Original Data Envelopment Analysis Research Articles

Comparative Analysis of Innovation Districts to Set Up Performance Goals for Tec Innovation District

Purpose: Innovation districts represent a way to create, foster, and manage innovation. Different regions apply their strategy according to the dominant stakeholder in the region, such as academia, industry, government, or entrepreneurs. This research aims to evaluate different innovation districts from a production system point of view to determine the output goals for a Tec Innovation District. Methodology/Approach: Data Envelopment Analysis (DEA) was determined to be the best tool for this study; the variable returns to scale output-oriented model was used to determine the goals for the new district; also, the bootstrap method was employed to analyse the efficiency sensitivity in the sample of districts. Findings: The average technical efficiency of the analysed innovation districts was 0.659, with the highest technical efficiency observed in the case of the Entrepreneurial type (0.831) and Industry Cluster (0.820) districts, whereas the Local government type registered the lowest technical efficiency (0.468). Research Limitation/Implication: The projections for the Tec Innovation District’s output variables were obtained using a set of U.S. innovation districts due to the similarity of the studied region to the available group. The research allowed us to determine realistic outputs for the studied innovation district. Originality/Value of paper: The study employs an original DEA for comparing innovation districts and performs a bootstrap to study the system’s robustness; within this research, the performance level of a new district was calculated to be within a specific efficiency level, according to their peers.

Comprehensive performance evaluation of coordinated development of industrial economy and its air pollution control

Exploring coordinated pathways that can promote not only the sustainable development of the industrial economy but also air quality is of great significance for the prevention and control of air pollution in China. Currently, the joint development pathways of the industrial economy-environment nexus remain unclear and poorly evaluated. In this study, we proposed a comprehensive performance evaluation combining objective and subjective weighting to identify industrial enterprises' economic-environment nexus benefits. It would be one of the most important steps to explore the coordinated pathways. Based on data envelopment analysis (DEA), the proposed method integrated with the index integration was used to evaluate the comprehensive performances of 41 industrial sectors in China's 13th five-year plan (2016–2020). Evaluation results showed that the comprehensive performances of the economy-environment nexus of the industrial sectors varied significantly, with the five-year average comprehensive technical efficiency (TE) of 0.11–1. Overall, the best two performances were realized by the industries of equipment manufacturing and living consumption, whereas the worst one belonged to the industry of bulk raw materials, with average comprehensive TE values of 0.50, 0.43, and 0.19, respectively. The results of the quantitative evaluation were consistent with those of the qualitative analysis in terms of the developmental status of the industrial sectors. According to the analyses of pure technical efficiency and scale effect, the proposed method identified the industrial sectors with the highest developmental value and with the highest need to control air pollution. Compared with those of the original DEA model, the results of the proposed method showed pronounced differences in terms of the performances of industrial sectors with high energy consumption and high particulate matter (PM) emissions and with low energy consumption and low PM emissions. The proposed evaluation method combining the weighting was suitable for identifying the comprehensive performance of the industrial economy-environment nexus and provides the basis for the prevention and control of air pollution.

How to Incorporate Preference Information in a Weight-Restricted DEA Model: A Straightforward Solution Applied in the Field of Economics, Based on Simos’ Revised Method

Data envelopment analysis (DEA) is one the most successful techniques in the field of Operations Research. DEA is a non-parametric and objective approach for evaluating the relative efficiency of a set of decision-making units. The original DEA proposal contemplated the total freedom of variation of weights. This free variation may lead to situations with non-realistic weights and to the impossibility of incorporating the judgments of decision-makers. This work studies the links between multicriteria decision analysis (MCDA) and DEA by introducing weight restrictions in a DEA model using a methodology developed to obtain criteria weights in a MCDA context: the so-called Revised Simos’ Procedure. The presented approach is suitable to be applied in the field of economics and management, being an intuitive and simple enough method for decision-makers who are not familiar with working with DEA models or multicriteria decision analysis. A classic example is presented, where the results found with this approach are compared with the results of other approaches which also use multicriteria decision analysis as a tool to obtain weight restrictions for a DEA model.

Public Hospitals Performance Measurement through a Three-Staged Data Envelopment Analysis Approach: Evidence from an Emerging Economy

This study proposes a three-staged approach to data envelopment analysis (DEA) modeling for hospital efficiency. The approach aims to overcome the constraint on the number of inputs/outputs relative to the number of DMUs. Initially, the principal components of all inputs and outputs are determined using principal component analysis (PCA). Next, these principal components enter a factor analysis (FA) process to construct a two-level hierarchy of inputs/outputs and to establish a weighting scheme based on explained variances of components. Finally, a two-level DEA (TLDEA) method is applied to the resultant framework to determine the relative efficiency of hospitals using data from the healthcare context of Iran as an emerging economy. The outcomes of applying the proposed PCA-FA-TLDEA approach are argued to offer a substantial increase in the discriminatory power of classical DEA methods and could incorporate a relatively large set of inputs/outputs already existing in the hospital efficiency literature. As demonstrated in the evaluated hospitals, the PCA-FA-TLDEA methodology improved the discrimination from 0% in the original DEA to 45%. The paper proposes a novel three-stage DEA model by using PCA to extract the principal components from the inputs and outputs; therefore, reducing the number of inputs and outputs and their inter-correlations. Secondly, a hierarchy of inputs and outputs by applying FA to the principal components is constructed. Finally, the TLDEA method to the hierarchy of inputs and outputs is applied to evaluate the performance of public hospitals.

Extending a fuzzy network data envelopment analysis model to measure maturity levels of a performance based-budgeting system: A case study

Performance-based budgeting (PBB) aims to formulate and manage public budgetary resources to improve managerial decisions based on actual performance measures of agencies. Although the PBB system has been overwhelmingly applied by various agencies, the progress and maturity of its implementation process are not satisfactory at large. Therefore, it warrants to find, evaluate and improve the performance of organisations in relation to implementing a PBB system. To do so, the composite indicators (CIs) have been proposed to aggregate multiple indicators associated with the PBB system, but their employment is contentious as they often lean on ad-hoc and troublesome assumptions. Data envelopment analysis (DEA) methods as a powerful and established tool help to contend with key limitations of CIs. Although the original DEA method ignores an internal production process, the knowledge of the internal structure of the PBB systems and indicators is of importance to provide further insights when assessing the performance of PBB systems. In this paper, we present a budget assessment framework by breaking a PBB system into two parallel stages including operations performance (OP) and financial performance enhancement (FPE) to open up the black-box structure of the system and consider the indicator hierarchy configuration of each stage. In situations of the hierarchical configuration of indicators, we develop a multilayer parallel network DEA-based CIs model to measure the PBB maturity levels of the system and its stages. It is shown that the discrimination power of the proposed multilayer model is better than the existing models with one layer and in situations of relatively small number of DMUs the model developed in this paper can be a good solution to the dimension reduction of indicators. Moreover, this research leverages fuzzy logic to surmount the subjective information that is often available in collecting indicators of the PBB systems. The major contribution of this research is to examine a case study of a PBB maturity award in Iran, as a developing country with a myriad of financial challenges, to adopt a PBB maturity model as well as point towards the efficacy and applicability of the proposed framework in practice.

Eco-innovation analysis of OECD countries with common weight analysis in data envelopment analysis

PurposeDespite unanimity in the literature that eco-innovation (EI) leads to sustainable development, evidence remains limited on measuring EI efficiency with the Malmquist productivity index (MPI). In conventional data envelopment analysis (DEA) models, decision-making units (DMUs) are inclined to assign more favorable weights, even zero, to the inputs and outputs to maximize their own efficiency. This paper aims to overcome this shortcoming by developing a common set of weights (CSW).Design/methodology/approachUsing goal programming, this study develops a CSW model to evaluate the EI efficiency of the organization for economic co-operation and development (OECD) countries and track their changes with MPI during 2010–2018.FindingsAchieving a complete ranking of DMUs, findings show the higher discrimination power of the proposed CSW compared with the original DEA models. Furthermore, results reveal that Iceland, Latvia and Luxembourg are the only OECD countries that have incessantly improved their EI productivity (MPI > 1) from 2010 to 2018. On the other hand, Japan is the OECD country that has experienced the highest yearly EI efficiency during 2010–2018. This paper also found that Iceland has the highest MPI over 2010–2018.Practical implicationsMore investment in environmental research and development (R&D) projects instead of generic R&D enables OECD members to realize more opportunities for sustainable development through minimizing energy use and environmental pollution in any form of waste and greenhouse gas emissions.Originality/valueIn addition to developing a novel common weights model for DEA-MPI to measure and evaluate the EI of OECD countries, this paper develops a CSW model by including the undesirable outputs for EI analysis.

Context-dependent DEASort: A multiple criteria sorting method for ecological risk assessment problems

• Context-Dependent DEASort is proposed based on BWM for multi-criteria sorting problem. • Categories can be simply obtained without predefined classification categorical rules. • A common set of weight model is introduced for evaluating attractiveness of each DMU. • Case study concerning risk assessment of Yangtze River Economic Zone is provided. • Sensitivity analysis is conducted to illustrate the stability of the proposed method. With the increasing urban economic development and awareness of environment protection, ecological risk assessment (ERA), as a management mode combined with ecological research and risk assessment, has become a highly relevant topic in environment sustainable development. In this paper, we propose a novel sorting model based on Data Envelopment Analysis (DEA) and Best Worst method (BWM) to solve multi-criteria sorting problems and apply it to ERA. The proposed Context-Dependent DEASort method based on the idea of Context-Dependent DEA to position decision-making units (DMUs) into diverse categories in uncertain circumstance. It also takes experts’ preference into full account as well, which makes the sorting solution more flexible and reasonable. Besides, a common set of weight-based model is introduced to deal with the situation when evaluating attractiveness and progress of each DMU. The common weight set model can ensure to provide an overall evaluation context compared to the original Context-Dependent DEA model, which only distinct the DMU from a single virtual DMU. Furthermore, a case study concerning ecological risk assessment of Yangtze River Economic Zone in China is provided to illustrate the applicability of the developed environment. Finally, comparative analysis and sensitivity analysis are carried out to demonstrate the practicality and effectiveness of the proposed method.

Evaluating the performance of Chinese fossil fuel power plants by data environment analysis: An application of three intermediate approaches in a time horizon

To our knowledge, the previous studies did not answer the following questions: how much does window analysis differ from the original Data Envelopment Analysis (DEA) approaches and how to develop DEA window analysis? To address these questions, we propose three intermediate approaches (cross-sectional approach, window efficiency analysis and window index approach) and statistically compare their performances. The underlying concern is that we need to examine whether a metrological bias exists in our study. The concern is important in guiding policy issues such as energy policy and climate change. Empirically, the proposed approaches are applied to measure the unified efficiency of fossil fuel power plants at a provincial level in China from 2007 to 2017, since these plants are important energy consumers and CO2 emitters. We obtain two important implications. First, considerable differences were confirmed among the three approaches, thus implying an existence of the methodological bias. Second, Beijing (the capital of China) outperformed the other provinces in terms of their measures, since the government has paid serious attention to air pollution emitted from fossil fuel power plants. The governmental regulation in Beijing is a good example of pollution prevention in China. It is expected that the success on Beijing will be disseminated to the other provinces.

Data envelopment analysis based on triangular neutrosophic numbers

Data envelopment analysis (DEA) is one of the best mathematical techniques to compute the overall performance of units with some inputs and outputs. The original DEA methods are developed to tackle the information based on the crisp number but no ability to handle the indeterminacy, impreciseness, vagueness, inconsistent, and incompleteness information such as triangular neutrosophic numbers (TNNs). This study attempts to establish a new model of DEA, where the information on decision-making units is TNNs. Initially, the concept and features of a conventional DEA model and the comparative TNNs are discussed. Besides, some new ranking functions of TNNs are presented. Furthermore, based on the mentioned ranking functions, an algorithm for solving the new model has been established. A comparison of the new model with an existing method and other kinds of uncertainty tools has been provided. In comparison with the existing methods, the significant characteristic of the new model is that it can handle the triangular neutrosophic information simply and effectively. Finally, the implementation of this strategy for an example has been applied for various models of DEA.

Are global cities sustainability champions? A double delinking analysis of environmental performance of urban agglomerations

Urban agglomerations – irrespective of their size or location – may act not only as engines of economic growth, but also as vehicles of environmental and climate sustainability that may stimulate both socio-economic achievements and environmentally-benign outcomes. Clearly, the efficiency of these outcomes may differ for different types of urban agglomeration in the world. This paper aims to present and test an advanced methodology for assessing economic and sustainability-oriented performance strategies for global cities, by developing and applying a super-efficient Data Envelopment Analysis (DEA) model. We compare 40 global cities – included in the Global Power City Index (GPCI) database – in a benchmark study in order to trace the highest-performing urban regions from both an economic and environmental-climatological efficiency perspective, by applying relevant quantitative GPCI indicators to these 40 cities. Our ultimate goal is to test what is termed the ‘delinking’ hypothesis, from the viewpoint of both economic prosperity and urban size externalities.This approach will be applied empirically by examining the GPCI data set comprised of various multi-dimensional and empirically verified indicators on economic performance and climatological-environmental conditions for the 40 global cities concerned. We regard both the size of these agglomerations and their economic welfare position as critical parameters for assessing their economic and environmental efficiency performance. In the framework of our original DEA approach, these urban areas are categorised according to 2x2 dimensions, viz. in terms of both agglomeration size (big or medium-sized) and the economic development level of the area (highly developed or developing/emerging). Our contribution serves to assess – by means of regression techniques – the highest performing agglomerations among the urban sustainability champions on the basis of the two above-mentioned assessment criteria. This approach provides the opportunity to test the so-called Kuznets sustainability curve under two different conditions, viz. agglomeration size and economic development. The study presents and interprets the empirical findings for these four classes of global cities.

Ranking efficient DMUs using the Tchebycheff norm with fuzzy data in DEA

In many real applications, the data of production processes cannot be precisely measured. Hence the input and output of Decision Making Units (DMUs) in Data Envelopment Analysis (DEA) may be imprecise or fuzzy-numbered. In original DEA models, inputs and outputs are measured by exact values on a ratio scale, therefore conventional DEA can't easily measure the performance of DMUs and rank them. The researchers have introduced mane deferent model for ranking DMUs by fuzzy number. In this paper, we proposed a new method by using the Tchebycheff norm for ranking DMUs with fuzzy data. We explain our method by numerical example with the triangular fuzzy number.

Stochastic p-robust approach to two-stage network DEA model

Data Envelopment Analysis (DEA) is a method for evaluating the performance of a set of homogeneous Decision Making Units (DMUs). When there are uncertainties in problem data, original DEA models might lead to incorrect results. In this study, we present two stochastic p-robust two-stage Network Data Envelopment Analysis (NDEA) models for DMUs efficiency estimation under uncertainty based on Stackelberg (leader-follower) and centralized game theory models. This allows a deleterious effect to the objective function to better hedge against the uncertain cases those are commonly ignored in classical NDEA models. In the sequel, we obtained an ideal robustness level and the maximum possible overall efficiency score of each DMU over all permissible uncertainties, and also the minimal amount of uncertainty level for each DMU under proposed models. The applicability of the proposed models is shown in the context of the analysis of bank branches performance.

A sequence of targets toward a common best practice frontier in DEA

Original data envelopment analysis models treat decision-making units as independent entities. This feature of data envelopment analysis results in significant diversity in input and output weights, which is irrelevant and problematic from the managerial point of view. In this regard, several methodologies have been developed to measure the efficiency scores based on common weights. Specifically, Ruiz and Sirvant (Omega 65:1–9, 2016) formulated an aggregated DEA model to minimize the gap between actual performances and best practices and identify a common best practice frontier. Their model is capable of determining target units for all units under evaluation, simultaneously, with the property that all of them are located on a common best practice frontier. However, in practice it is difficult for some units to achieve that specified target in a single step. Consequently, developing a methodology for assisting units to reach their corresponding targets, through a path of intermediate improving targets, is useful. This problem is investigated in this paper, and we propose a stepwise target setting approach which provides a path of intermediate targets for each unit. We study efficient and inefficient units separately and provide two distinct models for each category, although both of them are intrinsically similar. A simple numerical example and an application are also provided to illustrate our approach.

Closest target for the orientation-free context-dependent DEA under variable returns to scale

An important branch of data envelopment analysis (DEA) is context-dependent DEA, which evaluates efficiency by combining the attractiveness and progress for a particular decision-making unit (DMU). Traditionally, context-dependent DEA models are based on the assumption of constant returns to scale. Two limitations are found when directly extending original radial context-dependent DEA (ORCD-DEA) models into variable returns to scale versions. One is that it may not be possible to determine the attractiveness of a DMU that logically must be attractive in that context. The other problem is that the progress measure cannot ensure an inefficient DMU projects to a Pareto-efficient frontier. A small numerical example is used to illustrate these two issues. In order to overcome these deficiencies, the concept of closest target is introduced to determine the attractiveness and progress for each DMU. The closest target method can further improve DMUs’ performance with less wastes in inputs or underproduction in outputs. Finally, a practical application involving computer printers is presented.

A non-radial directional distance method on classifying inputs and outputs in DEA: Application to banking industry

The original Data Envelopment Analysis (DEA) models have required an assumption that the status of all inputs and outputs be known exactly, whilst we may face a case with some flexible performance measures whose status is unknown. Some classifier approaches have been proposed in order to deal with flexible measures. This contribution develops a new classifier non-radial directional distance method with the aim of taking into account input contraction and output expansion, simultaneously, in the presence of flexible measures. To make the most appropriate decision for flexible measures, we suggest two pessimistic and optimistic approaches from both individual and summative points of view. Finally, a numerical real example in the banking system in the countries of the Visegrad Four (i.e. Czech Republic, Hungary, Poland, and Slovakia) is presented to elaborate applicability of the proposed method.

Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical processes

The complex chemical process is a high pollution and high energy consumption industrial process. Therefore, it is very important to analyze and evaluate the energy and environment efficiency of the complex chemical process. Data Envelopment Analysis (DEA) is used to evaluate the relative effectiveness of decision-making units (DMUs). However, the traditional DEA method usually cannot genuinely distinguish the effective and inefficient DMU due to its extreme or unreasonable weight distribution of input and output variables. Therefore, this paper proposes an energy and environment efficiency analysis method based on an improved environment DEA cross-model (DEACM) method. The inputs of the complex chemical process are divided into energy and non-energy inputs. Meanwhile, the outputs are divided into desirable and undesirable outputs. And then the energy and environment performance index (EEPI) based on the cross evaluation is used to represent the overall performance of each DMU. Moreover, the improvement direction of energy-saving and carbon emission reduction of each inefficiency DMU is quantitatively obtained based on the self-evaluation model of the improved environment DEACM. The results show that the improved environment DEACM method has a better effective discrimination than the original DEA method by analyzing the energy and environment efficiency of the ethylene production process in complex chemical processes, and it can obtain the potential of energy-saving and carbon emission reduction of ethylene plants, especially the improvement direction of inefficient DMUs to improve energy efficiency and reduce carbon emission.

The robustness of performance rankings of Asia-Pacific super cities

Over the past decades, the Asia-Pacific Rim has exhibited an unprecedented high degree of economic and geographic dynamics. Clearly, cities in this region display heterogeneity in terms of economic performance, technological innovativeness, environmental conditions, and cultural recognition and interaction. It is, therefore, interesting to develop an efficiency ranking of the multi-dimensional performance of these large cities so as to identify ‘super cities’. The first aim of this paper is now to undertake a multi-faceted performance ranking of large cities in the Asia-Pacific region using a data envelopment analysis (DEA). However, there appears to be a wide variety of DEAs in the recent literature. And therefore, a second aim of the present paper is to perform a sensitivity analysis on the type of DEA employed, so as to test the robustness of the base ranking obtained from a standard DEA. A third aim of the paper is related to the question how much the ranking obtained by a DEA is influenced by the internal characteristics of the underlying data system. This calls for a sensitivity analysis of the precision or nature of the data used in the DEA. A fourth aim of the paper is to present a performance improvement projection based on the original DEA model and our new projection model for inefficient cities. These four aims of the research will be empirically addressed by a comprehensive data set on 7 quantitative main indicators regarding economic performance, technological innovativeness, environmental conditions, and cultural recognition and interaction for 13 Asia-Pacific super cities.

Applying a Ruggiero three-stage super-efficiency DEA model to gauge regional carbon emission efficiency: evidence from China

China’s climate change mitigation strategies and efforts are based on accurate regional carbon emission efficiency (CEE) estimates. Decision-making units which are all data envelopment analysis (DEA)-effective cannot be ranked by using the original DEA model. While previous studies omit environmental factors when gauging resources or environmental efficiency. In this study, we combine a Ruggiero three-stage model with a super-efficiency DEA model (SE-DEA) to solve these two problems. Following this method, we consider environmental factors and thereby compare provincial CEE in China in the new production frontier. The main results obtained are as follows: (1) provincial CEE values differ significantly in the first stage and the third stage; (2) in the third stage, only Eastern China reaches the SE-DEA relatively effective level, where CEE rankings in descending order are: Eastern, Central, and Western China; (3) provinces are divided into four categories in terms of provincial CEE values and per-capita GDPs, and therefore, regional climate and development policies could be oriented due to different categories. This efficiency evaluation methodology and the results obtained in our study not only contribute to understanding this issue, but also could be of specific interest to climate change policy makers in China.

Linear robust data envelopment analysis: CCR model with uncertain data

Data envelopment analysis (DEA) traditionally assumes that input and output data of the different decision making units (DMUs) are measured with precision. However, in many real applications inputs and outputs are often imprecise. This paper proposes a linear robust data envelopment analysis (LRDEA) model using imprecise data represented by an uncertainty set. The method is based on the robust optimisation approach of Bertsimas and Sim to seek maximisation of efficiency under uncertainty (as does the original DEA model). In this approach, it is possible to vary the degree of conservatism to allow for a decision maker to understand the tradeoff between a constraint's protection and its efficiency. The method incorporates the degree of conservatism in the maximum probability bound for constraint violation. Application of the proposed model (LRDEA) to analyse the technical and scale efficiency of potato production in 23 Iranian provinces demonstrates the reliability and flexibility of the model.

Linear robust data envelopment analysis: CCR model with uncertain data

Data envelopment analysis (DEA) traditionally assumes that input and output data of the different decision making units (DMUs) are measured with precision. However, in many real applications inputs and outputs are often imprecise. This paper proposes a linear robust data envelopment analysis (LRDEA) model using imprecise data represented by an uncertainty set. The method is based on the robust optimisation approach of Bertsimas and Sim to seek maximisation of efficiency under uncertainty (as does the original DEA model). In this approach, it is possible to vary the degree of conservatism to allow for a decision maker to understand the tradeoff between a constraint's protection and its efficiency. The method incorporates the degree of conservatism in the maximum probability bound for constraint violation. Application of the proposed model (LRDEA) to analyse the technical and scale efficiency of potato production in 23 Iranian provinces demonstrates the reliability and flexibility of the model.