Search

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]

Along with the popularity of Olympic Games, evaluating and ranking participating nations has become one of the hottest issues in recent years. Data envelopment analysis (DEA) is extensively used for participating nations' performance evaluation, while the prior literature mainly addresses the maximum efficiency by ignoring the weights diversity, which might lead to unreasonable and biased results. By considering the fixed-sum medals constraint, this paper proposes an integrated approach of stochastic multicriteria acceptability analysis (SMAA) and DEA for evaluating the participating nations. The proposed approach addresses the weights diversity and multiple equilibrium efficient frontiers by investigating the overall feasible space, and two strategies are considered to guarantee the fairness concern for calculating the efficiencies. Besides, the proposed approach provides both rank acceptability and holistic acceptability index to obtain the complete ranking from the best to the worst and to get the relative ranking relationship in the overall feasible space. The proposed approach can provide more reasonable performance measurements since it explores different rank positions and improves the whole satisfaction of all participating nations. Finally, the proposed approach is applied to empirically analyze the participating nations in the Winter Olympic Games from 2010 to 2022 and some valuable analytical results are provided. [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]

The outbreak and subsequent COVID-19 pandemic became an urgent public hygiene crisis and concern for international society. The significance of evaluating a healthcare system's performance when dealing with public hygiene hazards generally reflects the preparation and reaction level of a country. Data envelopment analysis (DEA) can assess efficiency by comparing outputs produced by inputs in each decision-making unit (DMU). This research thus employs a multiplicative DEA approach relative to a log-linear technology to construct a network DEA model that measures overall efficiency in a two-stage network structure. The stage efficiencies via a weighted geometric mean aggregate into overall efficiency. We decompose the weighted geometric mean efficiency through means of using the general two-stage structure as a numerical example. Some interesting findings about the change in overall and stage efficiencies appear. First, a variation in the weight of stage efficiency does not change the stage efficiency scores. Second, the stage efficiency scores for the most part remain unchanged under different weights of stage efficiency. Finally, we apply the proposed network DEA model in multiplicative form to evaluate the efficiency of COVID-19 treatment in OECD countries. [ABSTRACT FROM AUTHOR]

Purpose: This study examines the effects of CLS and DS on companies' WCME and analyses the differences in WCME at company and market levels. Design/methodology/approach: This study adopts the DEA approach, regression, differences, and additional analyses to achieve its objectives. This study employs 235 non-financial companies and 1,175 company-year observations from eight active industries in the United States from 2016 to 2020. Findings: The findings indicate that CLS and DS strategies positively influence companies' WCME. Additionally, WCME differed across size categories and industries, with large companies and those operating in the communication services industry showing better WCME. By contrast, WCME did not differ between the periods before and during the COVID-19 pandemic. Practical implications: This study scrutinizes the impact of CLS and DS strategies on companies' WCME to bridge the gap in this field. It extends the investigation of competitive strategies as explanatory variables for a company's WCME and examines the differences in companies' WCME at the company and market levels, which may assist decision-makers in improving their strategies and efficiencies for continuous improvement. Originality/value: This study enhances current knowledge by uncovering the influence of CLS and DS strategies on improving companies' WCME, an underexplored topic. It also explores companies' WCME trends and patterns regarding company size, industry type, and the pandemic period to draw interesting conclusions about the essence of WCME. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to measure and unveil the relationship between customer satisfaction and efficiency levels in the most relevant Brazilian airports. Design/methodology/approach: The authors utilize a two-stage network DEA (data envelopment analysis) and AHP (analytic hierarchy process) model as the cornerstones of the study. The first stage of the network productive structure focuses on examining the infrastructure efficiency of the selected airports, while the second stage assesses their business efficiency. Findings: Although the results indicate that infrastructure and business efficiency levels are heterogeneous and widely dispersed across airports, controlling the regression results with different contextual variables suggests that the impact of efficiency levels on customer satisfaction is mediated by a set of socio-economic and demographic (endogenous) and regulatory (exogenous) variables. Furthermore, encouraging investment in airports is necessary to achieve higher infrastructural efficiency and scale efficiency, thereby improving customer satisfaction. Originality/value: There is a scarcity of studies examining the relationships among customer satisfaction, privatization and airport efficiency, particularly in developing countries like Brazil. [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]

Purpose: This paper aims to propose an integrated use of balanced scorecard (BSC), data envelopment analysis (DEA) and game theory approach as an enhanced performance measurement technique to determine and rank the importance of manufacturing indicators of a steel company as a real case study. Design/methodology/approach: An efficiency change ratio is defined to examine the characteristic function of each coalition which is super-additive. Then, the Shapley value index is used as the solution of the cooperative game to determine the importance of the BSC indicators of the company and rank order them. Findings: The results reveal that "profitability rate" is the most important BSC indicator, whereas "customer satisfaction" is the least significant one. The ranking order of the importance of all BSC indicators makes it possible for the senior managers of the organization to realize the importance of each index separately and to improve the profitability and the number of customers by presenting programs according to the budget and time constraints. Originality/value: The main contribution of this paper lies in the adoption of a game theory approach to performance measurement in the industrial sector that determines and ranks the importance of manufacturing indicators. [ABSTRACT FROM AUTHOR]

While interests in Industry 4.0 technologies such as cloud computing and the Internet of Things (IoT) are growing, an ongoing challenge is to properly evaluate the performance of the providers of such technologies. Methods have been developed and applied to assess the efficiency of cloud service providers (CSPs) for Industry 4.0. However, most existing methods suffer from such shortcomings as subjective weights and computing complexity, rendering it difficult to succinctly differentiate the performance of CSPs in a competitive market. Besides, the literature has not taken into account many different types of data such as ratios, integers, and undesirable factors that can affect the performance of CSPs. Most importantly, most existing studies do not consider the sustainability of CSPs in evaluating their performance. To address the above issues, we present a comprehensive analytical method based on data envelopment analysis (DEA) to gauge the sustainability of CSPs for Industry 4.0. Validating the usefulness of our method using a real-life dataset, we provide not only a viable means with sound academic underpinning but also significant managerial insights for practitioners to assess the sustainability of CSPs for Industry 4.0. [ABSTRACT FROM AUTHOR]

Innovation is often seen as crucial for firm survival and as a way for firms to differentiate themselves from their rivals. However, innovation studies are vague about the actual importance of different innovation strategies. In this study, we distinguish between pure product, process, organizational, and marketing innovations and their combinations. We use a (balanced) panel data set with more than 15,000 firm-year observations for manufacturing firms to analyze the performance of firms with different innovation strategies. Additionally, we investigate from a societal perspective whether innovation facilitates less efficient firms to catch up, or whether firms already utilizing best practices are the main beneficiaries. Using Data Envelopment Analysis (DEA), we find the highest increase in firms' performance among the firms with innovation strategies that combine product innovation with other innovation types. This finding applies to both the short and the longer term. We also conclude that catch-up is strongest among the firms adopting pure process innovation, whereas the other innovation strategies are primarily associated with frontiers shifts. [ABSTRACT FROM AUTHOR]

Using data envelopment analysis (DEA) with large-scale data poses a big challenge to applications due to its computing-intensive nature. So far, various strategies have been proposed in academia to accelerate the DEA computation, including DEA algorithms such as hierarchical decomposition (HD), DEA enhancements such as restricted basis entry (RBE) and LP accelerators such as hot starts. However, few studies have integrated these strategies and combined them with a parallel processing framework to solve large-scale DEA problems. In this paper, a hybrid parallel DEA algorithm (named PRHH algorithm) is proposed, including the RBE algorithm, hot starts, and HD algorithm based on Message Passing Interface (MPI). Furthermore, the attribute of the PRHH algorithm is analyzed, and formalized as a computing time function, to shed light on its time complexity. Finally, the performance of the algorithm is investigated in various simulation scenarios with datasets of different characteristics and compared with existing methods. The results show that the proposed algorithm reduces computing time in general, and boosts performance dramatically in scenarios with low density in particular. [ABSTRACT FROM AUTHOR]

FinTechs have gained an important place in the financial sector with their dynamic and customer-oriented structures and innovative approaches and business models. In recent years, the competition between actors in the financial sector has intensified. In a rapidly developing sector, where competition is intense and challenging, efficiency is a very important performance indicator for fintechs. This study aims to measure and examine the efficiency performance of FinTech companies. Data Envelopment Analysis was used for efficiency measurement. By establishing a two-stage data envelopment analysis model, "profitability", "marketability" and "overall process" efficiencies of fintechs were measured and analyzed. Efficiency of FinTechs operating in eight different sub-sectors was measured for the period of 2010–2019. The results show that, fintechs' profitability efficiency performances are higher than their marketability efficiency performances. Besides, efficiency performance of the fintech companies differs on the basis of sub-sectors. This paper is a pioneer study in efficiency measurement of FinTechs. [ABSTRACT FROM AUTHOR]

Shipping is a highly cyclical, volatile, and unpredictable industry. In recent history, economic shocks like the COVID-19 pandemic have heavily disrupted supply chains and tested the resilience and adaptability of shipping lines from both operational and financial perspectives. Therefore, the commercial success of shipping lines is increasingly determined by an optimal balance between reliable logistics operations and financial performance. Given this, chartering strategies of shipping lines are pivotal in achieving fleet optimisation and financial sustainability. Using a Mann–Whitney U test and a data envelopment analysis (DEA), this study provides a view on chartering strategies and efficiency levels of shipping lines in response to the COVID-19 pandemic. The findings reveal that chartering strategies have changed considerably since 2020 and that most shipping lines have pursued a peculiar strategy with a different mix in terms of chartered vessels' size, age, and period of charter. Shipping lines which showed the most marked differences in chartering strategies also yielded higher efficiency scores under DEA, whereas those that did not sustain large changes scored lower in terms of efficiency. This study provides significant insights on and managerial guidance for understanding and benchmarking chartering strategies and their impact on efficiency gains. Highlights: Chartering strategies help achieve fleet optimization and financial sustainability Chartering strategies have changed since 2020 in response to the COVID-19 pandemic Most shipping lines pursued peculiar strategies Those lines that changed significantly chartering strategies yielded higher efficiency scores under DEA Those lines that did not sustain large changes scored lower in terms of efficiency [ABSTRACT FROM AUTHOR]

In the last decade, we have experienced a changing geopolitical context that has caused changes in the energy context. In addition, human activity contributes to global warming or sea level rising, i.e., climate change. A set of action policies have been implemented to continue fighting against this environmental situation (such as the Paris Agreement, the COP27, or the European Green Deal for 2030); therefore, it is necessary to determine whether we are on the right track. It is compulsory to develop predictive models that accurately analyze the current status and the already path undertaken. To this end, this article analyzes the environmental efficiency of the 27 countries of the European Union (excluding the UK) using the so-called data envelopment analysis (DEA). In particular, economic (GDP and GDP per capita), environmental (CO2 and CH4 emissions), electricity production data, the volume of vehicles, and the industrial production rate of the different countries were collected to calculate environmental efficiency. Once these data were collected, the environmental efficiency was calculated using two methods based on the DEA. The results show that out of the 27 countries, only 12 have a relatively high environmental efficiency, although it could be improved, implementing a set of corrections. However, other countries have a low eco-efficiency performance and they must improve in the coming years. We can highlight that rich countries are closer to achieving high environmental efficiency than less developed countries. [ABSTRACT FROM AUTHOR]

This study sought to assess disaster resilience in Korean metropolitan cities and explore the factors influencing disaster management in these cities. Statistical data from 17 metropolitan cities and provinces in Korea spanning the years 2016 to 2020 were collected. Data envelopment analysis (DEA), a method for estimating relative efficiency using multiple input and output variables, was employed to calculate the disaster resilience scores of these cities. The results indicate higher level of disaster resilience in capital regions and large cities in Korea, with social and economic characteristics significantly influencing resilience. Moreover, the study reveals that robust infrastructure, particularly in terms of housing, strongly impacts resilience scores. The use of DEA allows for the practical evaluation of relative resilience scores without requiring weighted value calculations, making it an effective methodology for assessing city resilience. This study aimed to assess how well Korean metropolitan cities are prepared for disasters and understand what factors affect their disaster management. We collected statistics data on the social, economic, infrastructure, community capital, and institutions of 17 Korean metropolitan cities and provinces from 2016 to 2020. We used a method called data envelopment analysis (DEA) to figure out how well these cities are handling disasters. The results show that capital regions and large cities in Korea are better at handling disasters. This is because factors such as social and economic characteristics have a significant impact on their ability to respond to disasters. Additionally, having strong infrastructure, especially when it comes to housing, plays a big role in how well a city can respond to disasters. Using DEA allowed us to evaluate how well cities are prepared for disasters without needing complicated calculations, making it a useful method for assessing city readiness for disasters. [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]

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]

The majority of studies of agricultural productivity support the view that there is a relationship between productivity and farm size. How the size of a farm is related to its efficiency is a constant problem in agricultural economics research. It also has important implications for agricultural development policy. For developed countries, including the countries of the European Union, numerous studies show that, with the increase in the size of farms, their efficiency usually increases. Moreover, there is a constant decrease in the number of farms and the concentration of land and labor. For this reason, the problem of the influence of the economic size of a farm is included in the scope of the research related to the analysis of the relationship between the farm size and its efficiency. The purpose of the paper was to investigate the relative technical efficiency of the agricultural sector in the European Union (EU-27) using the Data Envelopment Analysis (DEA). It was found that with the increase in economic size, the technical efficiency of the researched farms improved. Attention was paid to the possibility of reducing labor inputs, mainly in farms of economic size classes ES1-ES4. No need to reduce the level of capital expenditure has been demonstrated. This emphasizes the growing importance of the substitution of labor with capital. [ABSTRACT FROM AUTHOR]

Japan and Germany are both facing a rapidly aging population and have similar social insurance-based long-term care systems. However, there are significant differences in utilization and costs. This paper presents a microeconomic decision model validated by regression analysis, Blinder-Oaxaca decomposition, and Data Envelopment Analysis to contrast the utilization patterns in the Japanese and German long-term care sectors. The design of the welfare programs for low-income families has been identified as one of the main reasons. In Germany, the welfare system leads to a demand curve for nursing home care that is comparable to that of an inferior good which makes external long-term care in nursing homes the more attractive, the poorer the respective households are. In addition, the resulting inelastic demand of the population groups in need of social benefits seem to reduces competition among Germany's long-term care providers, which is associated with a loss of efficiency in how they use production factors. In Japan, this negative outcome is avoided by a comfort segmentation in the nursing home market. [ABSTRACT FROM AUTHOR]

In the 1990s, the united nations development programme (UNDP) introduced the human development index (HDI) to determine the development degrees of countries. One deficiency in the HDI calculation is the use of equal weights for its sub-indicators. Many scholars have tried to solve this problem using a data envelopment analysis (DEA) method, particularly the one enhanced by weight restrictions. Indeed no specific method has been yet suggested to determine the parameters of the weight restrictions. In this paper, we use four DEA/benefit of the doubt (BoD) models enriched by the assurance regions type I (AR-I) constraints to assess human development; we aim to objectively determine the AR-I bounds. Therefore, we consider a basis as the accepted human development values and propose a bi-level optimization problem to extract the AR-I bounds in such a way that the efficiency scores are almost the same as the basic values. On the other hand, the HDI is a globally accepted index that shows small changes year by year. So, if the UNDP decides to apply a BoD model for calculating the HDI instead of the traditional method, then it is better than the scores obtained by the BoD model, showing small changes in comparison with the HDI, at least in the first few years. Therefore, the HDI values are considered as the basis. Moreover, the objectively achieved AR-I bounds provide us with an insight into the way the sub-indicators affect the development scores. The bounds can be modified by the experts opinions, in the future. [ABSTRACT FROM AUTHOR]

In this article, we estimate the technical efficiency ratio for a sample of 24 financial institutions in Ecuador over the period 2015–2019. To do so, we first apply a non‐parametric method such as the data envelopment analysis (DEA). Under variable returns to scale and output‐oriented "Interest Income", the average technical efficiency for the 24 banks is 84.26% and, under output‐oriented "Other Operating Income", the average technical efficiency is 73.22%. We find that the group of large banks has higher levels of efficiency and that there are still opportunities for improvement in the group of medium and small banks. We also estimate that 80% of Ecuadorian banks need to make potential improvements either by increasing their output or reducing their inputs, such as salary costs, to increase the efficiency. In addition, the growth of the banking sector is crucial to achieve the financial development in Ecuador, being significant the efficiency indices, which show that a 1% increase in banking efficiency could increase its size by USD 605 million. By using the random effects panel data econometric model, we demonstrate that the growth of the banking industry is mostly influenced by the concentration of deposits and taxes. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization in recent years, China has witnessed large-scale construction across its provinces, generating massive amounts of construction waste that pose challenges to environmental protection and sustainable development. This study evaluated the impact of construction waste policy intensity on its environmental efficiency. Firstly, the content analysis method was used to analyze the construction waste policy text quantitatively. Second, this study constructed a slack-based measure (SBM) model based on data envelopment analysis (DEA), considering resource input and construction waste output to measure environmental efficiency. Finally, we built and tested an econometric model of how policies affect environmental efficiency using the system generalized method of moments (SYS-GMM). The findings indicate a non-linear U-shaped link between policy intensity and environmental efficiency. Among all five control variables, population density, urbanization level, and technological innovation enhance environmental efficiency, while economic development and highway density will lower it. This study advances the research on construction waste policies and offers some insights for the construction industry to pursue sustainable development. [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]

Liquidity risk was at the heart of 2007–2008 global financial crisis, which has led to a series of financial institutions failure. We test whether and how liquidity risk impacts European banks' stability (i.e., a bank risk-return profile) under different levels of engagement in non-traditional banking activities after the global financial crisis and during the implementation of the Basel III liquidity rules. To calculate financial stability, we adopt an efficiency perspective based on the combination of the CAMELS rating system with the data envelopment analysis technique. We implement a nonlinear panel smooth transition regression approach, where transitional factors of income diversification are endogenously captured from the data. We find that, liquidity risk stemming from liquidity creation has a positive impact on bank stability, implying that income diversification can serve as a "buffer" through which banks can ensure their liquidity creation and offset for the compression of intermediation margin in lending and deposit activities. This suggests that diversification does not impede the ability of banks to operate with lower liquidity holdings but allows them to make greater use of their balance sheets to fulfill their primary roles of credit provision and liquidity creation. The results offer interesting implications for regulators and bank managers in managing liquidity risk. [ABSTRACT FROM AUTHOR]

Background: This research conducts a contextual comparative analysis between Dar es Salaam and Mombasa ports, employing a hybrid data envelopment analysis (DEA) model that integrates the contextual value-added approach (CVA). The assessment incorporates various inputs (quay length, number of cranes, and storage area) and outputs (number of ship calls and cargo throughput) to compute efficiency scores, offering nuanced insights into the strengths, weaknesses, and areas for improvement of both ports. Methods: The hybrid DEA model with CVA is applied to calculate efficiency scores, considering the diverse inputs and outputs. This approach allows for a comprehensive evaluation of the relative performance of Dar es Salaam and Mombasa ports. The study also explores the influence of trade-related externalities on port efficiency, providing a holistic understanding of the factors shaping the ports' operational effectiveness. Results: The efficiency scores depict distinctive performance trends between Dar es Salaam and Mombasa ports. Notably, Dar es Salaam exhibits maximum efficiency (efficiency value of 1) in 2018 and 2021, while Mombasa attains optimal performance (efficiency value of 1) in 2021. However, efficiency values fluctuate for both ports in other years, ranging between 0.895 and 0.985 for Mombasa and 0.924 and 0.960 for Dar es Salaam. Conclusions: This study highlights the dynamic efficiency levels of Dar es Salaam and Mombasa ports over multiple years and identifies critical factors influencing their performance. The findings contribute valuable insights to the field of port analysis, offering guidance to port management and policymakers in optimizing the efficiency and competitiveness of these vital maritime hubs. [ABSTRACT FROM AUTHOR]

Congestion is a kind of inefficiency because it causes the decision-making units (DMUs) to be inefficient and reduces their output. Identifying inefficient DMUs and determining the cause of their inefficiencies has been one of the most important reasons for referring to the internal structure of DMUs and analyzing the effect of intermediate products on the subDMU's (or stages) performance. In this paper, to characterize the cause of the DMU's congestion, we refer to its internal structure as a two-stage network data envelopment analysis (DEA) and decompose the DMU's congestion into black-box (BB) and two-stage structure congestion. Also, inputs have two separate and simultaneous roles; black-box inputs role and stage1's input role, so three congestion types occur. Thus, we sought to analyze the relation between two types of initial inputs congestion, intermediate products congestion, and express their effects on BB congestion. Finally, we define three congestion definitions and model the relation between two types of input congestion, intermediate products congestion, and BB inputs congestion. Finally, a practical example illustrates the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a robust tool for ranking MHE prior to selecting the best one. The proposed ranking methodology is based on a new key performance indicator, identified as market weight scheme (MWS). The MWS of each MHE reflects the market's importance of its performance criteria and it is built through an ingenious integration of the most resonated appreciative (MRA) model with the ordered weighted averaging (OWA) operator. The flexibility of MRA is exploited to customize the weight profiles of the data envelopment analysis (DEA) peer-evaluations, whereas the OWA aggregation process is run to produce the associated MWS for a pre-set decision maker's (DM) subjective judgment towards the MHE market stability. Thus, the novelty of the present study is threefold. (1) The MHE is selected from a collective market perspective rather than individualistic manufacturer's viewpoint. (2) The ranking process is based on the aggregation of weight profiles instead of cross-efficiency (CE) scores, as traditionally done in DEA-CE approaches. (3) The DM's subjectivism is integrated into the assessment process through an optimism measure that mimics the market stability. The application of the proposed methodology on a sample of 27 robots asserts its robustness as a selection tool. [ABSTRACT FROM AUTHOR]

One of the important issues in Iran is to monitor imports and business processes as well as exchange rates. Given the government’s supportive policies for traders, how to implement these policies is challenging. An strategy to implement targeted oversight and policies is to control the actions of active traders based on their background. Data Envelopment Analysis (DEA) is one of the key tools to achieve this goal. The present research is based on Slack-Based Measure DEA Model (SBM-DEA) with an output-based approach which rates traders using real data as well as their background. In this model, 30 input indicators and six output indicators were first considered. Subsequently, given the correlation between them, the number was reduced to five input and four output indicators. After the extraction of effective input and output indicators, traders’ efficiency was measured and rated using DEA model. Then, wellrespected traders would receive facilities and various supports. To evaluate the performance of the model, the traders also were ranked using the Best Worst Method (BWM and after results shows better performance of the DEA model. Another result and application is the use of reference decision-making units, who indicate traders that are expected to have good performance by the market knowledge. Recognizing these units allows policy-makers to reduce other traders’ risk by disseminating their behavior. Another important application is traders’ classifications. By knowing the traders, the policy-maker reference can make a good classification of them, which is necessary for different resource allocation or facilitating policies. [ABSTRACT FROM AUTHOR]

This comprehensive study explored the efficiency landscape of the Jordanian banking industry from 2006 to 2021, utilizing a dual-pronged approach. First, we assessed the efficiency scores of 15 commercial banks, comprising 13 conventional and 2 Islamic institutions, through data envelopment analysis (DEA). Secondly, we investigated the determinants influencing relative efficiency using the Tobit regression model. Our dataset, spanning 240 observations over 16 years, provides a nuanced examination of industry dynamics. DEA, specifically focusing on variable return to scale (VRS), unveils efficiency scores by accounting for scale inefficiencies. The research contributes insights into the operational efficacy of Jordanian banks and provides a robust methodology for understanding efficiency dynamics in the broader financial landscape. The results reveal significant relationships between return on assets, return on equity, GDP growth, and efficiency. Furthermore, it is noteworthy that Islamic banks demonstrate higher efficiency compared to conventional banks. Additionally, non-significant associations were observed with credit risk, bank size, and the ratio of loan loss provision over net income. The findings hold implications for policymakers, industry stakeholders, and researchers aiming to bolster the resilience and competitiveness of Jordan's banking sector. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to benchmark the operational efficiency of fifty-eight public hospitals across Mexico between 2015 and 2018 and identifies the most critical inputs affecting their efficiency. In doing so, the study analyzes the impact of policy changes in the Mexican healthcare system introduced in recent years. Design/methodology/approach: To measure the operational efficiency of Mexican public hospitals, data envelopment analysis (DEA) window analysis variable returns to scale (VRS) methodology using longitudinal data collected from the National Institute for Transparency and Access to Information (IFAI). Hospital groups are developed and compared using a categorization approach according to their average and most recent efficiency. Findings: Results show that most of the hospitals in the study fall in the moving ahead category. The hospitals in the losing momentum or falling behind categories are mostly large units. Hospitals with initially low efficiency scores have either increased their efficiency or at least maintained a steady improvement. Finally, the findings indicate that most hospitals classified as moving ahead focused on a single care area (cancer, orthopedic care, child care and trauma). Research limitations/implications: This study examined the technical efficiency of the Mexican healthcare system over a four-year period. Contrary to conventional belief, results indicate that most public Mexican hospitals are managed efficiently. However, recent changes in public and economic policies that came into effect in the current administration (2018) will likely have long-lasting effects on the hospitals' operational efficiency, which could impact the results of this study. Originality/value: To the best of authors' knowledge, this is the first study that examines the efficiency of the complex Mexican healthcare system using longitudinal data. [ABSTRACT FROM AUTHOR]

Featured Application: The method is applied to the RFID system but can be implemented to assess the risks of any other equipment or process. The failure mode causes and effects analysis (FMCEA) is a commonly used reliability approach. It identifies, predicts, and analyzes potential failure modes affecting the proper function of equipment or the process under study, along with their roots and consequences. FMCEA aims to evaluate and assess the risks resulting from their occurrence, intending to suggest corresponding repair, adjustment, and precautionary measures to be planned during the conception, instruction, or implementation stages. However, the FMCEA has been criticized in the literature for its many inherent shortcomings related to risk assessment and prioritization. Therefore, this study presents an enhanced FMCEA method to address the deficiencies of the traditional risk priority number (RPN) and improve the reliability of risk assessments and corrective actions. A data envelopment analysis (DEA), as a non-parametric method, is used to evaluate the efficiency of these failures by considering their fixing time and cost and deciding on their final priority ranks. Sub-failure modes and their interrelationships are also taken into account. The radio frequency identification (RFID) system was chosen as an example due to its core role in Industry 4.0 and the Internet of Things (IoT) to demonstrate the effectiveness and usefulness of the proposed method. A total of 67 failures related to both hardware and software parts, including the environmental impacts of this technology, have been disclosed. The results of the conventional and the suggested FMCEA methods are found to be considerably different, with ten failure modes classified as being the most efficient. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Economia e Sociologia Rural is the property of Sociedade Brasileira de Economia e Sociologia Rural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: Uncertainty in data, whether in real-valued or integer-valued data, may result in infeasible optimal solutions or unreliable efficiency scores and ranking of decision-making units. To handle the uncertainty in integer-valued factors in data envelopment analysis (DEA) models, this study aims to propose a robust DEA model which is applicable in the presence of such factors. Design/methodology/approach: This research focuses on the application of fuzzy interpretation of efficiency to a mixed-integer DEA (MIDEA) model. The robust optimization approach is used to address the uncertain integer-valued parameters in the proposed MIDEA model. Findings: In this study, the authors proposed an MIDEA model without any equality constraint to avoid the arise problem by such constraints in the construction of the robust counterpart of the conventional MIDEA models. We have studied the characteristics and conditions for constructing the uncertainty set with uncertain integer-valued parameters and a robust MIDEA model is proposed under a combined box-polyhedral uncertainty set. The applicability of the developed models is shown in a case study of Malaysian public universities. Originality/value: This study develops an MIDEA model equivalent to the conventional MIDEA model excluding any equality constraint which is crucial in robust approach to avoid restricted feasible region or infeasible solutions. This study proposes a robust DEA approach which is applicable in cases with uncertain integer-valued parameters, unlike previous studies in robust DEA field where uncertain parameters are generally assumed to be only real-valued. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate agricultural sustainability as a collective issue involving multiple rather than individual farms. Through the utilization of energy consumption as a proxy, we propose a novel methodology that evaluates the impact of farm consolidations on agricultural sustainability while accounting for resource preferences. Our approach incorporates the ordered weighted averaging (OWA) operator within an inverse data envelopment analysis (IDEA) model to identify post-merger farms that meet preset efficiency targets. We employ a DEA cross-efficiency (DEA-CE) procedure to select merger plans that maximize agricultural sustainability for each preference scenario. By analysing a case study of 43 tomato greenhouse farms in Biskra, northern Algeria, our findings demonstrate that mergers can significantly enhance agricultural sustainability, surpassing the potential of individual farms by a factor of over 15. Additionally, the adoption of the most sustainable merger plan can lead to energy savings of more than 69%. Irrespective of the preference scenario, substantial energy savings in machinery, fertilizers, diesel, and electricity ranging from 22.92% to 73.73% were observed. These results emphasize the strategic role of merger processes in promoting agricultural sustainability and optimizing resource utilization. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Data envelopment analysis (DEA) is a leading approach in performance analysis and discovering newer benchmarks, and the traditional DEA models cannot forecast the future efficiency of decision-making units (DMUs). Machine learning, such as the artificial neural networks (ANNs), support vector machine/regression (SVM/SVR), projection pursuit regression (PPR), etc., have been viewed as beneficial for managers in predicting system behaviors. PPR is especially suitable for small and non-normal distribution samples, the usual cases in DEA analysis. This paper integrates DEA and PPR to cover the shortcomings we faced while using DEA and DEA-BPNN, DEA-SVR, etc. This study explores the advantages of combining these complementary methods into an integrated performance measurement and prediction model. Firstly, the DEA approach is used to evaluate and rank the efficiency of DMUs. Secondly, we establish two DEA-PPR combined models to describe the DEA efficiency scores (also called the production function) and the DEA-efficient frontier function. The first combined model's input variables are input–output indicators in the DEA model, and the output variable is the DEA efficiency. In the second model, its input variables are input or output indicators in the DEA model, and the output variable is the optimal input indicator for input-oriented DEA or the output indicator for output-oriented DEA. We conducted positive research on two examples with actual data and virtual small, medium-sized, and large samples. Compared with the DEA-BPNN and DEA-SVR models, the results show that the DEA-PPR combined model has more vital global optimization ability, better convergence, higher accuracy, and a simple topology. The DEA-PPR model can obtain robust results for both small and large cases. The DEA-BPNN and DEA-SVR models cannot obtain robust results for small and medium-sized samples due to overfitting. For large samples, the DEA-PPR model outperforms DEA-BPNN, DEA-SVR, etc. The DEA-PPR combined model possesses better suitability, applicability, and reliability than the DEA-BPNN model, the DEA-SVR model, etc. [ABSTRACT FROM AUTHOR]

X-bar control charts are widely used to monitor and control business and manufacturing processes. Design of control charts refers to the selection of parameters, including sample size, control-limit width, and sampling frequency. Many researchers have worked on this issue and have also proposed various solutions. However, despite the numerous advantages, the proposed methods also have their own set of problems. The biggest challenge is the complexity of solving these issues. Due to the fact that optimal design of control charts can be formulated as a multi objective optimization problem, in this paper to solve this problem, we used initial solution Spider's web data envelopment analysis method. In previous methods used multiple algorithms to resolve the issue. But in the proposed method once using Data Envelopment Analysis method and without any other algorithm can solve multi objective problem and this method can yield desirable efficient. Lastly, we compare our method with others and demonstrate its application in a real industrial context. [ABSTRACT FROM AUTHOR]

The study assesses the spatial efficiency of Romania's counties and development regions from the perspective of sustainable development. Data Envelopment Analysis (DEA) was used to calculate spatial efficiency, and A-P model (Andersen and Petersen) was used to rank efficient units (those with a score of 1.000) to assess superefficiency. The results of the study are graphically translated into four maps. The average efficiency of Romania's counties is 0.945 and the average efficiency of development regions is 0.986. Almost all development regions are efficient, with the only inefficient region being the Sud-Est region (0.887). The development regions with the highest superefficiency scores are located in the western part of Romania. The highest score of superefficiency is registered by the București-Ilfov region (30.997), the capital of the country and its surroundings, the economic engine of Romania. [ABSTRACT FROM AUTHOR]

China's New Energy Vehicle (NEV) industry has entered an accelerated development stage, with the technology innovation efficiency becoming an essential factor affecting the speed of its development. Therefore, to explore ways to improve the efficiency of technology innovation, this paper divides the technology innovation process into two stages: the research and development (R&D) stage and the commercialization stage. This paper introduces a framework for evaluating the technology innovation efficiency of NEV enterprises based on data envelopment analysis (DEA), meta-frontier, and tobit regression. Then, the technology innovation efficiencies of 45 vehicle, battery, and motor & electronic control NEV enterprises are analyzed. The results show that: (i) During the survey period, the overall technology innovation capability of Chinese new energy vehicle enterprises is not strong, especially regarding economic transformation. However, the general development trend is on the rise. (ii) Different types of enterprises have different technology gap ratios in the two stages. The technology innovation efficiency level of motor and electronic control enterprises is relatively weak in the new energy vehicle industry. (iii) Many measures can be implemented to help improve technology innovation efficiency: encouraging the large-scale development of enterprises, reasonably increasing the shareholding ratio of significant shareholders, improving the ratio of capital investment, stimulating the vitality of innovation within the enterprise, and optimizing the number of employees. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to evaluate the performance of mutual funds during the COVID-19 pandemic with environmental, social and governance (ESG) criteria. The main research question is whether mutual fund performance differs with respect to the level of the mutual fund's ESG score. Design/methodology/approach: The data set contains global fund data, and mutual fund performance is analyzed using two types of data envelopment analysis (DEA) models: the DEA portfolio index (DPEI) and the range direction measure (RDM) DEA. Propensity score matching and logistic regression are also applied. Findings: The results reveal that: nonequity mutual funds present significantly higher performance compared to the performance of equity mutual funds; mutual funds with high ESG scores are associated with significantly higher performance compared to those with low to medium ESG scores; funds with high ESG scores experience higher performance irrespective of their type; and efficiency scores derived from the RDM DEA are significantly higher than those derived from the DPEI model. Research limitations/implications: Investors, fund managers and market participants can benefit from the findings of this study and improve their investment decision-making process, including more sustainable funds in their portfolios. Regulators and policymakers should further promote or even require the inclusion of more sustainable investments in the financial products offered by institutional investors. The main limitation of the study is related to data availability regarding the ESG score of mutual funds. Originality/value: To the best of the authors' knowledge, this is the first study that provides robust evidence in support of a positive association between ESG scores and mutual fund performance during the pandemic-induced crisis applying a DEA methodology. [ABSTRACT FROM AUTHOR]

RESEARCH OBJECTIVES: Innovation is a significant factor in socio-economic development, and it is principal in achieving a competitive advantage among enterprises, regions, and countries. The purposes of this paper are: (1) to present, on the basis of the available literature, the essence and metrics of eco-innovation activities in EU countries, (2) to analyze the effectiveness of eco-innovation activities in EU countries using the Data Envelopment Analysis (DEA) method. THE RESEARCH PROBLEM AND METHODS: The research primarily focuses on a review of the recent literature and an analysis of the efficiency of eco-innovation activity using the DEA method. The empirical analysis is based on the Eco-innovation Scoreboard. THE PROCESS OF ARGUMENTATION: The analysis mainly determines the efficiency of eco-innovation. The theoretical basis for the considerations is a diagnosis of the existing state of eco-innovation in EU countries. RESEARCH RESULTS: The results illustrate the efficiency of eco-innovation activity in some EU countries against the backdrop of other EU countries. The analysis of eco-innovation activities and strategies to support them in the EU countries presented in this study is based on a diagnosis of the current state of their economies in this area using the methods of DEA and literature review. The results of this research enable us to draw concrete conclusions on the efficiency of eco-innovation activities. CONCLUSIONS, INNOVATIONS, AND RECOMMENDATIONS: The results of the Eco-innovation Scoreboard for these countries illustrate an inefficient use of materials, water and energy resources, as well as low resource productivity. The gaps in this scope between Central-Eastern and Western EU countries are diminishing, but there are still institutional, legislative, and economic shortcomings in eco-innovation that should be addressed. The analysis indicates the level of eco-innovation development in EU countries, its efficiency, and the potential for supporting activity in this field by these countries. [ABSTRACT FROM AUTHOR]

This paper deals with an inverse data envelopment analysis (DEA) based on the non-radial slacks-based model in the presence of uncertainty employing both integer and continuous interval data. To this matter, suitable technology and formulation for the DEA are proposed using arithmetic and partial orders for interval numbers. The inverse DEA is discussed from the following question: if the output of D M U o increases from Y o to β o , such the new DMU is given by (α o ∗ , β) belongs to the technology, and its inefficiency score is not less than t-percent, how much should the inputs of the DMU increase? A new model of inverse DEA is offered to respond to the previous question, whose interval Pareto solutions are characterized using the Pareto solution of a related multiple-objective nonlinear programming (MONLP). Necessary and sufficient conditions for input estimation are proposed when output is increased. A functional example is presented on data to illustrate the new model and methodology, with continuous and integer interval variables. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is an analysis of the process of digital transformation of enterprises, by measuring the efficiency of the use of information and communication technologies (ICTs) in business in 29 European countries in the period from 2012 to 2020. Design/methodology/approach: A Charnes, Cooper and Rhodes data envelopment analysis (CCR DEA, 1978) window model has been developed to measure the ICT efficiency of European countries. Several indicators of the use of information and communication technologies in enterprises are selected as the variables of the proposed models, which are available as such in the Eurostat database for European countries. Due to the sensitivity of the results obtained by applying the DEA method to measurement errors and output values, the robustness analysis of the obtained values of average efficiency is also performed, using the bootstrap method. Findings: The obtained results show that the highest average technical efficiency of the use of ICT in companies by windows, in the observed period, is recorded in Belgium, while Denmark is in the second place. Bulgaria, Romania, Greece and Latvia have the lowest average technical ICT efficiency per window. The analysis of the obtained results by years in the same period brings identical conclusions. Only Belgium has been ICT efficient many times. In general, for all observed countries, the movement of average ICT efficiency in the observed period shows a slightly growing trend, with the exception of a significantly decline in 2013. However, the fact is that the ICT efficiency of the observed countries in the past period is relatively low and for all countries it is 46.36%, with no country being 100% efficient and with eight countries whose average efficiency is below 50% of best practice. Research limitations/implications: To measure and evaluate the efficiency of ICT use in enterprises, four variables for efficiency assessment are identified, given the fact that only these data are available continuously for the observed period from 2012 to 2020 in the Eurostat database. Practical implications: Low efficiency of using digital potential in business of the observed countries indicates the need for better understanding of the nature and goals of the digital business transformation process by employees and management, to create conditions for effective implementation and optimization of business digitalization. Originality/value: Measurement of digital transformation is the subject of a very small number of studies and research, which mainly focus on measuring and assessing the impact of digital transformation on individual countries and perform a comparative analysis of technological development in those countries. Also, analyses are mainly based on identifying similarities and differences between countries or ranking countries according to adopted evaluation criteria using different digitization indices. A step forward in this research is the application of the DEA window method for measuring the relative efficiency of the use of ICT in enterprises, and the development of a model that can be extended if necessary with indicators for which data are available. [ABSTRACT FROM AUTHOR]

Objective: This research paper aims to evaluate the relative efficacy of pharmacognosy research on the top 40 tertiary hospitals and its pharmacy in India according to Newsweek's 2021 ranking. Purpose: India's pharmaceutical market is now estimated to be worth $50 billion. India is a significant pharmaceutical exporter, reaching more than 200 nations with its exports. This study will help to identify whether the pharma industry is working efficiently in selected Indian hospitals or not. Thus, to create a bench marking for the hospital pharmacies in India. Design/methodology/approach: Data for the study came from public and private hospitals' official websites, financial records, and government notifications. Crosssectional data analyses were done using a Slack-Based model and data envelopment analysis (DEA). STATA was the software used to scrutinize the results effectively. Results: Thirteen hospital pharmacies out of a total of 40 are judged to be relatively technically efficient overall, according to the research. The findings of the slack analysis demonstrate that government hospitals and pharmacies are better able to handle slack and effectively manage input and output. Conclusion: The researchers also set upped a benchmarking hospital both for private and government healthcare settings for pharmacognosy research. Thus, it can be utilized for the betterment of the pharmacies. [ABSTRACT FROM AUTHOR]

The world's industries, transportation systems, and households rely heavily on fossil fuels despite their limited availability and high carbon content. Therefore, it is of the utmost importance to improve fossil fuel energy efficiency in order to facilitate the shift towards a sustainable energy system with reduced greenhouse gas emissions. This paper employs a slacks-based measure network data envelopment analysis model with undesirable outputs to assess the efficiencies of fossil fuel energy in the Association of Southeast Asian Nations (ASEAN) countries during a span of seven years, specifically from 2015 to 2021. The inclusion of undesirable outputs in this study is important because it allows for a more realistic assessment of efficiency by considering factors like CO2 emissions, which are undesirable outcomes associated with fossil fuel use. The datasets utilised in this study are sourced from the U.S. Energy Information Administration and the open data website of Our World in Data. Based on the findings, it can be observed that Singapore and the Philippines have demonstrated outstanding performance in maximising the utilisation of fossil fuels. In contrast, Myanmar exhibits the lowest level of efficiency in this analysis. By identifying top-performing countries in terms of fossil fuel efficiency, it is possible to implement measures to boost efficiency in under-performing countries. This can be achieved through the promotion and adoption of cleaner energy alternatives, specifically renewable energy sources that exhibit a low or negligible carbon footprint. These findings offer significant contributions to policymakers exploring sustainable energy usage, environmental stewardship, and the formulation and execution of comprehensive strategies that aim to mitigate carbon dioxide emissions arising from the consumption of fossil fuels in the ASEAN region. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using Data Envelopment Analysis (DEA) oriented to input resources under variable returns to scale, the efficiency of production factors by administrative-territorial areas of Bulgaria as of 2021 in terms of gross value added (GVA) has been assessed. It is established that the factors of production are used effectively in only five of the 28 districts of Bulgaria. These efficiency regions are Vratsa, Gabrovo, Sofia, Sofia (capital) and Stara Zagora. The average value of the efficiency coefficient is 0.8178 and shows that the produced GVA in Bulgaria in 2021 should be achieved on average with 18.22% less production factors than the used ones – costs for acquiring fixed assets and employed persons. In connection with the latter, it is rational to direct production factors toward economic activities in which there are competitive advantages and potential for development on the territory of the respective area. [ABSTRACT FROM AUTHOR]

In the present world, calculating the efficiency (or the performance) of systems with an internal structure, such as two-stage systems, is principally imperative during multi-time periods. In the present approach, the traditional two-stage Data Envelopment Analysis (DEA) model is developed to a multi-period two-stage DEA model, which evaluates the overall and periods of efficiencies synchronously. This approach which is used, is not only alone incapable of having functional capacities under the assumption of variable returns to scale (VRS), but is also inattentive to the importance or magnitude of data in different periods. In this study, in order to surmount these shortcomings, we expand the existing approaches and introduce a generalized model to measure the overall efficiency of a multi-period two-stage system under the VRS assumption, wherein, the importance of data in time- periods is considered in a diverse manner. According to this generalized model, theorems are also being presented to determine the type of returns to scale (RTS) of both stages, as well as the system of the entire time periods and also each period. Finally, the real data of Taiwanese non-life insurance companies, which has been extracted from the extant literature, is used to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

We highlight the state of the art in the eco-efficiency measurement using data envelopment analysis, including Malmquist–Luenberger productivity index. We also consider productivity change over time, provide directions for future studies in the field and gather the most recent policy suggestions for governments, organizations and sectors for reducing CO2 emissions. A structured literature search of the Web of Science academic database reveals 311 papers published between 1989 and 2022. We carry out network analysis of citations to show the evolution of the literature in this research topic. In doing so, we (a) examine the key-route main path of knowledge flows, (b) provide basic bibliometric information about the most active journals and authors, (c) conduct a qualitative in-depth analysis of the identified most important studies and (d) identify the research fronts and relate them to the emerging issues on the topic researched, focusing on the most recent period between 2000 and 2022. Based on the insights of the literature review, the second part of this paper critically analyses the papers on the key-route (main path) of this subject. This review can be used as guidance and a starting point for researchers and practitioners who want to further investigate optimal policies to reach NetZero. [ABSTRACT FROM AUTHOR]