Network Data Envelopment Analysis Model Research Articles

The Technological Innovation Capability of The New Energy Vehicle Industry

Several subsidy programs are being implemented by nations all over the world in an effort to support the growth of the new energy vehicle industry and increase its capacity for innovation. In order to assess the sector’s capacity for technical innovation, the innovation capability level of China’s new energy vehicle industry from 2012 to 2017 was computed using a network DEA model. Technology development and innovation transformation were the two stages into which the sector was split. The findings demonstrate that a mismatch between the effectiveness of the technology development stage and the effectiveness of the innovation transformation stage is the cause of innovative technology’s inability to support business operations. This mismatch also contributes to the new energy vehicle industry in China’s overall low level of innovation capability. Based on the study’s findings, significant policy suggestions are made in order to progress the new energy vehicle industry’s technological capabilities within the constraints of China’s present new energy policy.

Measurement of the Operation Efficiency of the Basic Endowment Insurance System for Urban and Rural Residents in China: Based on the Two-Stage Network DEA Model

Measurement of the Operation Efficiency of the Basic Endowment Insurance System for Urban and Rural Residents in China: Based on the Two-Stage Network DEA Model

Demographic efficiency drivers in the Chinese energy production chain: A hybrid neural multi‐activity network data envelopment analysis

AbstractFor meeting the external requirements of the Paris Agreement and reducing energy consumption per gross domestic product, China needs to improve its energy efficiency. Although the existing studies have attempted to investigate energy efficiency from different perspectives, little effort has yet been made to consider the collaboration among different stages in the production chain to produce energy outputs. In addition, various studies have also examined the determinants of energy efficiency, however, they mainly focused on technology and economic factors, no study has yet proposed and considered the influence of geographical factors on energy efficiency. In this article, we fill in the gap and make theoretical and empirical contributions to the literature. In this study, a two‐stage analysis method is used to analyse energy efficiency and the influencing factors in China between 2009 and 2021. More specifically, from the theoretical/methodological perspective, a multi‐activity network data envelopment analysis model is used to measure energy efficiency of different processes in the energy production chain. From the empirical perspective, we attempt to investigate the influence of geographical factors on energy efficiency through a neural network analysis. Meanwhile, the comparisons among different provinces are made. The result shows that the overall energy efficiency is low in China, and China relies more on the traditional energy industry than the clean energy industry. The efficiency level experiences a level of volatility over the examined period. Finally, we find that raw fuel pre‐process and industry have a significant and positive impact on energy efficiency in China.

Evaluating Portuguese Public Hospitals Performance: Any Difference before and during COVID-19?

COVID-19 is a well-known respiratory disease that has spread worldwide since January 2020, causing many deaths and massive pressure on health systems. This pandemic’s appearance compromised health service sustainability and quality as many procedures were postponed or canceled, with an expected increase of adverse events like nosocomial infections, in-hospital deaths, and the worsening of the patient’s clinical status. For instance, the year 2020 featured an increase in undesirable results in Portugal: a rise of 10% in delayed first medical appointments, 0.02% in avoidable bloodstream infections, and more than 100 post-operative pulmonary embolisms and septicemia cases per 100,000 inpatients. Therefore, assessing whether the healthcare providers’ performance has changed is paramount. In this case, we evaluated public hospitals in Portugal. To this end, we developed a network data envelopment analysis model relating to efficiency and effectiveness. We observed consistent drops in efficiency when the pandemic started, followed by a recovery to levels above the pre-pandemic ones. Regarding effectiveness, we observed a positive trend during the evaluated period. We conclude that, apart from the great resilience of public healthcare providers, the Portuguese state was unprepared for a pandemic like COVID-19, but still the actions taken (including massive vaccination) were beneficial.

Design of humanitarian supply chain system by applying the general two-stage network DEA model

PurposeThe recent COVID-19 outbreak and severe natural disasters make the design of the humanitarian supply chain network (HSCN) a crucial strategic issue in a pre-disaster scenario. The HSCN design problem deals with the location/allocation of emergency response facilities (ERFs). This paper aims to propose and demonstrate how to design an efficient HSCN configuration under the risk of ERF disruptions.Design/methodology/approachThis paper considers four performance measures simultaneously for the HSCN design by formulating a weighted goal programming (WGP) model. Solving the WGP model with different weight values assigned to each performance measure generates various HSCN configurations. This paper transforms a single-stage network into a general two-stage network, treating each HSCN configuration as a decision-making unit with two inputs and two outputs. Then a two-stage network data envelopment analysis (DEA) approach is applied to evaluate the HSCN schemes for consistently identifying the most efficient network configurations.FindingsAmong various network configurations generated by the WGP, the single-stage DEA model does not consistently identify the top-ranked HSCN schemes. In contrast, the proposed transformation approach identifies efficient HSCN configurations more consistently than the single-stage DEA model. A case study demonstrates that the proposed transformation method could provide a more robust and consistent evaluation for designing efficient HSCN systems. The proposed approach can be an essential tool for federal and local disaster response officials to plan a strategic design of HSCN.Originality/valueThis study presents how to transform a single-stage process into a two-stage network process to apply the general two-stage network DEA model for evaluating various HSCN configurations. The proposed transformation procedure could be extended for designing some supply chain systems with conflicting performance metrics more effectively and efficiently.

Carbon Emission Abatement Allocation of a Recycling Production System in a Fuzzy Environment

Appropriate measurement of environmental performance and emission allowances in circular economy is of great significance for promoting sustainable development. Due to the vagueness involved in many circular indicators, a fuzzy network data envelopment analysis model for the carbon emission abatement (CEA) allocation of a recycling production system is proposed in this work. The proposed model employed the concept of environmental efficiency analysis while allocating the optimal CEA levels from a circular economy perspective. An empirical application to the CEA allocation of European Union (EU) countries is studied. Our results provide the EU policy makers and legislators a valuable information for emission allowances management in a fuzzy environment.

Two-stage dynamic data envelopment analysis measuring the overall efficiency and productivity changes of industry and agriculture in EU countries

Carbon emissions from industrial production affect agricultural production, and forests have the benefit of being carbon neutral. Therefore, this study adopts the dynamic network data envelopment analysis (DEA) model, takes carbon emissions as the link between industry and agriculture, and explores the production efficiency of industry and agriculture. It further incorporates forest area as a carry-over for evaluation and discusses 27 EU members' dynamic overall efficiency and productivity changes in 2014–2018. The empirical results are as follows. 1) The EU countries' industrial stage efficiency is better than their agriculture efficiency, with Germany, Luxembourg, Malta, and the Netherlands exhibiting the best efficiency performance. 2) The overall efficiencies of the Czech Republic, Portugal, and Croatia are the worst, because their misallocation of resources and excessive energy input cannot improve their output value. 3) The agricultural productivity of EU countries is better than that of industry productivity. 4) The EU has made great efforts at planting forests. 5) As energy consumption and carbon emissions are still rising, the EU still needs to do more to achieve the carbon reduction goals of the Paris Agreement and the Eu's 2030 zero carbon emissions.

Methodology selecting packaging materials combined multi‐sensory experience and fuzzy three‐stage network DEA model

AbstractMaterials selection involves thousands of material types and evaluation parameters. Considering multisensory experience for packaging materials selection is an important trend but a difficult duty that involves uncertainty due to the subjectivity of humans. The main aim of this study is to establish a model to determine the consumer sensory experience of packaging materials as a function supporting packaging material selection development. When the consumer interacts before and after opening the packaging, vision and tactility are the foremost sensory feedback types, which are difficult to measure for fuzziness and uncertainty. Thus, given the consumer senses of vision and tactility, this paper addresses the relationship between sensory experience and packaging material properties. This is creatively formulated in three stages from the physical properties and the psycho‐physical to the affective levels. The proposed approach that combines fuzzy theory and three‐stage network data envelopment analysis (DEA) is used to rank 12 toning lotion bottle packaging materials. It is validated that this model allows a clear decision‐making basis for material selection in packaging design, and physiological signal tests provided key data to support material selection. Moreover, it realized the function of predicting physiological signal according to material properties by backpropagation neural network (BPNN).

글로벌 금융위기와 유럽 금융위기 기간의 항공사 효율성 분석 연구

The global financial crisis and economic recession lead to a decline in airline returns and deterioration in the business environment. In this regard, we compared and analyzed the efficiency of airlines during the global financial crisis and the European financial crisis by using the two-stage Network Data Envelopment Analysis (DEA) model. Network DEA can complement the shortcomings of the traditional DEA model and explain the transformation process hidden in the intermediate process and compare the relationship between efficiency. For the analysis, 16 airlines in Europe and related countries were selected and the period from 2007 to 2016, when the financial crisis occurred, was used. As variables, ‘operating costs’ and the ‘number of employees’ were used as input, and

Operational Efficiency Evaluation of Chinese Internet Banks: Two-Stage Network DEA Approach

An in-depth study of the operational efficiency of Internet banks is essential to enhance banks’ competitiveness and sustainable development. We use the two-stage network data envelopment analysis (DEA) model to divide the operational process of Internet banks into the value operation stage (stage 1) and the value creation stage (stage 2). This paper adopts the R&D investment that reflects the characteristics of Internet banks as the input of the value operation stage in the two-stage DEA model. It examines the operating efficiency of China’s Internet banks from 2018 to 2019, including stage efficiency and comprehensive efficiency. The empirical analysis results indicate that the contribution of stage 2 to the comprehensive efficiency is higher than that of stage 1. Similarly, it can also be shown that the average values of the stage efficiency and comprehensive efficiency of Internet banking in 2019 are higher than those in 2018. In addition, the Kruskal–Wallis test shows no significant difference in the average comprehensive efficiency of Internet banks in the three major economic belts. These results have significant strategic implications for managers, regulators, and policymakers who share a common interest in boosting financial sustainability and performance.

Measuring Regional Innovation Efficiency in China using a Dynamic Network DEA Model

With the explosive growth in R&D investments and patent applications in recent decades, has China truly achieved improved innovation quality? To answer this question, it is necessary to correctly estimate China’s innovation efficiency. However, when measuring innovation efficiency, the dynamic and network features of the innovation process are seldom considered simultaneously. Therefore, this paper employs the method of dynamic network data envelopment analysis to estimate the overall, period, and sub-stage innovation efficiency of China’s 30 provinces between 2012 and 2016. We conclude that: (1) There is a regional imbalance in the overall scores, for example, developed provinces are more efficient than less developed areas. (2) The period and sub-stage values are not high in each period and represent a gap among the various provinces. (3) For most provinces, scores in the R&D stage are higher than those in the commercialization phase, indicating an uneven distribution of the innovation structure. Accordingly, policymakers should focus on innovation efficiency indicators, encourage innovation according to local conditions, and facilitate the long-run enhancement of both R&D and commercialization.

Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis

The main objective of this study is to propose a network data envelopment analysis (NDEA) model and a deep learning approach for forecasting the sustainability of healthcare supply chains (HSCs). Technological advances manifested in approaches such as deep learning, artificial intelligence (AI), and Blockchain are of substantial importance throughout HSCs and are understood as competitive advantages. Furthermore, applying advanced performance evaluation techniques, including DEA in HSCs for enhancing performance has attracted momentous attention over the last two decades. To make use of these approaches, a network DEA (NDEA) model and a deep learning approach are developed to predict the sustainability of HSCs. The developed model in this paper can determine the optimal value of bounded connections. Using the DEA capabilities, the threshold of each of these bounded connections is obtained to maximize the efficiency of decision making units (DMUs). It also identifies the role of the dual-role connections for each DMU. The results show that HSCs that use the least facilities and have the most desirable output, as well as the least undesirable output, are in the top ranks.

Evaluation of Water—Energy—Food—Economy Coupling Efficiency Based on Three-Dimensional Network Data Envelopment Analysis Model

In the process of human survival and sustainable development, water security, energy security and food security have become the three most prominent issues, but they are interrelated and directly affect each other, that is, to form a Water–Energy–Food (WEF) nexus. Scientific understanding and correct response to the relationship between WEF is important to realize the sustainable development of natural resources. There are some deficiencies in the existing research on the input–output efficiency of WEF system. There are few articles that can study the efficiency relationship between internal and external factors (such as the economy and environment) of the WEF system at the same time, or the research is not perfect. In view of the shortcomings of the existing research, this paper establishes a three-dimensional network structure to describe the water–energy–food–economy (WEF-Eco) system and establishes the corresponding network Data Envelopment Analysis (DEA) model. We use the data of 19 provinces in Northeast, East, and central China to show the application results of this model.

Efficiency measurement and spatial spillover effect of provincial health systems in China: Based on the two-stage network DEA model.

The effectiveness of a health care system is an important factor for improving people's health and quality of life. The purpose of this research is to analyze the efficiency and spatial spillover effects of provincial health systems in China using panel data from 2009 to 2020. We employ the two-stage network DEA model to evaluate their efficiencies and use a spatial econometric model for empirical estimation. The results suggest that the overall efficiency, resource allocation efficiency, and service operation efficiency of health systems in different regions of China generally have fluctuating upward trends, with large differences in efficiency among the various regions. Further analysis reveals that the efficiency of China's health system has a significant spatial spillover effect. The level of economic development, fiscal decentralization and old-age dependency ratio are important factors affecting the health system efficiency. Our findings help to identify the efficiency and internal operating mechanisms of China's health system at different stages, and are expected to contribute to policymakers' efforts to build a high-quality health service system.

An integrated game theoretic model and network data envelopment analysis to determine optimal export allocation to industrial units (A case study of the stone industry)

Despite the fact that Iran is one of the main world's stone producers with more than 6000 stone-cutting centers, its share barely holds one percent of the world market. The industry, hence, awaits effective measures to increase its exports of building stone products. It is assumed in the present study that public-private partnership based on a win-win approach might not only facilitate foreign trades by different industries but also enhance export opportunities and improve production efficiency. Accordingly, efforts are made in the present study to investigate both production and supply networks in conjunction with the hitherto less heeded demand allocation. It is the objective to exploit functional criteria in each of these networks that will guarantee efficiency and functional values based on domestic and international demands. The way each player participates in its respective network and handles feedback from other players depends on the his/her conditions and the performance of the various players/components of the network they are part of. To determine these parameters, use is made in this study of the network data envelopment analysis (NDEA) with the efficiency of each industrial unit in the network considered as the weight of that unit within a game theoretic framework. More specifically, given the importance of exports and trade commodities as well as the great impacts such industrial units as R&D and sales departments have on international markets and marketing, the weights associated with each industrial unit is calculated via the network data envelopment analysis that takes into account not only the effects of all the network components but also those of the parameters affecting their efficiency. Thus, all the factors involved in the evaluation of the supply chain are initially identified based on the SCOR model and the balanced scorecard that additionally includes sustainability criteria. The model is implemented on the data extracted from a survey of 10 stone-cutting plants, the supply chain network of which is determined based on a study of their structures and the criteria considered in the relevant sub-networks. In a subsequent stage, after implementing PCA, the NDEA model is solved for each of the subnetworks in GAMS software and the efficiency of each plant is obtained using the weights obtained from the demand allocation model within a game theoretic framework. The output from the model solved in GAMS indicates that, compared to the current situation, application of the integrated model proposed in this study leads to a significant increase in the profits gained from each customer demand; the profits earned is expressed as the number of orders placed with each plant.

Dynamic Network Data Envelopment Analysis Model Usage in Measuring and Ranking the Financial Performance of Social Security Hospitals Based on their Size

Background: Measuring the hospitals financial performance in the health care system is of great importance. This is because hospitals with good financial performance can maintain reliable systems and provide necessary resources to improve quality. The aim of this study was to measure, compare and rank the financial performance of social security hospitals based on their size using a dynamic network data envelopment analysis model.
 Methods: This descriptive-analytical study was conducted using information sources in social security hospitals. Data were collected from financial statements from 2016 to 2019. The network efficiency analysis of the units was performed by GAMS 28 software.
 Results: According to the findings from 50 hospitals, among the hospitals with less than 100 active beds, the highest financial performance score belonged to Aras Ardabil hospital (0.79) and the lowest to Shabikhani Kashan hospital (0.24). Among the hospitals with 100 to 200 active beds, the highest financial performance score was obtained by Gharzi Malayer hospital (0.78) and the lowest by Imam Reza Islamshahr hospital (0.27). Imam Reza Urmia hospital with a score of 0.87 and Beheshti Shiraz hospital with a score of 0.39, achieved the highest and lowest financial performance in the dynamic network usage, among the hospitals with more than 200 active beds.
 Conclusion: Using the dynamic network data envelopment analysis model, the researchers measured the input and output of each decision-making unit over time. They also provided information about the system and internal structure in order to achieve overall efficiency. It is suggested that policymakers and hospital managers abandon the idea that a higher input rate determines a higher level of efficiency. They need to consider the compatibility of hospital size and internal structure in order to improve efficiency.

The role of degree of opening in comprehensive water efficiency, derived from SHAN model

Abstract China's reform and opening-up strongly impact its local water resources. The most frequently stated problems are water supply scarcity and pollution issues in China's Yangtze River. What is not yet clear is the correlation between comprehensive water efficiency and its sub-stage efficiencies, water utility efficiency, and water governance efficiency. This study aims to investigate water utility and water governance together. This paper provides a fresh perspective on developing an improved DEA method called the SHAN model that evaluates and decomposes the comprehensive water efficiency to water utility efficiency and water governance efficiency for 30 cities in the Yangtze River Basin from 2005 to 2015. The SHAN model is a modified RAM Network DEA model with the extra consideration of Super SBM and undesirable output. A Tobit model will be adopted to analyze the relationship between comprehensive water efficiency and its sub-stages. This paper uses panel quantile regression to investigate the impact of the degree of opening and other efficiency determinants. The results show that the value of comprehensive water efficiencies in most cities along the Yangtze River Basin are lower than 1, as well as the water utility efficiencies and water governance efficiencies. The results indicate that the government should significantly improve water resource utilization and pollution problems. The findings also show that the degree of opening, population density, GDP per capita, urbanization, and industrial structure positively influence comprehensive water efficiency. Population urbanization lag has negative impacts on the comprehensive water efficiency. The Tobit regression results show that the water resource utilization efficiency and water resource management efficiency are 1.435 and 0.227, respectively. This result indicates that the water utility stage has a more significant effect on comprehensive water efficiency.

Applying an improved three-stage DEA model to evaluate the innovation resource allocation efficiency in industrial enterprises

PurposeThe existing three-stage network Data Envelopment Analysis (DEA) models with shared input are self-assessment model that are prone to extreme efficiency scores in pursuit of decision-making units (DMUs) efficiency maximization. This study aims to solve the sorting failure problem of the three-stage network DEA model with shared input and applies the proposed model to evaluate innovation resource allocation efficiency of Chinese industrial enterprises.Design/methodology/approachA three-stage network cross-DEA model considering shared input is proposed by incorporating the cross-efficiency model into the three-stage network DEA model. An application of the proposed model in the innovation resource allocation of industrial enterprise is implemented in 30 provinces of China during 2015–2019.FindingsThe efficiency of DMU would be overestimated if the decision-maker preference is overlooked. Moreover, the innovation resource allocation performance of Chinese industrial enterprises had a different spatial distribution, with high in eastern and central China and low in western China. Eastern China was good at knowledge production and technology development but not good at commercial transformation. Northeast China performed well in technology development and commercial conversion but not in knowledge production. The central China did not perform well in terms of technology development.Originality/valueA three-stage network DEA model with shared input is proposed for the first time, which makes up for the problem of sorting failure of the general three-stage network model.

Allocation of SO2 emission rights in city agglomerations considering cross-border transmission of pollutants: A new network DEA model

Currently SO2 emission rights are allocated without considering the cross-border transmission characteristics of pollutants, and the allocation scheme cannot reflect the fact that air pollutant overflows in space. In this paper, the WRF (Weather Research and Forecasting Model)-CMAQ (Community Multiscale Air Quality) is combined with the data envelopment analysis (DEA) allocation model for the first time, and a two-stage (Stage-1: SO2 formation, Stage-2: SO2 treatment) network DEA allocation model is proposed with the SO2 spatial transmission taken as the intermediate output or input index between decision-making units. Based on the principles of efficiency, economic protection and fairness, the model is applied to the study of SO2 emission rights allocation in the Yangtze River Delta city agglomeration with high homogeneity in China. The results show that: (1) When the weight coefficient (α1) in stage-1 is 0.7, 0.8 or 0.9, the per capita relative deprivation coefficient (PRDC) of each decision-making unit (DMU) is less than 0.5 in the allocation scheme, indicating that our scheme can be used as a reference to allocate emission rights in the Yangtze River Delta city agglomeration. (2) With great robustness and universality, our allocation model allows decision-makers to adjust the GDP constraint coefficient and the weight coefficients in the two stages according to their preferences, so as to obtain the optimal allocation results. The model proposed in this paper can help decision-makers to formulate effective policies to reduce pollutant emission, and promote energy conservation & emission reduction and coordinate environmental governance in regions of city agglomerations.

A three-stage network DEA approach for performance evaluation of BIM application in construction projects

BIM application in the construction industry is still low in China, mainly due to the lack of effective measures for BIM application evaluation. Therefore, this study takes BIM application as the research object to propose a three-stage network DEA approach for performance evaluation. The BIM application performance evaluation indicators are determined based on the balanced scorecard method and Delphi method, and a three-stage network DEA model is established to optimize the BIM application performance. The three-stage network DEA model established in this study can solve the problem that the traditional DEA model treats the internal structure as a “black box”. The model is then applied to the actual case of 9 construction projects, including relaxation value analysis and path optimization, to identify the critical path for the reallocation of resources. This paper studies the performance evaluation of BIM application, which is conducive to the further improvement of the theory of BIM management, and also can effectively improve the performance of construction projects and bring practical benefits to construction enterprises.