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The data envelopment analysis (DEA) model is extensively used to estimate efficiency, but no study has determined the DEA model that delivers the most precise estimates. To address this issue, we advance the Monte Carlo simulation-based data generation process proposed by Kohl and Brunner (2020). The developed process generates an artificial dataset using the Translog production function (instead of the commonly used Cobb Douglas) to construct well-behaved scenarios under variable returns to scale (VRS). Using different VRS DEA models, we compute DEA efficiency scores with artificially generated decision-making units (DMUs). We employ five performance indicators followed by a benchmark value and ranking as well as statistical hypothesis tests to evaluate the quality of the efficiency estimates. The procedure allows us to determine which parameters negatively or positively influence the quality of the DEA estimates. It also enables us to identify which DEA model performs the most efficiently over a wide range of scenarios. In contrast to the widely applied BCC (Banker-Charnes-Cooper) model, we find that the Assurance Region (AR) and Slacks-Based Measurement (SBM) DEA models perform better. Thus, we endorse the use of AR and SBM models for DEA applications under the VRS regime.

The Strategic Framework for European Cooperation in Education and Training (ET 2020) aimed to promote the exchange of best practices among the Member States. This paper assesses the performance evolution of European countries in terms of the common objectives for the education sector. The framework used to evaluate European education systems is based on constructing a composite indicator adopting a “benefit-of-the-doubt” approach. The evaluation of performance change over time is done using a Global Malmquist Index. Sigma and beta convergence of EU countries are also explored using non-parametric frontier techniques. The results are analysed for the period 2009–2018 and discussed in light of the goals envisaged and the national policies adopted. The results revealed a trend of improvement in the performance of education systems in most European countries in the period analysed. Although most European countries moved closer to the European best practice frontier over time, as confirmed by the values of sigma-convergence, a few countries are still lagging considerably below their peers, as revealed by the existence of divergence in beta.

This study uses three data envelopment analysis models to determine the production efficiency of biohydrogen which is produced from macroalgae and other sources by dark fermentation. The efficiency of macroalgae is greatest in batch mode for S. Japonica using a sDFMEC process at pH 5.3, 35 °C, 1 g COD/L and a hydrogen production rate (HPR) of 0.34 L/L/h. The highest efficiency is using an internal circulation batch reactor in continuous mode for beverage waste water. The HPR and substrate concentration are the most important factor of biohydrogen efficiency, and efficiency and temperature are the most important factors of HPR. Malaysia and India are the two economies that most benefit from increased production efficiency due to the use of macroalgae. Increasing biohydrogen yield efficiency will improve macroeconomic growth and establish a renewable hydrogen and biohydrogen industry, which is especially efficient related to the economic recovery during the COVID-19 pandemic.

The transition from fossil fuels to more clean, affordable, and sustainable energy resources is of strategic importance for developing Asian countries like Vietnam, in which solar energy is an attractive option. The government of Vietnam is implementing various policy measures and has substantial investment plans to accelerate the national spread of solar energy. In this regard, location optimization for solar power installation is a vital strategic decision that needs to be analysed in-depth to harness solar power. This study aims to determine suitable areas for solar power installation in a case study in Vietnam. For the decision-making that influences solar plant site selection, while some criteria can be evaluated based on a numerical database, numerous qualitative criteria must be under experts’ judgements in linguistic terms and can be represented through grey numbers. To deal with the complex nature of the prioritization problem posed by the coexistence of multiple factors, this study proposes a combination of the Data Envelopment Analysis (DEA) method, Grey Analytic Hierarchy Process (G-AHP), and Grey Technique for Order Preference by Similarity to Ideal Solution (G-TOPSIS). With the utilized integrated approach, both quantitative and qualitative effective criteria are considered to evaluate locations. DEA is used in the first phase to select high-efficiency locations based on various measurable criteria. In the second phase, these locations are further assessed in terms of qualitative criteria: technical, economic, environmental, and socio-political factors. For this evaluation, G-AHP is used for criteria weighting, and G-TOPSIS ranks the locations. While the proposed approach is expected to help save costs and resources towards determining hot spots for solar power plants installation all around the country, it is recommended that governments and local authorities take into account suitability policies and support mechanisms for sustainable solar energy development.

Chinese sea perch (Lateolabrax Maculatus) is one of the main marine fish culture species in China, its green development has received increasing attention and its ecological economic study is rare. Based on field survey, this paper takes water pollution tax of China as conversion indicator for aquaculture eutrophication to simulate aquaculture waste fee for further analyzing the ecological impact of Chinese sea perch's aquaculture. For data analysis, data envelopment analysis model has been used. The comparison for Chinese sea perch's economic performance and ecological economic performance under its main aquaculture systems has been done. The results show that: 1. The simulated waste fee has no significant impact for the comparison. 2. Ranking for ecological economic performance's ecological economic, technical and scale efficiency is as follow: pond farming, offshore cage culture and cage culture. 3. Offshore cage culture is the only system presenting positive increase for ecological economic performance, which makes its aquaculture activity more sustainable than other systems. 4. The key point of green development is to maximize the ecological economic efficiency of main aquaculture systems. Thus, offshore cage culture is the most sustainable aquaculture system among China's current marine fish culture systems, and other systems need innovation for achieving the green development of China's aquaculture.

This paper studies the relationship between logistics, intermodality and airport efficiency. To this end, using Data Envelopment Analysis (DEA), the efficiency of the 21 largest European airports has been estimated over the 2009–2014 period. In a second stage, this research analyses the effect of Logistics and Modal Integration on airport efficiency. The airport's ability to compete globally depends, greatly, on the passengers and cargo’ access to efficient networks. The results show evidence of the positive impact of the quality of the national logistics system on Airport efficiency. The effect of Logistics on overall airport efficiency is estimated at 0.15% for every 1% increase in the Logistic Performance (LPI), ceteris paribus. Furthermore, airports that have a direct link to a High-Speed Rail (HSR) network are expected to perform, on average, 27 per cent above the rest of the airports. Finally, airports that offer more facilities to optimise the interface between airport and aircraft are expected to achieve higher efficiency levels.

Over the last decade, many studies have adopted the data envelopment analysis (DEA)-type metafrontier framework to relax the constraint of technological heterogeneity. In conventional metafrontier analyses, either radial or nonradial DEA models are used in two separate DEA efficiency analyses. The results of a metafrontier analysis may be biased if this approach is adopted in a nonradial DEA model. One possible problem is that the value of the technology gap ratio is greater than one because the technology gaps to be estimated for decision-making units (DMUs) under different technology groups relative to the metatechnology have no connection between group frontiers and metafrontiers. Furthermore, the direction of projection of each DMU onto the metafrontier is totally independent of the direction to the efficiency frontier of the respective group frontier. Our proposed integration of group technology and metatechnology constraints into a union model offers a possible solution to the existing metafrontier problem and provides more robust metafrontier results regardless whether radial or nonradial DEA models are used. Four numerical examples and one empirical application are demonstrated, and the results appear to support the use of our proposed model.

In this paper, we focus on a two-level production and distribution planning problem in the field of supply chain management, and examine the situations where the leader does not fully know the manufacturing technologies of the follower. In such a situation, the parameters representing the manufacturing technologies cannot be explicitly used to formulate the follower’s production planning problem. To overcome this difficulty, we propose formulations that implicitly express manufacturing technologies by using the input-output data observed from the production activities of the follower, incorporating the idea of data envelopment analysis (DEA). Assuming that the follower has multiple production facilities, we consider two possibilities of the observable input-output data and formulate two corresponding production planning problems; firstly, that only the input-output data aggregated for all the production facilities can be observed collectively, and secondly, that the input-output data for each of the production facilities can be observed separately. To clarify the validity of these DEA approaches, we compare them with the conventional formulation with technological coefficients using a numerical example.

In recent years, implementation of pay-for-performance (P4P) programmes in health care has become a worldwide initiative. However, most P4P programmes incorporate systems of Performance Indicators (PI) without accounting for trade-offs between indicators. This article has two objectives: (1) to develop a Data Envelopment Analysis (DEA) methodology for performance assessment of primary care providers; and (2) to propose an innovative methodology for P4P contracting based on the DEA assessment results. To achieve the first objective, we modify the standard DEA model to account for the necessary relations between the weights attributed to each PI and domain of performance and to account for the effect of the relevant environmental variables. To achieve the second objective, we combine relative and absolute performance assessments in the elaboration of several bases for reward, and then we link these assessments to a system of graduated rewards. A benchmarking programme is also proposed to contribute to the dissemination of best practices. This article contributes to the literature by proposing an enhanced methodology for performance assessment of primary care providers which can form the basis for P4P rewards planning. The applicability and advantages of the proposed methodology are illustrated with data from Portugal, but it can easily be adapted to different sets of PIs or domains, making it relevant for performance assessment and for P4P reward setting in other contexts and countries. info:eu-repo/semantics/publishedVersion

Efficiency has become the basis for boosting the environmental satisfaction and economic performance of gardens. The major goal of this research is to optimize the date production process by minimizing the quantity of inputs in the process using parametric and non-parametric approaches, as well as to investigate the amount of energy consumption and economic indicators in the date palm production in Iran's Khuzestan region. For this purpose, data envelopment analysis (DEA: CRS & VRS and Anderson-Peterson) was used to optimize energy efficiency and present the ideal pattern of input consumption. Also, the transcendental production function was developed to represent the relationship between energy consumption and product performance. The outcomes revealed that the average entire energy expenditure in date palm production was 35.52 GJ ha−1. Among inputs, irrigation, diesel and manure have contributed to the highest energy consumption and their contribution was calculated to be 43.92%, 32.86% and 9.49%, respectively. The average of energy ratio, energy productivity, energy intensity and net energy gain were equal to 3.66, 0.39 kg MJ−1, 2.70 MJ kg−1 and 91290.66 MJ ha−1, respectively. Moreover, direct and indirect energy, as well as renewable and non-renewable energy, were computed at 76.64%, 26.46%, 56.28%, and 43.72%, respectively. DEA approach showed that there is potential to prompt sustainability, and based on the constant return to scale 10.15% of input energy is able to be alleviated. The coefficient determination of the transcendental production function model was obtained as 0.92 and gross return, benefit-cost ratio, economic productivity, payback period, invest return rate, percentage of production at the break-even point and profit per kg were calculated as 3550.63 $ ha−1, 0.44, 1.67, 2.23 year, 0.44, 2.26 and 0.26 $ kg−1, respectively. To save energy, it is suggested that solar energy be used instead of diesel fuel to extract water, and that simultaneous cultivation be implemented to limit the usage of chemical fertilizers.

This paper introduces a modelling framework which combines Data Envelopment Analysis and Markov Chains into an integrated decision aid. Markov Chains are typically used in contexts where a system (e.g. staff profile in a large organisation) is at the start of the planning horizon in a given state, and the aim is to transform the system to a new state by the end of the horizon. The planning horizon can involve several steps and the system transits to a new state after each step. The transition probabilities from one step to the next are influenced by both organisational and external (non-organisational) factors. We develop our generic methodology using as a vehicle the homogeneous Markov manpower planning system. The paper recognizes a gap in existing Markovian manpower planning methods to handle stochasticity and optimization in a more tractable manner and puts forward an approach to harness the power of DEA to fill this gap. In this context, the Decision Maker (DM) can specify potential anticipated future outcomes (e.g. personnel flows) and then use DEA to identify additional feasible courses of action through convexity. These feasible strategies can be evaluated according to the DM's judgement over potential future states of nature and then employed to guide the organisation in making interventions that would affect transition probabilities to improve the probability of attaining the ultimate state desired for the system. The paper includes a numerical illustration of the suggested approach, including data from a manpower planning model previously addressed using classical Markov modelling.

Funding Information: We wish to thank the reviewers for useful suggestions and comments, which greatly improved our paper. Publisher Copyright: © 2021 The Authors Our paper focuses on a robustness analysis of efficiency scores in the context of Data Envelopment Analysis (DEA) assuming interval scale data, as defined in A. Dehnokhalaji, P. J. Korhonen, M. Köksalan, N. Nasrabadi and J. Wallenius, “Efficiency Analysis to incorporate interval scale data”, European Journal of Operational Research 207 (2), 2010, pp. 1116–1121. We first show that the definition of the efficiency score used in our paper is a well-defined measure according to Aparicio and Pastor (J. Aparicio and J. T. Pastor, “A well-defined efficiency measure for dealing with closest targets in DEA”, Applied Mathematics and Computation 219 (17), 2013, pp. 9142–9154.). Next, we characterize how robust the efficiency scores are with respect to improvements and deteriorations of inputs and outputs. We illustrate our analysis with two examples: a simple numerical example and a more complex example using real-world data.

To help enhancing affordability and availability in the U.S. individual health insurance markets, we evaluate whether expanding interstate markets is associated with efficiency improvement, and the potentials of “Medicaid for All” and “Medicare for All”. This research aims to provide insights and evidence for data-driven decision making in reforming individual health insurance markets and optimizing individual health insurance operations. We employ traditional, non-oriented slack-based, order-α partial frontier, bootstrapped bias-corrected, and modified context-dependent data envelopment analysis (DEA) models, as well as generalized linear, Tobit, and residual inclusion regression models. We find that higher competition or expansion is not associated with higher consumer efficiency or societal efficiency. Our results also indicate that, in minimizing premiums or expenses given enrollment and utilization of medical services, individual health plans are less efficient than Medicaid managed care plans, but more efficient than Medicare Advantage plans. Our findings imply that, for individual plans, expanding interstate markets is not accompanied with lower premiums or expenses without the sacrifice of medical services. This research suggests that it should be advisable to structure individual health insurance markets following the Medicaid managed care model but not the Medicare Advantage model. To “Medicaid-ize” individual markets, we propose to structure the individual coverage in two layers: a conditionally subsidized Medicaid managed care program with mandatory essential benefits, and an unsubsidized “Medicaid Supplement” program for optional additional coverages.

Data envelopment analysis is a technique widely used to measure the relative efficiency of a set of decision making units and for ranking alternatives based on the measured efficiency. When there are data that need to be estimated, a group of experts is consulted. The opinions of the experts can be aggregated at either the data level, based on which the efficiency is calculated, or the efficiency level, where the efficiencies calculated from the data provided by individual experts are aggregated. However, the results may not be consistent, which leads to a puzzling situation as which result to follow. In this paper, a method of determining appropriate weights by which the opinions of the experts can be used to calculate the efficiency of alternatives is proposed. One radial model and one slacks-based measure (SBM) model are constructed based on this idea. It is shown that, for both models the results obtained from aggregating the opinions at the data level and at the efficiency level are the same. The final efficiencies are thus reliable. A case of a robot selection problem for a manufacturing company in Taiwan is used for the purpose of illustration. The results show that the radial efficiency is dependent on the non-Archimedean number and will overstate the performance of the robot. The SBM efficiency, which does not have this drawback, is more reliable than the radial efficiency to be used for ranking. A robot is suitably selected based on the SBM efficiency.

Within the framework of data envelopment analysis (DEA) methodology, the problem of determining the closest targets on the efficient frontier is receiving increased attention from both academics and practitioners. In the literature, the number of approaches to this problem are increasing, most of which are based on the computation of closest targets. Some of the existing approaches satisfy the important property of strong monotonicity. However, they tend to either propose a complex conceptual framework and multi-stage procedure or change the original definition of Holder distance functions. Clearly, these approaches cannot be solved easily when there are many “extreme” efficient units with multiple inputs and multiple outputs. To solve this problem, we consider the notion of the extended facet production possibility set (EFPPS). In particular, we propose a Mixed Integer Linear Program (MILP) to find the closest efficient targets and that is related to a measure that satisfies the strong monotonicity property. Additionally, in this paper, the proposed approach is applied to real data from 38 universities involved in China's 985 university project.

Classical efficiency studies on data envelopment analysis (DEA) consider all its inputs and outputs are desirable factors and real valued-data. Additionally, the DEA models only focus either on input-oriented projection minimizing inputs for an inefficient decision making unit (DMU) while keeping outputs at their maximum level, or output-oriented projection maximizing outputs under the present level of input consumption. To simultaneously deal with input excesses and output shortfalls maximizing both projections, this paper proposes a bi-objective DEA model in the context of undesirable factors and mixed integer requirements. These factors and requirements were integrated into the objective function and constraints of the existing bi-objective models. In addition, the proposed model estimates the returns to scale of DMUs that depends on the projections of input reduction and output augmentation. The applicability and usefulness of the proposed model were tested using the dataset of 39 Spanish airports retrieved from the literature. Besides, the proposed model was compared with the three existing bi-objective DEA models in the literature to test its validity.

Despite the surge of literature on customer engagement (CE) in social media, few studies shed light on how to leverage CE to improve firms’ operational efficiency. This research proposes a fresh framework using social media data to improve demand forecasting accuracy, resulting in a cost-efficient inventory control strategy. Drawing upon the resource mobilization perspective in particular, this research quantifies the construct of CE from the view of input–output efficiency evaluation using the Data Envelopment Analysis (DEA) model, and then leverages CE to forecast consumer online demand and reconfigure inventory management strategy. Using a 71-week data set from a social commerce start-up in China, this research shows that this new framework dramatically increases demand forecasting accuracy and reduces operational costs in inventory management. This study contributes to the literature by demonstrating the value of social media data in improving operational efficiency, particularly regarding inventory management.

Efficiency measurement has been a major indicator of the performance of a firm. In the study the level of efficiency of the steel firms operating in India was ascertained by adopting an input oriented data envelopment analysis (DEA). Twenty steel producing firms for the period 2018–19 was considered for the study. The study was conducted by using the input variables such as raw material cost, power and fuel cost, gross fixed assets, current assets, and the output variables considered to measure the efficiency was sales turnover. The result of the study indicates the efficient and less efficient firms with respective to their technical efficiency scores. It also indicates the bench marking firms. Out of twenty firms, eleven firms were on the efficient frontier and rest nine were below the frontier. For the relatively less efficient firm the slack and target values were calculated and further analysis was done to indicate the area of improvement for the inputs, which will result in improving the efficiency of the firms.

With the growing severe challenges of the climate change, improving low-carbon technical efficiency (LTE) is considered as the most important way to facilitate the low-carbon development. Whether or not China's carbon emission trading (CET) can facilitate the LTE deserves deep exploration. Through the introduction of a more scientific semi-disposability assumption for undesirable output, this paper first calculates China's provincial LTE based on a non-radial and non-oriented data envelopment analysis (DEA) method and analyses the reasons for inefficiency. Furthermore, we explore the impacts and mechanisms of CET on the LTE using augmented difference in differences (augmented DID) model. The results show that China's provincial LTE is not satisfactory which presents a downward trend after experiencing a transient rise. The insufficient low carbon innovation output is the crucial impeding the LTE. The implement the CET pilots has improved the LTE and the promotion effect are mainly through strengthening innovation support and capital concentration, while the mediating effects of innovation synergy degree and talents concentration are not significant. Therefore, it is essential for the Chinese government to push forward the scientific construction of national carbon trading market rather than increase the inputs blindly, so as to stimulate the innovative vitality of enterprises as innovation subjects.

This study assesses the sustainable development of countries worldwide and the impact of renewable energy on country-level sustainability for the period 1990–2014. The nonparametric method “data envelopment analysis” is employed to measure the performance of the countries by merging environmental and economic elements into unified sustainability measures. Reflecting the different attitudes with which the countries treat environmental and economic factors in pursuing sustainability, the assessment procedure assigns priority to either economic or environmental course. We find that if economic criterion is prioritized over environmental criterion, then the developed countries significantly outperform the developing countries until early 2000s. However, the gap quickly diminishes after the period. If environmental criterion takes precedence, then developed and developing countries have similar performance over the entire horizon. Moreover, no matter which criterion is prioritized, dispersion in sustainability levels for countries in different regions has shrunk over time. Panel analysis shows that the share of renewable energy in total energy consumption has positive and strongly significant impact on sustainability, no matter whether economic prosperity or environmental protection is the priority. This implies that despite legitimate concerns about its cost efficiency, renewable energy has shown a strong positive effect in enhancing sustainability of the world.

In this paper we propose hinging hyperplanes (HHs) as a flexible nonparametric representation of a concave or an S-shaped production function. We derive the HHs using expressions with focus on the distinction between hinge location and the bending along each hinge. We argue that the HHs approximation can be estimated using a fixed endogenous determined partitioning of the input space. Assuming a homothetic production function allows us to separate the S-shape scaling law and the underlying core function. We propose an estimation procedure where two HHs function approximations of the core function and the scaling law are estimated simultaneously. A closed form expression of the inverse of the piecewise linear inverse scaling law is proposed and proved. We stress the known result that the HHs formulation is equivalent to the Canonical Piecewise-Linear representation of a piecewise linear function and exploit the result that the HHs formulations for the core function and for the scaling law provide global representations of all piecewise linear functional forms. A simulation study evaluates the performance of the proposed estimation procedure of an S-shaped production function.

The current paper estimates the overall inefficiency indicator of a bank under a network Data Envelopment Analysis (DEA) in the presence of undesirable inputs and outputs. We primarily contribute to the DEA literature in four ways. First, we decompose banks’ overall inefficiency performance indicator into five component indicators, namely, the sub-indicators of innovation, two kinds of stability, profitability and corporate social responsibility. Second, since the proposed model, which is an absolute value optimization problem, does not give the overall inefficiency score directly, we introduce a two-step procedure to obtain all sub-indicator scores and the overall inefficiency score. Third, we extend the by-production framework by proposing not only a concept and interpretation of “strategic disposability” for an intermediate undesirable output in estimating the stability sub-indicators, but also another concept of “strategic disposability” for an intermediate undesirable input in gauging the sub-indicator related to corporate social responsibility. Our results show that 1) the Chinese banking industry has a high level of innovation inefficiency and profitability inefficiency is higher than primary business stability inefficiency, strategic management stability inefficiency and corporate social responsibility inefficiency for all the bank ownership types except the state-owned commercial banks; 2) state-owned commercial banks have a higher level of corporate social responsibility inefficiency compared to the other inefficiencies; 3) state-owned commercial banks, rural commercial banks and joint-stock commercial banks are the best three performers with different ownership types performing well in different perspectives; 4) all different ownership types experience a level of performance volatility over the examined period.

Radiotherapy treatment (RT) irradiates a patient's tumour volume while minimising damage to healthy tissue and surrounding critical organs at risk (OAR). In the conventional RT planning process, the RT planner has to iteratively adjust either the planning objectives (tumour or OAR dose levels) or the weights of the planning objectives until an acceptable plan is obtained that satisfies the minimum requirements. At the end of this iterative process, it remains unknown whether this plan is the best that can be obtained for the patient. The oncologist reviews each plan and decides to either treat using this plan or request further plan development, which may or may not lead to an actual improvement of the reviewed plan. We describe how Data Envelopment Analysis (DEA) is used as a real-time decision support tool to assess quality of RT plans for head and neck cancer patients by applying a knowledge-based comparison of each new plan to a library of previous clinically approved plans. This library allows benchmarking, which gives planners and oncologists a better idea of the relative quality of their plan and its improvement potential, resulting in improved use of resources and better quality treatments for patients. Our DEA-based approach provides a novel way of capturing multiple measures of plan quality as well as anatomical differences between patients in the benchmarking process. We present the developed DEA model and results for a set of benchmark instances. Initial results of integrating DEA-based quality feedback into the RT planning process are presented showing that operations research can contribute significantly to planning quality in this setting.

Data envelopment analysis (DEA) is a technique used to measure the relative efficiency of a set of production units that applies multiple inputs to produce multiple outputs. In its original settings, the data is required to be nonnegative. To allow for negative data, several methods have been proposed. While these methods have merits, they also have weaknesses and limitations. This paper generalizes the construction of the production possibility set from production units with nonnegative observations to those with real values. Given the signs of the aggregate target and aggregate observed outputs of the production units to be evaluated, different models are developed to calculate the efficiencies under both variable and constant returns to scale technologies, and an additive model is used to identify the signs of the aggregate target and aggregate observed outputs. Since the efficiencies are calculated from the original observations without transformation or manipulation, the proposed method does not have the drawbacks of the existing methods. A case of the Detroit National Bank shows that the results obtained from the proposed method are more representative and reliable as compared to those obtained from a data transformation method.

Data envelopment analysis (DEA) is a non-parametric approach for measuring the relative efficiencies of peer decision making units (DMUs). In recent years, it has been widely used to evaluate two-stage systems under different organization mechanisms. This study modifies the conventional leader–follower DEA models for two-stage systems by considering the uncertainty of data. The dual deterministic linear models are first constructed from the stochastic CCR models under the assumption that all components of inputs, outputs, and intermediate products are related only with some basic stochastic factors, which follow continuous and symmetric distributions with nonnegative compact supports. The stochastic leader–follower DEA models are then developed for measuring the efficiencies of the two stages. The stochastic efficiency of the whole system can be uniquely decomposed into the product of the efficiencies of the two stages. Relationships between stochastic efficiencies from stochastic CCR and stochastic leader–follower DEA models are also discussed. An example of the commercial banks in China is considered using the proposed models under different risk levels.

The property insurance industry grows fast in China and it is necessary to further investigate the profitability of the Chinese property insurance industry. This study investigates the evolution and determinants of the profitability of 53 Chinese property insurers during the year 2013–2017. Profitability is measured by profit ratio efficiency by data envelopment analysis (DEA) methodology and a profit ratio change index is applied to compare the performance of these insurers over different periods. Tobit regression models are used to investigate several influencing factors of profitability. The empirical results show the importance of proper arrangement of costs and revenues for an insurer and help to better understand the effect of firm size, age, and product specification on profitability. Some policy implications and suggestions are also proposed.

The DEA is a nonparametric method of assessing the efficiency of decision-making units using mathematical programming. The classic DEA model assumes that input and output variables are deterministic. However, there are many applications where the variables are of a stochastic nature. Based on the consideration of input and output levels as random variables, the Stochastic Data Envelopment Analysis (SDEA) was developed. Statistical distributions therefore play a major role in this regard. By considering the dependency between input and output variables, and also their simultaneous dependencies in this study, we have introduced three copula-SCCR models. We used three copulas of Gaussian, Clayton, and Gumbel to estimate the dependence between the random variables with normal distribution. We evaluated the proposed models using real data from 20 bank branches. The results showed that considering stochastic dependency between the inputs or outputs causes different results. A comparison between the Copula-SCCR models and the SCCR models revealed that the efficiency of the DMUs using the Copula-SCCR models differed from the SCCR model by a significant margin of at least 20%.

Performance evaluation for universities or research institutions has become a hot topic in recent years. However, the previous works rarely investigate the multiple departments’ performance of a university, and especially, none of them consider the non-homogeneity among the universities’ departments. In this paper, we develop data envelopment analysis (DEA) models to evaluate the performance of general non-homogeneous decision making units (DMUs) with two-stage network structures and then apply them to a university in China. Specifically, the first stage is faculty research process, and the second stage is student research process. We first spit each DMU (i.e. department) into a combination of several mutually exclusive maximal input subgroups and output subgroups in terms of their homogeneity in both stages. Then an additive DEA model is proposed to evaluate the performance of the overall efficiency of the non-homogeneous DMUs with two-stage network structure. By analyzing the empirical results, some implications are provided to support the university to promote the research performance of each department as well as the whole university.

Southern Vietnam is a key economic region for the country, producing 45% of Vietnam’s GDP, despite only accounting for 9% of the country’s total landmass and 21% of its population. Specifically, the Southern Region dominates seaport-based container transportation, contributing 55% of Vietnam’s total vessel calls and 74% of its total container cargo volume, with the North and Central regions trailing far behind. However, despite the sector’s importance, the efficiency of container terminals in Southern Vietnam has not been evaluated by the literature in recent years. This study fills this research gap by using data envelopment analysis (DEA) to evaluate the efficiency of major container terminals in Southern Vietnam. The results of the DEA Malmquist analysis show that most container terminals demonstrated productivity growth between 2017 and 2019, and that this mainly derived from advancements in catch-up operational efficiency and in technological innovation. Moreover, the DEA slack-based measure and DEA undesirable output analyses demonstrate that three container terminals (Cai Mep International Terminal, Tan Cang Cat Lai, and Tan Cang–Cai Mep International Terminal) achieved the maximum efficiency level when both applying and not applying the undesirable output variable. However, other container terminals displayed severe operational inefficiency, with these terminals’ efficiency values decreasing by between 9% and 40% when considering undesirable outputs. The ineffective terminals can therefore be categorized as consuming excessive inputs and lacking in the production of desirable outputs or creating large amounts of undesirable outputs. Therefore, using these results, port authorities and policymakers should be better placed to develop appropriate strategies for managing container terminals in Southern Vietnam.

In today's world, the transportation industry is globally known for its high energy consumption and intensive use of fossil fuels. However, the search for more sustainable, less resource-carrying, and environmentally friendly transportation alternatives is growing. In this sense, this paper aims to analyze the eco-efficiency of transports using Data Envelopment Analysis (DEA) in the Brazilian automotive industry and so to propose a method to measure the performance of transports from the perspective of sustainable development. In this case study, there were considered eleven alternatives and four variables: load capacity, fuel efficiency, total emissions, and energetic consumption. As a reference, it was used data provided by the Brazilian program for the rationalization of the use of petroleum derivatives and natural gas, Inmetro and Petrobras, and by the automobile manufacturers of the vehicles analyzed. Findings indicate that the use of DEA provided evidence of a closer view of operational reality, and proved to be an adequate and complementary tool, allowing greater discrimination for eco-efficiency analysis of vehicles. Thus, the proposed approach can bring important insights to improve the Government evaluation regarding the energy efficiency of vehicles and is up to enrich the body of knowledge in organizational sustainability management and its performance measurement in the transports sector. Finally, the main contributions of this study include: (i) it is an extension of previous DEA works that assesses the efficiency of the road transport sector, considering also undesirable variables; and it (ii) allows a comparative assessment of car eco-efficiency using DEA model, combining desirable and undesirable results, and provides the unified efficiency of the Brazilian transportation industry.

Data with large dimensions will bring various problems to the application of data envelopment analysis (DEA). In this study, we focus on a “big data” problem related to the considerably large dimensions of the input-output data. The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation, including principal component analysis (PCA-DEA), which is based on the idea of aggregating input and output, efficiency contribution measurement (ECM), average efficiency measure (AEC), and regression-based detection (RB), which is based on the idea of variable selection. We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test. In addition, we discuss the effect of initial variable selection in RB for the first time. Based on the results, we offer guidelines that are more reliable on how to choose an appropriate method.

The existing literature lack of a comprehensive framework for measuring and comparing the efficiency of different types of enterprises. Distinguished from the traditional efficiency, this paper constructs the comprehensive efficiency from a multi-output productivity model including net profit and external enterprise social responsibility based on the Data Envelopment Analysis. This research measures the comprehensive efficiency of sample enterprises from 2003 to 2016. With regress method and robust test, the results show that the comprehensive efficiency of central SOEs is higher than its traditional efficiency and enterprise ownership has a significant and differentiated effect on the two different efficiency performance. In terms of industry heterogeneity, central SOEs in resource-intensive and labor-intensive industries have higher comprehensive efficiency.

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA.

In recent years, the use of Public Service Obligations (PSOs) in air transport policy has substantially increased. In remote regions, where air transport services are not profitable, the application of this subsidy program is crucial for the economic and social development of these regions. However, there is a great diversity in the provision of PSO services among different regions in Europe. Therefore, it is essential to assess the performances of the air carriers operating in different PSO routes. The present study uses Data Envelopment Analysis (DEA) to assess the efficiency of PSO routes within the European Union and compares them with the performances of the operating airlines, both when operating under this subsidy program and when operating their regular flights. First, DEA was applied to the annual data of several European airlines, and then applied to the data of their PSO routes, so that a comparison can be made between the performance obtained on regular flights and flights under the PSO regime. The results identify which airlines had the best and worst performances, as well as the routes and respective regions of Europe where the airlines tend to score higher and lower in terms of efficiency. Results suggest that the Nordic European countries and the United Kingdom were the regions where air carriers obtained the lowest results, and thus, this empirical evidence should support the public authorities to consider revisiting their PSO contracts, in order to improve their operation. info:eu-repo/semantics/acceptedVersion

Due to sectoral interactions in the economy, the overall green efficiency (GE) of China’s industrial system relies heavily on fundamental sectors that contribute substantial energy to the supply chain production of other sectors but shows low sectoral GE. For the three fundamental sectors in China’s industrial systems, namely the smelting and pressing of nonferrous metals (SPNFM); the processing of petroleum, coking, and nuclear fuel (PPCNF); and the manufacturing of nonmetallic mineral products (MNMMP), we employed a three-stage data envelopment analysis (DEA) model to measure GE in the fundamental sectors in 30 provinces from 2010 to 2015. We then adopted a stochastic frontier analysis (SFA) model to evaluate the influence of technological innovation (TI), industrial agglomeration (IA), environmental regulation (ER), and intraindustry competition (IC). The results showed that GE in the three fundamental sectors varied spatially. Specifically, TI promoted GE in MNMMP, but the effect was not obvious in the SPNFM and PPCNF sectors. Moreover, ER had positive impacts on GE in the fundamental sectors. The effects of IA and IC on GE in the fundamental sectors varied in direction and strength. After eliminating the impacts of environmental effects and statistical noise, the real GE in the three fundamental sectors varied significantly compared to the comprehensive GE. Policy opportunities for enhancing GE in the fundamental sectors mainly lie in region-specific policy and regulations that avoid a “one-size-fits-all” governance approach.

Congestion is an economic phenomenon of overinvestment that occurs when excessive inputs decrease the maximally possible outputs. Although decision-makers are unlikely to decrease outputs by increasing inputs, congestion is widespread in reality. Identifying and measuring congestion can help decision-makers detect the problem of overinvestment. This paper reviews the development of the concept of congestion in the framework of data envelopment analysis (DEA), which is a widely accepted method for identifying and measuring congestion. In this paper, six main congestion identification and measurement methods are analysed through several numerical examples. We investigate the ideas of these methods, the contributions compared with the previous methods, and the existing shortcomings. Based on our analysis, we conclude that existing congestion identification and measurement methods are still inadequate. Three problems are anticipated for further study: maintaining the consistency between congestion and overinvestment, considering joint weak disposability assumption between desirable outputs and undesirable outputs, and quantifying the degree of congestion.

Under the effects of internal factors and the external environment, some customer requirements can be extremely sensitive to change; these are known as sensitive requirements. These requirements drive the identification and design of the relevant engineering characteristics. This study defines such characteristics as agile engineering characteristics (AECs), which are crucial for redesigning products and prolonging their service life but attract limited research and practice attention. The research question of identifying the product AECs is answered in this study, and three key issues are solved in the proposed methodology. First, the contribution of product engineering characteristics to customer requirements is calculated using an orthogonal experimental design, and the optimal level combination of engineering characteristics to satisfy the customer requirements is determined through the analysis of variation. Subsequently, the relative importance of engineering characteristics is calculated based on data envelopment analysis. Next, an agility index is defined by integrating the customer satisfaction degree, the relative importance degree of engineering characteristics, and the variation of the level of engineering characteristics, to identify the AECs. Finally, based on the identification of the AECs of an electric vehicle air-conditioning heat pump scroll compressor, the results are analysed and compared to illustrate the feasibility and effectiveness of the proposed method.

The finance literature on efficiency and crisis at the macro-level and efficiency and default at the micro-level has hitherto grown surely but distinctly. In this comprehensive paper, we globally review and theoretically unify these two strands of research in studying the record level of bank failures and the deepest financial crisis of an emerging market, Turkey, with sixteen distinct measures of efficiency, stemming from two alternative methods, stochastic frontier analysis (SFA) and data envelopment analysis (DEA). The results show that efficiency scores tend to deteriorate gradually before crisis, hit bottom during crisis, and rebound after crisis. Inelastic inputs and elastic outputs seem to produce this pattern. The efficient banks have the highest survival rates. Managers of survivor banks are evidently better at controlling costs and scales, utilizing and allocating resources, generating assets, revenues and profits. Demotion to a lower efficiency class is a rare event in normal times but widespread during crises. The least efficient banks are the least likely to be acquired by private bidders. Default prediction models notably improve with DEA scores, off-balance sheet items, definition of failure with “factual insolvency”, deciles of efficiency, changes in some key variables, homogenous dataset, and efficiency scores based on quantities of inputs and outputs rather than their noisy prices.

Banks have two primary strategies for adapting to a regulation change in the era of big data which can be characterized as natural disposability and managerial disposability. Natural disposability implies a negative strategy by which a bank attempts to decreases its vector of inputs to decrease undesirable outputs. In contrast, managerial disposability indicates a positive strategy by which a bank considers a regulation change as an opportunity and adapt the regulation change by utilizing big data technology. The operational process of a bank can be decomposed into a productivity stage and a profitability stage. Furthermore, the operation costs, a shared resource, can be used to characterize natural disposability and managerial disposability. Based on natural disposability and managerial disposability, this paper proposes two network models to estimate the efficiencies of banks. To test their practical implications, the proposed models were applied to examine the efficiencies of Chinese commercial banks in the period 2014−2018. Our key findings are as follows. (1) There exist great disparities in the inefficiencies between two adaptive strategies. The inefficiencies are primarily driven by the profitability stage under natural disposability, whereas the inefficiencies are equally attributed to both stages under managerial disposability. (2) The efficiency differences among different types of banks are insignificant under natural disposability but are significant under managerial disposability. (3) Joint-stock commercial banks are more oveall efficient than state-owned commercial banks, city commercial banks and rural commercial banks, while state-owned commercial banks show worst practice for overall efficiency and profitability stage efficiency.

Data envelopment analysis (DEA) is a useful tool for measuring the relative efficiencies of participating nations in the Olympic Games. DEA models with restricted multipliers have been used to refine efficiency evaluations by imposing additional information. Existing DEA models for evaluating Olympic medals do not focus on multiplier restrictions regarding input. To fill this research gap, this study incorporates a data fitting technique of medal prediction using ordinary least squares regression in input multiplier restrictions of the conventional DEA model. We show that the efficiency of the proposed model can be decomposed into the achievement ratio of substantial medal total and the unit value index of medals. Such decompositions can be used to analyze the effectiveness of host nations and athlete development initiatives. For an illustrative empirical application, we examine the target that the Brazilian Olympic Committee (BOC) set for the 2016 Summer Olympic Games (Rio 2016). Our results explain the extremely high feasibility of Brazil’s target of being in the top 10 medals table in Rio 2016.

With the increasing environmental pollution and environmental awareness, ecological efficiency has become a hot issue. Based on the traditional DEA model of unexpected output, a new super data envelopment analysis and slacks-based measure considering undesirable outputs (S-DEA-SBM-UO) efficiency model is proposed. Moreover, the weight S-DEA-SBM-UO (W–S-DEA-SBM-UO) is applied to evaluate the ecological efficiency of industrial enterprises. The experimental results show that the W–S-DEA-SBM-UO can measure the ecological efficiency including unexpected output more effectively than the conventional DEA-SBM to solve unexpected output. Finally, it is suggested that enterprises should make great efforts to improve the ecological efficiency and it is pointed out that the results have strong theoretical significance and research value for ecological improvement.

Ghasemi, Ignatius, and Rezaee (2019) (Improving discriminating power in data envelopment models based on deviation variables framework. European Journal of Operational Research 278, 442– 447) propose a procedure for ranking efficient units in data envelopment analysis (DEA) based on the deviation variables framework. They claim that their procedure improves the discriminating power of DEA and can be an alternative to the super-efficiency model that is well-known to have the infeasibility problem and the cross-efficiency approach which suffers from the presence of multiple optimal solutions. However, we demonstrate, in this short note, that their procedure is developed based upon inappropriate use of deviation variables which leads to the development of a ranking approach that does not meet their expectations and as a result, an unreasonable ranking of decision making units (DMUs). We also show that the use of deviation variables, if interpreted and used correctly, can lead to developing a cross-inefficiency matrix and approach.

We develop a new network data envelopment analysis (DEA) approach for two-stage network systems considering a match between the production scale of the substages and the intermediate measure levels. Several explicit production axioms are introduced to build a production possibility set. New models are developed based on the production possibility set and a frontier projection procedure with the production scale matching process. Unlike the existing approach which assumes the intermediate measures are free-setting decision variables, the new envelopment network DEA models project the intermediate measures of a unit using the radial projection technique. Correspondingly, the resulting multiplier network DEA models allow for weight flexibility on the intermediate measures while holding a total value flow equivalence between a unit's two stages. We show that our approach does not suffer the known network DEA pitfalls. It identifies the overall efficiency, divisional efficiencies, and frontier projection using either an envelopment or a multiplier network DEA model, i.e., the primal-dual correspondence holds in our approach. Our approach also avoids uncertainties in determining divisional efficiencies by generating a unique pair of divisional efficiencies for each unit. Additionally, the adoption of the production scale matching process explains clearly the frontier projection procedure from the practical point of view. The proposed approach is illustrated with a numerical example and compared with the existing approaches with a case study of commercial bank branches.

Energy quota trading (EQT) is a market-driven method to reduce distortions in energy allocation. In 2016, an EQT policy was proposed in China. However, the economic and environmental effects of this new policy remain unclear. To study these problems, this paper conducts an empirical analysis using data envelopment analysis (DEA) and index decomposition analysis (IDA). Following a simulation of the optimal energy input and maximum output in three sectors of the 30 Chinese provinces that have adopted China's EQT policy, the main conclusions are as follows: (1) The energy consumption of the industrial sectors is excessive, yet the primary and service sectors need further energy input, while there is vast economic potential in China's western provinces and the service sectors. (2) The optimal energy intensity effect generated by the trading policy is the largest impetus of carbon intensity reduction in China, but the obstacles to carbon intensity reduction are different in each region. (3) EQT has the potential to drive a significant decline in carbon intensity in China, resulting in a simulated average annual decline of approximately 4.09% from 2005 to 2016.

Grasping the intricacy and diversity of complex systems dealing with ever-growing amounts of data is essential to public and private institutions’ continuous improvement. Composite indicators (CIs) emerge as aggregators of key performance indicators, providing a single measure that reflects those multidimensional performance aspects. One way to build such measures is based on the use of data envelopment analysis (DEA). Several DEA models can be used to generate CIs. Still, not many of them can deal concurrently with desirable and undesirable outputs, and incorporate the decision-making actors’ preference information. Based on the directional ‘Benefit-of-the-Doubt’ model, we propose a novel approach consisting of the simultaneous use of weight restrictions and an artificial target reached via a range directional vector. The resulting CI assesses the Portuguese public hospitals’ performance under two perspectives of hospital activity: users and providers. In the end, managerial and policy implications are withdrawn from the results of this study conducted in cooperation with the Portuguese Ministry of Health.

The literature on data envelopment analysis (DEA) and, more broadly, production theory employs different notions of efficiency for the characterization of boundary points of production technologies. These include the strong and weak (Pareto) efficiency, and the Farrell input and output efficiency. For the conventional constant and variable returns-to-scale technologies, the relationship between the different notions of efficiency has been explored in the literature and is well understood now. In this paper we show that, in the general case, which includes many recently developed technologies, the conventional relationship between the different notions of efficiency is no longer valid. We show that such relationship depends on the properties of a particular technology such as convexity, disposability and returns-to-scale characteristics. Our results are applicable to many new technologies for which the different notions of efficiency and methods of their testing have not been fully explored.

Most of the previous cross efficiency evaluation methods consider the evaluated system as a “black box” system. This probably leads to misleading results. For example, the system is efficient but all of its divisions are inefficient. This paper firstly investigates the cross efficiency for a general two-stage system. In such system, some outputs of one stage are taken as inputs of the other stage, and some other outputs directly become the final products. Since the optimal weight vector for the self-evaluation of a decision making unit (DMU) may be non-unique, we apply the leader-follower method for the decomposition of the system's efficiency and then adopt the optimal weights to obtain the peer-evaluation. Then, multiplicative hesitant fuzzy elements (MHFEs) are applied to denote the ratio of all possible cross efficiencies of any two DMUs. Based on MHFEs, multiplicative hesitant fuzzy preference relations (MHFPRs) are used to express these relationships comprehensively. Furthermore, the optimization models for deriving acceptably consistent MHFPRs with the minimum total adjustment and the minimum number of adjusted elements are built when the consistency of MHFPRs is unacceptable. An algorithm for decomposing the overall efficiency according to the cross efficiency and acceptably consistent MHFPRs in the setting of the general two-stage system is provided. Finally, our method is applied to the analysis of nine top universities in China.

This paper applies the additive relational network DEA model to examine the multi-period efficiency and productivity of Regional Banks I and II in Japan between 2002 and 2017. The examined timeframe covers two turbulent periods; the Global Financial Crisis and the economic recession in the aftermath of the Great East Japan Earthquake. We extend the additive relational network DEA model into a multi-period structure in order to evaluate the overall efficiency for the time period in question, as well as the annual period efficiencies. We show that the overall efficiency can be expressed as a weighted average of the period efficiencies. In addition, we examine the implications of different returns to scale assumptions. The newly proposed model is able to calculate common-weight Malmquist Productivity Indices. The results reveal a dispersion of inefficiency levels for Japanese Regional Banks in general, and a difference between Regional Banks I and II in particular. Furthermore, the Global Financial Crisis caused a significant negative effect on the productivity growth and the technical progress of Japanese Regional Banks, while the Great East Japan Earthquake had a negative effect on the technical efficiency change.

In cognizance of the role that the traditional loan-to-deposit (LTD) ratio fulfils for financial stability policy, the paper proposes extending this metric of liquidity and funding towards measuring attainment in financial intermediation. The proposed metric, financial intermediation measure (FIM), is a normative indicator and amends the descriptive nature of the LTD ratio, and is thus more suited to the needs of macroprudential regulation of financial systems. The metric is grounded in ideas of data envelopment analysis and is developed in conjunction with a data set on structural indicators provided by the World Bank. The FIM focuses upon the banking part of financial intermediation and measures whether a financial system is successful in transforming (bank) deposits into loans subject to feasibility restrictions applied to both the banking and securities markets. Utilization of the FIM is demonstrated for 48 financial systems of the world using data from 1993 to 2016 under different classifications applied to financial systems. The demonstration shows that the FIM is useful also for performance assessments of market-based financial systems without distinction.