Benchmarking Urban Energy Efficiency with Deterministic and Stochastic Methods

The main aim of the study was to evaluate the efficiency of Islamic Azad University branches in East Azerbaijan using stochastic frontier analysis (SFA) and to compare the findings with the results of data envelopment analysis (DEA). In this respect, SFA and DEA techniques were applied to measure production efficiency of 28 branches of East Azerbaijan Islamic Azad University considering the following variables as either output or input: 3 variables as output (including the number of graduated students and students accepted for the further pursuit of their studies, as well as the number of articles, books, and research projects) and 6 variables as input (including the number of university faculty members, educational fields, current students, staff working in different sections of the university, and the total university campus in square meters as well as university costs). Estimation of DEA and obtaining of the units' efficiency indicated that the average efficiency of the universities under investigation were 0.66 and 0.80 in SFA and DEA techniques, respectively. In addition, efficiency distributions in SFA and DEA methods were tremendously different from each other so that in SFA method nearly 40% while in DEA method more than 60% of unit efficiency was between 0.8-1 intervals. The minimum level of estimated efficiency (0.14) was observed for Hadishahr using SFA method, and in DEA approach, the minimum level of efficiency belonged to Khoda Afarin branch (0.065).

This study proposes a new efficiency measurement technique CDS as combination of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) and compares the CDS efficiency score with the DEA and SFA efficiency scores. The financial companies listed in Malaysian Stock Exchange for the period 2007-2016 are used to estimate the different types of efficiency score. Besides, linear regression analysis and ULFR (unreplicated linear functional relationship) analysis are used to analyze the performance of this CDS technique with the DEA and SFA techniques. The result suggests that the most efficient model is CDS which has a significant positive correlation with profit risk. Among the CDS, DEA and SFA techniques, the recommended technique (CDS) shows higher coefficient of determination values for both ULFR (0.9994) and linear regression (0.292) analysis. Also, based on the results of CDS, this study postulates that the most efficient firm is ACSM (Aeon Credit Service (M) Bhd) and the least efficient firm is MAY (Malayan Banking Bhd).

Non-parametric Data Envelopment Analysis (DEA) and parametric Stochastic Frontier Analysis (SFA) have become very popular in the analysis of productive efficiency. This study undertakes a comparison of the SFA and the constant returns to scale (CRS) and variable returns to scale (VRS) output-oriented DEA models, based on a sample of 240 farms in Bangladesh. However, the aim of this paper is not only to compare estimates of technical efficiency obtained from the two approaches, but also to examine the determinants of inefficiency. The detection and measurement of technical inefficiency and its determinants can be used as a basis of policy recommendations. The results from both the approaches indicated that there is a potential for increasing farm production through efficiency improvement and that farmers' age and educational level have a significant effect on efficiency.

BackgroundThe present study has been undertaken with the aim to evaluate performance and ranking of various universities of medical sciences that are responsible for providing public health services and primary health care in Iran.MethodsFour models; Weighted Factor Analysis (WFA), Equal Weighting (EW), Stochastic Frontier Analysis (SFA), and Data Envelopment Analysis (DEA) have been applied for evaluating the performance of universities of medical sciences. This study was commenced based on the statistical reports of the Ministry of Health and Medical Education (MOHME), census data from the Statistical Center of Iran, indicators of Vital Statistics, results of Multiple Indicator of Demographic and Health Survey 2010, and results of the National Survey of Risk Factors of non-communicable diseases.ResultsThe average performance scores in WFA, EW, SFA, and DEA methods for the universities were 0.611, 0.663, 0.736 and 0.838, respectively. In all 4 models, the performance scores of universities were different (range from 0.56–1, 0.53–1, 0.73–1 and 0.83–1 in WFA, EW, SFA and DEA models, respectively). Gilan and Rafsanjan universities with the average ranking score of 4.75 and 41 had the highest and lowest rank among universities, respectively. The universities of Gilan, Ardabil and Bojnourd in all four models had the highest performance among the top 15 universities, while the universities of Rafsanjan, Ahvaz, Kerman and Jiroft showed poor performance in all models.ConclusionsThe average performance scores have varied based on different measurement methods, so judging the performance of universities based solely on the results of a model can be misleading. In all models, the performance of universities has been different, which indicates the need for planning to balance the performance improvement of universities based on learning from the experiences of well-performing universities.

Purpose The purpose of this paper is to provide an outline of the major contributions in the literature on the determination of the least distance in data envelopment analysis (DEA). The focus herein is primarily on methodological developments. Specifically, attention is mainly paid to modeling aspects, computational features, the satisfaction of properties and duality. Finally, some promising avenues of future research on this topic are stated. Design/methodology/approach DEA is a methodology based on mathematical programming for the assessment of relative efficiency of a set of decision-making units (DMUs) that use several inputs to produce several outputs. DEA is classified in the literature as a non-parametric method because it does not assume a particular functional form for the underlying production function and presents, in this sense, some outstanding properties: the efficiency of firms may be evaluated independently on the market prices of the inputs used and outputs produced; it may be easily used with multiple inputs and outputs; a single score of efficiency for each assessed organization is obtained; this technique ranks organizations based on relative efficiency; and finally, it yields benchmarking information. DEA models provide both benchmarking information and efficiency scores for each of the evaluated units when it is applied to a dataset of observations and variables (inputs and outputs). Without a doubt, this benchmarking information gives DEA a distinct advantage over other efficiency methodologies, such as stochastic frontier analysis (SFA). Technical inefficiency is typically measured in DEA as the distance between the observed unit and a “benchmarking” target on the estimated piece-wise linear efficient frontier. The choice of this target is critical for assessing the potential performance of each DMU in the sample, as well as for providing information on how to increase its performance. However, traditional DEA models yield targets that are determined by the “furthest” efficient projection to the evaluated DMU. The projected point on the efficient frontier obtained as such may not be a representative projection for the judged unit, and consequently, some authors in the literature have suggested determining closest targets instead. The general argument behind this idea is that closer targets suggest directions of enhancement for the inputs and outputs of the inefficient units that may lead them to the efficiency with less effort. Indeed, authors like Aparicio et al. (2007) have shown, in an application on airlines, that it is possible to find substantial differences between the targets provided by applying the criterion used by the traditional DEA models, and those obtained when the criterion of closeness is utilized for determining projection points on the efficient frontier. The determination of closest targets is connected to the calculation of the least distance from the evaluated unit to the efficient frontier of the reference technology. In fact, the former is usually computed through solving mathematical programming models associated with minimizing some type of distance (e.g. Euclidean). In this particular respect, the main contribution in the literature is the paper by Briec (1998) on Hölder distance functions, where formally technical inefficiency to the “weakly” efficient frontier is defined through mathematical distances. Findings All the interesting features of the determination of closest targets from a benchmarking point of view have generated, in recent times, the increasing interest of researchers in the calculation of the least distance to evaluate technical inefficiency (Aparicio et al., 2014a). So, in this paper, we present a general classification of published contributions, mainly from a methodological perspective, and additionally, we indicate avenues for further research on this topic. The approaches that we cite in this paper differ in the way that the idea of similarity is made operative. Similarity is, in this sense, implemented as the closeness between the values of the inputs and/or outputs of the assessed units and those of the obtained projections on the frontier of the reference production possibility set. Similarity may be measured through multiple distances and efficiency measures. In turn, the aim is to globally minimize DEA model slacks to determine the closest efficient targets. However, as we will show later in the text, minimizing a mathematical distance in DEA is not an easy task, as it is equivalent to minimizing the distance to the complement of a polyhedral set, which is not a convex set. This complexity will justify the existence of different alternatives for solving these types of models. Originality/value As we are aware, this is the first survey in this topic.

At the moment, there is an increase in the importance of efficiency in another sector of the economy. Evaluating the efficiency of an enterprise makes it possible to implement a correct and profitable resource allocation strategy, which shows its potential level. In connection with the annual increase in the number of bankrupt enterprises, the problem of assessing the efficiency of enterprises' activities is relevant both for their owners and managers, and for creditors. There are various methods and models for assessing the efficiency of enterprises. One study focuses on the improvement of a parametric method for assessing the performance of objects - the SFA (Stochastic Frontier Analysis) method. The classical SFA method is based on the production function of an enterprise, linking the volume of output with the volume of consumed resources. In this case, the SFA model uses several input (volumes of consumed resources) and only one output parameter - the volume of output. The essence of the proposed modification of the model based on the SFA method is to use financial indicators of enterprises' activities instead of data on consumed resources and output, on the basis of evaluating the financial activity of the economic objects under study. The developed model of the situation for assessing the efficiency of the enterprise in terms of its financial indicators.

This paper extends the concept of methodological crosschecking by examining whether bank efficiencies computed by the two frontier techniques, stochastic frontier analysis (SFA) and data envelopment analysis (DEA), are consistent. The study used a panel of 220 unbalanced observations from 27 Ghanaian banks between 2007 and 2016 to estimate cost and technical efficiencies and check for consistency using five criteria: efficiency distribution, ranking, ability to identify best or worst banks, stability of efficiencies, and relationship with accounting ratios. The results suggest that there is no consistency in the way parametric and non-parametric techniques rank or identify the best or worst banks. Also, there exists a weak relationship between the efficiency scores generated by both SFA and DEA and the non-frontier accounting ratios of Ghanaian banks. This suggests that the latter may contain some exogenous variables that make them weak measures of efficiency and should be used with caution, especially for bank supervision. However, the SFA approach yielded efficiency scores that were comparatively more stable over time. Therefore, the study concludes that the SFA approach is more practical and thus more appealing for regulatory purposes in Ghana due to the relatively consistent efficiency scores under the SFA approach compared to those under the DEA.

Existing multi-criteria decision-making (MCDM) methods often face challenges when evaluating a large number of alternatives, leading to skewed results in selecting the optimal choice. Similarly, conventional efficiency analysis (EA) methods, such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), often yield incomplete solutions due to their reliance on theoretical assumptions. To address these limitations, we propose a novel EA method that integrates Virtual Gap Analysis (VGA) models to evaluate the performance of each decision-making unit (DMU) in relation to others based on best practices. Unlike DEA and SFA, our VGA models are linear programming-based, assumption-free, and capable of delivering robust and reliable solutions. The proposed method enables each DMU to identify achievable benchmarks for inputs and outputs. Based on the estimated virtual gaps, DMUs are classified as inefficient (with scores below one) or efficient (with scores of one or higher). Additionally, our new MCDM method incorporates existing MCDM techniques to analyze the few identified efficient DMUs, significantly reducing the effort required to select the best DMU.

Existing multi-criteria decision-making (MCDM) methods often face challenges when evaluating a large number of alternatives, leading to skewed results in selecting the optimal choice. Similarly, conventional efficiency analysis (EA) methods, such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), often yield incomplete solutions due to their reliance on theoretical assumptions. To address these limitations, we propose a novel EA method that integrates Virtual Gap Analysis (VGA) models to evaluate the performance of each decision-making unit (DMU) in relation to others based on best practices. Unlike DEA and SFA, our VGA models are linear programming-based, assumption-free, and capable of delivering robust and reliable solutions. The proposed method enables each DMU to identify achievable benchmarks for inputs and outputs. Based on the estimated virtual gaps, DMUs are classified as inefficient (with scores below one) or efficient (with scores of one or higher). Additionally, our new MCDM method incorporates existing MCDM techniques to analyze the few identified efficient DMUs, significantly reducing the effort required to select the best DMU.

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.

This study aimed to measure the efficiency scores of 75 dairy buffalo farms in the province of Nueva Ecija, Central Luzon, Philippines, using an input-oriented, variable-return-to-scale Data Envelopment Analysis (DEA) model. The farmer-informants or decision-making units (DMUs) were categorized as smallholders, family modules, and semi-commercial in operations. Personal interviews using structured questionnaires were done to gather various information on the socio-economic and management practices of the DMUs. Output in the form of volume and value of milk produced and inputs such as quantities and costs of biologics, feeds, forage, and labor were also collected and evaluated among individual DMUs. The efficiency scores were computed using PIM-DEA software, which identified fully efficient DMUs lying on the frontier line (scores of 1.0) and those enveloped by it (inefficient DMUs with scores of less than 1.0). The overall mean Technical Efficiency (TE), Allocative Efficiency (AE), and Economic Efficiency (EE) scores among the DMUs were 0.80, 0.81, and 0.65, respectively. Most of the inefficient DMUs were in the smallholder category. In sum, smallholder DMUs classified under low and moderate TE clusters should reduce their inputs by 53.31% and 40.01%, respectively, to become fully efficient. Likewise, higher lambda values among efficient peer DMUs indicate the best practice frontiers that the inefficient peer DMUs can benchmark with. Extension and advisory services can help promote the best management practices of the frontiers to improve the TE, AE, and EE of the inefficient DMUs.

The reasons for the spectacular collapse of so many centrally‐planned economies are a source of ongoing debate. In this paper, we use detailed farm‐level data to measure total factor productivity (TFP) changes in Mongolian grain and potato farming during the 14‐year period immediately preceding the 1990 economic reforms. We measure TFP growth using stochastic frontier analysis (SFA) and data envelopment analysis (DEA) methods. Our results indicate quite poor overall performance, with an average annual TFP change of – 1.7% in grain and 0.8% in potatoes, over the 14‐year period. However, the pattern of TFP growth changed substantially during this period, with TFP growth exceeding 7% per year in the latter half of this period. This suggests that the new policies of improved education, greater management autonomy, and improved incentives, which were introduced in final two planning periods in the 1980s, were beginning to have a significant influence upon the performance of Mongolian crop farming.

Stochastic Nonparametric Envelopment of Data: Combining Virtues of SFA and DEA in a Unified Framework

Two popular approaches for efficiency measurement are a non-stochastic approach called data envelopment analysis (DEA) and a parametric approach called stochastic frontier analysis (SFA). Both approaches have modeling difficulty, particularly for ranking firm efficiencies. In this paper, a new parametric approach using quantile statistics is developed. The quantile statistic relies less on the stochastic model than SFA methods, and accounts for a firm's relationship to the other firms in the study by acknowledging the firm's influence on the empirical model, and its relationship, in terms of similarity of input levels, to the other firms. Copyright © 1999 John Wiley & Sons, Ltd.

Background: Hospitals are one of the greatest and most costly operational units of the health system and a study on its performance may be a criterion for operation and efficiency assessment of its resource consumption. The present study was done in 2017 with the purpose of assessing technical efficiency of the selected hospital under the coverage of Yazd University of Medical Sciences using Data Envelopment Analysis (DEA) in context of Health Reform Plan. Methods: This research is a descriptive-analytical study was done in 2017. In this research in order to determine assessing the efficiency of Yazd University of Medical Sciences hospitals, 10 hospitals affiliated with this university were chosen. To collect data the information collection form including specifications of hospital as well as input and output index is used and the data before and after executing Health Reform Plan were collected and compared. The input-oriented DEA method is used for estimating technical efficiency of hospitals and data analysis is done by DEAP software and two-sample paired-t test SPSS21. Results: The average of hospitals that have been studied before and after the reform plan is 0.985 and 0.990 respectively. In management efficiency, it is respectively 0.986 and 0.992 before and after the reform plan and the efficiency average of the hospital's scale before and after executing the health reform plan is respectively 0.999 and 0.997. There is no meaningful relationship observed between hospitals' efficiency grades before and after the reform plan in these three technical, management and scale types of efficiency (P-value > 0.05). Conclusion: According to results we could say the efficiency of the hospitals was not so affected by reform plan. It is recommended to assess the efficiency of the Stochastic Frontier Analysis (SFA) method on a linear basis in addition to identifying indexes that are effective in efficiency of hospitals.