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The importance of technology and managerial risk management in banks has increased due to the financial crisis. Banks are the most affected since there are so many of them with poor financial standing. Due to this problem, an unstable and inefficient financial system causes economic stagnation in both the banking sector and overall economy. Data envelopment analysis (DEA) has been used to examine decision-making units (DMUs) performance to enhance efficiency. Currently, with the rapid growth of big data, adding more DMUs will likely require a large amount of memory and CPU time on the computer system, which will be the biggest challenge. As a result, machine learning (ML) approaches have been used to analyze financial institution performance, but many of them have variances in predictions or model stability, making measuring bank efficiency extremely difficult. For this, ensemble learning is commonly used to evaluate the performance of financial institutions in this context. This paper presents a robust super learner ensemble technique for assessing bank efficiency, with four machine learning models serving as base learners. These models are the support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and AdaBoost classifier (ADA) which represent the base learners and their results utilized to train the meta-learner. The super learner (SL) approach is an extension of the stacking technique, which generates an ensemble based on cross-validation. One important benefit of this cross-validation theory-based technique is that it can overcome the overfitting issue that plagues most other ensemble approaches. When SL and base learners were compared for their forecasting abilities using different statistical standards, the results showed that the SL is superior to the base learners, where different variable combinations were used. The SL had accuracy (ACC) of 0.8636–0.9545 and F1-score (F1) of 0.9143–0.9714, while the basic learners had ACC of 0.5909–0.8182 and F1 of 0.6897–0.9143. So, SL is highly recommended for improving the accuracy of financial data forecasts, even with limited financial data. [ABSTRACT FROM AUTHOR]

We estimate the efficiency of health spending in 145 middle and high-income and the potential gains from improving efficiency for a range of health system outputs using Robust Data Envelopment Analysis for 2010–2014 and 2015–2019 and examine associations with health system characteristics. Focusing on Latin American and Caribbean countries, we find large variability in efficiency and overall substantial potential gains in the later period, despite improvements over time. Our results suggest that, for example, improving spending efficiency could increase life expectancy at birth by 3.5 years (4.6%), or slightly more than the 3.4-year improvement in average life expectancy in the region between 2000 and 2015. Similarly, improved efficiency could reduce neonatal mortality by 6.7 per 1,000 live births (62%), increase service coverage by 6 percentage points (8.7%), and reduce the rich-poor gap in birth attendance by 10 percentage points (12.6%). We find that governance quality is positively associated with efficiency. Overall, the findings indicate an urgent need to improve efficiency in the region and substantial scope for realizing the potential gains of such improvements. [ABSTRACT FROM AUTHOR]

Physical reservoir computing (PRC), a brain-inspired computing method known for its efficient information processing and low training requirements, has attracted significant attention. The key factor lies in the number of computational nodes within the reservoir for its computational capability. Here, we explore co-multiplexing reservoirs that leverage both temporal and spatial strategies. Temporal multiplexing virtually expands the node count through the use of masking techniques, while spatial multiplexing utilizes multiple physical locations (e.g., Hall bars) to achieve an increase in the number of real nodes. Our experiment employs a strain-mediated reservoir based on multiferroic heterostructures. By applying a single voltage across the PMN-PT substrate (acting as global input) and measuring the output Hall voltages from four Hall bars (real nodes), we achieve significant efficiency gains. This co-multiplexing approach results in a reduction in the normalized root mean square error from 0.5 to 0.23 for a 20-step prediction task of a Mackey–Glass chaotic time series. Furthermore, the single input and four independent outputs lead to a fourfold reduction in energy consumption compared to the strain-mediated PRC with temporal multiplexing solely. This research paves the way for future energy saving PRC implementations utilizing co-multiplexing, promoting a resource-efficient paradigm in reservoir computing. [ABSTRACT FROM AUTHOR]

Improving energy efficiency by adjusting the structure of energy consumption types is of great significance for reducing carbon emissions in the short term. The present paper constructs new data envelopment analysis models for evaluating energy utilization under different structural conditions and calculating potential emissions reductions. We conducted empirical research on 30 provinces in China from 2003 to 2019—a time frame that coincides with the instituting of China's “coal‐to‐gas” program. Our results show that technological progress is the main way for China to reduce carbon emissions and that it is possible to reduce the total amount of carbon emissions by 35%. Additionally, optimizing the energy consumption structure following the coal‐to‐gas program guidelines could reduce the country's carbon emissions by a further 25%. Finally, this paper provides specific policy recommendations based on the efficiency analysis results to guide each province in reducing carbon emissions under the conditions of energy demand growth. [ABSTRACT FROM AUTHOR]

In interval data envelopment analysis (DEA), the production possibility set is variable, which causes traditional resource allocation optimization methods to yield results with limited reachability. This study aims to improve existing resource allocation optimization models so that they can produce meaningful results when handling interval data. Addressing this topic can enhance the applicability of existing models and improve decision-making accuracy. We grouped decision-making units (DMUs) based on heterogeneity to form production possibility sets. We then considered the characteristics of the worst and best production possibility sets in the interval DEA to establish multiple benchmark fronts. A staged optimization procedure is proposed; the procedure provides a continuous optimization solution, offering a basis for decision-makers to formulate strategies. To illustrate this, we provide a numerical example analysis and a case study on forest carbon sequestration. Finally, by applying our method to China's forest carbon sink data, we show that it better meets the practical needs in China. The practical implication of this procedure is that it provides a basis for decision makers to formulate strategies based on interval data. The theoretical implication is that it extends the application of DEA models to interval data. [ABSTRACT FROM AUTHOR]

Context: The reduced utilisation of inputs in milk production holds significant importance from both sustainability and profitability perspectives. Aim: This study examines the efficiencies of each input separately utilised in specialised milk farms across the European Union (EU) countries and explores the factors that influence input efficiencies by using non-radial data envelopment analysis (DEA) model with Russell measure method. Methods: Data from the dairy report of the EU milk-specialised farms compiled by the European Commission (EC) and the Farm Accountancy Data Network (FADN) covering the period from 2013 to 2020 were analysed. The study employed a DEA model to assess efficiency, with a focus on energy, labour, dairy cows, homegrown feed, purchased feed, and operating costs. Key results: Ireland, Italy, Malta, and the Netherlands demonstrated full efficiency across various aspects of milk production, highlighting their exemplary performance in resource utilisation within the EU. Furthermore, the study identified significant potential for efficiency improvements across the EU countries, with potential reductions ranging from 48.8% to 65.616% in various input usage categories. Conclusions: The analysis underscored the importance of improving input efficiency to enhance competitiveness in the dairy sector. Implications: As milk prices and economic performance improve, specialised milk farms tend to become more efficient in managing operating expenses. However, expanding the land area for forage may lead to diminishing returns in resource efficiency, indicating complex interactions and trade-offs affecting input efficiency. Understanding input efficiency in specialised milk farming is crucial for enhancing sustainability and productivity in the European Union (EU) dairy sector. This study addressed the problem of inefficient resource utilisation in EU milk farming, discovering that countries such as Ireland and the Netherlands achieve full efficiency, whereas others have significant potential for improvement. The findings indicated that improving resource efficiency can lead to significant cost savings, environmental benefits, and increased competitiveness within the dairy industry. [ABSTRACT FROM AUTHOR]

The operating efficiency of fishermen's associations is significant for developing the fishery industry and fishing villages. Since fishermen's associations have economic, financial, and service sectors, this study evaluates their efficiency through multiactivity data envelopment analysis. In addition to exploring the overall efficiency of each fishermen association, this study further investigates the differences in the operating efficiency of each of the economic, financial, and service sectors. Fishermen's associations were divided according to their sizes and geographic areas for analyses and comparisons by considering the differences in their organizational scales and fishing activities. In the empirical study, 24 fishermen associations in Taiwan were selected as the research subjects, and the shared inputs of economic, financial, and service sectors were taken to conform to their practical operation. The overall efficiency of fishermen's associations was affected by their size and mainly varied by economic sector. The research results indicate room for improvement in the overall efficiency of fishermen's associations, but improving the efficiency of different fishermen's associations should occur in various sectors. [ABSTRACT FROM AUTHOR]

This article explores the development and testing of a new metric to assess and improve productivity in primary care clinics. The metric focuses on the value of care rather than just the quantity, and integrates four primary care outputs into an efficiency calculation. The study found wide variation in efficiency across clinics, with only a small percentage classified as efficient. The metric shows promise for supporting improvement initiatives and providing feedback to healthcare system leaders. [Extracted from the article]

In 2015, the United Nations (UN) proposed the 2030 Agenda, a resolution that outlines the global goals for sustainable development. This report focuses on promoting health and well-being, emphasizing the importance of primary healthcare (PHC). However, Brazil faces challenges in managing its healthcare system, exacerbated by fiscal policy changes that have reduced public sector expenditures after 2015. This study was motivated by these recent changes in Brazil's fiscal policy. We evaluated potential fluctuations in total factor productivity (TFP) in 5,524 Brazilian municipalities' primary healthcare from 2015 to 2019. TFP, a measure of efficiency and production technology in PHC, was computed using the bootstrapping Malmquist productivity index (MPI) derived from the Data Envelopment Analysis (DEA) algorithm. This study focused on the economic recession and austerity measures initiated in 2015. Moreover, it's essential to highlight that the SARS-CoV-2 pandemic in Brazil in 2020 brought significant changes to the country's economic, social, and health dynamics. Therefore, this research intentionally concentrated on the pre-pandemic period. Our results revealed that TFP in the PHC sector in Brazil decreased between 2015 and 2019. It was possible to note that three out of every four Brazilian municipalities experienced either stagnation or a decline in the TFP of primary healthcare, with more pronounced challenges in the Midwest and Northeast regions. Looking at the TFP elements, we could see a continuous drop in technical efficiency everywhere in the country, markedly in the Midwest and Northeast. Conversely, there was a considerable uptick in Brazil's technological component. These results implied that there might have been advancements in PHC's technological infrastructure, preventing even less favorable outcomes in the TFP indicator. We concluded that the productivity of primary healthcare in Brazil declined when the 2015 fiscal ceiling was implemented. This decrease in performance was evident in the sluggish growth rate of primary healthcare inputs, a diminution in vaccination coverage indicators, and an increase in hospitalizations for preventable causes. Given that health is a crucial component of social well-being, it is vital to highlight the need for comprehensive efforts to guarantee the effective operation and advancement of primary healthcare services, even during economic downturns. [ABSTRACT FROM AUTHOR]

Despite the potential cost reduction and environmental friendliness of inland lock waterways, there is a noticeable lack of scientific research on their efficiency and performance. Moreover, empirical insights into the impact of waterway locks on logistics systems are insufficient. Similarly, despite extensive research on data envelopment analysis (DEA) in various logistics contexts, the efficiency of waterway locks remains underexplored. This study addresses these gaps by evaluating inland waterway transposition efficiency based on operational aspects and handling capacity. In this study, data from nine primary inland waterway locks in an emerging economy were examined, comparing their relative efficiency by input-output relationships. Efficiency indices, performance objectives, benchmarks, composite efficiency, and separability conditions were calculated and analyzed. Our results highlight external factors like vessel numbers and volume as efficiency influencers. The study uncovers strategies to enhance efficiency, mainly through operational improvements or leveraging performance indices from other units. Empirical findings present opportunities to optimize maritime logistics operations, especially in countries with expansive territories. This is a novel research that introduces efficiency analysis models for inland waterway locks and offers actionable insights for an underexplored research area. Lastly, the article offers promising research opportunities for maritime logistics systems' performance. [ABSTRACT FROM AUTHOR]

Ensuring food security is not only vital to the adequate supply of food in the world, but also the key to the economic development and social stability of various countries. Based on the panel data of 29 provinces in China from 2016 to 2020, this paper selects the number of foodborne diseases patients and iodine deficiency disease patients as reference objects, uses stunting rate of children under 5 years old, malnutrition rate of children under 5 years old, obesity rate of children under 5 years old, and newborn visit rate to measure improving nutrition, proposes Meta Entropy Two-Stage Dynamic Direction Distance Function (DDF) Under an exogenous Data Envelopment Analysis (DEA) model to measure the efficiency of hunger eradication, food security, and improving nutrition under the influence of exogenous variable rainfall. The research results indicate that the sustainability of China's agricultural economy is insufficient, and the focus of attention should be different in different stages. In addition, the average efficiency of the three regions generally shows a decreasing level in the eastern, western, and central regions. In order to improve China's ability to guarantee food security, we must continue to strengthen the construction of agricultural infrastructure, increase policy support for green agricultural production, promote the diversification of agricultural production, and enrich people's agricultural product consumption varieties. [ABSTRACT FROM AUTHOR]

Background: Primary 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. Methods: To 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. Results: The 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. Conclusions: DEA 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Green hydrogen can be generated through electrolysis using electricity from renewable sources, such as wind and solar, to split water into hydrogen. This study evaluates the green hydrogen supply chain (GHSC) upstream process using the two-phase integrated multi-criteria decision analysis (MCDA) framework. In the first phase, the data envelopment analysis (DEA) technique is applied to measure the relative efficiency of provincial alternatives with multiple criteria. The input criteria include provincial area, population density, gross domestic product value, and land cost data. In contrast, the sustainability-based governmental criteria concerning people, prosperity, planet, peace, and partnership indices are used as output criteria. Then, the technique for order of preference by similarity to ideal Solution (TOPSIS) is further applied to evaluate regional districts for Ubonratchathani province, one of the twelve relatively efficient provinces, to obtain the ranking list of potential renewable energy sites in the GHSC. Criteria related to geographic and climate data relevant to the efficiency of solar and wind are, thus, collected and analyzed. Our results show that the top three district areas are Kut Khaopun District, Pho Sai District, and Na Tan District, respectively. Finally, the obtained results are verified to evaluate the robustness of the assessment. Our results offer a strategic and practical analysis for policymakers involved in the energy site appraisal process. [ABSTRACT FROM AUTHOR]

China's economic development has achieved great success in recent years, but the problems of energy scarcity and environmental pollution have become increasingly serious. To enhance the reliability and efficiency between energy, the environment and the economy, sustainable development is an inevitable choice. In the context of measuring sustainability efficiency, a network data envelopment analysis model is proposed to formulate the two-stage process of energy use and operations management. A double frontier is derived to optimize the available energy for sustainable development. Due to nonlinearity, previous linear methods are not directly applicable to identify the double frontier and calculate stage efficiencies for inefficient decision-making units. To address this problem, this study develops the primal-dual relationship between multiplicative and envelopment network models based on the Lagrange duality principle of parametric linear programming. The newly developed approach is used to evaluate the sustainability efficiency of 30 administrative regions in China. The results show that insufficient sustainability efficiency is a systemic problem. Different regions should take different measures to conserve energy and reduce pollutant emissions for sustainable development. To increase sustainability efficiency, regions should support energy-saving and emission-reducing technologies in production processes and strengthen their capacity for technological innovation. Compared with energy use efficiency, operations management efficiency in China has a wider range of changes. During the operations management stage, there is not much difference between the capacity and quantity of each region. Based on benchmark regions at the efficiency frontier, there is an opportunity to improve operations management in the near future. Blockchain technology can effectively improve energy allocation efficiency. [ABSTRACT FROM AUTHOR]

The efficiency and productivity evaluation process commonly employs Data Envelopment Analysis (DEA) as a performance tool in numerous fields, such as the healthcare industry (hospitals). Therefore, this review examined various hospital-based DEA articles involving input and output variable selection approaches and the recent DEA developments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilised to extract 89 English articles containing empirical data between 2014 and 2022 from various databases (Web of Science, Scopus, PubMed, ScienceDirect, Springer Link, and Google Scholar). Furthermore, the DEA model parameters were determined using information from previous studies, while the approaches were identified narratively. This review grouped the approaches into four sections: literature review, data availability, systematic method, and expert judgement. An independent single strategy or a combination with other methods was then applied to these approaches. Consequently, the focus of this review on various methodologies employed in hospitals could limit its findings. Alternative approaches or techniques could be utilised to determine the input and output variables for a DEA analysis in a distinct area or based on different perspectives. The DEA application trend was also significantly similar to that of previous studies. Meanwhile, insufficient data was observed to support the usability of any DEA model in terms of fitting all model parameters. Therefore, several recommendations and methodological principles for DEA were proposed after analysing the existing literature. [ABSTRACT FROM AUTHOR]

Amid rising global demand for renewable energy, geothermal power emerges as a vital, low-carbon solution to enhance energy security and sustainability. Taiwan, strategically located on the seismically active Pacific Ring of Fire, possesses an untapped geothermal potential that is underutilized due to complex site selection challenges. This study specifically addresses the need for a more precise and psychologically attuned site selection process, aiming to optimize the development of geothermal resources in regions with complex geological settings. Utilizing the Modified Bounded Rationality Data Envelopment Analysis (MB-DEA) model, this research integrates traditional DEA with bounded rationality to factor in the risk preferences of decision-makers, offering a novel approach that enhances accuracy in evaluating geothermal sites. This study addresses the critical challenge of accurately selecting geothermal energy sites in geologically complex regions like Taiwan, where traditional methods fall short, aiming to significantly boost the efficiency and effectiveness of geothermal energy exploitation as part of Taiwan's transition to renewable energy sources. Applied to 30 potential sites across Taiwan, our model provides a detailed assessment based on technical, economic, and psychological criteria, revealing variations in site suitability influenced by stakeholder risk attitudes. Key locations such as Datun Mountain, Maoxing, and Taolin consistently rank highly, confirming their robust potential irrespective of risk preferences. At the same time, other sites show marked sensitivity to shifts in decision-making attitudes. This work significantly advances the methodology of renewable energy site selection by demonstrating the utility of incorporating psychological factors into analytical models, which not only refines decision-making processes but also aligns with Taiwan's strategic energy planning goals. This study also underscores the importance of accurate geographical data in complex terrains, suggesting further refinement and dynamic integration of bounded rationality for future research. [ABSTRACT FROM AUTHOR]

As one of the most popular techniques for performance evaluation, Data Envelopment Analysis (DEA) has been widely applied in many areas. However, the self-evaluation used in DEA leaves it open to much criticism. Moreover, most researchers have ignored the fact that reality abounds with uncertainty and have assumed that the data used for evaluation is deterministic and accurate. Both assumptions make it difficult to evaluate the efficiency of real-world production processes correctly and reasonably. In this paper, we propose a series of robust cross-efficiency (RCE) models based on robust optimization theory and cross-efficiency to deal with these problems. First of all, the proposed RCE models allow the conservatism level to be adjusted easily to suit the attitude of the decision-maker towards uncertainty. In addition, the RCE models have better discrimination power than the existing robust CCR models. We present two applications to demonstrate the effectiveness and stability of our models. [ABSTRACT FROM AUTHOR]

Mutual fund (MF) is one of the applicable and popular tools in investment market. The aim of this paper is to propose an approach for performance evaluation of mutual fund by considering internal structure and financial data uncertainty. To reach this goal, the robust network data envelopment analysis (RNDEA) is presented for extended two-stage structure. In the RNDEA method, leader–follower (non-cooperative game) and robust optimization approaches are applied in order to modeling network data envelopment analysis (NDEA) and dealing with uncertainty, respectively. The proposed RNDEA approach is implemented for performance assessment of 15 mutual funds. Illustrative results show that presented method is applicable and effective for performance evaluation and ranking of MFs in the presence of uncertain data. Also, the results reveal that the discriminatory power of robust NDEA approach is more than the discriminatory power of deterministic NDEA models. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is one of the widely used methods to measure the efficiency scores of decision making units (DMUs). Conventional DEA is unable to consider both uncertainty in data and decision makers' (DMs) judgments in the evaluations. This study, to address the shortcomings of the conventional DEA, proposes a new best worst method (BWM)- robust credibility DEA (BWM-RCDEA) model to estimate the efficiency scores of DMUs considering DMs' preferences and uncertain data, simultaneously. First, to handle uncertainty in input and output variables, fuzzy credibility model has been applied. Additionally, uncertainty in constructing fuzzy sets is modeled using robust optimization with fuzzy perturbation degree. In this paper, two new types of RCDEA models are proposed: RCDEA model with exact perturbation in fuzzy inputs and outputs and RCDEA model with fuzzy perturbation in fuzzy inputs and outputs. In addition, to deal with flexibility of weights and incorporating DMs' judgement into the RCDEA model, a bi-objective BWM-RCDEA model is introduced. Finally, the proposed bi-objective model is solved using min–max approach. To illustrate the usefulness and capability of the proposed model, efficiency scores of 39 distribution companies in Iran is investigated and results are analyzed and discussed. Finally, based on the results, recommendations have been made for policy makers. [ABSTRACT FROM AUTHOR]

This study investigates the impact of activity‐based funding (ABF) on the performance of hospitals by exploiting a natural experiment that happened in the state of Queensland, Australia. To examine the outcome of the reform, the performance of hospitals is measured by the technical efficiency estimated from data envelopment analysis (DEA) models. We try to identify the causal effect of ABF on the technical efficiency of hospitals by incorporating difference‐in‐differences approach in the popular two‐stage DEA framework. We find empirical evidence that ABF improves the technical efficiency of hospitals. [ABSTRACT FROM AUTHOR]

Background: Medical diagnostic laboratories are an essential work environment that plays an important role in diagnosing, treating, and being sensitive to diseases. One way to evaluate laboratories' performance is to calculate their efficiency. This study investigates the efficiency of laboratories that are related to health centers in the south of Iran. Methods: This study was conducted in 2021. The input numbers include: the number of technical personnel and the number of cell counters, and the output data includes: the scores obtained from the level 2 health laboratory evaluation list. And efficiency was calculated with DEAP software. The analysis is accomplished by the assumption of input-oriented. Findings: The efficiency of laboratories of Orzueeyeh and Ravar Cities had the highest efficiency with the assumption of variable returns to scale efficiency 1, and the model of all laboratories is the laboratory of Ravar City. The laboratories of Kuhbanan and Rabor cities had the lowest efficiency with the assumption of variable returns to scale efficiency of 0.859 and 0.899, respectively. The average scale efficiency, Variable returns to scale, and constant returns to scale for laboratories in the cities of Kerman province are 0.842, 0.943, and 0.895, respectively. Conclusions: To increase the efficiency of laboratories, significant resources and funds should be used, as well as few studies have been done on the efficiency of laboratories, which requires more attention. [ABSTRACT FROM AUTHOR]

Combining the super‐efficiency model based on data envelopment analysis (DEA) with the Malmquist index model, this paper evaluated the efficiency of the logistics distribution center comprehensively considering the truck traffic restriction and provided decision suggestions to improve the efficiency of the logistics distribution center. This paper takes 20 logistics distribution centers as the research objects and uses economic factors, transportation factors, quality of distribution center business activities, and quality of customer service as the primary input indicators; selects eight indicators such as construction cost, transportation cost, labor cost, road facilities, accessibility, business demand, number of laborers, and customer satisfaction as the secondary input indicators; chooses distribution time and profit as the output indicators; and measures the static efficiency of logistics distribution centers from two perspectives, including the traditional unconstrained super‐efficiency model and the truck‐ restricted conditions, using the super‐efficiency model of data envelopment analysis (DEA). The Malmquist index model was used to measure the dynamic efficiency and change trend efficiency of the logistics distribution center, and a unified and comprehensive analysis was also made. The results of the case study show that the average efficiency of the logistics distribution center in the driving and nondriving restriction area is 0.872 and 0.914, respectively, and the average efficiency in the driving restriction area is about 4.5% lower than that of the nondriving restriction area, and variance is 1.58 times of the latter. Therefore, it can be concluded that the measures of truck driving restriction have an impact on the efficiency of the logistics distribution center, and the results of the super‐efficiency model with the restriction constraint have a greater impact on the logistics efficiency of the logistics distribution center than the traditional unconstrained super‐efficiency model. According to the evaluation results, suggestions on reasonable assignment of labor and other resources input are put forward for logistics distribution centers in areas where driving is restricted to improve efficiency. [ABSTRACT FROM AUTHOR]

The aims of this research are: 1) To determine the effectiveness of BUMDes workforce on the village economy in South Jailolo District, West Halmahera Regency. 2) To find out the effectiveness of per capita income on the village economy in South Jailolo District, West Halmahera Regency. 3) To determine the effectiveness of the role of the Village Fund Budget on the Village Economy in South Jailolo District, West Halmahera Regency. The number of samples of the research object is 11 villages and researchers only found 5 active BUMDes Villages in South Jailolo District, West Halmahera Regency. The test tool used is the DEA analysis method (Data Envelopment Analysis) to determine the efficiency by using the Windeap Software program. Sources of data are secondary data obtained through the Village Office, BUMDes Workforce, and several other sources. The results showed that: 1) Labor in Ratem Village and Gamlenge Village was not effective on the economy of the community in South Jailolo District, West Halmahera Regency. 2) Village income per capita in 5 villages is effective for the economy of the community in South Jailolo District, West Halmahera Regency. 3) Activity budget allocations in Ratem Village and Gamlenge Village are not effective for the economy of the community in South Jailolo District, West Halmahera Regency. [ABSTRACT FROM AUTHOR]

The high carbon emissions and pollution of China's thermal power industry chain have exacerbated environmental and climate degradation. Therefore, accelerating the green transformation process is of great significance in promoting the sustainable development of enterprises. This study selected 30 listed thermal power enterprises in China as research objects, analyzed their data from 2018 to 2022, set targeted input–output indicators for different stages, and used a two-stage dynamic data envelopment analysis (DEA) model to evaluate and measure the efficiency of the green transformation of Chinese thermal power enterprises. In addition, this study also uses the logarithmic mean Divisia index (LMDI) method to analyze the driving effects of green transformation. The results indicate that in terms of overall efficiency, there is a significant difference in the overall performance of these 30 thermal power enterprises, with a large difference in average efficiency values. Efficiency values are related to enterprise size. In terms of stage efficiency, the average efficiency value of thermal power enterprises in the profit stage was significantly higher than that in the transformation stage, and the profitability of Chinese thermal power enterprises was better. In terms of sub-indicator efficiency, the efficiency of each indicator shows a "U"-shaped trend, and there is a certain correlation between the operating costs and revenue of thermal power enterprises, the market value of green transformation, and related indicators. In addition, the most important factor affecting the efficiency of green transformation is the sewage cost they face, whereas their operational capabilities have the least impact on their green transformation. In this regard, thermal power enterprises should increase their investment in the research and development of key technologies for thermal power transformation and continuously optimize their energy structure. The government will increase financial support for thermal power green transformation enterprises and correspondingly increase emission costs. [ABSTRACT FROM AUTHOR]

This study employs a hotspot areas-based data envelopment analysis (DEA) to investigate the coupling efficiency between bike-sharing demand and land use. Hotspot areas are used as decision-making units and re-sampling data are imposed spatial constraints. The elbow method is used to select the crucial indicators after exploring the correlation between coupling efficiency evaluation indexes using the grey correlation model. Since this coupling efficiency evaluation belongs to a 'multi-input and single-output' system, DEA was introduced to build a quantitative model for bike-sharing demand and land-use coupling efficiency. This paper also identifies the factors that restrict bike-sharing demand generation. More importantly, different spatial scenarios are deployed to demonstrate heterogeneous characteristics of DEA efficiency in different locations. The results of a case study in Beijing, China show that DEA efficiency generally exhibits a trend from low to high from the inside to the outside of the urban centre. The land-use factors with relatively high redundancy rates are financial insurance facilities, hotels and living facilities. [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]

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]

This paper presents a method of equipment demonstration AoA method considering RMS factors. It puts forward the equipment demonstration AoA framework, and elaborates equipment demonstration AoA measure system. It analyses the RMS and cost factors, and establishes their quantitative models. An AoA model based on Data Envelopment Analysis (DEA) and an application example are given to illustrate the feasibility and effectiveness of the method. [ABSTRACT FROM AUTHOR]

The accurate evaluation of green innovation efficiency is a critical prerequisite for enterprises to achieve sustainable development goals and improve environmental performance and economic efficiency. This paper evaluates the green innovation efficiency of 72 new-energy enterprises by using a hybrid method of Data Envelopment Analysis (DEA) and a random forest model. The non-parametric DEA model is combined with the parametric SFA model to analyze the real green innovation efficiency on the basis of removing environmental factors and random factors. Then, the random forest model based on a nonlinear relationship is used to evaluate factors impacting green innovation efficiency. This paper proposes a comprehensive evaluation method designed to assess the green innovation efficiency of new-energy enterprises. By applying this method, companies can gain a comprehensive understanding of the current performance in green innovation, facilitating informed decision-making and accelerating sustainable development. [ABSTRACT FROM AUTHOR]

A strong and attractive educational system serves normally as a cornerstone for enhancing a nation's long-term socio-economic development potential. In recent years, bullying among pupils or students has become a pressing issue in many schools, with significant negative repercussions for both pupils (or students) and their educational environment. Bullying not only diminishes the quality of school education but also erodes the students' motivation and wellbeing. Thus, it plays a critical role in educational performance, prompting an urgent need for an assessment of its negative implications. This paper seeks to design and test a new model-based approach to evaluate the negative role of bullying at school in educational performance. A prominent avenue of evidence-based research on the quantitative evaluation of educational outcomes can be found in the use of Data Envelopment Analysis (DEA), a multidimensional comparative assessment tool for judging the efficiency of a set of relevant decision-making agents by examining the ratio of outputs to inputs. Among the various applications of DEA, the Distance Friction Minimization (DFM) approach has emerged as a promising tool. Nevertheless, the conventional DFM approach has also a serious limitation: it considers normally only one input and one output element in its projection. To address this shortcoming, this paper introduces a new Improved Ratio Minimization (IRM) approach. The IRM method overcomes the above-mentioned constraint, by allowing for the distribution of efficiency improvement projections among all input and output items contributing to efficient outcomes. Subsequently, this paper seeks to demonstrate the practical relevance of the IRM approach in DEA by applying it to an assessment of educational efficiency, with a particular focus on the effects of bullying in secondary education in European countries. Drawing from an extensive international dataset, the IRM-DEA model generates a variety of comparative empirical findings regarding the overall wellbeing and efficiency loss caused by bullying among students in European countries. The paper also explores new policy avenues for enhancing educational performance in the context of bullying at school across Europe. [ABSTRACT FROM AUTHOR]

Background: Health Complex Model was implemented to provide primary health care services in urban, especially in slum areas. As a pilot at a provincial level, Chamran Health Complex offers healthcare for more than 57,000 residents of Tabriz. Despite the necessity of cost information in healthcare decision-making, there was limited knowledge about the unit cost of services. This study aims to analyze the cost and efficiency of health centers. Methods: Activity-Based Costing method with direct and step-down allocation methods was adopted. We estimated unit costs in a hypothetical scenario according to national standards to quantify the gap between current and standard practice. Input-oriented Data Envelopment Analysis was administered to measure the efficiency of health centers. Results: The total cost of the complex was $2,841,897, of which 67% ($1910373) and 33% ($931523) were accounted for direct and indirect costs, respectively. The vaccination center had the lowest ($9), and the occupational health center had the highest average unit cost ($76). The average technical efficiency of the health centers was 0.519, where the HC1 and HC3 showed the best performance. Conclusion: There is remarkable variability in service costs across health centers, which must be addressed in performance management and contracting practices. Although we found a gap between current and standard practice in terms of staff and facilities according to national standards, Chamran Health Complex has an untouched capacity that can be utilized with better planning and without incurring additional costs. It raises the need for revising national standards by the Iran Ministry of Health. [ABSTRACT FROM AUTHOR]

The paper proposes a joint use of two global sensitivity analysis methods to evaluate the influence of model inputs on spatially explicit outputs. The analysis is conducted on spatial outputs of geodiversity assessment. First, the screening method is used to identify inputs with low importance and interactions, which can be discarded in later analysis. A simplified model is compared with a complete model using the mean and variance calculated on spatial outputs to evaluate outcome uncertainty. Finally, the variance decomposition method computes two sensitivity index maps per input. The results are mapped using bivariate classification, allowing for the simultaneous identification of regions with higher geodiversity scores and their corresponding variability. The research reveals that the streamlined approach preserves the overall patterns of outcome uncertainty and sensitivity observed in an original model while simultaneously reducing the number of outcome maps to interpret. Additionally, bivariate mapping proves efficient in concurrently depicting spatial sensitivity and interaction effects. The analytic efficiencies afforded by the presented approach and bivariate mapping enhance the operational knowledge of spatially explicit uncertainty and sensitivity analysis by simplifying the computational procedure and offering an alternative way of visualizing and interpreting input factors driving the sensitivity of model output. KEY POLICY HIGHLIGHTS: Spatially explicit uncertainty and sensitivity analysis extends the geodiversity assessment methods and comprises the next step in improving the accuracy of geodiversity assessments for land management decisions. An integrated uncertainty and sensitivity analysis helps focus on high-priority areas (high sensitivity and low uncertainty) to improve assessment reliability. The uncertainty and sensitivity analysis in models with spatially explicit output results depicted on maps can help reduce field research expenses by identifying a particular area or a specific natural component that warrants better-quality data. Bivariate maps employed to visualize the results of spatially explicit uncertainty and sensitivity analysis can potentially reduce the cognitive effort of visually switching between multiple single-variable (theme) maps. [ABSTRACT FROM AUTHOR]

Global seaport network efficiency can be measured using the Liner Shipping Connectivity Index (LSCI) with Gross Domestic Product. This paper utilizes k-means and hierarchical strategies by leveraging the results obtained from Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) to cluster 133 countries based on their seaport network efficiency scores. Previous studies have explored hkmeans clustering for traffic, maritime transportation management, swarm optimization, vessel trajectory prediction, vessels behaviours, vehicular ad hoc network etc. However, there remains a notable absence of clustering research specifically addressing the efficiency of global seaport networks. This research proposed hkmeans as the best strategy for the seaport network efficiency clustering where our four newly founded clusters; low connectivity (LC), medium connectivity (MC), high connectivity (HC) and very high connectivity (VHC) are new applications in the field. Using the hkmeans algorithm, 24 countries have been clustered under LC, 47 countries under MC, 40 countries under HC and 22 countries under VHC. With and without a fuzzy dataset distribution, this demonstrates that the hkmeans clustering is consistent and practical to form grouping of general data types. The findings of this research can be useful for researchers, authorities, practitioners and investors in guiding their future analysis, decision and policy makings involving data grouping and prediction especially in the maritime economy and transportation industry. [ABSTRACT FROM AUTHOR]

Offshore wind turbine (OWT), a sustainable energy source, has recently gained wide attention. The energy demand for India is soaring high as it is a fast-developing nation in terms of industrialization; however, the interest shown by India toward renewable energy is low, especially for OWTs. This study aims to identify, categorize, and evaluate the criteria needed to be considered in the installation of OWTs and selection of potential locations in India. Based on literature analysis and exploratory interviews with experts, six aspects, namely, climatic conditions, regional features, investments and benefits, environmental impact, economic impact, and social and technical impact, with a total of twenty-six criteria, were identified and evaluated. An integrated approach of data envelopment analysis (DEA) with grey analytical hierarchy process (GAHP) and grey Complex proportional assessment (GCOPRAS) is used to evaluate the criteria and also to identify the locations for OWTs. Soil condition, extreme wind speed, seismic movement, tidal flow, and closeness to the power transmission grid have been identified as the top five criteria to be considered in the installation of OWTs. Gujarat, Tamil Nadu, Odisha, the Lakshadweep Islands, and the Andaman and Nicobar Islands have been identified as potential locations for installing OWTs in India. The outcomes of this study will deliver better insights for the practitioners about the criteria that need to be considered in OWTs. Further, this study sheds light on the importance of OWTs in an Indian context, which can possibly attract more investments. [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]

Monel K 400 is a potential superalloy that is used in heat exchanger piping, process vessels, gasoline, and portable water owing to superior mechanical properties at zero temperature. Square holes are needed in the high-temperature application components and are difficult to machine using conventional methods. Hence, in this study, the square holes have been fabricated on Monel K 400 superalloys with varying pulse duration, current, pulse interval, and servo voltage and its influences on Recast Layer Thickness (RLT), Corner Radius (CR), and Surface Roughness (SR) have been analyzed. Experiments are planned on using Taguchi design and the responses are analyzed with mean surface plots. The overall analysis found that the SiC powder mixed in EDM oil has a larger improvement % than that of CFRP and ZrSiO4 powder. The performance of SiC powder mixed in EDM oils increased to 62 % for RLT, 32 % for CR, and 51 % for SR. The current and pulse duration are revealed to be the most significant parameters using Analysis of Variance (ANOVA). Quadratic and Adaptive Neuro-Fuzzy Inference System (ANFIS) models are developed for prediction. The Mean Absolute Percentage Error (MAPE), coefficient of determination (R), and Root Mean Square Error (RMSE) are calculated for evaluating the models and it is discovered that both models have low RMSE, MAPE, and high R. Finally, the multiple response decision making based on Taguchi based Data Envelopment Analysis based Ranking (DEAR) methodology is an improvement of 28 % for RLT, a 16 % for CR, and a 21 % for SR. [ABSTRACT FROM AUTHOR]

The main aim of this research is to present an innovative method known as fuzzy network data envelopment analysis (FNDEA) in order to assess the performance of network decision-making units (DMUs) that possess a two-stage structure while taking into account the uncertainty of data. To attain this goal, we utilize various methodologies including the non-cooperative game (leader-follower) NDEA method, the concept of Z-number, credibility theory, and chance-constrained programming (CCP) to develop a model for the fuzzy NDEA approach. The FNDEA approach offers several advantages, such as the linearity of the presented FNDEA models, the ability to rank two-stage DMUs in situations of ambiguity, the provision of a unique efficiency decomposition method in an uncertain environment, and the capability to handle Z-information. To demonstrate the applicability and effectiveness of the proposed approach, we implement the Z-number network data envelopment analysis (ZNDEA) approach in assessing the performance of Iranian private insurance companies. The results of this implementation reveal that the proposed ZNDEA method is suitable and effective for measuring and ranking insurance companies in situations where data ambiguity is present. [ABSTRACT FROM AUTHOR]

This study explores the critical determinants of efficiency in European Higher Education Institutions (HEIs). The contemporary landscape presents a wide range of challenges and opportunities, necessitating unprecedented levels of efficiency. Our primary objective is to unravel the fundamental elements that drive the efficiency of European HEIs. To achieve this, we employ a novel methodological approach, integrating Data Envelopment Analysis (DEA) with a Machine Learning technique, specifically Feature Selection. This combination sheds new light on areas previously less explored in DEA, including the intricate interplay among variables. Our empirical investigation contributes to the academic discourse by presenting a comprehensive, multi-country analysis from a multifaceted viewpoint. We assess the efficiency levels of European universities, highlighting the influence of factors such as student fee funding, Ph.D. program intensity, and international mobility on achieving high efficiency scores. The findings of this study have some implications for policy-making, suggesting strategies to enhance the efficiency levels of HEIs. [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]

This article aims to demonstrate that the Data Envelopment Analysis method can be employed to evaluate the operational efficiency of public utility enterprises under state ownership or local government jurisdiction, such as cities and municipalities. These enterprises are tasked with delivering high-quality public services to citizens at affordable prices. Achieving this goal necessitates efficient management and judicious use of public resources. The research evaluated the efficiency of public water utility enterprises in the Federation of Bosnia and Herzegovina using the Data Envelopment Analysis method. Three indicators were used as inputs in the model: asset value, number of employees, and total expenses. The output indicator was total revenue, which reflects the number of users under relatively uniform service tariffs. The findings reveal that 92.31% of these public utilities exhibit technical efficiency levels above 90%. Additionally, 53.85% demonstrate 100% technical efficiency in input utilisation. The results highlight enterprises that inefficiently use assets incur above-average costs or employ more workers than necessary. The study concludes that the Data Envelopment Analysis model effectively identifies state-owned public utility enterprises that consume above-average resources to deliver the same scope and quality of services. [ABSTRACT FROM AUTHOR]

In times of prolonged economic doldrums across the globe, multinational firms (MNFs) offshoring blunders can undermine their competitiveness in the marketplace. To help the MNF formulate a more resilient offshoring strategy and identify the most desirable offshoring destination, this article aims to identify dynamic risk factors that significantly hinder the efficiency of offshoring and then measure specific offshoring risks over time using two different versions of data envelopment analysis (DEA) models and Malmquist productivity index (MPI). After assessing the degree of risk resiliency of the offshoring host countries over extended periods and then conducting Tobit regression analysis to identify key factors that significantly influence offshoring risks, we found that the host country's logistics efficiency (i.e., logistics performance index [LPI]) and domestic market size were critical indicators of offshoring success in that country. Since low‐cost sourcing countries (LCCs) tend to have relatively low LPIs and smaller domestic market sizes, they are not attractive offshoring destinations. This finding defies conventional wisdom. This article is one of the first longitudinal studies to assess the comprehensive risk resilience of 87 different offshoring destinations (countries) during multiple periods (6‐year span). [ABSTRACT FROM AUTHOR]

For the past several decades, the container shipping industry has grown rapidly. As a result, container shipping companies (CSCs) play a vital role in the global cargo transportation market. Thus, measuring the efficiency of CSCs is a primary concern of managers and practitioners. When the COVID‐19 pandemic broke out, the industry was severely disrupted and presented with unprecedented challenges. For this reason, evaluating how CSCs performed and learning the practices they took to achieve successful business results during this period have become necessary. To serve that purpose, this article used a hybrid method that combined data envelopment analysis Malmquist index with window analysis to analyze the world's nine leading CSCs' performance over time and their efficiency relative to each other, using data collected within a 5‐year period from 2016 to 2020. Our findings revealed different characteristics and trends in efficiency among these CSCs in terms of technical efficiency change, technological change, and total productivity change. Then, managerial implications were discussed to understand the best practices of successful CSCs as well as the circumstances in which some of them underperformed during this time. In closing, the study summarized a fact‐based guideline CSCs could take as management reference should a similar crisis occur. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present an example of the measurement of airport efficiency, a topic of great interest in civil aviation today. The methodology used is data envelopment analysis (DEA) and the Malmquist Index. The calculation of airport efficiency with up-to-date data for each period is of great importance in the context of sustainability. The study selected ten airports with high air traffic in Turkey as the sample set. The objective of this study is to evaluate the current state of airport efficiency, identify the sources of inefficiency and make appropriate policy recommendations based on the findings obtained. Design/methodology/approach: The study is based on DEA and Malmquist Index Analysis. The number of personnel and terminal size data of ten selected decision-making units (DMU) are used as inputs, while passenger, cargo and aircraft traffic data are used as outputs. A five-year period, spanning from 2018 to 2022, is considered as the data set in the study. Findings: Upon analysis of the data from the ten airports included in the study, it was found that the current input-output combination yielded efficient results, with the exception of certain characteristics, such as the impact of seasonal conditions or tourism. Concurrent with the growth in aircraft, passenger and freight traffic, the number of personnel employed at these airports has also increased. It was concluded that technological efficiency is of paramount importance for each airport, and that investments in technology should be increased. Practical implications: A separate assessment was conducted for each of the ten airports included in the study sample. Each airport was evaluated in terms of its strengths and weaknesses, and areas of low efficiency were identified. Consequently, more general conclusions were reached than airport-specific evaluations. Originality/value: In order to ensure the long-term sustainability of the sector, it is essential that the efficiency measurements of airports are calculated using up-to-date data on a regular basis. The results obtained from these calculations provide guidance for the strategic plans to be implemented in the long term, as well as for the solution proposals for operational problems. In this context, this study not only provides information to policymakers and airport managers about the current situation, as it includes recent data, but also contributes to the literature in this sense, as it includes policy recommendations. [ABSTRACT FROM AUTHOR]

AbstractMultinational firms establish foreign affiliates to reduce production and transaction costs and open markets. This study was conducted to determine whether foreign affiliates are efficient in production—the efficiency of foreign affiliates established by Korean multinationals through foreign direct investment from 2007 to 2018. DEA efficiency was measured by dividing it into pure, technical, and scale efficiency. An empirical analysis using the panel Tobit and Probit models examined the factors affecting efficiency decisions. It was confirmed that the total number of foreign affiliates’ employees significantly affects their efficiency. In addition, it was analyzed that establishments with traditional FDI motives, such as taking advantage of low wages and promoting exports, harm the efficiency of foreign affiliates. [ABSTRACT FROM AUTHOR]

This paper addresses the question whether energy consumption variables and the disaggregation of output matter in the context of efficiency analysis of container terminals. While it is obvious that the energy consumption of refrigerated cargo is higher than the energy consumption of non-refrigerated cargo, this work investigates whether those differences show in an overall efficiency analysis of terminals. This would point to a potentially important input for efficiency measures, to be considered in future productivity and efficiency analysis of terminals. Starting with a discussion on theoretical concepts and variable selection for measuring the energy dimension of terminal efficiency, this is the first paper that applies data envelopment analysis (DEA) comparing results with and without energy consumption, as well as differentiating productive outputs (dry and reefer container handling). The results reveal how the output disaggregation leads to substantially different efficiency scores and are a first step to show the relevance of output disaggregation and the inclusion of the energy variables as inputs in container terminal efficiency studies. [ABSTRACT FROM AUTHOR]

Background: With Primary Health Care (PHC) being a cornerstone of accessible, affordable, and effective healthcare worldwide, its efficiency, especially in developing countries like China, is crucial for achieving Universal Health Coverage (UHC). This study evaluates the efficiency of PHC systems in a southwest China municipality post-healthcare reform, identifying factors influencing efficiency and proposing strategies for improvement. Methods: Utilising a 10-year provincial panel dataset, this study employs an enhanced Data Envelopment Analysis (DEA) model integrating Slack-Based Measure (SBM) and Directional Distance Function (DDF) with the Global Malmquist-Luenberger (GML) index for efficiency evaluation. Tobit regression analysis identifies efficiency determinants within the context of China's healthcare reforms, focusing on horizontal integration, fiscal spending, urbanisation rates, and workforce optimisation. Results: The study reveals a slight decline in PHC system efficiency across the municipality from 2009 to 2018. However, the highest-performing county achieved a 2.36% increase in Total Factor Productivity (TFP), demonstrating the potential of horizontal integration reforms and strategic fiscal investments in enhancing PHC efficiency. However, an increase in nurse density per 1,000 population negatively correlated with efficiency, indicating the need for a balanced approach to workforce expansion. Conclusions: Horizontal integration reforms, along with targeted fiscal inputs and urbanisation, are key to improving PHC efficiency in underdeveloped regions. The study underscores the importance of optimising workforce allocation and skillsets over mere expansion, providing valuable insights for policymakers aiming to strengthen PHC systems toward achieving UHC in China and similar contexts. [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]