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In a ranked voting system, voters select a subset of candidates and rank them from most to least preferred. Data envelopment analysis (DEA)-based voting models, among others, are used to determine the rank-position weights most favorable for each candidate, with the goal of achieving the highest aggregate score. However, concerns have been raised about the weights assigned to each rank position, as well as the potential for rank reversal of some candidates resulting from changes in votes earned by other candidates. To address these issues, some authors have developed two improved models. These models aim to incorporate the constraints of candidates that are not being evaluated into a single restriction, preventing inefficient candidates from influencing the order of efficient candidates. Moreover, these models treat the parameters used to make the distance between successive ranks as variable weights, and calculate average efficiency scores of candidates while considering the entire range of parameters. In this study, we revisit the two improved models and explore an alternative approach based on results from linear algebra and convex analysis, which is more intuitive and easier to understand. Furthermore, we provide closed-form optimal solutions for DEA-based voting models that share the common goal of maximizing the distance between successive ranks while considering both efficiency-related and weight constraints. The analysis of these four models offers a better understanding of their similarities and differences. [ABSTRACT FROM AUTHOR]

Purpose: This study provides a bibliometric analysis of bank efficiency and competition over the past years (from 1993 to 2022) to (1) discover the past and current state of knowledge on bank competition and efficiency, (2) identify leading and authoritative journals and scholars who made significant contributions to the distribution of knowledge and impact, (3) identify nations that made a significant contribution and impact to the literature and (4) identify the structure of collaboration that exists between scholars in the areas of bank competition and efficiency and key thematic areas. Design/methodology/approach: A total number of 868 documents made up of articles, reviews, book chapters, book and conference papers from the Scopus database were gathered. This study used a bibliometric analytic approach. Findings: The number of documents on bank competitiveness and efficiency has increased significantly, as have their total publications, citations and national output. Additionally, the most esteemed and prestigious academic journals of eminent academics who have had a significant impact on the dissemination of knowledge on bank efficiency and competition literature champion papers on banking efficiency and competition. In terms of citation performance and collaborative efforts, the United States tops the developed countries, led by China, which is also the most productive. Additionally, single-country publications predominate in the literature, with China ranking first among the top five countries with corresponding authors. While the Lerner index, H-statistic, concentration index and market power were used to measure bank competitive behaviour, the data envelopment analysis approach predominates efficiency estimation techniques that are linked to cost, profit or revenue, scale, technical and productivity indexes. Originality/value: This study is one of the first to offer bibliometric evidence of both bank competition and efficiency. It also offers proof of the distribution of knowledge and intellectual structure of the concepts and concerns in bank competition and efficiency. [ABSTRACT FROM AUTHOR]

Industrial economic activities produce pollutants and environmentally sustainable production systems in forestry aim to minimize these undesirable outputs while maintaining high production and economic growth. In this contribution, we assume that in addition to plot-specific inputs and outputs, there are some contextual variables that may be exogenously fixed or may be under the control of the decision-makers. In this sense, we first propose a novel and practical approach to calculate environmental efficiency by reducing undesirable products. Then, we utilize an inverse data envelopment analysis (IDEA) model to effectively manage and reduce CO2 emissions. In doing so, the applied models have been utilized to evaluate the efficiencies of 89 forest plots in the USA. Given our estimations in a real application to the forest plots, the study revealed that the average environmental efficiency score is nearly 0.75 (out of 1). However, there is potential for improvement by adjusting the impacts of contextual factors, which could raise the score to approximately 0.8. Furthermore, the analysis indicates a positive correlation between ownership and environmental efficiency, suggesting that increased ownership leads to higher environmental efficiency. Conversely, temperature exhibits a negative correlation with environmental efficiency. Finally, the results obtained from the IDEA indicate that in order to reduce undesirable outputs by a specific level of 5–10%, it is necessary to decrease other inputs and outputs. This is because, under the assumption of weak disposability, reducing the level of undesirable outputs requires a reduction in certain factors that influence production capacity. In other words, achieving the desired reduction in undesirable outputs inevitably involves diminishing certain aspects of the production process. As the major conclusion, the emergence of IDEA as a powerful tool for sensitivity analysis, along with its flexible nature, offers exciting opportunities for research and practical applications in various fields, including forestry activities. It has the potential to enhance overall environmental efficiency and enable better control over GHG emissions levels. [ABSTRACT FROM AUTHOR]

Annual preventive care is essential for diabetes patients to reduce the risk of complications including hypoglycemic events and blindness. Our aim was to examine the relative efficiency of Diabetes Belt (DB) and non-Diabetes Belt (NDB) counties in providing recommended preventive care for Medicare beneficiaries with diabetes using available health professional resources and to understand county-level socioeconomic factors associated with inefficient provision of preventive care. A data envelopment analysis (DEA) model was developed to assess relative efficiency of counties in providing diabetes preventive care. Logistic regression was performed to identify socioeconomic characteristics associated with inefficiencies. We used Medicare claims data to extract individual-level information of diabetes preventive service use and obtained county-level estimates of health resources information from the Area Health Resources File. More than 80% of counties had more than 10% inefficiencies on average. Compared to counties in the NDB, the odds of being inefficient were 2.44 times more likely in the DB (OR 2.44, CI 1.67–3.58). Counties with lower median income, with a smaller proportion of non-Hispanic Black population, and in a rural area had higher odds of being inefficient in providing preventive care. Our DEA results showed that counties in the DB and NDB were mostly inefficient. The availability of care providers may be less of a problem than how efficiently the resources are used in providing preventive care. Identifying sources of inefficiency within each community with low resource utilization and developing targeted strategies is needed to improve uptake of preventive care cost-effectively. [ABSTRACT FROM AUTHOR]

The match rate of medical schools in the U.S. has been extensively studied from the perspective of applicants and influential factors. A method to objectively estimate the efficiency of a medical school's match rate has not been described in the literature. Such a method constitutes a significant improvement opportunity for medical schools via benchmarking best practices. This research fills the gap and proposes a bootstrap data envelopment analysis (DEA) framework to assess the residency match rate efficiency of medical schools. The efficacy of the proposed method is confirmed when benchmarking the Texas allopathic medical schools to representative samples of allopathic medical schools in the United States. The model allows to determine the statistical significance of differences in the residency match rate efficiency between groups of medical schools. The proposed bootstrap DEA approach is used to estimate the real efficiency's density function of 40 medical schools in the U.S. over the 2018–2020 period. The aggregate efficiency estimation showed that the medical schools are performing at a high competitive level; they have experienced a slight decline in scale efficiencies and have preserved high managerial performance. The study measured four groups: Texas medical schools, top ten ranked, middle ten ranked, and bottom ten ranked U.S. medical schools. The overall major improvement opportunity for medical schools is the scale of operations. Results confirm that medical schools are shown to be efficient in training future physicians. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis is among the most frequently used methods to create composite indices in sustainability measurement. The present study aims to contribute toward the use of this methodology thanks to its ability either to seek an individual weighting scheme for each destination or to use a common set of weights for all the alternatives. The first alternative is similar to the CCR model, while the other is comprised of two parts: one that maximizes the total efficiency of the units and the other that minimizes the minimum efficiency. The study was conducted in Cuba using both parts, objective and subjective indicators attained from the regional proposal known as the Caribbean Zone of Sustainable Tourism. The results demonstrate the reliability of the selected indicators to measure tourism sustainability, the feasibility of the proposal for being combined with other approaches in supporting the decision-making process, and the great explanatory power of the outcomes. [ABSTRACT FROM AUTHOR]

The Russian invasion of Ukraine has generated a new impetus towards an enhanced European defence pillar, while many EU members have announced significant increases in their respective defence budgets over the coming years. Expectedly, a large percentage of the capital and technological inputs required to support the efforts towards a common European defence and the increased defence needs of many EU members will be procured from the European Defence Technological and Industrial Base (EDTIB). The European defence companies are among the major suppliers of conventional arms in the world. Their cumulative share of world arms exports is the second largest globally. This paper uses a Benefit-of-the-Doubt (BoD) model via Data Envelopment Analysis (DEA) to comparatively evaluate the sales performance of major European defence industries vis-à-vis other major international arms producers. The sample used in the analysis contains 72 firms and spans the period 2016–2020. The findings indicate that on the whole US defence manufacturers have a dominant presence among the biggest global producers and outrank many major European companies. These findings offer valuable policy recommendations. [ABSTRACT FROM AUTHOR]

We measure the performance of a real-world triadic supply chain (SC) network consisting of suppliers, 3PL, and retailers. Three-stage network slacks-based measure (NSBM) and multi-parametric bias correction generalised directional distance function (MPBC-NGDDF) approach are developed to deal with conventional and non-discretionary inputs, desirable and undesirable linking activities and outputs. The sample consists of 786 combinations of triad members. Using both NSBM and MPBC-NGGDF facilitates a sound SC performance evaluation and identification of optimal inputs and outputs. We also compare the NSBM and MPBC-NGDDF estimates against the network epsilon-based measure (NEBM) efficiencies. This comparative analysis reveals the weaknesses and strengths of each method in the context of a complex SC network. Identifying weaknesses and strengths draws on the appropriateness of optimal solutions and practical implications. The MPBC-NGDDF approach meets both appropriateness criteria. According to this approach, considerable reductions are needed in the number of employees used by suppliers, the production agility, the origin port distance from the suppliers, the destination port distance from the retailers, the time from placing an order to delivery to the retailers' premises, and distribution costs. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently, a PV panel parameters estimation method based in neural network and numerical current predictor methods has been developed. However, in order to further improve the estimation accuracies, a new approach of PV panel parameter estimation is proposed in this paper. The output current and voltage dynamic responses of a PV panel are measured, and the time series of the I–V vectors will be used as input to an artificial neural network (ANN)-based PV model parameter range classifier (MPRC). The MPRC is trained using an I–V dataset with large variations in PV model parameters. The results of MPRC are used to preset the initial particles' population for a particle swarm optimization (PSO) algorithm. The PSO algorithm is used to estimate the PV panel parameters and the results could be used for PV panel health monitoring and the derivation of maximum power point tracking (MMPT). Simulations results based on an experimental I–V dataset and an I–V dataset generated by simulation show that the proposed algorithms can achieve up to 3.5% accuracy and the speed of convergence was significantly improved as compared to a purely PSO approach. [ABSTRACT FROM AUTHOR]

This study employs modified data envelopment analysis (DEA) models and spatial autocorrelation methods to analyze the characteristics of red tourism transformation efficiency and categorize them into efficiency zones. By utilizing geographic detector models, the interactive driving mechanisms behind spatial differentiation are revealed, providing valuable insights for the high-quality transformation and development of China's red tourism economy. The application of modified DEA models facilitates the evaluation of red tourism resource transformation efficiency by decomposing comprehensive efficiency into single-factor efficiency for individual input and output variables. The results indicate that: (1) Expansion of tourism factors is crucial for achieving red tourism resource transformation in China, with low efficiency in resource endowment investment acting as the primary constraint. (2) Local spatial correlation between production efficiency and resource transformation efficiency demonstrates a decreasing trend from east to west, leading to the classification of China's red tourism resources into five types of efficiency zones. (3) Endogenous ability factors predominantly affect red tourism resource transformation efficiency, with interaction between internal and external factors driving spatial differentiation. [ABSTRACT FROM AUTHOR]

This study assessed the marketing efficiency of two crucial fish species in Egypt, the Nile tilapia (Balti) and mullet (Bouri), contributing 73.4% to the nation's total fish production. Utilizing data envelopment analysis (DEA) and the variable returns to scale model, marketing efficiency for the Nile tilapia ranged from approximately 62.4 to 100%, with an average of 87.5%, while mullet fish exhibited a range of 36.2 to 100%, averaging 83.4%. The findings suggest a marketing efficiency deficit of 12.5% for the Nile tilapia and 16.6% for mullet fish, attributable to factors not considered in the model. To enhance efficiency, increasing marketing margins or fish production is recommended. Scale efficiency analysis revealed that some fishers failed to achieve an optimal production efficiency. The Nile tilapia could increase productivity by 24.1%, through optimization at the minimum point on the average total cost curve (MC = AC). Similarly, mullet fish could increase productivity by 27.4%, using the same approach. Furthermore, 70% of the Nile tilapia volume demonstrated increasing returns to scale (IRS), while 25% exhibited decreasing returns to scale (DRS). For mullet, 80% demonstrated IRS, and 10% exhibited DRS. Identified marketing challenges included high summer temperatures causing significant losses, financial constraints, insufficient liquidity, high labor costs, lack of marketing information, and elevated taxes. Recommendations include providing comprehensive marketing information, ensuring well-equipped transportation, attempting to enhance liquidity through lending, and supporting energy to reduce costs for fishing-related activities. [ABSTRACT FROM AUTHOR]

Our objective in this paper is to evaluate the benefits of bus transit. To obtain the more accurate and highly discrimination results, this study establishes an evaluation indicator system from a stakeholder perspective and proposes a novel combined method consisting of dynamic network data envelopment analysis, cross-efficiency evaluation, and Shannon entropy aggregation method. An empirical study of bus transit systems in 33 key Chinese cities from 2016 to 2019 is provided. The results show that the benefit of bus transit in most key cities was ineffective. In contrast to production efficiency, focusing on improving service effectiveness is more conducive to improving the benefits of bus transit. Small cities have higher benefits than large cities. Meanwhile, the benefits of bus transit vary geographically. This evaluation outcome provides a more appropriate basis for decision-making related to the benefits of public transit services, as well as for operational and management studies. [ABSTRACT FROM AUTHOR]

The Solow residual method, traditionally pivotal for calculating total factor productivity (TFP), is typically not applied to green TFP calculations due to its exclusion of undesired outputs. Diverging from traditional approaches and other frontier methodologies such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), this paper integrates undesired outputs and three types of spatial spillover effects into the conventional Solow framework, thereby creating a new spatiotemporal econometric Solow residual method (STE-SRM). Utilizing this novel method, the study computes the industrial green TFPs for 280 Chinese cities from 2003 to 2019, recalculates these TFPs using DEA-SBM and Bayesian SFA for the same cities and periods, and assesses the accuracy of the STE-SRM-derived TFPs through comparative analysis. Additionally, the paper explores the statistical properties of China's urban industrial green TFPs as derived from the STE-SRM, employing Dagum's Gini coefficient and spatial convergence analyses. The findings first indicate that by incorporating undesired outputs and spatial spillover into the Solow residual method, green TFPs are computable in alignment with the traditional Solow logic, although the allocation of per capita inputs and undesired outputs hinges on selecting the optimal empirical production function. Second, China's urban industrial green TFPs, calculated using the STE-SRM with the spatial Durbin model with mixed effects as the optimal model, show that cities like Huangshan, Fangchenggang, and Sanya have notably higher TFPs, whereas Jincheng, Datong, and Taiyuan display lower TFPs. Third, comparisons of China's urban industrial green TFP calculations reveal that those derived from the STE-SRM demonstrate broader but more concentrated results, while Bayesian SFA results are narrower and less concentrated, and DEA-SBM findings sit between these extremes. Fourth, the study highlights significant spatial heterogeneity in China's urban industrial green TFPs across different regions—eastern, central, western, and northeast China—with evident sigma convergence across the urban landscape, though absolute beta convergence is significant only in a limited subset of cities and time periods. [ABSTRACT FROM AUTHOR]

This research explores the nexus between corporate governance and sustainable development, focusing on State-Owned Enterprises (SOEs) in Vietnam. Recognizing the pivotal role of SOEs in the national economy, this study employs a Multi-Criteria Decision-Making approach (MCDM) to assess and enhance the corporate governance frameworks of these entities. First, the Data Envelopment Analysis (DEA) model is employed to identify the most qualified prospective SOEs firms based on several quantitative criteria. Then, the spherical fuzzy analytic hierarchy process (SF-AHP) model is used to identify priority weights for a given set of qualitative criteria, the Evaluation based on distance from average solution (EDAS) model is implemented to rank enterprises in the SOEs sector. To validate the proposed models, a case study conducted within the Vietnamese electric power industry is utilized. The MCDM methodology integrates diverse factors such as business management, corporate social responsibility, and corporate governance shareholder to construct a comprehensive evaluation framework. By applying this approach, the study aims to identify the key drivers and barriers influencing corporate governance practices within Vietnamese SOEs. The study's findings illustrate the efficacy of the suggested approach in evaluating corporate governance performance, providing valuable insights for policymakers, corporate leaders, and stakeholders involved in shaping the governance landscape of SOEs in Vietnam. By aligning corporate governance with sustainable development principles, the research aims to contribute to the ongoing discourse on responsible business practices, offering practical recommendations to enhance the performance and resilience of SOEs in the pursuit of long-term socio-economic and environmental sustainability. [ABSTRACT FROM AUTHOR]

This paper extends the literature on economic consequences of corruption. We simultaneously consider corruption's impacts on output, production efficiency, and environmental performance in an eco-efficiency framework. Pollution is considered as undesirable output in the production process. We construct an eco-efficiency measure from a non-oriented data envelopment analysis model that incorporates a non-convex metafrontier, super-efficiency, and undesirable outputs into a directional distance function. By using panel data from 30 Chinese provinces from 1998 to 2016 in a spatial analysis, we find that corruption, measured by filed-cases of corruption-related crimes, has a significantly negative direct impact on eco-efficiency. The regional spillover effect is not significant for provinces in the same geographical region, but is marginally significant for provinces sharing a common border. [ABSTRACT FROM AUTHOR]

The banking industry is one of the most important pillars of the economy of any country, which plays a decisive role in the economic development and growth of countries due to the provision of various financial and credit services. With the globalization of the banking industry, it is very important to use income diversification strategies to improve value and efficiency. Using the data and information obtained from the financial statements of banks, this research has investigated the effect of income diversification strategies on efficiency in banks admitted to the Tehran Stock Exchange. The statistical population of this research includes all the banks admitted to the Tehran Stock Exchange during 2011-2021, and 10 banks were selected as a statistical sample using the targeted elimination method. In this research, the data envelopment analysis (DEA) approach was used to measure efficiency, and the autoregressive model approach with distributed intervals (ARDL) was used to estimate the research model. The present research findings showed that the strategy of income diversification (interest and non-interest) both in the short term and in the long term has a positive and significant effect on the efficiency of banks admitted to the Tehran Stock Exchange. [ABSTRACT FROM AUTHOR]

This study constructs a composite indicator system covering the core dimensions of medical equipment input and output. Based on this system, an innovative cone-constrained data envelopment analysis (DEA) model is designed. The model integrates the advantages of the analytic hierarchy process (AHP) with an improved criterion importance through intercriteria correlation (CRITIC) method to determine subjective and objective weights and employs game theory to obtain the final combined weights, which are further incorporated as constraints to form the cone-constrained DEA model. Finally, a bidirectional long short-term memory (Bi-LSTM) model with an attention mechanism is introduced for integration, aiming to provide a novel and practical model for evaluating the effectiveness of medical equipment. The proposed model has essential reference value for optimizing medical equipment management decision-making and investment strategies. [ABSTRACT FROM AUTHOR]

The quest to eradicate poverty, central to the United Nations Sustainable Development Goals (SDGs), poses a significant global challenge. Advancement in sustainable rural development is critical to this effort, requiring the seamless integration of environmental, economic, and governmental elements. Previous research often omits the complex interactions among these factors. Addressing this gap, this study evaluates sustainable rural development in China by examining the interconnection between agricultural production and government-led poverty reduction, with annual rainfall considered an influential factor of climate change impacts on these sectors and overall sustainability. Utilizing a Meta-frontier entropy network dynamic Directional Distance Function (DDF) within an exogenous Data Envelopment Analysis (DEA) model, we categorize China's 27 provinces into southern and northern regions according to the Qinling-Huaihe line for a comparative study of environmental, economic, and governmental efficiency. This innovative approach overcomes the limitations of previous static analyses. The findings reveal: (1) Rainfall, as an exogenous variable, significantly affects agricultural production efficiency. (2) The overall efficiency in both southern and northern regions increases when accounting for rainfall. (3) Government effectiveness in poverty reduction is comparatively lower in the northern region than in the southern region when rainfall is considered. These insights underscore the importance of including climatic variables in sustainable development policies and emphasize the need for region-specific strategies to bolster resilience against climatic challenges. [ABSTRACT FROM AUTHOR]

Since the 1990s, market liberalization of the electricity industry has advanced all around the world. To survive in the drastically changing business environment, incumbent electric utility companies have conducted operational reforms, including Mergers and Acquisitions (M&As), to enhance and/or complement existing business capabilities. The purpose of this study was to measure the operational efficiencies of 31 of the world's largest electric utility companies using data from 2010 to 2020 and examine regional differences in and the impacts of M&As on the efficiencies. For this purpose, we applied a new type of Data Envelopment Analysis (DEA) and Tobit model regression. We provide findings from the empirical analyses and discuss the business implications of M&As for electric utility companies. The operational efficiency measures were different among regions, but did not show statistically significant changes over the study period from 2010 to 2020. Furthermore, the results of regression analyses indicate that the increasing number of M&A buying transactions and M&A total transactions has a negative marginal impact on the operational efficiency or leads to a lower operational efficiency for utility companies. Since electricity utility companies have not received gains in operational efficiency from increasing the number of M&A transactions, they need to be more cautious about whether M&A transactions can provide value to the operation and technology management. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine a productive efficiency index that incorporates two new production factors of sustainability—an environmental variable as an undesirable output and a well-being indicator as a desirable output—for 12 years of data from 2007 to 2018 pertaining to 47 prefectures in Japan. This study proposes a combination of a new data envelopment analysis (DEA) intermediate approach with the DEA super-efficiency model to measure the integrated productive efficiency. The approach incorporates CO2 emissions and a well-being indicator into the conventional productivity index. A three-stage analysis is conducted by sequentially adding new factors, CO2 emissions, and a well-being indicator. We also conduct a club convergence analysis of the productive efficiency and observe how clubs are formed, what their characteristics are, and how the efficiency changes over time. Through these approaches, we examine the practicality of the new efficiency measure and discuss regional policy implications. We found that higher labor productivity and carbon productivity in major industries caused increased productive efficiency. Adding sustainability factors to the conventional production factors in efficiency measurement widened the efficiency gap among prefectures. [ABSTRACT FROM AUTHOR]

This swift progression of urbanization has led to increasingly prominent conflicts over the use of land, particularly around its supply and demand. Researchers, both in China and internationally, have underscored the inherent interconnection between urbanization and land utilization. This relationship has gradually become more complex with the development of urbanization. With the implementation of the Yellow River Basin's strategy to preserve the environment while ensuring high-quality development, the Yellow River Basin has become a focal point of attention for numerous scholars. This study centers on the 57 county-level administrative divisions within the Gansu segment of the Yellow River Basin. We employed an extensive array of methodologies, such as GIS technology, the entropy method, data envelopment analysis, the coupling coordination degree model, and the panel vector autoregressive model. We established an index system and a measurement model to evaluate the degree of urbanization and the efficiency of land use. We also investigated the coupling coordinated dynamics between these two variables, to further explore the dynamic interplay between urbanization and land use and reveal their underlying mechanisms. The conclusions are as follows. The urbanization level and efficiency of land use in the Gansu section of the Yellow River Basin have exhibited a consistent upward trajectory, albeit at levels that are not particularly high, indicating substantial room for improvement in the future. The level of coupling coordination between urbanization and land use efficiency in the Gansu section of the Yellow River Basin has shown a generally upward trend. However, the overall coordination level remains relatively low, characterized by an imbalance, with "high coupling but low coordination". Regarding spatial distribution patterns, considerable disparities exist in the level of coordination development, which generally decreases from the eastern toward the western regions. A strong reciprocal and interactive relationship exists between the urbanization level and land use efficiency. An elevated level of economic urbanization can initially stimulate land use efficiency. Similarly, the improvement in the level of population urbanization, social urbanization, and ecological urbanization tends to exert a restraining influence on the augmentation of land use efficiency. Conversely, the enhancement of land use efficiency makes a distinct contribution to promoting the elevation of the urbanization level. [ABSTRACT FROM AUTHOR]

The capacity of transport infrastructure is one of the very important tasks in transport engineering, which depends mostly on the geometric characteristics of road and headway analysis. In this paper, we have considered 14 road sections and determined their efficiency based on headway analysis. We have developed a novel interval fuzzy-rough-number decision-making model consisting of DEA (data envelopment analysis), IFRN SWARA (interval-valued fuzzy-rough-number stepwise weight-assessment-ratio analysis), and IFRN WASPAS (interval-valued fuzzy-rough-number weighted-aggregate sum–product assessment) methods. The main contribution of this study is a new extension of WASPAS method with interval fuzzy rough numbers. Firstly, the DEA model was applied to determine the efficiency of 14 road sections according to seven input–output parameters. Seven out of the fourteen alternatives showed full efficiency and were implemented further in the model. After that, the IFRN SWARA method was used for the calculation of the final weights, while IFRN WASPAS was applied for ranking seven of the road sections. The results show that two sections are very similar and have almost equal efficiency, while the other results are very stable. According to the results obtained, the best-ranked is a measuring segment of the Ivanjska–Šargovac section, with a road gradient = −5.5%, which has low deviating values of headways according to the measurement classes from PC-PC to AT-PC, which shows balanced and continuous traffic flow. Finally, verification tests such as changing the criteria weights, comparative analysis, changing the λ parameter, and reverse rank analysis have been performed. [ABSTRACT FROM AUTHOR]

Operational risk assessment has received considerable attention in bank risk management. However, current assessment methods are primarily designed to assess the risk profile of individual banks. To enable cross-bank operational risk assessment, we propose an integrated AHP-DEA (analytic hierarchy process–data envelopment analysis) method. This method determines the importance of assessment criteria by calculating the weighted sum of rank votes after obtaining the importance values for specific rankings with DEA. This procedure replaces the pairwise comparisons in AHP and addresses the challenge of traditional AHPs in determining appropriate importance values when dealing with a large number of indicators. We applied this method to assess the operational risks of three Chinese commercial banks, and the empirical results indicate that this integrated AHP-DEA method is simple and user-friendly, making it suitable for cross-bank operational risk assessment. [ABSTRACT FROM AUTHOR]

Wastewater management decision‐making is complicated because of: (1) a complex regulatory structure, (2) the wide variety of conflicting expectations by stakeholders external and internal to the responsible utility, and (2) constrains including regulatory requirements, available technologies and practices, and customer willingness to pay. This review synthesizes the results from over 200 papers published since 2000 and presents a decision‐making structure and process which is (1) science and fact‐based, (2) reflects sustainability, (3) clear and transparent, (4) inclusive, (5) produces an objective‐oriented decision, (6) scalable, (7) repeatable, and (8) efficient. Tools supporting the decision‐making process are reviewed, including Multi‐Criteria Decision Analysis (MCDA), Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), process modeling, economic assessments, Life Cycle Assessment (LCA), and Social Life Cycle Assessment (SLCA). Ultimately it was determined that engagement of decision‐makers and relevant stakeholders to assess their values and preferences, coupled with supporting data and analyses, is necessary to reach a decision that, critically, has the support needed for it to be implemented. The results demonstrate that an understanding of the components of the decision process, coupled with an orderly process, enables good wastewater management decision‐making. Practitioner Points: A decision‐making structure and process leading to the selection of implementable solutions is presented.The process possesses the following attributes: (1) science and fact‐based, (2) reflect sustainability, (3) clear and transparent, (4) inclusive, (5) produce an objective‐oriented decision, (6) scalable, (7) repeatable, and (8) efficientAn extensive summary and analysis of tools supporting the decision process are provided, including Multi‐Criteria Decision Analysis (MCDA), Data Envelopment Analysis (DEA), Analytic Hierarchy Process (AHP), process modeling, economic assessments, Life Cycle Assessment (LCA), and Social Life Cycle Assessment (SLCA).The critical role of internal and external stakeholders and differentiating their involvement relative to decision‐makers is emphasized. [ABSTRACT FROM AUTHOR]

The objective of this study was to propose an index based on the IPAT model to quantify the performance of countries concerning their emissions, also taking into account the level of climate technology and the level of inequality. In this study, two quantitative techniques were applied to a sample of 100 countries and 21 indicators. The main results were: (a) a 1% increase in social inequality contributes to an increase in emissions levels by 0.105; (b) the 1% increase in climate techs contributes to reducing emissions levels by 0.141; (c) The proposed IPA index shows that only Estonia and Slovenia are the best performers, mainly in overcoming the inequality barrier, while South Africa and Nigeria are the worst performers in reducing their emissions. The results provide evidence that climate policies should contain social targets to improve country performance and should use a representative value to identify their effectiveness. [ABSTRACT FROM AUTHOR]

COVID-19 has affected the health systems of countries. The purpose of this study is to assess hospital resource and bed utilization efficiency during the COVID-19 pandemic in member states of the European Union. The Pabon Lasso model was utilized to assess the efficiency of hospital bed utilization in this particular context. Data Envelopment Analysis was utilized to assess the efficacy of hospitals' resource utilization with respect to the selected input and output variables. The outcomes of Data Envelopment Analysis and Pabon Lasso were contrased. Among the Pabon Lasso parameters, Sweden was determined to have the highest values in the bed turnover rate parameter, Portugal in the average length of stay parameter, and Ireland in the bed occupancy rate parameter. Among the output variables, the highest discharge rate in 2020 and 2021 was observed in hospitals in Bulgaria. The ratio of physicians, as a health manpower, and considered the most important input variable, had the lowest value in hospitals in Cyprus in both years. According to Data Envelopment Analysis, hospitals in Cyprus, Latvia, Sweden, and the Netherlands were found to be efficient in both 2020 and 2021, respectively. According to Data Envelopment Analysis in 2020, %25 of hospitals were efficient, and when it comes to Pabon Lasso model, %70 were efficient. In 2021, %20 of hospitals were evaluated as efficient according to Data Evelopment Analysis, and %75 were evaluated as efficient according to the Pabon Lasso model. There is a need for plans for the efficient use of resources in hospitals against public health crises such as COVID-19. [ABSTRACT FROM AUTHOR]

Efficiency measurement plays an increasingly important role in the regulation and management of railway organizations. Despite its proven usefulness in efficiency measurement, data envelopment analysis (DEA) lacks predictive capability. In order to benefit from their learning and mapping capabilities, machine learning (ML) algorithms have been used as a complementary method to DEA, recently. However, the majority of the existing ML-DEA studies focused on efficiency estimation while disregarding the prediction of DEA projected inputs/outputs toward better performance. This study proposes a novel framework using the adaptive neuro-fuzzy inference system (ANFIS) and the support vector machines (SVM) models in conjunction with the context-dependent DEA model to predict efficiency scores and the best input/output levels for 37 railway companies of OECD countries. Despite drawing on a small sample size, the proposed DEA-ANFIS and DEA-SVM models successfully predicted the efficiency scores and the best output levels of the organizations via approximating the efficient frontiers. [ABSTRACT FROM AUTHOR]

Recent pandemic outbreaks, including the COVID-19 and SARS, have revealed that supply chains (SCs) are unable to respond to such disasters. To mitigate the destructive impacts and improve the performance of SCs, Operations Research (OR) techniques have been applied to address the issues over the last two decades. The objective of this paper is to develop a network data envelopment analysis (NDEA) model to measure the resilience and sustainability of healthcare SCs in response to the COVID-19 pandemic outbreak. In the proposed NDEA model, for the first time, outputs' weak disposability, chance-constrained programming (CCP), the convexity assumption, and the semi-oriented radial approach are aggregated. Moreover, a modified directional distance function (DDF) measure is developed to measure the overall and divisional efficiency scores. Furthermore, the proposed model can deal with different types of data such as integer-valued data, negative data, stochastic data, ratio data, and undesirable outputs. Also, several useful and interesting properties of the novel efficiency measure are presented. Finally, we measure the performance of 28 healthcare SCs to demonstrate the applicability and capability of our proposed approach. [ABSTRACT FROM AUTHOR]

Applications of data envelopment analysis (DEA) often include inputs and outputs that are embedded in some other inputs or outputs. For example, in a school assessment, the sets of students achieving good academic results or students with special needs are subsets of the set of all students. In a hospital application, the set of specific or successful treatments is a subset of all treatments. Similarly, in many applications, labour costs are a part of overall costs. Conventional variable and constant returns-to-scale DEA models cannot incorporate such information. Using such standard DEA models may potentially lead to a situation in which, in the resulting projection of an inefficient decision making unit, the value of an input or output representing the whole set is less than the value of an input or output representing its subset, which is physically impossible. In this paper, we demonstrate how the information about embedded inputs and outputs can be incorporated in the DEA models. We further identify common scenarios in which such information is redundant and makes no difference to the efficiency assessment and scenarios in which such information needs to be incorporated in order to keep the efficient projections consistent with the identified embeddings. [ABSTRACT FROM AUTHOR]

Developing China's high-tech industry is important to make the country innovation-oriented. This requires optimising the allocation of innovation resources and improving innovation efficiency. However, few studies have investigated this topic and the realisation path for the high-tech industry. This study develops an input-oriented inverse data envelopment analysis (DEA) model with frontier changes to analyse the optimisation of resource allocation in China's high-tech industry during 2019–2025. With this method, decision makers can scientifically analyse the specific amount of resource investment. We also construct an analysis framework from short- and long-term perspectives. The results show that the excessive input of research and development (R&D) personnel and unbalanced allocation of capital resources are the main barriers to the development of high-tech industries in the short term, and in the mid- and long terms, the demands for investment in talent and capital will continue to increase. Improvement directions for promoting the development of China's high-tech industry are discussed. Finally, we present valuable information for policymaking to promote progress in high-tech industries in different regions. [ABSTRACT FROM AUTHOR]

Park-and-ride (P&R) facilities, when combined with the advantages of urban rail transit, have the potential to alleviate congestion problems and promote multimodal transportation. However, some facilities face operational and utilization issues in terms of efficiency and capacity, which may not fully meet users' needs. Therefore, it is essential to conduct research that addresses challenges related to user parking and facility operation. A critical step in this direction is to study relevant indicators that comprehensively measure the operational efficiency and capacity of the facilities, and to find effective methods to tackle problems in P&R operation. This study examined and selected data indicators for P&R facilities around targeted stations, taking those around Nanjing urban rail transit stations as an example. Data envelopment analysis was employed to establish a P&R operational efficiency and capacity evaluation model. This model was enriched with a comprehensive evaluation of multiple factors that affect P&R, with these factors significantly influencing the recorded evaluation results. By identifying operational issues in P&R facilities around Nanjing urban rail transit stations, this study proposed optimization solutions to enhance their operational efficiency and capacity. The findings can serve as a reference for the coordinated development of P&R facilities and urban rail transit. [ABSTRACT FROM AUTHOR]

This study combines the general equilibrium efficient frontier data envelopment analysis (GEEFDEA) approach, which handles fixed-sum CO2 emissions, with the meta-frontier Malmquist-Luenberger productivity index (MMLPI). We use this novel technique to measure the CO2 emission performance (CEP) of 30 of China's provincial-level thermal power industries during 2006–2020, both statically and dynamically, and reveal the drivers of CEP changes. Our proposed approach considers the fixed-sum feature of CO2 emissions and also captures the impact of technology heterogeneity. The empirical results show that the CEP of China's thermal power industry was good during the sample period, showing average annual increases of 2.42%, mainly due to technical efficiency gains and technological progress. The CEP of most provinces grew at various rates, between 0.23% and 8.38%; and the drivers of CEP changes differ between provinces. Policy implications are discussed, such as the need to strengthen technology innovation and optimize production processes to improve CEP. [ABSTRACT FROM AUTHOR]

While there is a broad agreement in academia and the business community that hydrogen (H2) could significantly contribute to energy policy goals, there is no single shared vision of a sustainable hydrogen supply chain (HSC). The large number of sources for hydrogen production (various fossil fuels, biomass, water) and the different methods for extracting, distributing, and storing it make creating an efficient supply chain a challenge. The commonly used optimization techniques based on linear programming require a new mathematical problem to be formulated and solved for each case. Instead, this study aims to develop an innovative optimization approach for the hydrogen supply chain that does not require complete knowledge of the end-user hydrogen demand and applies to any region. Based on the Network Data Envelopment Analysis (NDEA) method, hybrid Life Cycle Analysis (LCA) and Value Sensitive Design (VSD) approach, the integral eco-efficiency indicator constructed by this approach is a weighted linear combination of economic parameters (CAPEX and OPEX) and environmental parameters (ecotoxicity, oxidation potential, eutrophication potential and climate change). The proposed approach has been tested on 22 different hydrogen supply chain options, confirming its efficiency and ability to inform national and international decision-making. For example, a pan-European decision support system could be based on continuously updated data from the EcoInvent database on environmental parameters of hydrogen production, storage, and transport technologies, and updated data on economic parameters from the European Hydrogen Observatory. [Display omitted] • We propose a new multi-criteria technique to optimise the eco-efficiency of the hydrogen supply chain. • We propose a Value Sensitive Design (VSD) approach to environmental impact selection. • Hydro/Wind/Nuclear energy – PEM electrolysis are the most eco-efficient hydrogen supply chain options. • Thermal generation based on coal – PEM/AWE/SOE electrolysis are the least eco-efficient options for the hydrogen supply chain. [ABSTRACT FROM AUTHOR]

China has steadfastly pledged its dedication to accomplishing carbon peaking by 2030 and achieving carbon neutrality before 2060, a commitment widely recognized as the Dual Carbon Goal (DCG). The proposition of China's DCG presents formidable challenges for the advancement of the transport sector due to its significant contribution to carbon emissions. Consequently, new carbon abatement constraints have been enforced on carbon emissions within the Chinese transport sector. In pursuit of the carbon peaking goal, this research introduces the environmental context-dependent data envelopment analysis approach. The approach is employed to identify sustainable performance targets for the Chinese transport sector at the city level, while considering carbon abatement constraints. These sustainable performance targets not only provide insights on enhancing the environmental performance of these cities with minimum effort, but also offer a comprehensive pathway towards carbon peaking cities in the Chinese transport sector. • A pathway towards carbon peaking cities in the Chinese transport sector is examined. • An environmental context-dependent data envelopment analysis approach is proposed. • Policy implications for the Chinese transport sector are presented. [ABSTRACT FROM AUTHOR]

In this article, we analyse India's transport sector policy. The study examines the two important modes of transport, viz., road and rail, and analyses the efficiency of transport infrastructure from 2011 to 2019. Through Data Envelopment Analysis (DEA), we evaluate modes in terms of the capacity of registered road goods vehicles and railway goods wagons, truck km/train km and energy consumption and corresponding freight traffic. The analysis gives valuable insight into the fact that rail infrastructure is more efficient than road infrastructure. Still, current transport sector policies promote energy-intensive carbon-fuel road vehicles rather than energy-efficient alternative transport modes. The study suggests the changes that may be incorporated in inputs to improve the efficiency of the road infrastructure. The Data Envelopment Analysis (DEA)-Malmquist Index analysis reports the need for total factor productivity improvement in rail transport and the introduction of technological innovations in road and rail transport sector management. The study employs a well-to-wheel (WTW) based index to ascertain the energy consumed and pollutants emitted by freight-carrying road and rail modes between two leading business hubs of India, i.e., Ludhiana and Mumbai. The paper identifies policy gaps and suggests a holistic approach for need-based roadway investment and a time-bound development of railway infrastructure in India. Our study generates valuable implications for India's policy planners and government authorities and provides new insights to other countries. • The study examines road and rail transport, and analyses the efficiency and energy consumption of these modes. • The study suggests the changes to improve the efficiency of the road infrastructure. • The Data Envelopment Analysis (DEA)-Malmquist Index analysis and well-to-wheel (WTW) based index have been employed for the analysis. • The paper suggests a need-based roadway investment approach and a time-bound development of railway infrastructure. [ABSTRACT FROM AUTHOR]

Accepted by: Prof. Ali Emrouznejad Non-parametric data envelopment analysis (DEA) is susceptible to the curse of dimensionality, a challenge that can be mitigated through the use of the multi-criteria decision-making (MCDM) method. Conversely, DEA can overcome the limitations of the MCDM method by defining the weights of the decision-making unit to calculate the data envelop. This study addresses this issue by introducing a novel model, the Trigonometric Envelopment Analysis for Ideal Solutions (TEA-IS). TEA-IS combines DEA and the Technique for Order Performance by Similarity to Ideal Solution approaches. The proposed method is employed to assess the efficiency and performance of 367 Chinese banks over a 19-year period using various financial variables. The TEA-IS model leverages machine learning techniques to predict positive or negative outcomes for Chinese banks, taking into account various influencing factors. Our results indicate that TEA-IS scores demonstrate superior discriminatory power and reliability compared with non-parametric and MCDM methods. Furthermore, our findings reveal the presence of synergy amongst Chinese banks and illustrate a pattern of volatility in the Chinese banking industry's performance. Notably, performance improved from 2000 to 2005, declined during the period from 2006 to 2013 and subsequently experienced a recovery until 2018. The majority of Chinese banks in the sample are categorized as medium performers with lower synergy levels. Additionally, the study underscores the positive impact of bank listing and age on bank performance, suggesting that regional banks outperform domestic ones. [ABSTRACT FROM AUTHOR]

The development of rural sports depends on many factors, but the complex causal relationship between various factors and the level of rural sports development is not clear. Using data envelopment analysis (DEA) and fuzzy set qualitative comparative analysis (fsQCA), this study aims to examine the driving role of various factors on rural sports development and construct various grouping paths to improve the level of rural sports development in China. The results shows that the area of fitness venues and social capital participation are sufficient conditions for the development of rural sports in China. Resource endowment through government fing, social capital participation and the construction of sports venues and facilities is the key driving factor for rural sports development in China. There are four grouping paths for the high-quality development of rural sports, which are divided into three configurations by combining the grouping characteristics: the state-social capital jointly controlled type driven by economic development and the resource endowment driven by the modernization of the agriculture promotes production and the grassroots organizations that are supported by the advantage of resource endowment. The results of the study highlight the complex causal relationships and key driving factors of rural sports development in China, highlight the differences in rural sports development patterns in different regions, and provide new ideas and guidelines for improving the level and quality of rural sports development. [ABSTRACT FROM AUTHOR]

Background: Besides the scarcity of resources, inefficient utilization of available health service resources has been the bottleneck to deliver quality health services in Ethiopia. However, Information regarding the efficiency of health service providers is limited in the country. Health service managers and policy makers must be well informed about the efficiency of health service providers and ways of using limited resources efficiently to make evidence-based decisions. This study aimed to assess the level of technical efficiency and associated factors among health centers in East Gojjam Zone, Northwest Ethiopia. Methods: A facility-based cross-sectional study was conducted among 27 randomly selected health centers in East Gojjam zone, Northwest Ethiopia, from October 30, 2022, to April 30, 2023. Using an interviewer-administered questionnaire and document review checklist, health centers' data was collected and entered to Epi-Data version 4.6. The data was exported to Microsoft office excel and Stata version 14 for analysis. A two-stage output-oriented data envelopment analysis with a variable return to scale assumption was employed to determine the level of technical efficiencies. Finally, the tobit regression model was applied to identify the associated factors at 5% level of significance. Results: In this study, 59.3% of the health centers were technically efficient. The mean technical efficiency score of the health centers was 0.899 ± 0.156. Inefficient health centers could provide more 22, 433 outpatient visits, 1,351 family planning visits, 155 referral services, 206 skilled deliveries and 385 fully vaccinations of children if they were technically efficient as their peer health centers for the same year. From the tobit regression, the catchment population and number of administrative staffs were statistically significant determinants of the technical efficiency of health centers. Conclusions: The mean technical efficiency of the health centers in East Gojjam zone, Northwest Ethiopia was high. However, nearly half of the health centers were technically inefficient, which indicates the exitance of a space for further improvements in the productivity of these health centers. Employing excess number administrative staffs (above the optimal level) should be discouraged and selecting appropriate sites where the health centers to be constructed (to have large catchment population coverage) could improve the productivity of health centers. [ABSTRACT FROM AUTHOR]

Higher education evaluation presents itself as a worldwide trend. It aims to improve performance due to its importance for economic and personal growth. Graduate activities are essential for Brazilian research and innovation systems. However, previous studies have disregarded the importance of this educational level and have evaluated efficiency by jointly considering teaching and research or only undergraduate courses. Therefore, this study contributes to Brazilian reality by proving a national graduate activities efficiency evaluation that considers them as a two-stage system (formative and scientific production stages). The study provides three main methodological contributions by presenting a new centralized two-stage dynamic network data envelopment analysis (DNDEA) model with shared resources. Besides measuring efficiency, an efficiency decomposition based on a leader–follower assumption shows managers how much efficiency can alter when one of the stages needs to be prioritized. Finally, a new framework based on modified virtual inputs and outputs provides a bi-dimensional representation of the efficiency frontier. Results indicate the usefulness of the approach for ranking universities, and the need to improve scientific production, highlighting the negative impacts of COVID-19 on the formative process efficiency and showing no significant regional discrepancies regarding performance. [ABSTRACT FROM AUTHOR]

The evaluation research on integrated transport has mainly focused on urban agglomeration or cities. Yet the integration of various modes of transportation is increasingly being seen at smaller scales, such as counties, due to population concentration and rising affluence levels. Consequently, it is becoming necessary to evaluate the efficiency and convenience of transportation services for people at each location in the county space. This study has evaluated the efficiency and equity of integrated transport from a county perspective. With respect to efficiency indexes, in addition to transportation infrastructure investment, transportation energy consumption, transportation turnover, and other general indicators, we also take into account the accessibility of people to various types of hubs in the county. We use the BCC (Banker, Charnes, Cooper) model in DEA (Data Envelopment Analysis) for efficiency evaluation. Besides, we evaluate transport equity by using the Gini coefficient to reflect the population distribution of the county and GIS-based spatial accessibility of each hub site. Finally, we establish an evaluation matrix of efficiency and equity; we employ a spatial overlay method to classify research counties into different quadrants. By comprehensively considering the relationship between efficiency and equity in integrated transport, this study can identify areas for improvement in transportation development. The case study evaluation in Ningbo counties, using data from 2017 to 2020, provides practical questions and constructive suggestions for integrated transport development in these areas. Overall, this research has important implications for local transportation development and could inform policy decisions related to integrated transport. • The research is focused on evaluating the efficiency and equity of integrated transportation. • We expand the evaluation work to include a county-level perspective. • We consider the accessibility of transportation services for residents. • We utilize an efficiency-equity matrix and a DEA model to conduct the evaluation. • This study can assist decision-makers in gaining a comprehensive understanding of the transportation system's advantages and disadvantages at the county level. [ABSTRACT FROM AUTHOR]