Data Envelopment Analysis Frontier Research Articles

Financial efficiency and financial resource allocation of Beijing–Tianjin–Hebei urban agglomeration

This study focuses on improving financial efficiency and rationally allocating financial resources in the Beijing–Tianjin–Hebei, based on data from 2011 to 2019. Firstly, the Slack-Based Measure (SBM) model based on Shannon Entropy is adopted to measure financial efficiency in Beijing–Tianjin–Hebei. Secondly, a Beijing–Tianjin–Hebei inter-regional financial resource allocation scheme is proposed using the Generalized Equilibrium Efficient Frontier Data Envelopment Analysis (GEEFDEA) model. Finally, the external environmental factors influencing financial efficiency are explored using spatial and other econometric models. Efficiency measurement reveals that financial efficiency in Beijing–Tianjin–Hebei is unevenly developed. The financial efficiency of Beijing, Tianjin, and Zhangjiakou is high and stable, whereas the financial efficiency of Tangshan, Langfang, and Shijiazhuang is high, but fluctuates significantly. In comparison, the financial efficiency of other cities is low. The factors and the adjustment amount to improve urban financial efficiency are obtained by studying the allocation of financial resources. And it is found that Tianjin and Tangshan have more financial resource surpluses than other cities. By exploring influencing factors, it is found that the financial development level, innovation level, and infrastructure construction level of Beijing–Tianjin–Hebei significantly affect financial efficiency.

A meta‐analysis of aquaculture technical efficiency studies

AbstractAs the world population grows, the aquaculture sector faces challenges in the use of production systems to ensure the adequacy of the food supply. Technical efficiency analysis has been shown to be useful for improving resource management and production. This study provides a literature update and uses meta‐regression analysis to examine the variations in aquaculture mean technical efficiency (MTE) scores based on 101 studies published from 1998 to 2023. The findings show that studies with larger model dimensions, different output measures, an input‐oriented model specification and that analyse the exogenous variables of efficiency scores, freshwater aquaculture, and European data produce a narrower variance in MTE scores. In contrast, studies that use panel data and nonparametric data envelopment analysis (DEA) generate wider variance in MTE scores. As a result of these investigations, the findings suggest the importance of selecting appropriate variables and sample sizes for efficiency estimates. Robustness checks of efficiency scores from the DEA and stochastic frontier analysis (SFA) approaches are recommended for future studies. Different policies and regulations should be implemented in different countries and across different farming systems.

Using data envelopment analysis to measure and improve organizational performance

AbstractOrganizations are complex and have many goals while almost all analytical tools measure performance using only one goal. Thus, analysts often rely on multiple analytical tools to produce a bewildering array of performance measures that often lack internal consistency and a clear focus. In this article, we show how data envelopment analysis (DEA) builds a performance frontier (analogous to a production frontier) that measures organizational performance in the presence of multiple organizational measures. The DEA frontier produces target values for each organizational measure based on the observed performance of organizations in the comparison set. In addition, DEA provides factor performance levels for each performance measure for each organization and can detect circumstances in which an organization has a strong overall performance measure but still has weaknesses in one or more measures. We will illustrate this approach with applications to several examples using real data. Analyzing organizational performance data is critical in the organizational improvement process. The data must directly reflect the organization's goals and the analytical tools used must be appropriate. However, organizations are complex and have many goals while univariate analytical tools measure performance relative to only one goal. Thus, analysts often rely on multiple analytical tools to produce a collection of performance measures, sometimes resulting in a bewildering array of measures that lack focus.

Estimating the potato farming efficiency: A comparative study between stochastic frontier analysis and data envelopment analysis.

The government of Bangladesh has been trying to encourage potato consumption to reduce pressure on rice consumption and earn foreign currency along with ensuring zero hunger that helps to achieve the Sustainable Development Goal. It is necessary to use farmers' resources and current technology more efficiently to meet the demand. Therefore, the authors aimed to evaluate the farm-level efficiency of potato farming in Bangladesh. The Cobb-Douglas Stochastic Frontier Analysis (SFA) and the input-oriented Data Envelopment Analysis (DEA) methods are used to compute farm-level technical, allocative, and economic efficiencies and inefficiency of potato farming. The primary data were collected through interviews of 300 potato farmers from Munshigonj, Rangpur, Dinajpur, and Joypurhat districts of Bangladesh. The findings revealed that the efficiency score of the SFA model is higher than the DEA model, which implies that the SFA frontier fits better than the DEA frontier. In the case of DEA, variable returns to scale (VRS) technical efficiency (TE) enveloped data more closely than constant returns to scale (CRS) TE. Results of efficiency suggest significant economic, technical, and allocative inefficiencies in potato farming and there is a scope to increase potato production levels through efficiency improvement. Inefficiency analysis shows that infrastructure and socio-economic factors jointly influence potato production variability. The authors suggest for using the SFA to find efficiencies in the agriculture sector. To achieve efficiency in potato production, the government needs to pay attention for improving the allocative and economic efficiencies along with emphasizing to choose the appropriate technology and efficient use of resources for the scale of operation.

Efficiency evaluation for decision making units with fixed-sum outputs using data envelopment analysis and stochastic multicriteria acceptability analysis

The generalized equilibrium efficient frontier data envelopment analysis (GEEFDEA) approach, an extension of the DEA method, has been widely used to solve the problem of evaluating decision making units (DMUs) producing fixed-sum outputs. It constructs a common equilibrium efficient frontier through a minimum reduction strategy for fixed-sum outputs and uses this frontier as a benchmark to achieve a complete ranking of DMUs. However, the existence of multiple feasible equilibrium efficient frontiers may lead to inconsistency in the evaluation criteria, and this possibility limits the method’s usefulness. In this paper, an integrated framework for solving this problem is proposed to rank DMUs by using stochastic multicriteria acceptability analysis (SMAA-2) method combined with the GEEFDEA approach. Instead of using a certain common equilibrium efficient frontier as in conventional GEEFDEA approaches, we explore all possible frontiers to answer various robustness questions by computing rank acceptability indices and pairwise winning indices. Furthermore, we derive the complete ranking from the dominance relationships among the DMUs. Two numerical examples are used to demonstrate the effectiveness and rationality of the proposed hybrid approach.

Analysis of CO2 emission performance of China’s thermal power industry: a meta-frontier Malmquist-Luenberger approach with fixed-sum CO2 emissions

This study combines the general equilibrium efficient frontier data envelopment analysis (GEEFDEA) approach, which handles fixed-sum CO2 emissions, with the meta-frontier Malmquist-Luenberger productivity index (MMLPI). We use this novel technique to measure the CO2 emission performance (CEP) of 30 of China’s provincial-level thermal power industries during 2006–2020, both statically and dynamically, and reveal the drivers of CEP changes. Our proposed approach considers the fixed-sum feature of CO2 emissions and also captures the impact of technology heterogeneity. The empirical results show that the CEP of China’s thermal power industry was good during the sample period, showing average annual increases of 2.42%, mainly due to technical efficiency gains and technological progress. The CEP of most provinces grew at various rates, between 0.23% and 8.38%; and the drivers of CEP changes differ between provinces. Policy implications are discussed, such as the need to strengthen technology innovation and optimize production processes to improve CEP.

How to peel a data envelopment analysis frontier: A cross-validation-based approach

Data Envelopment Analysis (DEA) presents the typical characteristics of a data-driven approach with the specific objective of determining technical efficiency and production frontiers in Engineering and Microeconomics. However, by construction, the frontier estimator generated by DEA suffers from overfitting problems; something that contrasts with currently accepted models in machine learning. In this regard, DEA can be seen as a preliminary stage of a more complex approach, where the aim is to avoid overfitting in order to determine a proper description of the underlying Data Generating Process that is behind the generation of the observations in a production process. In this paper, we introduce a possible solution to overcome the overfitting problem associated with DEA that is based on cross-validation. This process “peels” the standard DEA frontier (removing certain supporting hyperplanes) until a new convex technology, which also satisfies free disposability in inputs and outputs but not the principle of minimal extrapolation, is determined. Our approach is tested by resorting to a computational experience. Additionally, we illustrate how the new method could be used as a complement to the standard DEA technique through an empirical application based on a PISA (Programme for International Student Assessment) dataset.

The max-reward and min-penalty frontier: A benchmark for research of supply and demand mismatches

The traditional production-oriented frontier in data envelopment analysis is defective in dealing with supply and demand mismatches, where the emphasis is placed on mismatches between input and supply as well as output and demand, instead of simply how input is used to produce output. Previous techniques to evaluate demand effectiveness and supply-demand effectiveness limited the value ranges of penalty parameters and thus failed to consider some extreme situations. To fill this gap, we abandon the traditional concept of frontier and map production units into a new type of frontier. Supply and demand mismatches can increase production costs but can also bring extra revenues in some situations; these outcomes are called penalty and reward, respectively. We map the production possibility set onto a dataset of input and output penalties, namely, the reward/penalty possibility set. A new frontier, called the max-reward and min-penalty frontier, is provided as the benchmark to evaluate decision-making units’ performance in supply management and demand fulfilment and accordingly define reward-penalty effectiveness. Much further research can be done based on this new frontier.

Economic Efficiency of Processed Hibiscus Sabdanriffa (Roselle) Drink in Imo State, Nigeria

The study developed a model to evaluate efficiencies of Zobo drink processors in Imo State using Data Envelopment Analysis (DEA). Data were collected from 163 Zobo drink processors across 36 markets in the study area and was analyzed under the constant returns to scale (CRTS) and variable returns to scale (VRTS) assumptions. Result showed that Zobo processing was highly profitable with about 44.34% net margin on total revenue generated. Mean economic efficiency scores estimated from the DEA frontier for both CRTS and VRTS were 0.537 and 0.683. Sex, educational level and alternative to income were most statistically significant factors. It was recommended that more male involvements are encouraged in Zobo drink; educated individuals particularly unemployed are advised to engage and create a cliché for themselves through the use of more innovative processing and packaging activities and adopting cost-minimizing input mixes of best-practice to enable them become fully efficient.

Does economic freedom fosters Islamic rural banks efficiency? Evidence from Indonesia

PurposeThis paper aims to examine the relationship between economic freedom and Islamic rural banks' efficiency in the case of Indonesia.Design/methodology/approachThe study covers 40 Islamic rural banks in 34 Indonesian regions from 2014 to 2020. Tobit regression is utilized to expose the impact of economic freedom on the efficiency of Islamic rural banks, and nonparametric frontier data envelopment analysis is used to acquire banks' technical efficiency.FindingsThe findings reveal that overall economic freedom has a strong favorable impact on the efficiency of Islamic rural banks. The study’s breakdown components suggest that business freedom, government spending and investment freedom are favorable indicators, whereas government integrity and tax burden are negative indicators, and all indicators agree with previous studies.Practical implicationsThis research can serve as a guideline for Islamic rural bank management in terms of maintaining financial efficiency. The government should think about the ramifications of financial sector liberalization and reforms, according to these findings. When financial intermediaries operate in a less constrained environment, they are more likely to pursue competitive practices that increase their operating rate and other efficiency metrics. Finally, academics might utilize this information to investigate the economic flexibility of Islamic rural banks.Originality/valueThe novelty of this study is in using data envelopment analysis and Tobit regression to identify economic freedom and Islamic rural banks' efficiency. To the best of the authors' knowledge, the study of the role of economic freedom in Islamic rural bank's efficiency is limited, particularly in the context of Indonesia.

Operational efficiency assessment of oil refineries using data envelopment analysis and Tobit model: evidence from India

PurposeIn India, the operational performance of the refinery is influenced by many factors. It is important to identify those key drivers which can assist the refineries to uphold and succeed in day-to-day production activities. Therefore, the purpose of this study is to evaluate the operational efficiency of seven Indian oil refineries during the period 2010 to 2018.Design/methodology/approachIn this work, a two-stage empirical analysis is proposed. In the first stage, the data envelopment analysis (DEA) – variable return to scale model is used to evaluate the operational efficiency of the Indian oil refineries. The ordinary least square (OLS), random effect generalized least square (GLS) and Tobit model are used in the second stage to identify the key determinants of efficiency and to explain the variation in refinery efficiency.FindingsThe first-stage DEA results showed that the Numaligarh Refinery Limited and Chennai Petroleum Corporation Limited are found to be more efficient than the rest of the sampled refineries and attained their efficiency scores of 0.993 and 0.981, respectively, during the study period. The second-stage regression analysis suggested three explanatory variables: refinery structure, utilization rate and distillate yield, which are found to be significant in explaining variations in refinery efficiency.Practical implicationsThis study provides valuable information that would help policymakers to formulate policies toward improving the efficiency of underperforming Indian refineries, which reduces the excessive use of resources and gives a competitive advantage.Originality/valueThis study proposes the first-ever application of the profit frontier DEA model for assessing the operational efficiency of oil refineries and explains the variation in refinery’s efficiency using OLS, GLS as well as the Tobit model.

A data envelopment analysis model integrated with portfolio theory for energy mix adjustment: Evidence in the power industry

A reasonable energy mix would effectively improve resource utilization and reduce environmental impact. Decision makers are concerned not only with the current energy efficiency but also with the adjustment of energy mix. However, previous studies mostly focus on energy efficiency and seldom analyse energy mix adjustment. To fill in this literature gap, we address the research question: How to adjust the current energy mix to improve resource utilization and reduce emission? To answer this research question, based on portfolio theory, we first describe the return and risk of energy technologies and build the energy portfolio. Then, we propose a novel data envelopment analysis (DEA) model to measure the energy efficiency and adjust the energy mix. Furthermore, according to the method of improving the DEA frontier, we obtain the optimal energy mix adjustment plan. Moreover, we consider energy technical constraints caused by resource scarcity or geographical conditions while searching for the benchmark. Finally, we apply the proposed methods to the power industry. Interestingly, we find that after adjusting the energy mix, the proportion of renewable energies can be greatly increased and the carbon emission can be significantly reduced on the premise of not increasing other inputs. By our methods, the proportion of renewable energy can rise from 29.40% to 55.53%, and the proportion of coal can decrease from 64.59% to 26.15%. Meanwhile, the average carbon emission per unit of energy mixes can drop from 0.86kg/kWh to 0.65kg/kWh, which will reduce the carbon emissions of the power industry by 24.54%.

Carbon mitigation by quota allocation

To avoid the calamitous consequences of an even warmer world, the efforts are focused on overarching and immediate solutions to reduce the greenhouse gases. The emissions trading scheme is deemed worldwide as an ecological and robust management mechanism to curb carbon emissions. The challenge is how to design such a scheme to attain the basic purpose of installing a uniform system and equilibrium efficiency achievement. For the first time, existing idea of group method of data handling (GMDH) type neural network (NN) is developed to predict capital stock, labor force, CO2 emission, energy consumption, and gross domestic product (GDP) based on past information for the top-26 emitting countries. Then, this study deals with the allocation of emission quotas by proposing a two-step optimization mechanism that takes full advantages of the context-dependent data envelopment analysis (DEA) and equilibrium efficient frontier DEA (EEFDEA) models. In the first step, under the premise of constant total carbon emissions, a pragmatic efficiency-oriented carbon quota trading system is established to attain equilibrium state. In the second step, under the measured total emission mitigation target, the carbon quotas allocation mechanism is formulated to translate the top emitters' mitigation target into national purposes from two features of efficiency and fairness as well as to specify the comprehensive targets for the top emitters to maintain their equilibrium state. Two of the main findings are: 1) The top emitters should decrease the total CO2 emission by at least 37% by 2023. 2) In light of the CO2 emission mitigation allocation, the countries with larger potentials are China, Japan, and the US yet to receive the larger portions of 20%, 9% and 22%, respectively. Finally, the allocation method that takes regional heterogeneity into account is more logical since it alleviates pressure on the emitters to decrease carbon emissions and establishes a baseline for distributing CO2 emission quotas across the emitters to enhance adaptation to nations' present circumstances.

Directional scale elasticity considering the management preference of decision-makers

Most data envelopment analysis (DEA) studies on scale elasticity (SE) and returns to scale (RTS) of efficient units arise from the traditional definitions of them in economics, which is based on measuring radial changes in outputs caused by the simultaneous change in all inputs. In actual multiple inputs/outputs activities, the goals of expanding inputs are not only to obtain increases in outputs, but also to expect the proportions of such increases consistent with the management preference of decision-makers. However, the management preference is usually not radial changes in outputs. With the latter goal into consideration, this paper proposes the directional SE and RTS in a general formula for multi-output activities, and offers a DEA-based model for the formula of directional SE at any point on the DEA frontier, which is straightforward and requires no simplifying assumptions. Finally, the empirical part employs the data of 16 basic research institutions in Chinese Academy of Sciences (CAS) to illustrate the superiority of the proposed theories and methods.

Estimating the confidence intervals for DEA efficiency scores of Asia-Pacific airlines

Data envelopment analysis (DEA) is a popular non-parametric approach to examine performance and productivity of airlines; however, it could not provide statistical information such as confidence intervals on the estimated efficiency scores. We combined stochastic frontier analysis and DEA into a single framework to disentangle noise and ‘pure’ inefficiency from the DEA inefficiency scores and accordingly provide confidence intervals for the estimated efficiency scores. Monte-Carlo simulation verified that our novel model is a good alternative for the conventional DEA as well as the bootstrap DEA. Empirical application using Asia-Pacific airlines’ data (2008‒2015) shows that after accounting for the ‘pure’ random errors, the sampled Asia-Pacific airlines performed well during the study period but their ‘pure’ efficiency was declining, hence, there is still room for improvement.

Modelling Efficiency of Electric Utilities Using Three Stage Virtual Frontier Data Envelopment Analysis with Variable Selection by Loads Method

Electric utility regulators and policy makers implement incentive-based regulation to improve electric utilities efficiency or to manage the cost of electricity. However, poorly implemented regulation may produce undesired results such as low reliability or poor quality of service. Moreover, the competition within the electricity sector is likely to be low because of the high barriers to entry, vertically integrated electric utilities, and high capital requirements. Therefore, benchmarking exercises allow policy makers and regulators to gauge the relative efficiency of electric utilities and help them to reward or penalize the electric utilities accordingly. In this study, we examined the variables that significantly influence the efficiency of electric utilities and developed an optimum method to measure the efficiency of the electric utilities. The results of the efficiency measurement were then used to rank the electric utilities. The result of this study indicates that there are 13 variables that significantly affect the efficiency score of electric utilities and three stage virtual frontier data envelopment analysis (3S-VF-DEA) is the optimum method to measure the efficiency of the electric utilities.

Robust worst-practice interval DEA with non-discretionary factors

Traditionally, data envelopment analysis (DEA) evaluates the performance of decision-making units (DMUs) with the most favorable weights on the best practice frontier. In this regard, less emphasis is placed on non-performing or distressed DMUs. To identify the worst performers in risk-taking industries, the worst-practice frontier (WPF) DEA model has been proposed. However, the model does not assume evaluation in the condition that the environment is uncertain. In this paper, we examine the WPF-DEA from basics and further propose novel robust WPF-DEA models in the presence of interval data uncertainty and non-discretionary factors. The proposed approach is based on robust optimization where uncertain input and output data are constrained in an uncertainty set. We first discuss the applicability of worst-practice DEA models to a broad range of application domains and then consider the selection of worst-performing suppliers in supply chain decision analysis where some factors are unknown and not under varied discretion of management. Using the Monte-Carlo simulation, we compute the conformity of rankings in the interval efficiency as well as determine the price of robustness for selecting the worst-performing suppliers.

Provincial carbon emission performance analysis in China based on a Malmquist data envelopment analysis approach with fixed-sum undesirable outputs

The environmental issue has attracted ever-increasing attention from governments and academics in recent years. Environmental performance analysis is widely considered a crucial and useful tool for effectively protecting the environment and developing a sustainable society. Many analytical techniques have been used to assess carbon emission performance, among which data envelopment analysis (DEA) is prominent. However, few previous DEA-related carbon emission performance studies recognize that the total amount of carbon dioxide emissions is limited to a specific level by authorities; ignoring this fixed-sum requirement may lead to distortions and deviations in empirical results. This paper proposes an alternative Malmquist DEA approach for evaluating the carbon emission performance while considering fixed-sum undesirable outputs. For this purpose, we develop a generalized equilibrium efficient frontier DEA model with fixed-sum undesirable outputs and combine the model with the Malmquist productivity index (MPI). The proposed approach is applied to assess the carbon emission performance of 30 provincial regions in China from 2009 to 2015. Then we provide analytical results and policy suggestions regarding the provincial carbon emission performance in China.

Sensitivity assessment of metafrontier data envelopment analysis for soil carbon sequestration efficiency

The data envelopment analysis (DEA) is useful for the measuring of efficiencies of functions in various research fields. In previous studies the soil carbon sequestration (SCseq) efficiency index (SCI) was assessed at regional scale; however, the effects of various calculation factors—including returns-to-scales (RTSs), selection of orientation (input or output), and global (or meta-)/local (or group) frontiers—on the SCI scores are still unclear. Thus, in this paper, the sensitivity of DEA frontier models with various types of RTSs and orientations was investigated. The models examined the SCseq rates as target outputs in three land use/land cover (LULC) types (pinelands, improved pastures, and wetlands). The rates were calculated based on lab-measured soil organic carbon concentration collected from the topsoil (~20 cm depth) between 1965 and 2009 in Florida, United States. Pedogenic, hydrologic, and environmental variables relevant to the carbon sequestration function were chosen as inputs, including soil available water capacity, sand concentration, pH, soil total nitrogen and phosphorus, Enhanced Vegetation Index, and Normalized Difference Vegetation Index. The input and output variables for the three LULC types informed multivariate normal distributions that served to simulate the local frontiers. Results show that the SCseq rates in wetland soils were statistically lowest (p<0.05), while the SCI scores were highest. This suggests that the efficiency of the soil carbon sequestration function was distinctively different from the SCseq rates. The SCI scores also demonstrated to identify internal causes (i.e., local management practices) for inefficiency in the SCseq in pasture soils and suitability of the function among the LULC types. A higher ability in output orientation to discern efficient from inefficient sites for the SCseq function was also observed than the input orientation. The preferred choice of RTSs in either conservative or progressive approaches to SCseq efficiency management was also identified in the study. Sensitive scores reflected regions that are in need for efficient soil management (such as no-tillage) to improve the performance of individual soils in terms of SCseq efficiency. SCseq efficiency scores enable land resource managers to refine their approaches to optimize soil carbon and possibly other soil and ecological functions. Further research is invited to identify potential cause(s) that determine(s) the local/global frontiers.

Performance evaluation of China’s industry: a generalized equilibrium data envelopment analysis approach with fixed-sum undesirable output

With the rapid development of economy, China faces a considerable challenge from environmental problems especially with the waste of gas pollution emitted by China’s industry. To assess the environmental performance of China’s industry, this paper proposes a new generalized equilibrium efficiency frontier data envelopment analysis (GEEFDEA) approach that considers waste gases as undesirable outputs. It is assumed that the total amount of waste gases that the industry is permitted to emit is limited (or fixed), that is, increasing air pollution of one area should reduce that of other areas for offset. Compared with the prior evaluation approaches, the proposed approach is the only one that satisfies the following conditions simultaneously. 1) The approach considers the property of the “fixed sum” of undesirable outputs. 2) The approach avoids the case in prior approaches (e.g., fixed sum output and zero sum gains DEA approaches) that an infeasible solution may occur when accessing decision making unites (DMUs) with fixed-sum undesirable outputs. 3) The proposed approach inherits all advantages of the GEEFDEA approach such as achieving an equilibrium state by only one step and obtaining a complete ranking order. Finally, the proposed approach is applied to evaluate the performance in China’s industry from 2008 to 2013. Results show China’s industry has significant regional characteristics.