Data Envelopment Analysis Research Articles

Optimizing public sector resources: A centralized DEA model for effective allocation of shared inputs

This study presents a novel centralized Data Envelopment Analysis (DEA) model with shared inputs to optimize the allocation of public sector resources and enhance management efficiency. Recognizing the need for a comprehensive approach to public sector performance evaluation, the study integrates the strengths of the centralized DEA framework and the two-stage DEA model with shared inputs. The centralized DEA model shifts the focus from individual decision-making units to optimizing resources across the entire public sector system. This allows for the reallocation of shared inputs among decision making units, leading to potential efficiency gains and improved overall performance. Incorporating shared inputs within the centralized structure enables a more nuanced understanding of the interdependencies and interactions between distinct functions and stages within the public sector. The empirical application of the proposed model in the context of public sector management and cultural subsidies provides valuable insights. The findings highlight inefficiency and offer guidance for policymakers and administrators on optimizing shared resource use. The centralized DEA model with shared inputs serves as a practical decision-support tool, informing the development of targeted policies and strategies to enhance the efficiency and effectiveness of public service delivery, particularly in resource-constrained environments. This research contributes to public sector performance evaluation's theoretical and methodological advancement, offering a comprehensive framework for resource optimization and improved management practices.

Application of DEA Models to Develop Strategies for Improving the Energy Efficiency of National Economies: Focusing on SDGs and Best Practices in European Countries

Objective: This study aims to develop a strategy for improving the energy efficiency of national economies through the application of DEA (Data Envelopment Analysis) models. The research focuses on adopting the best practices from European countries in the context of SDGs. Theoretical Framework: The research is grounded in the framework of sustainable development and energy efficiency, focusing on the UN Sustainable Development Goals (SDG 12). DEA models are utilized to assess energy efficiency across various European economies, considering economic, environmental, and energy-related factors. Method: DEA models were applied to assess energy efficiency by analyzing input variables such as energy intensity, CO₂ emissions, and energy consumption. The model used a comparative analysis of 38 European countries, with special attention to the weights (λ) in the VRS model, allowing for variable returns to scale. The data were sourced from Eurostat and the World Bank. Results and Discussion: The results indicated that countries like Ireland and Albania serve as key reference units for Ukraine, highlighting the effectiveness of transitioning to renewable energy sources and reducing dependence on fossil fuels. The analysis demonstrated that inefficient countries could improve their performance by following the strategies of their reference units, specifically in reducing energy consumption and emissions. Research Implications: The study provides practical insights into how less efficient countries, like Ukraine, can enhance their energy policies by adopting successful practices from European nations, ultimately contributing to global sustainability goals. Originality/Value: This research contributes to the literature by offering a structured methodology for enhancing energy efficiency in national economies through DEA modeling. It emphasizes the significance of renewable energy in sustainable development and presents a comparative framework applicable to a broad range of countries.

Evaluating and Improving Relative Efficiency The Banking Sector in Iraq Using Data Envelopment Analysis of The Output-Oriented VRS Model With Application

The banking sector is one of the important sectors in Iraq, and the last stage witnessed the emergence of several private banks in the competition market, and in order to determine the efficiency of existing banks, the resources available to government and private banks were analyzed and compared with two international banks to determine their efficiency ratios for the surveyed banks using data envelopment analysis, the research problem was diagnosed in the lack of interest in measuring the efficiency of banks by exploiting the resources available to maximize profits, the aim of the research is to determine the efficiency of the banks studied according to the direction of outputs and to conduct continuous analysis of the results in order to come up with recommendations that contribute to improving the banking sector. The research focused on the direction of increasing outputs with constant inputs in order to maximize returns. The( Deap2.1) data analysis program was used, and the research adopted data for 2022 to measure efficiency. For 22 banks operating in Iraq (governmental and private) and two banks outside iraq, the variable volume returns (VRS) model was used, and the most prominent results achieved were the achievement of banks The following banks (Rasheed, industrial, TBI, Al-Ahli, Asia, National,trans-Iraq, Jordanian capital, Lloyds Banking Group London) achieved high efficiency100% as they used their resources optimally. The investment bank achieved 90.8%, which is a good percentage, with the ability to benefit from the expertise of industrial and national banks, Jordanian capital, and Lubd's Banking Group London to reach 100% efficiency in 2022.

Data envelopment analysis for performance measurement in the construction field: a systematic review

Data envelopment analysis for performance measurement in the construction field: a systematic review

Quantitative simulation and validation of energy carbon emission efficiency changes in Chinese urban agglomerations

In the context of the global energy dilemma, enhancing energy efficiency has inevitably become a pivotal strategy for mitigating energy waste and promoting sustainable development. Urban agglomerations, as growth poles leading high-quality regional economic development, play a significant role in this process. Within these urban agglomerations, the aggregation of various factors and the substitution of energy resources substantially influence the energy carbon emission efficiency (ECEE) of surrounding cities during their development and maturation phases. This study initially employs a three-stage Data Envelopment Analysis (DEA) to quantitatively analyze the ECEE values of 19 urban agglomerations from 2006 to 2020. It then establishes a methodology for calculating the joint intensity (JI) and joint threshold (JT) of ECEE within these urban agglomerations. Finally, a validation process is executed using MATLAB simulation and verification methodology to fit and authenticate the evolutionary patterns of ECEE in Chinese urban agglomerations. The primary objective of this research is to model the evolution of ECEE, thereby exploring the sustainable development pathways for urban agglomerations in China. The research findings indicate that: (1) The results from the three-stage DEA demonstrate that, after removing the influences of external environmental factors and random errors, the comprehensive technical efficiency (TE) of energy carbon emissions for the 19 urban agglomerations in China experienced a wave-like increase, rising from 0.410 to 0.506 between 2006 and 2020. (2) The calculation results of the constructed JI model indicate that the JT values of ECEE for national-level, regional-level, and local-level urban agglomerations are 1006.92, 226.60, and 160.14, respectively. The national-level urban agglomerations have significantly higher JT values and JI than the other two levels of urban agglomerations. (3) The results of the Matlab simulation verification show that 16 urban agglomerations fit well with the wave-like ascending evolutionary pattern. However, four urban agglomerations exhibit an opposite fitting effect due to the influence of their pillar industries or the small number of cities within these accumulations, making them unrepresentative. Consequently, the evolutionary curve of ECEE in Chinese urban agglomerations generally exhibits a wave-like upward trend over time.

Performance measurement and management in complex environments: a system of systems approach for the public sector

Issues related to rapidly changing digital environments and turbulent or complex settings, such as the public sector, often hinder successful implementations of Performance Measurement and Management (PMM) systems. Despite the availability of PMM methods, the challenge of implementing successful systems in turbulent settings persists. This article introduces a novel PMM framework employing a System of Systems (SoS) approach, integrating Data Envelopment Analysis and Machine Learning to guide organisational changes by enhancing the efficiency of Decision-Making Units (DMU). Validated by a case study within the Brazilian Navy, the framework demonstrates a significant capacity to interpret the impact of DMU characteristics on operational efficiency and draw actionable insights. Our findings suggest that leveraging SoS principles emphasises adaptability and ongoing improvement through a data-driven process, suggesting the framework’s potential to navigate through complexity by enabling more effective management practices and decision-making. Future research directions include longitudinal studies and the incorporation of technologies to support managerial decision-making.

Principal Component Analysis for Aggregating Environmental Variables Affecting Electricity Distributors' Operations in Brazil

Theoretical Framework: The National Electric Energy Agency (NEEA) applies benchmarking, especially Data Envelopment Analysis (DEA), to establish distribution tariffs and calculate efficient operational costs. The environmental context of the areas where distributors operate impacts their efficiency, indicating the need to integrate environmental factors into benchmarking models to correct methodological biases and consider unmanageable aspects, ensuring a more accurate analysis of distributor efficiency. Objective: The study applied Principal Component Analysis (PCA) to the environmental variables of electricity distributors in Brazil, facilitating the evaluation of companies under similar operational conditions. Method: The research utilized PCA to condense environmental information from the utilities into a reduced set of variables, simplifying the understanding of impacts on the companies' operational costs. Research Implications: The 21 environmental variables were synthesized into four main components: Territoriality, Climate Factor, Wildfires, and Accessibility/Infrastructure, allowing their use in advanced models that consider environmental factors. Originality/Value: The application of PCA to the environmental variables of electricity distributors in Brazil enables a more homogeneous evaluation based on similar environmental conditions. This approach, still underexplored by ANEEL, assists in analyzing the particularities of the distributors.

Evaluation of real estate investment performance of insurance companies by NHF Act in curbing housing deficit in Nigeria

PurposeThe purpose of this study is to evaluate the real estate investment performance portfolio decision-making of the insurance firms by the National Housing Fund (NHF) Act in reducing the housing deficit in Nigeria. The insurance companies' gross premium, real estate investment and return on investment trends for the period 2014–2019 were evaluated, to determine the extent of their investment in real estate in Nigeria, this ought to have reduced the shortfall in housing.Design/methodology/approachBoth primary and secondary sources were used. Cronbach’s alpha was used for testing the reliability. The Friedman mean rank with Chi-square was used for different types of real estate investment properties and for reasons for investing in real estate by insurance companies in Nigeria. The test for normality was conducted using the Shapiro–Wilk and Spearman correlation for the significance. The percentage and the data envelopment analysis frontier model were used for measuring performance and efficiency.FindingsThe results showed that the majority of real estate investments made by insurance companies were in commercial and land buying and reselling and that their performance was below average. However, their motivation for investing is not toward the NHF Act but rather for diversification and increasing their capital.Originality/valueTo the best of the authors’ knowledge, this is one of the first studies in Nigeria that looked at how well insurance firms have performed investing in real estate as required by the NHF Act. However, the new Act, the National Housing Fund (Establishment) Act of 2018 which was put on hold due to flaws, affected data availability beyond 2019.

Data Envelopment Analysis and Models in Healthcare Services: A Review

Data Envelopment Analysis (DEA) is a widely used method for measuring efficiency and performance in healthcare services. In this article, we will examine the role of DEA and the models used in the healthcare sector. DEA is a method used to measure the efficiency of units (such as hospitals, clinics) with multiple inputs and outputs. Essentially, data envelopment analysis conducts a performance analysis that shows how units utilize their existing resources and how they can optimize these resources. Units such as hospitals, clinics, and healthcare organizations must deliver more services with limited resources. DEA helps these organizations to use their resources most efficiently. DEA is used to increase operational efficiency, reduce costs, and improve service quality in healthcare services. There are various models available for data envelopment analysis. The most common ones include Data Envelopment Analysis (DEA) and Stochastic Data Envelopment Analysis (SDEA). While DEA is used for measuring efficiency that can be either constant or variable in scale, SDEA considers uncertainties and random effects. There are various advantages and disadvantages. Facilitates efficient use of resources, brings a data-driven approach to the decision-making process and objectively evaluates performance in healthcare services. Disadvantages are data deficiencies or poor quality may pose challenges in some cases and the complexity of the model may complicate the application and interpretation process. Data envelopment analysis in healthcare services is a powerful tool for measuring and improving the efficiency of organizations. However, proper use of data and careful application of the model are essential.

EFFICIENCY MEASUREMENT OF ARTIFICIAL INTELLIGENCE: A RESEARCH ON COMPANIES IN TÜRKİYE

The use of technology is increasing due to Industry 4.0. Both countries and organizations have had to invest in the field of artificial intelligence (AI) to compete with their rivals in global competitive conditions and to adapt to the ever-changing world. An organization or a country needs to evaluate its performance to ensure its sustainability constantly. The Data Envelopment Analysis (DEA) method is widely used in performance evaluation. This study aimed to evaluate Türkiye AI performance for the nine years between 2014 and 2022. In the research, years were included in the analysis as the decision-making unit. Two input and two output variables were used in the analyses. The study was carried out by using the input-oriented CCR DEA model and its super-efficiency model. According to the results of the analysis, efficient/inefficient decision-making units were determined. Several potential improvement suggestions have been put forward for inefficient decision-making units.

On value efficiency analysis and cone-ratio data envelopment analysis models

AbstractIn this paper we relate the Value Efficiency Analysis (VEA) efficiency scores with those of three different Cone-Ratio Data Envelopment Analysis (CR-DEA) models, when the chosen model Decision Making Units (DMUs) in CR-DEA jointly comprise the Most Preferred Solution (MPS) in VEA. In particular, we consider the cases where the set of feasible weight vectors is given respectively by (i) the weight vectors that are jointly optimal in DEA for all the model DMUs, (ii) every weight vector that is optimal in DEA for at least one of the model DMUs, and (iii) only those weight vectors with strictly positive input and output weights, each of which is jointly optimal in DEA for all the model DMUs. In each of these cases we show that the CR-DEA efficiency scores can be obtained or approximated by estimating either a VEA model or a series of VEA models, without the need of identifying a priori the set of feasible weight vectors. We illustrate the usefulness of our results by means of an empirical application using data on Japanese regional banks.

Economic and Environmental Effects of Farmers’ Green Production Behaviors: Evidence from Major Rice-Producing Areas in Jiangxi Province, China

This study examines the economic and environmental impacts of green production practices among farmers. It aims to contribute to sustainable agricultural development, mitigate agricultural non-point source (NPS) pollution, and align environmental protection with economic growth. This paper utilizes survey data from 1345 farm households in the main rice production areas of Jiangxi Province, China, using the example of reduced fertilizer application (RFA) among rice farmers. This study constructs a slack-based measure data envelopment analysis (DEA—SBM) model with undesirable outputs to measure environmental effects and applies an endogenous switching regression model (ESRM) to test the economic and environmental effects of farmers’ adoption of green production technologies. We found the following: (1) The RFA behavior of farmers has a significant positive impact on their net profit per hectare (NPH), helping farmers increase their income, with the increase ranging from 2.05% to 6.54%. (2) Farmers’ RFA behavior has a significant positive impact on agricultural green productivity (AGP), contributing to the improvement of the environment, ranging from 44.09% to 45.35%. (3) A heterogeneity analysis found inconsistencies in the income-enhancing and environmental-enhancing effects at different quantiles of NPH and AGP. Therefore, attention should be placed on improving the agricultural product quality supervision system under the market circulation mechanism, creating land scale conditions conducive to the promotion and application of fertilizer reduction technologies and promoting the implementation of externality internalization compensation systems.

How to optimize government R&D budget allocation? A review of recent studies and research gaps

Purpose Research and Development (R&D) activities are important for technological innovation and present opportunities for entrepreneurship. These activities depend on the flow of funding. This paper aims to review approaches used in R&D project selection and budget allocation. Design/methodology/approach This study conducts a systematic review, examining the content of 60 relevant papers (spanning 2000–2022) concerning public R&D budget allocation. The analysis focuses on allocation methodology, R&D output evaluation, budget allocation efficiency and the management of uncertainty in the allocation process. Findings The systematic review reveals different methods proposed for allocating government R&D budgets. These methods range from classical optimization, multi-criteria analysis and hierarchical analysis to techniques such as balanced scorecard, data envelopment analysis and analytic hierarchy process, including fuzzy approaches. Recent trends indicate an increase in the use of advanced optimization, integration and simulation algorithms. Performance indicators for reflecting R&D project outputs or goals can be categorized into four main groups: output (e.g. publications, patents, graduates), outcome, productivity (e.g. citations, patent references, articles and patents per capita) and sector-specific metrics. Practical implications Future research directions in government R&D budget allocation may include optimizing allocation to maximize social, economic and political benefits, developing ranking models, decision-making frameworks, simulations and evaluations of factors influencing allocation type and strategy. Additionally, there is a growing interest in novel budget allocation algorithms leveraging artificial intelligence and self-adjusting meta-heuristic algorithms. Originality/value The systematic review showed that some important research gaps in (government) R&D budget allocation could be considered in future studies; for example, long-term social, economic and political benefits in budget allocation optimization models, comprehensiveness of allocating government R&D budgets to universities, higher education and research institutes, R&D budget allocation to strategic technology development, e.g. renewable energy sector, supply chain issues and renewable energy value chain; new budget allocation algorithms based on artificial intelligence and self-adjusting meta-heuristic algorithms; methods for optimizing the structures of government budget allocation to R&D, considering executive and regulatory conflicts.

Technical efficiency analysis of advanced medical centers in Burkina Faso

IntroductionBurkina Faso faces many challenges in the health domain, with no real opportunity for an increase in public health expenditures. In Burkina Faso, as in all low-income countries, health spending efficiency is crucial. The objective of this paper is to assess the efficiency of Advanced Medical Centers (AMCs)—which correspond to district hospitals—in Burkina Faso over the 2017–2020 period and identify the factors that promote—or, on the contrary, limit—the efficiency of these health structures.MethodWe first assessed the efficiency level of the 45 AMCs running in the country between 2017 and 2020 using a bootstrap Data Envelopment Analysis (DEA) methodology. Inputs include the number of doctors, nurses, other medical staff, non-medical staff, and beds, while output variables correspond to the number of inpatients, surgeries, outpatients, and inpatient days. In a second step, determinants of AMC’s efficiency levels were explored using a double-bootstrap procedure. The roles of AMCs’ internal and environmental factors were both considered.ResultsWe found a mean efficiency score of 0.51 over the study period, indicating that AMCs could have almost doubled their healthcare production without needing additional resources. The size, education level, and health status of the covered population and the density of the healthcare supply in the district appeared to be the driving factors of AMCs’ efficiency.ConclusionOur results indicate that improving the efficiency of AMCs should be a high-level priority for the Burkinabe health policy. Resources could be reallocated across AMCs to increase the overall efficiency of the health system.

Pathway Selection for Enhancing the Efficiency of Psychological Crisis Intervention Resource Integration during Major Emergencies: Evidence from China

In major emergencies, psychological crisis intervention plays a critical role in safeguarding public mental health and supporting post-disaster recovery. However, uneven resource allocation underscores the urgent need for more efficient resource integration pathways. This study, based on data from Chinese governmental and health departmental reports in 2022, employs a Data Envelopment Analysis (DEA) and a fuzzy-set Qualitative Comparative Analysis (fsQCA) to evaluate the efficiency of psychological crisis intervention resource integration across various regions. It investigates the combined effects of internal and external factors on improving integration efficiency through a configurational approach. The findings reveal that while the overall efficiency of resource integration is relatively high, there are notable differences between institutions. The analysis identifies five key pathways: policy support-driven (H1), professional capability-driven (H2), comprehensive synergy-driven (H3), resource optimization (NH1), and community empowerment (NH2). Despite regional disparities, effectively integrating key resources can enhance overall efficiency. Coordinating internal and external factors and optimizing essential resources are crucial for improving the effectiveness of psychological crisis interventions. This research offers actionable insights into integration strategies to strengthen psychological crisis intervention during emergencies. The findings also provide practical solutions to improve disaster preparedness and intervention efficiency, addressing a key gap in crisis resource management research.

Performance-Based Efficiency and Effectiveness Analysis of Technology Companies Listed on Borsa Istanbul Using Data Envelopment and Logistic Regression Analysis

This study analyses the performance-based efficiency and effectiveness of technology companies listed on Borsa Istanbul using Data Envelopment Analysis (DEA) and Logistic Regression (LR). The research focuses on evaluating the relative efficiency of selected technology firms over the period 2015-2023. DEA, a non-parametric technique, is used to measure the efficiency of decision-making units, while logistic regression helps identify the factors influencing efficiency. The study aims to assess the impact of financial ratios, such as current ratio, net profit margin, accounts receivable turnover, and others, on the firms' technical efficiency. The results indicate that Net Profit Margin (NPM) and Accounts Receivable Turnover (ADH) have the most significant positive effects on efficiency, while the Fixed Assets to Equity ratio (MDVOZK) negatively impacts efficiency. This analysis provides insights into the financial health and operational performance of technology firms, offering actionable recommendations for improving efficiency and guiding investment decisions. Key Words: Data Envelopment, Logistic Regression, BİST JEL: C45, C88, G21

The solution of super-scale DEA models based on preference character

The basic relationships and intrinsic laws in the data envelopment analysis (DEA) are the basis for designing fast solutions for big data DEA models. By delving into these relationships, it becomes feasible to address super-scale DEA problems more swiftly, aligning with the need for timeliness in real-world efficiency evaluations and reducing time costs. This paper leverages the intrinsic characteristics of DEA to formulate a DEA model scale reduction approach, offering reliable theoretical support for solving super- scale DEA problems. Initially, the partially ordered set theory to prove the relationship between the efficient decision making units (DMUs) of DEA and the maximal element of the partially ordered set. Building on this, a DEA model based on maximal element set (DEA-PS) is constructed based on the theory of partially ordered set theory, and the equivalence between DEA-PS model and the original DEA model is proved. Furthermore, it is proved that the dominant theorem exists in the solution of multiple classical DEA models, and a method of super-scale DEA models based on dominant theorem is given. The conclusions of this paper theoretically reveal the relationship between efficient DMUs and maximal elements, from this relationship, two key insights emerge: (1) Regardless of how huge the number of DMU is, the proposed method in this paper can realize the scale reduction of the DEA model quickly, (2) The algorithm has good compatibility with all current big data DEA model algorithms.

Assessment of carbon efficiency in wastewater treatment plants through Stochastic non-parametric data envelopment analysis (StoNED): Insights from Spanish facilities

Evaluating the carbon efficiency (CE) of wastewater treatment plants (WWTPs) is crucial for guiding these facilities towards carbon neutrality. Nevertheless, enhancing carbon performance ought not to be pursued to the detriment of pollutant removal efficiency. In this study, the CE of WWTPs is calculated as a composite index that integrates carbon emissions with the volume of pollutants removed from wastewater, including organic matter, suspended solids, nitrogen, and phosphorus. To achieve this, the Stochastic Non-parametric Envelopment of Data (StoNED) method is employed. This approach, distinct from the commonly used Data Envelopment Analysis (DEA), accounts for both empirical data and random variations in the estimation of CE, thereby enhancing the reliability of the CE evaluation. The study assesses the CE of 109 Spanish WWTPs, finding that none of them are fully carbon efficient. This involves that all plants have potential to reduce greenhouse gas emissions without sacrificing pollutant removal efficiency. The average CE of the WWTPs is 0.529, indicating a possible reduction in carbon emissions by approximately 0.076 kg of CO2 equivalent per cubic meter of wastewater treated. The analysis also reveals that neither the volume of wastewater treated nor the type of reactor used for secondary treatment has a significant impact on the CE of the facilities. The CE metric proposed in this study serves as an important decision-support tool for advancing towards the carbon neutrality of wastewater treatment processes. By providing a more comprehensive understanding of the environmental performance of WWTPs, it helps identifying areas for improvement and guiding policy and operational decisions.

EVALUATING PERFORMANCE, COMBUSTION, AND EMISSION CHARACTERISTICS OF WASTE PLASTIC OIL BLENDS IN CRDI DIESEL ENGINES USING DATA ENVELOPMENT ANALYSIS

As fossil fuels run out quickly, researchers studying engines are increasingly interested in alternative fuels. This paper covers the use of plastic paralysis oil as biodiesel in diesel engines. This was done using a four-stroke, multi-cylinder CRDI diesel engine running between 2000 and 2500 rpm. From zero loads to fifty percent load, the performance, combustion, and emission characteristics were computed for a variety of load scenarios. The analysis revealed that the blend D80WPO20 at 2000 rpm reduced hydrocarbon emissions from 0.059 g/kW to 0.045 g/kW, which is lower than that of 25% diesel at 26 kW. At high speed, the blend D80WPO20 at 2000 rpm gave the maximum BTE of 34.53%. The nitric oxide (NOx) emissions from D50WPO50 and diesel at a 26 kW load are 6.48 g/kWh and 5.37 g/kWh, respectively, according to reports. Data envelopment analysis, a multi-response linear programming optimization tool, was used to evaluate the output and emissions of DI diesel engines using waste plastic oil mixtures.

New insights and theoretical conditions for determining minor and major consolidations in the inverse DEA

Abstract Accepted by: Ali Emrouznejad In the current paper, we propose new conditions for determining major and minor consolidation using the inverse data envelopment analysis models. We categorized the causes of major consolidation into pre and post-effect. Pre-effects may happen due to the omission of pre-merged units and post-effects are associated with the fact that the merged unit may not belong to the production set. The idea is described by numerical examples and graphical illustrations and an empirical analysis in banking also helps us to show the necessity, strength, and applicability of the proposed models for real-world applications.