Data Envelopment Analysis Methodology Research Articles

Monitoring sustainable waste management in OECD countries: A Malmquist productivity approach

Sustainable waste management (SWM) practices are crucial for tackling environmental concerns under a circular economy (CE) scope. Thirty-one OECD countries have been evaluated on their national SWM performance via Malmquist data envelopment analysis (DEA) methodology from 2000 to 2021. The Malmquist (MPI) and Malmquist-Luenberger (MLPI) productivity indices are compared based on their total factor productivity (TFP) and its elements (i.e., efficiency and technological changes). Both models have the gross domestic product (GDP) as a desirable output, but MLPI also incorporates municipal solid waste (MSW) generation and greenhouse gases (GHGs, i.e. CO2 and CH4) as undesirable outputs. MLPI has a lower average performance (1.74%) than MPI (4.42%), meaning that the incorporation of waste and GHGs has diminished an OECD country’s SWM performance by 2.5 times on average. In essence, it is innovation that drives TFP in the selected countries. Overall, the synergistic implementation of SWM practices, Malmquist DEA-based methodology, and CE principles is crucial for advancing sustainable development and achieving a circular and resilient future. The manuscript also offers policy implications regarding the need of financial incentives, vocational training, and fostering stakeholder encouragement in the waste sectors to implement CE solutions.

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.

A comparative study on the efficiency of the public and private sector banks post-merger: A Data Envelopment Analysis

The paper compares the operation efficiency of the public service banks against the private service banks post-merger. The paper also makes an attempt to rank the individual banks in the years before and after mergers. Hence, the study taken a period of five years from 2017 to 2022. The researchers employed the data envelopment analysis (DEA) methodology to calculate the efficiency. From this viewpoint, the merger of the public service banks enhanced their performance but still lagged behind the private sector banks.

Background of Data Envelopment Analysis Implementation in Moscow Healthcare System: International and Russian Experience

Background. The efficiency of healthcare organization activity should be improved and measured using new techniques, as the healthcare system has widely applied advanced technologies and optimized the workflow of all medical facilities. The methodology of data envelopment analysis (DEA) is one of the fast-evolving approaches to assessing the performance of healthcare organizations. The article examines Russian and international experience in applying the DEA method, which can be used for assessing the performance of Moscow public healthcare organizations. Purpose. To study the basics of DEA and the experience of its use for assessing the efficiency of healthcare organizations in Russia and abroad, and to consider its potential implementation in the Moscow healthcare system. Materials and methods. The publications were selected using the PRISMA guidelines. A complex of modern general scientific methods of cognition was also used: system and structural approach, analytical method, comparison, and generalization. Results. The practice of using the DEA method in Russian regions and abroad was analyzed. The main advantages and disadvantages of DEA, as well as the main parameters used in constructing the model of efficiency measurement for healthcare organizations, were categorized. The prerequisites for using the DEA method to assess the efficiency of Moscow healthcare organizations were identified.

Efficiency evaluation of waste management: A systematic literature review of DEA applications

This paper aims to implement a systematic literature review to survey the Web of Science scientific database and review papers with an emphasis on the application of Data Envelopment Analysis (DEA) in the efficiency evaluation of waste management. This approach includes the identification, screening, eligibility, and inclusion phase of the relevant papers. The article also provides a theoretical overview of waste management and the DEA methodology. Researching the Web of Science scientific database according to the keywords “waste management”, “waste disposal” and “data envelopment analysis” led to 12 full-text published papers written in English. Qualitative analysis of papers was carried out with an emphasis on the analyzed country, i.e. region, time frame, applied DEA model, and included input and output variables in the model. Research of this type complements the scientific literature on the importance of waste management and provides key guidelines to scientists, policymakers, and authorities on efficient waste management as one of the key problems of today's society.

Environmental and social performance relationships to firm efficiency: Evidence from the semiconductor industry

Escalating global demand for high-performance computing and smart devices has driven rapid expansion in the semiconductor industry. This expansion raises environmental and societal sustainability concerns among stakeholders. Although there is a growing commitment to environmental and social responsibilities by semiconductor companies, the impact of these responsibilities and initiatives on corporate performance remains unclear—especially with respect to general firm operational efficiency. Thus, this study employs a sample of 129 listed Chinese semiconductor companies and a slack-based data envelopment analysis (SBM-DEA) methodology to examine the relationship between corporate environmental and social performance and firm efficiency. We also investigate how supply chain position—an important industrial characteristic—moderates these relationships. The findings reveal that: (1) China's semiconductor industry efficiency is relatively low, particularly in the chip design sub-sector; (2) there is a positive relationship between corporate environmental performance and firm efficiency; and (3) supply chain position moderates the relationship between both corporate environmental and social performance and firm efficiency. These findings offer important implications for managers and researchers aiming to foster greater semiconductor industry sustainable development.

Modelling efficiency in primary healthcare using the DEA methodology: an empirical analysis in a healthcare district

BackgroundPrimary healthcare management efficiency conditions the functioning of specialized care and has a direct impact on the outcomes of the health system and its sustainability. The objective of this research is to develop models to evaluate the efficiency, including health outcomes, of the primary healthcare centres (PHC) of the Clínico – La Malvarrosa Health District in Valencia.MethodsTo evaluate efficiency, Data Envelopment Analysis (DEA) was used with output orientation and variable returns to scale, with panel data from the years 2015 to 2019. In rates per 10,000 inhabitants, the inputs are: medical and nursing staff and pharmacy cost. The outputs are: number of consultations, hospital emergencies, referrals, avoidable hospitalisations, avoidable mortality and pharmaceutical prescription efficiency. As exogenous variables: the percentage of population over 65 years old, over 80 and case-mix. Three models were developed, all of them with the same inputs and different combinations of outputs related to: healthcare activity, outcomes, and both, in order to study the influence of the different approaches on efficiency. Each model is analysed both without exogenous variables and with each of them.ResultsThe efficiency results vary depending on the model used, although certain PHCs are always on, or very close to, the efficient frontier, while others are always inefficient. When healthcare activity outputs are considered, efficiency scores improve and the number of efficient PHCs increases. However, in general, the PHC score decreases throughout the evaluated period. This decrease is more pronounced when only activity outputs are included.ConclusionsDEA allows the inefficiencies of PHCs to be analysed and the efficient ones are clearly distinguished from the inefficient, although different efficiency scores are obtained depending on the model used. Evaluation can be according to healthcare activity, health outcomes or both, making it necessary to identify the expected objectives of the PHCs, as the perspective of the analysis influences the results.

Spatial dynamics of transport accessibility and regional efficiency in the Yangtze River Delta Urban Agglomeration

Transport accessibility is crucial for defining urban clusters and evaluating a region's centrality and connectivity. To foster equitable growth in urban conglomerates, it is crucial to understand the relationship between transport accessibility and regional development. This study introduces the concept of 'regional efficiency'—the effectiveness of converting regional inputs into outputs—determined via the super-efficiency Data Envelopment Analysis methodology, to assess the progression of regional development. Employing a combination of bivariate spatial autocorrelation and spatial regression analysis, this research elucidates the spatial dynamics between transport accessibility and regional efficiency, both at macro and micro levels. A binary Logit model identifies the factors affecting the Moran's I correlation between regional efficiency and transport accessibility. Based on the diverse urban characteristics within a single conglomerate, tailored development strategies are suggested. Focusing on the Yangtze River Delta Urban Agglomeration in China as a case study, four distinct spatial clusters—High–High (HH), Low–Low (LL), High–Low (HL), and Low–High (LH)—are identified, based on the local correlation between regional efficiency and transport accessibility. The findings reveal that regions with a High–High Moran's I correlation between road accessibility and regional efficiency predominantly located in the eastern sectors of the Yangtze River Delta, exhibiting effective regional efficiency. These regions are characterized by superior road accessibility, aligning well with their regional efficiency metrics. For air accessibility, cities classified under the HH category, including Shanghai and its neighboring cities, demonstrate a significant spatial correlation with regional efficiency. Population density and urban tier are key predictors of the significance of the Moran's I correlation between road accessibility and regional efficiency. In contrast, for air accessibility, these factors assume an inverse role. The identified cluster types (HH, LL, LH, and HL) are proposed as predictive indicators for the significance of the Moran's I relationship between air accessibility and regional efficiency.

Discrepancy in efficiency scores due to sampling error in data envelopment analysis methodology: evidence from the banking sector

Background Data Envelopment Analysis (DEA) methodology is considered the most suitable approach for relative performance efficiency calculation for banks as it is believed to be superior to traditional ratio-based analysis and other conventional performance evaluations. This study provides statistical evidence on the sampling error that can creep into performance evaluation studies using the DEA methodology. Inferences are drawn based on samples, and various preventive measures must be taken to eliminate or avoid sampling errors and misleading results. This study demonstrates the possibility of sampling error in DEA with the secondary data available in financial statements and reports from a sample set of banks. Methods The samples included 15 public sectors and five leading private sector banks in India based on their market share, and the data for calculating efficiencies were retrieved from the published audited reports. The sample data was collected from 2014 to 2017 because the banking sector in India witnessed a series of mergers of public sector banks post-2017, and the data after that would be skewed and not comparable due to the demonetization policy implementation and merger process-related consolidation implemented by the Government of India. The efficiency measures thus computed are further analyzed using non-parametric statistical tests. Results We found statistically significant discrepancies in the efficiency score calculations using DEA approach when specific outlier values. Evidence is provided on statistically significant differences in the efficiencies due to the inclusion and exclusion of particular samples in the DEA. Conclusion The study offers a novel contribution along with statistical evidence on the possible sampling error that can creep into the performance evaluation of organizations while applying the DEA methodology.

Integrating the water-energy-food nexus and LCA + DEA methodology for sustainable fisheries management: A case study of Cantabrian fishing fleets

The fishing sector constitutes an important source of economic revenue in northern Spain. In this context, various research studies have focused on the application of the five-step Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) methodology to quantify environmental impacts of fishing systems. However, some of them have used environmental indicators that focus on individual environmental issues, hindering the goal of achieving integrated resource management. Therefore, in this study, the Water-Energy-Food (WEF) Nexus is employed as an integrative perspective that considers the synergies and trade-offs between carbon footprint, energy requirements, and water demand.The main objective of this study is to evaluate the operational efficiency and environmental impacts of Cantabrian fishing fleets. To this end, the combined use of LCA and DEA, along with the WEF Nexus, was applied to the Cantabrian purse seine fleet. DEA matrices were generated using the LCA-derived WEF nexus values as inputs to calculate efficiency scores for each vessel. Subsequently, based on the efficiency projections provided by the DEA model, a new impact assessment was performed to understand the eco-efficiency and potential environmental benefits of operating at higher levels of efficiency within this fleet.The average efficiency of the fleet was above 60 %. Inefficient units demonstrated a greater potential to reduce their environmental impacts (up to 65 %) by operating according to efficiency projections. Furthermore, the results revealed a strong dependence of environmental impacts on one of the operational inputs, i.e., fuel consumption. These findings highlight the significance of embracing holistic approaches that combine technical, economic, and social factors to achieve a sustainable balance in fisheries systems. In this regard, the five-step LCA + DEA method applied in conjunction with the WEF Nexus emerged as a suitable tool for measuring operational and environmental objectives.

Multi-choice conic goal programming model-based network data envelopment analysis

In multi-stage processes, classical Data Envelopment Analysis (DEA) acts like a black box and measures the efficiency of decision-making units (DMUs) without considering the internal structure of the system. Unlike classical DEA, recent studies have shown that the overall system efficiency scores are more meaningful if researched using the Network DEA (NDEA) methodology. NDEA performs simultaneous efficiency evaluations of sub-processes and the entire system. Recently, the composition method integrated with multi-objective programming (MOP) has been preferred by many authors to alleviate the drawbacks of earlier methods such as decomposition, slack-based measure (SBM) and the system-centric approach. This study proposes a novel approach incorporating Multi-Choice Conic Goal Programming into the NDEA (MCCGP-NDEA). It provides a more accurate representation of the Pareto front by revealing potential Pareto optimal solutions which are overlooked by the composition methods. Due to its ability to modify stage weights based on the decision makers’ (DMs) preferences, it is likely to gather more samples from the Pareto surface. Computational results on available benchmark problems confirm that the proposed MCCGP-NDEA is a viable alternative to existing methods.

Wind turbine end-of-life options based on the UN Sustainable Development Goals (SDGs)

The installed wind power capacity is rapidly growing worldwide, and large volumes of waste materials would need to be treated due to the decommissioning of wind turbine systems in the next years. The purpose of the present work is to evaluate the sustainability performance of decommissioning options for wind turbines and suggest policies to improve the sustainability of the wind turbine end-of-life phase by jointly applying Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) methodologies. Unlike most of the relevant literature, which focuses mainly on technical aspects of wind turbine decommissioning, the proposed approach considers all three dimensions of sustainability using Sustainable Development Goals (SDGs) as a guide to identify economic, environmental, and social indicators. The methodology is applied to a representative case study of a wind turbine operating in the Greek territory using real data. Repurposing was found to be the most sustainable end-of-life alternative for composite waste. In contrast, the waste treatment option of the foundation concrete contributes substantially to the sustainability performance of the examined scenario. The sustainability performance of these technologies could be enhanced by the operation of dedicated concrete and composite materials recycling facilities close to wind parks, following cross-sectoral approaches. The results also indicate that the social aspects of the sustainability framework are equally important and should be considered when strategies towards more sustainable waste management of wind turbine systems are designed.

Sustainable Governance for Promotional Market Competitiveness in Chinese Airports after COVID-19

Like many countries, China’s airports faced severe operational challenges after the COVID-19 outbreak. However, this predicament has been better mitigated by the Chinese government’s strong support for the aviation industry. However, whether this supportive policy can ensure the sustainable development of the industry needs to be further evaluated. In order to better understand the role of government promotional policies, this paper attempts to explore the driving mechanisms behind the sustainable development performance of Chinese airports. Using the bootstrap Data Envelopment Analysis (DEA) methodology, this study evaluates the operational efficiency of 43 major airports over the period from 2015 to 2022. The efficiency scores are compared with the regional distributions of supply and demand factors, as well as the different sizes based on their annual passenger throughput. The overall operational efficiency showed a fluctuating trend of an initial increase followed by a decline due to COVID-19, with significant regional disparities in airport efficiency. Regional differences are evident, with the eastern region having the highest efficiency value of 0.719, while the central region has the lowest efficiency of 0.594. The findings show that airports in the eastern region perform the best due to the proactive market support with rapidly increasing logistics demand, while airports in the west and northeast regions supported by the strong promotional policies follow closely in terms of operational efficiency. In contrast, airports in the central region are much less efficient due to the lack of market or policy support. To analyze more precisely this conflict on the governance, further analyses show that airport size also has a significant impact on the operational efficiency. Larger airports are typically more efficient, while smaller airports are less efficient, implying the market demand is slightly better to promote the airport operational efficiency than the promotional policies. These findings help us to gain a deeper understanding of the mechanism in the sustainable governance behind the operational efficiency of airports and suggest that more market-oriented appropriate policy direction is required to enhance the competitiveness of the aviation industry. These findings offer valuable insights for policymakers and stakeholders of the developing countries as well, underlining the necessity not for supply-driven policies, but for the demand–pull efforts to optimize resource allocation and adapt to evolving market dynamics.

Performance evaluation of the global airline industry under the impact of the COVID-19 pandemic: A dynamic network data envelopment analysis approach

The COVID-19 pandemic posed unprecedented challenges to the airline industry, necessitating a focus on maintaining high efficiency for profitability. This study assesses the efficiency of 26 international airlines from 2019 to 2022 using a dynamic network data envelopment analysis (DNDEA) methodology. The model accounts for the dynamic effect between two consecutive periods and incorporates an internal structure to evaluate airline performance across multiple dimensions. It enables the assessment of overall, period-specific, and stage-specific efficiencies. The findings reveal that while overall efficiency is moderately high on average, no airline achieved full efficiency during the pandemic. Efficiency decreased notably from 2019 to 2020, with a partial recovery but not a return to pre-pandemic levels by 2022. Operational performance remains satisfactory and stable, while service and financial performance exhibit lower efficiency, especially among low-cost airlines compared to full-service counterparts. Additionally, the study explores airlines' environmental impact by considering greenhouse gas emissions. Comparative analysis with a dynamic DEA model without internal structure highlights theoretical contributions, and the study offers managerial insights for airline leaders and policymakers.

Evaluating Technical and Scale Efficiencies of Cooperative Banks in Jammu and Kashmir

This article examines the performance of cooperative banks in Jammu and Kashmir (J&K) during 2005–2018. We use data envelopment analysis (DEA) methodology to obtain technical and scale efficiency scores for each bank over the years. Income-based approach has been employed while selecting our inputs and outputs. Our results indicate a large asymmetry as far as the performance across different banks is concerned. The dominant source of the technical inefficiency in the cooperative banks is found to be scale related. Further, the results from returns to scale suggest that majority of the cooperative banks in J&K are operating on the zone of decreasing returns to scale.

Measuring countries relative efficiencies in using development assistance: a data envelopment analysis approach

Despite the presence of a considerable corpus of literature investigating the impact of aid on nations' development, the efficiency of utilizing this finite pool of development finance remains ambiguous. The main aim of this study is to address the existing research gap by examining the efficiency of utilizing such development assistance in achieving three specific Sustainable Development Goals (SDGs) from 2002 to 2020 using a Data Envelopment Analysis (DEA) methodology. Moreover, this study examines the theoretical underpinnings that show a correlation between the impact of aid on development and the governance and political structure of countries. The findings indicate that the efficiency of development assistance often falls short of optimal, underscoring the necessity for more attention to its administration, particularly in low-income countries. The efficiency of development assistance can be significantly enhanced by organizational improvements, resulting in a significant increase beyond 80%. The confirmation of the robustness of the findings was attained by the application of the bootstrapping methodology. Hence, it is crucial to recognize that while augmenting the levels of development assistance may hold significance, it alone may not be adequate to guarantee efficient utilization in bridging the financial gap required to meet the desired objectives of the SDGs by 2030.

Choice modelling of eCooking adoption by households in Ghana

ABSTRACT The use of electric cooking appliances, also referred to as eCooking devices, presents a practical and sustainable solution for achieving universal access to clean cooking facilities by 2030. A choice modelling analysis on the factors that influence the selection of an eCooker in Ghana has been conducted to facilitate market expansion for organisations seeking to offer clean cooking services. The study utilised Analytical Hierarchy Process (AHP), Fuzzy-TOPSIS, and Data Envelopment Analysis (DEA) methodologies to assess and rank electric cookers based on crucial factors such as kWh consumption rate, cooking efficiency, affordability, time-saving features, ergonomics, space utilisation capabilities, compatibility with cookware types, ease of cleaning and maintenance procedures, accessibility, among others. The rate of consumption was identified as the most critical factor, emphasising the significance of energy efficiency in the decision-making process. The analysis showed that the induction stove was optimal, considering both Fuzzy-TOPSIS and DEA evaluations. Although the induction stove may consume a considerable amount of electricity, its efficiency, performance, and other attributes make it the preferred choice. This study presents a systematic approach to assessing and ranking electric cooking stoves in Ghana, offering valuable insights for both consumers and policymakers.

Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984-2022.

This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals.We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.

Comparative Network Efficiency Analysis of the Airlines in Turkey After Deregulation

Deregulation has significantly developed the civil air transport industry in an ever-globalizing world. Even though deregulation has caused significant structural transformations in airline companies, the effect of deregulation effect on the production, marketing efficiency, and competitiveness of airline carriers worldwide, especially in Turkey, has not been fully revealed yet. Therefore, this study aims to analyze the efficiency of Turkish air carriers after the deregulation process in Turkish civil aviation by dividing the efficiency into production and market efficiency. Production and marketing efficiencies of airlines were estimated using the window network data envelopment analysis methodology. Efficiency analysis results showed production efficiency at 0.887, marketing efficiency at 0.764, and system efficiency at 0.796. Results also indicate that low-cost airlines have a higher production efficiency score (0.918) than full-service airlines (0.825). In comparison, the marketing efficiency of full-service airlines (0.879) is higher than that of low-cost carriers (0.708). The study determined that the system efficiency does not change according to the business model. The system efficiency score of the full-service provider airlines with a larger market share is higher and more balanced. The close and dynamic monitoring of the air transport market and the continuation of operations under a business model incorporating an appropriate marketing mix will increase the marketing efficiency of the airlines.

Sustainable approach towards alternatives for the use of iron ore tailings in the construction sector using Data Envelopment Analysis methodology.

Iron ore tailings (IOTs) need to be properly managed to mitigate the environmental, social, and economic impacts of mining activities. To cope with this issue, we use data envelopment analysis (DEA) to evaluate alternatives for using IOT in the construction sector. The classical and weight restriction output-oriented DEA models were used in this analysis. The results show that the ranking of alternatives depends on the aspect being evaluated. Concrete block is the most environmentally friendly alternative when analysing both models. For both social and economic aspects, ceramics produced better results in the classical model, whereas Portland cement showed better outcomes in the weight restriction model. In this sense, the results suggest great potential for the use of IOT in the construction sector, enabling the reduction of risks and social and environmental impacts of tailings dams.