases by 6.89 % annually, with Shandong having the greatest efficiency level and Qinghai the lowest.

The Effect of Financial Development on Industrial Green Technology Innovation Efficiency: Experience Analysis from 288 Cities in China

Green industrial growth is at the core of the green and low-carbon transformation of the real economy. Financial services provide new channels for green innovation financing for enterprises. How to utilize financial development to enhance the industrial green technology innovation efficiency (GTIE) is the focus of this paper. Using data from 288 cities in China, this paper employs the Super-SBM model and the Network DEA model to measure the industrial GTIE and then investigates the complex relationship between financial development and industrial GTIE. The results show that, overall, financial development can promote the industrial GTIE. And alleviating enterprise financial constraints and increasing investment openness are effective pathways for this influence. In addition, we discuss the heterogeneous effects of financial development on industrial GTIE across different types of cities, manifested as a dual effect of “supporting” and “predation”. In regions with stronger financial power such as coastal and eastern cities, the “predation effect” is stronger, while the “support effect” is stronger in inland and central/western cities. Furthermore, the expansion of city size weakens the impact of financial development on industrial GTIE. This paper points out that financial development plays a crucial role in enhancing the industrial GTIE, but excessive financial power may crowd out the positive impact of financial development on the industrial GTIE. When promoting financial development, it should be matched with region development to avoid crowding out investment in green innovation by enterprises.

Developing an efficiency evaluation model for the circular economy in Europe

ABSTRACT The aim of this work is to develop a model for evaluating the efficiency of the Circular Economy (CE) in Europe. In this context, we have developed a dynamic efficiency model of the CE, the Dynamic Network Data Envelopment Analysis (DNDEA) model (Tone and Tsutsui 2014), with the purpose of it being utilized to determine the degree of progress of the policies on the CE, the degree of compliance with those policies, as well as to determine the factors that are associated with a greater or lesser efficiency. The results obtained indicate that the factors which have most affected the dynamic inefficiency have been: the Imports from the EU countries, the Exports to the non-EU countries, and the Gross Value Added. The patents that are related to Waste Management, Recycling, and Persons Employed in the CE sector have been the factors that have added the greatest efficiency. The contribution of this work lies in the development of a new model for the measurement of the dynamic efficiency of the CE at the macroeconomic level in Europe, while considering the relationships between the results and the resources that are allocated to the CE, and establishing a period of implementation and execution.

Mobile payment-banking efficiency nexus—A concise review of the evolution and empirical exploration of the Taiwan banking industry

This paper utilizes an advanced Network Data Envelopment Analysis (DEA) model to examine the impact of mobile payment on the efficiency of Taiwan banking industry. Inheriting the literature, we separate the banking operation process into two stages, namely profitability and marketability. Mobile payment is then considered as the core factor in the second stage. Our paper discovers network DEA model can effectively enhance the analysis of banking industry’s efficiency, and mobile payment has a notable impact on Taiwan banking industry. Regarding the profitability stage, there is only one efficient bank in 2019 and 2022, respectively. These banks also perform better in terms of “mobile payment production”. In the marketability stage, there is also only one bank in 2021 and one bank in 2022, that can reach to unique efficiency score. This indicates many banks attempt to increase earnings per share through investing in mobile payment services. However, the achievement still needs more wait. This leads to the fact that no bank can reach the ultimate overall efficiency. Within our sample, we also find that regarding promoting mobile payment services, Private Banks outperform Government Banks.