Oriented Data Envelopment Analysis Model Research Articles

Modelling environmental energy efficiency in the presence of carbon emissions: Modified oriented efficiency measures under polluting technology of data envelopment analysis

Reductions of carbon dioxide (CO2) emissions and energy consumption are two vital methods contributing in improvement of the environmental energy efficiency. Since data envelopment analysis (DEA) provides a synthetic standardised index of environmental efficiency through considering real factors, such as undesirable outputs and integer-valued data, it has acquired major popularity in environmental energy efficiency. Previous studies regarding applications of oriented DEA models to environmental efficiency did not consider the simultaneous impact of undesirable outputs under the polluting technology and mixed integer-valued data on efficiency measures. The objective of this research is to obtain comprehensive environmental energy efficiency measurements by extending the output-oriented slacks-based measure (SBM) model of Tone (2001) and the output-oriented BCC model of Banker et al. (1984). Thus, new output-oriented SBM and BCC models in mixed integer-valued data and undesirable outputs under the polluting technology are modelled. The models have been utilised to examine scale energy efficiency, pure energy efficiency, output mix-efficiency measure, carbon efficiency, and potential carbon reduction of 30 provinces in China to evaluate their land transportation systems. Additionally, the results are remarkable and interesting compared to those reported in Chang, Zhang, Danao, and Zhang (2013) since many provinces, performed environmental efficiency. There are 12 and 14 provinces, performed environmental output mix-efficiency under CRS and VRS, respectively. Moreover, 50% of the total evaluated provinces performed carbon efficiency. In regard of environmental-inefficient provinces, the proposed efficiency measures showed that provinces produce high levels of carbon emissions have risky potential to save energy.

Efficiency Analysis of Foreign and Domestic Banks and its Determinants: Evidence from Gulf Cooperation Council (GCC) Countries

This paper aims to assess the foreign and domestic banks efficiency and its determinants in Gulf Cooperation Council (GCC) countries. In total, 50 foreign and domestic banks were examined for the period of five years from 2015- 2019. The two stage procedures approach was used by this paper to analyse banks efficiency. First input- oriented Data Envelopment Analysis model was used to analyze the efficiency scores and in the second Tobit regression model was applied to investigate the efficient drivers. Furthermore, nonparametric Kruskall-Wallis and Mann Whitney tests and the parametric (t-test) were applied to compare the efficiency between foreign and domestic banks. On average, the findings showed that many domestic and foreign banks in GCC countries were inefficient with mean efficiency scores of less than 100%. The overall banks inefficiency could be explained by managerial inefficiency in exploiting their resources to the fullest; second domestic banks groups on average were more efficient than foreign banks groups supporting home field advantage theory. Finally the multivariate regression analysis highlights that; bank size, credit risks, management quality and liquidity were the most significant efficiency drivers. The findings were useful to the regulators and bankers in GCC countries on operating performance of banking industry and its future evolution. The study results were important because the study pinpoints the drivers of efficiency and its sources of inefficiency, it would help regulator and banks with strategic planning. This will assist the countries to maintain a healthy and sustainable financial system in an economy.

Cross-efficiency evaluation and decomposition with directional distance function in series and parallel systems

As an extension tool of data envelopment analysis (DEA), cross-efficiency evaluation has been widely applied in ranking decision making units (DMUs). Series and parallel are two basic network structures. Current cross-efficiency evaluation in both the structures has been developed with oriented DEA models that minimize inputs while producing at least the given outputs. The extension proposed here is aimed at providing a non-oriented cross-efficiency evaluation based on measures accounting for input excesses and output shortfalls simultaneously. This paper extends the cross-efficiency evaluation in series and parallel structures for use with the directional distance function (DDF). In the proposed DDF cross-efficiency evaluation, the system cross-efficiency score can be decomposed into a weighted average of process cross-efficiency scores, whether for the series or parallel system. The problem with the alternate optima for the multipliers is also addressed and models of avoiding zero multipliers are provided in this new context. Numerical examples from the literature and an empirical one illustrate the validity of the proposed network cross-efficiency evaluation and decomposition, and the superiority of the models in avoiding zero multipliers.

Evaluation of the energy system through data envelopment analysis: Assessment tool for Paris Agreement

Various energy related studies use the data envelopment analysis (DEA) approach to measure the efficiency of decision making units (DMUs). However, heterogenous DMUs and either inappropriate input or output-oriented DEA model lead to unreasonable results. K-Mean clustering method was applied to select homogenous countries from energy data perspective. The input oriented DEA model was performed for power stations (PS) under renewables. The PS under non-renewables and refineiries as well as demand side were analyzed via the output-oriented model. The energy related quality of life (QoL) was the output of the demand efficiency analysis. The overall energy efficiency was calculated by multiplying the efficiency of both sides of energy. The results of the paper specified that the highest potential energy saving (PES) source in the supply side belongs to the non-renewables in power stations, followed by refineries, and finally deployment of renewables. Demand side analysis identified that the highest PES belongs to countries with high population, and high-income economy. In conclusion, the results of overall energy efficiency relying on QoL, suggested an allowance for non-renewables deployment in countries with low economic and low population. The allowance was proposed to support energy poverty, health improvement, and promotion of education.

Potential energy saving via overall efficiency relying on quality of life

Various studies utilize the data envelopment analysis (DEA) technique to measure the efficiency of decision making units (DMUs) in energy system. An inappropriate input- or output-oriented DEA model as well as heterogeneous DMUs can cause unreasonable results in efficiency analysis. K-Means clustering algorithm was applied to classify homogenous countries in the demand and supply sides of an energy system. An input- oriented DEA model was used to analyze power stations (PS) under renewable energy. While, an output-oriented model was used not only to examine PS’ efficiency under non-renewables and refineries, but also in the demand analysis. Further, the output variable for the demand analysis consisted of an energy-related quality of life (QoL) indicator. The overall energy efficiency was calculated through multiplying the supply efficiency by the demand efficiency. The results of the paper showed that the highest potential energy saving (PES) sources on the supply side were the non-renewables in power stations, followed by refineries, then renewables. The demand-side analysis identified that the highest PES belonged to countries with high populations and high-income economies. In conclusion, the overall energy efficiency analysis based on QoL suggested allowances be made to use fossil fuels in poorer countries with weak economies and smaller populations. This allowance was proposed to support energy poverty reduction, improve health, and promote education.

Joint carbon footprint assessment and data envelopment analysis for the reduction of greenhouse gas emissions in agriculture production

Operations management tools are critical in the process of evaluating and implementing action towards a low carbon production. Currently, a sustainable production implies both an efficient resource use and the obligation to meet targets for reducing greenhouse gas (GHG) emissions. The carbon footprint (CF) tool allows estimating the overall amount of GHG emissions associated with a product or activity throughout its life cycle. In this paper, we propose a four-step method for a joint use of CF assessment and Data Envelopment Analysis (DEA). Following the eco-efficiency definition, which is the delivery of goods using fewer resources and with decreasing environmental impact, we use an output oriented DEA model to maximize production and reduce CF, taking into account simultaneously the economic and ecological perspectives. In another step, we stablish targets for the contributing CF factors in order to achieve CF reduction. The proposed method was applied to assess the eco-efficiency of five organic blueberry orchards throughout three growing seasons. The results show that this method is a practical tool for determining eco-efficiency and reducing GHG emissions.

Carbon Emission Performance of China's Power Industry: Regional Disparity and Spatial Analysis

SummaryThis study employs an undesirable‐output–oriented data envelopment analysis model to measure the carbon emission performance of the power industry throughout China's 30 administrative regions during the period of 2003–2012. Also, it further studies the regional disparity and spatial correlation of the carbon emission performance of China's power industry. The main findings are as follows: (1) The carbon emission performance of China's power industry is at a relatively low level, but shows a rising trend. (2) The regional carbon emission performance of China's power industry is extremely unbalanced: The eastern area ranks first, with the highest performance of 0.851, followed by the central area, whereas the western area falls behind, with the lowest performance of 0.760. Provinces in the eastern area generally perform better than those in other areas. (3) According to spatial analysis, the global Moran's I values of carbon emission performance are significantly positive during the sample period, which indicates that the carbon emission performance is a positive spatial correlation and has an obvious clustering effect. The estimate of the local spatial autocorrelation index confirms the imbalance of spatial distribution of the power sector's carbon emission performance. Based on the above findings, several policy suggestions are presented in this article.

Evaluation of world’s largest social welfare scheme: An assessment using non-parametric approach

Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is the world’s largest social welfare scheme in India for the poverty alleviation through rural employment generation. This paper aims to evaluate and rank the performance of the states in India under MGNREGA scheme. A non-parametric approach, Data Envelopment Analysis (DEA) is used to calculate the overall technical, pure technical, and scale efficiencies of states in India. The sample data is drawn from the annual official reports published by the Ministry of Rural Development, Government of India. Based on three selected input parameters (expenditure indicators) and five output parameters (employment generation indicators), I apply both input and output oriented DEA models to estimate how well the states utilize their resources and generate outputs during the financial year 2013–14. The relative performance evaluation has been made under the assumption of constant returns and also under variable returns to scale to assess the impact of scale on performance. The results indicate that the main source of inefficiency is both technical and managerial practices adopted. 11 states are overall technically efficient and operate at the optimum scale whereas 18 states are pure technical or managerially efficient. It has been found that for some states it necessary to alter scheme size to perform at par with the best performing states. For inefficient states optimal input and output targets along with the resource savings and output gains are calculated. Analysis shows that if all inefficient states operate at optimal input and output levels, on an average 17.89% of total expenditure and a total amount of $780million could have been saved in a single year. Most of the inefficient states perform poorly when it comes to the participation of women and disadvantaged sections (SC&ST) in the scheme. In order to catch up with the performance of best performing states, inefficient states on an average need to enhance women participation by 133%. In addition, the states are also ranked using the cross efficiency approach and results are analyzed. State of Tamil Nadu occupies the top position followed by Puducherry, Punjab, and Rajasthan in the ranking list. To the best of my knowledge, this is the first pan-India level study to evaluate and rank the performance of MGNREGA scheme quantitatively and so comprehensively.

Decomposition of potential efficiency gains from hospital mergers in Greece.

This paper evaluates the technical efficiency of 71 Greek public hospitals and examines potential efficiency gains from 13 candidate mergers among them. Efficiency assessments are performed using bootstrapped Data Envelopment Analysis (DEA) whilst merger analysis is conducted by applying the Bogetoft and Wang methodology which allows the overall potential merger gains to be decomposed into three main components of inefficiency, namely technical (or learning), scope (or harmony) and scale (or size) effects. Thus, the analysis provides important insights not only on the magnitude of the potential total efficiency gains but also on their sources. The overall analysis is conducted in the context of a complete methodological framework where methods for outlier detection, returns to scale identification, and bias corrections for DEA estimations are also applied. Mergers are analyzed under the assumptions of constant, variable and non-decreasing returns to scale in an input oriented DEA model with three inputs and three outputs. The main finding of the study indicates that almost all mergers show substantial potential room for efficiency improvement, which is mainly attributed to the pre-merger technical inefficiencies of the individual hospitals and therefore it might be possible to be achieved without the need of implementing full-scale mergers. The same -though, at a lower extent- applies to the harmony effect whilst the size effect shows marginal or even negative gains.

An Evaluation of Investment Performance of Private Life Insurance Industry in India

Data Envelopment Analysis (DEA) model is used to provide valuable information on investment efficiency of private life insurance industry in India. This study utilizes two inputs (shareholders’ investments and policyholders’ investments) and two outputs (net returns on investments to the shareholders and net returns on investments to the policyholders). This study focuses upon 20 private life insurance companies operating in India over a period of 4 years from 2010-11 to 2013-14. Since this study attempts to maximize output, an output oriented DEA model is used. The study finds that investment efficiency of private life insurance industry has improved on Banker, Charnes and Cooper (BCC) model and Charnes, Cooper and Rhodes (CCR) model. The study further highlights that during all years under study, 15% to 40% life insurance companies have been found on the CRS frontier and 40% to 60% life insurance companies have been found on the VRS frontier. With regard to scale efficiency issues, 15% to 40% companies have een operated at their most productive scale over the study period.

Performance evaluation for Egyptian's Computers and Information Faculties: Using a Stochastic Input Oriented Data Envelopment Analysis Model

Envelopment Analysis (DEA) is a great approach used for measuring relative efficiencies and performance of a collection of Decision Making Units (DMUs). These used in the various forms, such as hospitals, universities, air force, banks, courts, business firms, and others, including the performance of countries, regions, etc. One of the earliest applications of DEA called Education. It was an ideal introduction for DEA because it typically deals with comparisons of many similar and autonomous 'non-profit' entities described by inputs and outputs. Therefore, education served to test and validate DEA as a tool for analysis and decision support. Recently DEA has been extended to examine the efficiency of Higher Education operations. In this paper, a Stochastic Input Oriented Data Envelopment Analysis (SIODEA) Model is conducted for the comparison of evaluating the relative efficiency scores of Faculties of Computers and Information (FCIs) each with some of inputs are stochastic with normally distributed, recent inputs are deterministic and outputs.

Teaching hospitals in Brazil: Findings on determinants for efficiency

Hospital efficiency is an important dimension for health services evaluation and suffers the influence of different non-discretionary variables, not necessarily under the control of the hospital manager. These must be considered when establishing goals for efficient and socially committed health, which in many countries is now the primary goal. To test and further understand the context of non-controllable variables in healthcare delivery, this study examined 104 teaching hospitals in Brazil. The efficiency score was generated using weight restricted-output oriented data envelopment analysis model. A second-stage logistic regression analysis was deployed to examine the predictors of efficiency among the non-discretionary variables. The efficiency results indicate that, it requires reduction in labor and in bed capacity, while it increases necessary admissions and high complexity procedures. The main predictors of efficiency were the size of the hospital, high teaching intensity, and low teaching dedication. The model predicts no association between efficiency and ownership. Teaching load, as an important source of qualified human resources to society, although associated with higher health costs, when not considered in service delivery, may distort the analysis of efficiency for the teaching hospitals. These findings could have implications for any healthcare system that has teaching hospitals among its components.

Determining and Optimizing Effective Factors in Laser Irradiation on Skin Tensional Strength using a Hybrid DOE and DEA Approach

Introduction: We investigated the characteristic of a suitable irradiation on skin's tensional strength using design of experiments (DOE). The experiments in this research are designed in two phases and data envelopment analysis (DEA) is used for performance measurement of each phase. Material and Methods: Samples were provided from pleura as surface tissue made of collagen and elastin fibers. In each experiment, the sample was stretched before and after irradiation. Variation of the sample length was measured. Then force-length data were plotted and the slope of the fitted line was calculated. Variation in these slopes was used as a criterion to determine tissue strength variation after laser irradiation. Furthermore, the output oriented DEA model by variable return to scale was used to examine performance of the designed experiments for each phase. Results: Results of the first phase experiments showed that the main effect of time duration was significant; but this was not the case for beam radius. Regarding polarization, only its interaction effect with time duration was significant. Results of the second phase indicated that laser irradiation with parallel polarization for 10 seconds caused a greater increase in tensional strength. Resultant efficiencies of applying DEA showed that the first phase experiments were more efficient. Discussion and Conclusions: This research has combined DEA and DOE to investigate the effects of laser on skin elasticity. Comparing the results of the two phases indicates that it is more efficient to use the experimental design of phase 1 in our experiment. So for similar future studies, we suggest using more levels for experiments of phase 1 instead of doing the experimental design in two phases.

Intangibles and business performance – a technical efficiency approach

This research focuses on the efficiency performance measurement challenge of two groups of technological companies: 1 those with a history of three to five years; 2 those with a history of longer than five years. Data envelopment analysis (DEA) models are used in the evaluation of 431 different technological companies financed by a Finnish venture capital company. As the companies were from three to five years old, roughly one third of the companies showed on average negative profitability, and therefore these companies could not be managed solely with revenue generating innovations. Companies having more than five years of financial history seemed to be generating revenues as well as profits, and revenue as well as profit oriented DEA models seemed to produce similar results. Interestingly, our research also shows that each of the analysed groups and different DEA models have average efficiency below 50%. This observation leaves space for the development of operational management methods as well as venture capital funding decisions in the future.

The Assessment of Irrigation Efficiency in Buyuk Menderes Basin

While extending irrigated areas to augment agricultural production, irrigation efficiency should be increased not only to improve the agricultural production but also to obtain the sustainable use of valuable and limited water resources. Through the use of Data Envelopment Analysis (DEA), which is a linear programming technique to determine the relative efficiency of a decision-making unit, it is possible to decide whether the use of water in an irrigation district is efficient or not. In this study, an input oriented DEA model is constituted to focus on the efficient use of inputs, and the method is applied to the irrigation districts having similar types of agriculture in the Buyuk Menderes Basin, Turkey. This paper aims to determine the efficient irrigation district(s), in other words where the application of water is the most profitable, considering two inputs; water volume supplied and the total irrigated area, and one output, the total value of agricultural production. The weight restrictions consistent with decision makers’ value judgements are added as constraints into the DEA models to prevent excessive weight flexibility assigned to inputs and outputs. The results have provided the efficiency scores of the irrigation districts and numerically delineated desired features of the irrigation districts for maximum efficiency. The analyses for three study years have inferred the robustness of the results. It is concluded that DEA is a practical tool for detecting local inefficiencies and proposing possible improvements for irrigation districts that could offer the greatest potential for growth.

Railway efficiency analysis from larger Europe during the period 1994 2003

This paper represents efficiency analysis of European railways during the period of 1994?2003 with the measurement interval of 1 year. We have separated freight and passenger transports in our Data Envelopment Analysis (DEA) models, and have used in each case two different input oriented DEA models. However, freight and passenger models are brought together in terms of graphical joint-evaluation. Results of our research show that in a 10-year period, European railways have either been efficient in cargo or freight operations, and the number of countries showing good performance in both of the dimensions is scarce. In addition to this, we also find that in Central and Eastern European (CEE) countries, efficiency has declined during the observation period, and therefore, especially passenger operations in these countries, are in need of considerable output increase or input restructuring. However, the sample also contains a number of CEE countries, where both freight and passenger operations are in need of considerable efficiency improvement. In a 'better performing' Europe, improvement need lies primarily in freight operations.

Investigating Sources of Inefficiency in Residential Mental Health Facilities

Data Envelopment Analysis (DEA) was used to measure efficiency of residential mental health facilities. The sample consisted of 50 half-way houses, 8 nursing homes, and 32 sheltered homes. In total, 68 facilities belonged to the public sector and were 22 supervised by private non-profit organizations. Variables chosen to characterize production were: structure size (m2), staff, salaries and operational costs, and the output measure was patient numbers. An input oriented DEA model, allowing for variable returns to scale, was applied and units were ranked according to a benchmarking approach. Mean efficiency, for the whole sample, was 73.2% and 18 best practice units were found, on average, 33.1% over-efficient. The other 72 were under-performing, with 54 appearing more than 20% inefficient. The mean efficiency scores for public and private non-profit units were 68.8 and 86.6%, respectively, and significantly different (p < 0.001). Results suggest that efficiency improvements are possible with better use of resources but more research employing various data sets is required.

AN ε-FREE DEA AND A NEW MEASURE OF EFFICIENCY

This study presents a DEA model without the conventional non-Archimedian infinitesimal E. This article also introduces a new DEA efficiency measure. Incorporating slacks in inputs and shortages in outputs, the new DEA measure expresses the relative efficiency of decision making units more properly than traditional one. 1 Introduction and Historical Background In their ingenious paper (lOJ, Charnes, Cooper and Rhodes introduced a fractional pro­ gramming method to measure the relative efficiency of a Decision Making Unit (DMU), which was solved by transforming the fractional programming into a linear programming problem via the Charnes-Cooper scheme (6J. The method was referred to as DEA (Da.ta En­ velopment Analysis). The DEA model proposed in (10J maintained an assumption; all the weighting values to inputs and outputs were assumed to be nonnegative. In the subsequent short communication (11), Charnes et. al. changed their DEA problems and required that the weights be strictly positive. Thus, t.he introduction of the non-Archimedian in­ finitesimal e was anticipated to distinguish bet.ween nonnegative and positive values. ( This problem was already discussed in (10J implicitly.) Although the subsequent discussions can be found in (5), (7), and (12), and the role of e has become unclear and weakened, it is still frequently used in the literature (e.g., (4),(8)) and in particular, in some cases of compu­ tational situations, values such as e = 10- 5 ,10- 6 (single precision) or e = 10- 12 (double precision) are conveniently employed to substitute for the non-Archimedian infinitesimal e. However, the approach may produce a theoretically contradicting issue. That is, we cannot uniquely determine what is the best~. Different e values yield different DEA re­ sults. Therefore, we need a completely e-free development of DEA from both theoretical and computational points of view. This article is organized as follows. Section 2 defines an input oriented DEA model based on the production possibility set. Its dual corresponds to the Charnes-Cooper­ Rhodes (CCR) model with the weights to inputs and outputs as variables. Then, we define a DMU as slackless if, for every optimal solution to the DEA model, it has no slack in inputs and no shortages in outputs. By a theorem of the alternative or the strong theorem of complemantary slackness, it will be proved that for a slackless DMU there is a strictly positive weight solution in the corresponding CCR model. Subsequently, for a DMU with non-zero slacks in an optimal solution to the DEA model, there exist no positive weight solutions in the CCR model. Section 3 defines the max-slack solution and shows a procedure to find it. The max-slack solution can be used for deciding whether the DMU is slackless or not. Then, we propose a method for finding positive weights for slackless DMUs. Thus, all jobs of the CCR model can be successfully achieved with no recourse to e. Section 4 introduces a new measure of relative efficiency, based on the max-slack solution, which