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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]

Copyright of African Journal of Reproductive Health is the property of Women's Health & Action Research Centre 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.)

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Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

The efficiency of banks in any country is a key factor in the growth and development of that country's economy. Recently, the evaluation and improvement of the efficiency of the banking industry in Iran has received a lot of attention. The purpose of this article is to evaluate the efficiency of selected Iranian banks in 2021. Although in many studies of the banking industry, classical DEA models have been used without considering the internal structure and partial processes of banks. To overcome the limitations of the classic DEA model, the use of network data envelopment analysis (NDEA) models with the Nash bargaining game approach is the innovation of this research. In this model, all the internal components of the process and its overall coherence are considered to evaluate the efficiency of partial processes and to identify the sources of inefficiency of the entire banking system. Another advantage of the results of these evaluations is that they can provide managers with sufficient management information to identify the causes and factors affecting efficiency and gain competitive advantages. The results show that only future banks, Middle East, Mellat Saderat, Tejarat, export development in both stages and the whole performance system are efficient. Bargaining game model has also been used to fairly analyze efficiency and identify the stage that caused inefficiency. According to the private bank's goal to earn profit, banks that do not perform properly have not been able to use their physical resources and capacities optimally to attract deposits and generate more profit. [ABSTRACT FROM AUTHOR]

Urban development is facing increasingly complex disturbances. Assessing large cities' urban resilience is important for improving their ability to withstand disturbances and promoting sustainable development. Therefore, this paper establishes an improved assessment model for urban resilience based on the driving force–pressure–state–impact–response (DPSIR) and data envelopment analysis (DEA) model. The Malmquist index, Dagum Gini coefficient, and Markov chain were sequentially used for spatiotemporal evolution and differential resilience analysis. Then, 105 large Chinese cities were selected as case studies. The results indicate their overall resilience is relatively high; each year's average resilience efficiency can achieve DEA effectiveness. The distribution pattern of resilience level presents a healthy olive-shaped structure. However, there is also a significant difference between the two poles. During the research period, the combined effect of technological efficiency improvement and technological progress resulted in the overall resilience slowly improving, and this process was more driven by technological innovation. At the same time, the overall regional difference in resilience also shows a narrowing trend, and the current spatial differences mainly come from the difference within subregions and super-density. In future transfer predictions, the resilience of large cities will show good stability with a higher probability of maintaining stability; if the resilience undergoes a transition, the probability of an increase will be higher than a decrease. Based on the life cycle process of resilience, this study selects indicators that can characterize the level of resilience according to the DPSIR model, which comprehensively reflects the characteristics of urban resilience. This study's results can provide particular reference values for urban disaster response emergency planning and sustainable development construction, and it also provides new ideas for the assessment research of urban resilience. [ABSTRACT FROM AUTHOR]

One of the paramount challenges in natural resource management revolves around the delicate equilibrium between the demand for and the supply of diverse Ecosystem Services (ESs) within a cultural landscape. Recognizing the centrality of cultural landscapes to human well-being, the sustainability of these landscapes hinges upon the health and stability of ecosystems that can effectively provide the required ESs. Over the long term, the sustainable supply of ESs is constrained by the potential supply of ESs. Understanding the potential supply of ESs is crucial for averting compromises to the ecosystems within a landscape. This article introduces a novel perspective on evaluating the ESs of a landscape by means of efficiency analysis. Instead of presenting the potential supply of ESs in absolute terms, we offer a comparative analysis of ESs' relative supply to associated management costs. In principle, the efficiency of Landscape Units (LUs) is defined as the ratio of the potential supply of multiple ESs to the costs associated with land use and land cover management. The resultant efficiency maps serve as hot and cold spot maps, revealing efficient ecosystem compositions that yield multiple ESs. This composition reflects management efforts, incorporating various management costs. Forests emerge as pivotal ecosystems in landscapes, delivering the most ESs at the lowest costs. These efficiency maps offer valuable insights for regional planners, enabling them to enhance the supply of ES in inefficient LUs by studying the ecosystem structure and associated costs of the most efficient LUs. [ABSTRACT FROM AUTHOR]

Education 5.0@UiTM is transforming Universiti Teknologi MARA (UiTM). This new approach emphasizes flexible learning paths that teach life and cross-disciplinary skills. To make higher education future-ready, universities must adapt fast to the significant global and technical changes caused by the 4th Industrial Revolution and transition from content-based to individualized learning. To ensure students meet Education 5.0@UiTM standards and corporate needs, the evaluation process must be reviewed. Competency-based academic achievement evaluation outperforms grades. This assessment is rarely systematically analyzed. This study was conducted to thoroughly explore the selection of input and output factors in determining student achievement efficiency using the data envelopment analysis (DEA) approach. The study reviewed publications from Scopus, Google Scholar, and Science Direct and found six themes for input variables: human resources, facilities condition and equipment, finance, curriculum, students characteristics, and community resources. output variables included student achievement, satisfaction, graduation rate, employment, research, and community resources. In DEA analysis, input and output selection affects scope, aim, production frontier, efficiency scores, accuracy and completeness. So, characterizing student achievement requires choosing relevant input and output variables. This study can enhance student evaluation processes to prepare college graduates for the next industrial revolution. [ABSTRACT FROM AUTHOR]

Over the past few decades, microfinance institutions have attracted the interest of governments and academics alike, given their unique nature of being financial institutions with a dual mission of promoting social development and reducing poverty. However, concerns have been raised about their effectiveness in achieving these goals while remaining financially sustainable. In this study, we attempt to examine the factors that have the greatest impact on the social, financial, and overall efficiency of microfinance institutions in African regions. We adopt a two-step approach: First, we assess the efficiency scores of 95 microfinance institutions in Africa between 2005 and 2018 using a data envelopment analysis (DEA) approach. We then regress their efficiency scores on a set of determinant variables, capturing the microfinance institutions' characteristics. Our findings suggest that a majority of institutions prioritize profitability over social outreach. Furthermore, the panel data regression indicates that factors such as profitability, equity capitalization, types of loans, and low gross domestic product (GDP) have a positive influence on microfinance institutions' efficiency. Conversely, variables including their risk portfolio, grants, microfinance institution status (Non-Governmental Organization (NGO), cooperative, etc.), operational area, political environment, and size exert a negative impact on efficiency. Through this study, we seek to enhance our understanding of microfinance institutions and to identify the factors that impact their operational efficiency, thereby reinforcing their crucial role in advancing financial inclusion, empowering marginalized communities, and fostering inclusive economic growth. [ABSTRACT FROM AUTHOR]

Micro and large-sized enterprises are important elements to enhance the economic growth of any country, and even more so for developing countries such as Mexico. These enterprises highly contribute to job generation, competitiveness, and gross domestic product, factors that are important for the developing of a nation. The aim of this paper is to study the impact of human capital investments in the efficiency of the 21 economic activity subsectors for micro and large-sized enterprises in the Mexican manufacturing industry between 2009–2021. The database come from Mexico Annual Manufacturing Industry Survey. Four Data Envelopment Analysis models are developed to study the relationship between annual average working days, annual average wages, and annual average investment in training with average sales per year. Data indicate that, most of the micro-sized enterprises of the Mexican manufacturing sector do not invest in human capital training, contrary to their large-sized enterprises. The results show that investing in human capital training increase sales and wages in micro-sized enterprises of the Mexican manufacturing industry, but it is not evident in large-size enterprises of the Mexican manufacturing industry. The calculation of the economic activity subsectors efficiencies using the developed Data Envelopment Analysis models indicate that all the economic activity subsectors with scale efficiency equal to one optimally invest, and the average amount of investments in human capital training needed to increase the global and pure technical efficiencies of the others are calculated with the developed Data Envelopment Analysis models. In the three main economic activity subsectors of the Mexican manufacturing industry, a significant increase—in 83.33% of cases—in wages and salaries is seen in both micro and large-sized enterprises. Particularly, the results indicate that the Chemical industry economic activity subsectors show the highest efficiency in both micro and large-sized enterprises when the human capital training variable is present. This paper demonstrates the importance of investing in human capital to enhance the efficiency of micro and large-sized enterprises. [ABSTRACT FROM AUTHOR]

Purpose: In China, engineering insurance has been questioned as not being beneficial as expected. This paper seeks to further understand how China's engineering insurance industry functions and to provide a macro perspective explanation for engineering insurance's underdevelopment. Design/methodology/approach: Three industrial organization hypotheses were extended to the engineering insurance context: structure conduct performance (SCP), relative market power (RMP) and efficiency structure (ES) hypotheses. This paper employed the Generalized Method of Moments (GMM) and Data Envelopment Analysis (DEA) bootstrap to test the hypotheses using panel data from 2008 to 2017. Findings: The results suggest that the SCP paradigm is validated in China's engineering insurance market, indicating a concentrated market where the welfare of consumers (e.g. owners, contractors and designers) may be eroded. Several factors are identified to have significant impacts on engineering insurers' performance, such as the investment return, percentage of engineering business, the ratio of outstanding claims, the number of large contractors, market rivalry and entry barriers. Originality/value: Despite the sheer size of China's construction industry and the urgent need to improve risk management, the insurance industry that serves construction firms engineering insurance is underdeveloped. Engineering insurance is yet to be understood from a macro perspective, which may reveal the underlying reasons for engineering insurance's underdevelopment. The industrial organization theories provided a theoretical framework to test the functioning of this specific industry. The disaggregated data (engineering line specific) is employed to ensure effective regulation and policymaking. [ABSTRACT FROM AUTHOR]

Green development and solutions to environmental problems rely on local conditions and green connections between regions during their development. However, it is less understood whether a region's emission of carbon and pollutants is moderate compared to its development. The study proposes the 'green connection of regional development' concept from the consumption perspective to enhance the understanding and measurement of regional capacity towards green development and responsible production and consumption. Data envelopment analysis was performed to evaluate the local and embodied emissions of eight pollutants for 30 provinces in China from 2012 to 2017. Twenty provinces had optimal capacity of green growth within the region and 15 had an optimal green connection of regional development. Suboptimal levels of overall green growth and green connection of regional development efficiencies were due to insufficient pure technical and scale efficiency levels, with a stronger impact of the former. [ABSTRACT FROM AUTHOR]

Improving green productivity (GP) is critical for achieving global sustainable green development. Yet, past efficiency studies on the African continent have ignored Africa's GP, estimation, and driving factors. Specifically, the role of sovereign debt vulnerability, environmental entrepreneurship, and institutional quality in ensuring Africa's green productivity development is lacking. Therefore, this study built a meta‐frontier biennial weighted modified data envelopment analysis (DEA) model to evaluate green productivity across resource‐intensive and non‐resource‐intensive African countries from 2010 to 2019. Further, the study adopted the global Malmquist‐Luenberger productivity index (GML) to investigate the dynamic changes in African green productivity growth. The results suggest that only a few African countries are efficient, implying more room for improvement for the other countries. The heterogeneity of green productivity across the resource's classification exhibits significant disparity. The technology gap also prevails among the two resource classifications of African countries. The GML index indicates that Africa's green productivity is mainly explained by technical change instead of efficiency change. The bootstrap truncation regression suggests an inverted U‐shaped nexus between growth and green productivity in Africa and resource‐intensive countries (RIC). Green environmental entrepreneurship positively affects green productivity in Africa. On the components of institutional quality, we observed that the rule of law positively influences green productivity in non‐resource‐intensive countries (NRIC) and the whole of Africa. In contrast, political stability, government effectiveness, voice and corruption negatively affected green productivity in NRIC. Also, foreign direct investment (FDI) and sovereign debt vulnerability negatively correlate to green productivity in Africa as a whole and NRIC, respectively. The findings of this study aid in raising awareness of green productivity in developing countries, which is crucial in boosting global green development. [ABSTRACT FROM AUTHOR]

Evaluation of green development is crucial for policy analysis and decision‐making. To address the research gap on green development assessment systems, this study first presents the Super‐efficiency Bounded Adjusted Measure model to gage the green growth index and economic growth index. This analysis involves evaluation of the coupling coordination degree. Through the analysis of the coordination between green growth and economic growth leads to recommendations for each region under consideration in regards to promotion of the components of the sustainable growth. This study presents the model that melds the objectives of the green and economic growth and uses the results for constructing the sustainable development Lorenz curve and Gini coefficient for the analysis of the differences in the sustainable development levels across the regions. The empirical case is presented to show the possibilities for application of the proposed approach. [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]

The COVID-19 pandemic posed unheralded challenges to people, business, government at all levels (federal, provincial, regional), and society at large. In addition to the direct consequences of taking care of infected people, which in some countries led to a virtual collapse of the healthcare system, the pandemic strained eldercare, employment, economic growth, and exacerbated mental health and social problems. During the first year of the pandemic, researchers' and policy makers' main focus was on 'flattening the curve,' and on predictive modeling of infections and deaths. In this paper we present a non-parametric data-driven descriptive analysis based on Data Envelopment Analysis to assess COVID-19 in ten Canadian provinces over the two year period 2020 to 2021. The objective is to derive worst- and best-case intra-provincial benchmarks to assess if and to what extent the situation could have been worse respectively better. To take account for any indirect socio-economic impact our analysis incorporates official monthly unemployment rates and a stringency index reflecting the level of social policy restrictions imposed by the provincial governments. A major contribution of the model framework is that it provides a mechanism for measuring the impact of the two main strategies in curbing the pandemic, namely vaccination and social policy restrictions. As a robustness check, the bench-mark results are compared against bias-corrected efficiency measures. [ABSTRACT FROM AUTHOR]

The logistics performance index (LPI) is a comprehensive and comparable composite index developed by the World Bank to assess a country's logistics and trade facilitation environment. However, the existing LPI relies on externally assigned weights. To enhance LPI scores, this study adopts an effectiveness-based hierarchical data envelopment analysis method that internally allocates objective weights based on the dataset. Such endogenous weight information can provide additional valuable insights for countries to prioritize and strategize efforts to enhance their performance in the future. The results of this study indicate that focusing on improving the policy category yields greater benefits than improving the service category in terms of ranking national logistics performance. Furthermore, this study finds that logistics performance is influenced by income levels and geographical area. Income levels impact the regulatory and trade facilitation environment, with varying income levels leading to different priority policy areas. Geographical location also plays a crucial role in regional economic integration and trade facilitation. A favorable geographical location reduces costs and time while enhancing supply chain predictability and reliability. It is hoped that this study serves as a valuable resource for countries in identifying optimization strategies to improve their logistics performance. [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]

Generic data envelopment analysis (DEA) models are based on deterministic input and output. However, input and output vectors are often interrupted by random factors, such as measurement errors and data noise, in real economic situations. This study proposes a new chance-constrained network DEA model based on the modified directional distance function (DDF) and enhanced Russell measure (ERM) model for assessing government management and culture-led urban regeneration. In addition to exploring the randomness of data, this study integrates the advantages of both ERM and DDF and considers the inefficiency level from a non-oriented viewpoint, the direction vector, and each input and output simultaneously. Each input and output of the two production stages can use non-radials to measure efficiency. Results show that the urban-rural gap has gradually widened since 2015. To validate the legitimacy of the model, this study utilizes the bootstrapping method to verify the results of the stochastic network DEA model and the conventional two-stage network DEA approach. This study also considers different alpha values as basis for comparison to confirm whether the results obtained differ by uncertainty level. [ABSTRACT FROM AUTHOR]

The sharing economy affords new opportunities to the global tourism industry, accelerating the development of tourism at a striking rate. This paper examines productivity of the bed and breakfast (B&B) industry and how to improve the performance of the B&B industry in a sharing economy. A modified global Malmquist index using slack-based measures is developed to measure productivity, and benchmark selection models using technology forecasting are proposed to improve future performance. The future technology is forecasted by averaging the frontier shifts (technology changes) in previous periods. This study challenges the implicit assumption that the technology will be stationary in traditional benchmarking, and suggests that how the information of frontier shifts derived from Malmquist index can be used to forecast future technology in a rational manner. The empirical results inform that there is a productivity growth in the B&B industry in a sharing economy, and the main driver of the growth is technology progress rather than performance improvement. In particular, a counterintuitive but interesting result is that the B&B industry experienced a slight productivity growth after the outbreak of COVID-19. The benchmarks in 2021 and 2022 are forecasted to improve the performance of the B&B industry. [ABSTRACT FROM AUTHOR]

This paper presents an investigation of the interconnection between firm size and efficiency under the financial constraints lens. Specifically, we used the Data Envelopment Analysis (DEA) technique to measure the efficiency of a sample of large, medium-sized and small private Italian firms, using the firms' default risk as an undesirable output. Our findings indicate that larger companies perform better than medium-sized and smaller companies in terms of efficiency (across all business profiles), including default on bank loans. Based on indicators widely employed to characterize the bank-firm relationship, our study demonstrates the need to improve the efficiency of the Italian entrepreneurial system, consisting mainly of small companies, through their dimensional growth. [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]

There are still important theoretical and empirical gaps in understanding the role of best practices (BPs), such as quality management, lean and new product development, in generating firm's performance advantage and overcoming trade-offs across distinct performance dimensions. We examine these issues through the perspective of performance frontiers, integrating in novel ways the resource-based theory with the emergent practice-based view. Hypotheses on relationships between BPs, performance advantage, and trade-offs are developed and tested with stationary and longitudinal (recall) data from a global survey of manufacturing firms. We use data envelopment analysis, which overcomes limitations of mainstream methods based on central tendency. Our findings support the view that BPs may serve as a source of enduring competitive advantage, based on their ability to lead to a heterogeneous range of dominant and difficult-to-imitate competitive positions. The study provides new insights on contemporary debates about the role of BPs in generating performance advantage and how practitioners can sustain internal support and extract benefits from them. [ABSTRACT FROM AUTHOR]

The nonparametric estimator of the Extended Facet reference technology for the Constant Returns to Scale case has attracted some attention, because the associated production frontier by construction does only include strongly efficient faces of maximal dimension, or strongly efficient faces that are part of such strongly efficient faces of maximal dimension. The strongly efficient faces of maximal dimension are denoted Full Dimensional Efficient Facets (FDEFs). The identification of such strongly efficient facets is facilitated by removing all inefficient and all strongly efficient but not extreme efficient DMUs from the estimation procedure of the technology set. Any face that i) is passing through the origin and with (s + m − 1) linear independent extreme efficient observed DMUs positioned on it and ii) with a normal vector with strict positive (strict negative) output (input) components, is a FDEF, where s (m) is the number of outputs (inputs). It is, however, not correct that every face that satisfies only i) is a FDEF. We denote a face (a subface) that satisfies only condition i) but not condition ii) for an AP-face (an AP-subface). It is proved that a radial projection of any output input combination in the estimated EXFA technology set is positioned on the strongly efficient frontier if and only if i) no AP-(sub)faces exist, ii) a regulaty condition RC1 is satisfied and only dual multiplier constraints corresponding to extreme efficient DMUs are included in the estimation. A test for the fulfillment of the condition that no AP-(sub)faces exist is provided. [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]

Background: Policymakers, who are constantly discussing growing health expenditures, should know whether the health system is efficient. We can provide them with such information through international health system efficiency evaluations. The main objectives of this study are: (a) to evaluate the efficiency of health systems in 28 developed countries by multiple-criteria decision analysis (MCDA) and data envelopment analysis (DEA) and (b) to identify reasonable benchmark countries for the Czech Republic, for which we collect information on the relative importance of health system inputs and outputs. Methods: We used MCDA and DEA to evaluate the efficiency of the health systems of 28 developed countries. The models included four health system inputs (health expenditure as a relative share of GDP, the number of physicians, nurses, and hospital beds) and three health system outputs (life expectancy at birth, healthy life expectancy, and infant mortality rate). The sample covers 27 OECD countries and Russia, which is also included in the OECD database. To determine the input and output weights, we used a questionnaire sent to health policy experts in the Czech Republic. Results: We obtained subjective information on the relative importance of the health system inputs and outputs from 27 Czech health policy experts. We evaluated health system efficiency using four MCDA and two DEA models. According to the MCDA models, Turkey, Poland, and Israel were found to have efficient health systems. The Czech Republic ranked 16th, 19th, 15th, and 17th. The benchmark countries for the Czech Republic's health system were Israel, Estonia, Luxembourg, Italy, the UK, Spain, Slovenia, and Canada. The DEA model with the constant returns to scale identified four technically efficient health systems: Turkey, the UK, Canada, and Sweden. The Czech Republic was found to be one of the worst-performing health systems. The DEA model with the variable returns to scale identified 15 technically efficient health systems. We found that efficiency results are quite robust. With two exceptions, the Spearman rank correlations between each pair of models were statistically significant at the 0.05 level. Conclusions: During the model formulation, we investigated the pitfalls of efficiency measurement in health care and used several practical solutions. We consider MCDA and DEA, above all, as exploratory methods, not methods providing definitive answers. [ABSTRACT FROM AUTHOR]

Regional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge‐specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high‐fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis‐based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge‐model‐ground‐motion sample pairs to conduct NRHA with much‐improved efficiency. Both independent and correlated multi‐output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL‐GPR framework and the associated component‐level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL‐selected samples would enable the GPR to derive bridge‐specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL‐GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi‐output independent GPR, a stable and reliable fragility model can be achieved using 50 AL‐selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi‐component bridge portfolios. [ABSTRACT FROM AUTHOR]

China's higher education system is shifting from quantitative expansion to connotative development to advance its quality. Since 2015, Chinese governments have been implementing a strategic policy for higher education called "double first-class", which aims to promote a number of Chinese top universities to construct world-class universities or to establish world-class disciplines. "Double first-class" universities have received a large amount of educational resources through this policy. Taking advantage of resources efficiently is an important element in promoting the development of higher quality higher education. However, research on resource allocation in China's "double first-class" universities is incomplete. Current research has not clarified the level of resource allocation efficiency or the factors affecting China's "double first-class" universities. With the help of the superefficient data envelopment analysis (DEA)-Malmquist–Tobit model, this study actively explores the current status of the resource allocation efficiency of China's "double first-class" universities to fill this gap in the field. Specifically, the development level and change trend of the resource allocation efficiency of 13 "double first-class" universities in China from 2015 to 2019 were measured with the help of the superefficient DEA-Malmquist model. The internal and external factors affecting the resource allocation efficiency of "double first-class" universities are also analysed with the help of the Tobit model. The overall level of resource allocation efficiency of "double first-class" universities is high, but the internal variability is large. From the perspective of efficiency decomposition, it is found that both technical efficiency change (EFch) and technical progress efficiency (TEch) play important roles in improving the total factor productivity (TFP) of resource allocation. Compared with TEch, EFch plays a more significant pulling role. This study confirms that the factors affecting resource allocation efficiency are complex. Among them, the regional economic environment, faculty title structure, and degree of international exchange have significant roles in promoting the resource allocation efficiency of "double first-class" universities, but local financial support and the time of policy implementation have certain negative effects. [ABSTRACT FROM AUTHOR]

The socio-labor inclusion of people with disabilities represents a substantial social challenge in European countries, particularly in Spain. Sheltered Workshops (SWs), a type of Work Integration Social Enterprise, are companies specifically designed to provide employment opportunities for individuals with disabilities, offering ongoing support throughout the integration process. SWs need to be profitable to sustain their social contributions. This study investigates the determinants of profitability for SWs in Spain, analyzing a sample of 1133 entities from 2010 to 2020. Using a static panel data model (random effects) and a dynamic panel data model (Generalized Method of Moments, GMM system), we estimate, among other factors, the impact of efficiency on profitability, with efficiency scores calculated via data envelopment analysis (DEA). Our findings reveal that the positive effect of operational efficiency outweighs the negative effect of social efficiency, resulting in a net positive impact of overall efficiency on profitability. The SWs were able to operate effectively to meet the needs of people with disabilities while achieving financial viability by combining social and economic goals. The study shows no significant differences in these effects when considering the legal status of the SWs (for-profit vs. non-profit). Additional factors influencing SW performance include size, low risk, and leverage, which have a positive impact, while age, liquidity, and tangibility generally have a negative and significant effect. During both the financial and COVID-19 crises, SWs experienced lower profitability; however, a positive and significant relationship between efficiency and profitability was observed during the COVID-19 crisis. The robustness of our results was confirmed through alternative measures of efficiency and performance and by addressing potential endogeneity issues. Understanding how financial and social factors influence profitability helps policymakers develop growth and sustainability policies for SWs and enables practitioners to identify strengths and weaknesses, facilitating better decision-making. [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]

Salt farming has been a hereditary occupation in the coastal communities of Madura Island; however, salt productivity in this area is still relatively low. The government has introduced a new production technology, called a geomembrane, as part of their efforts. The application of the latest technological innovations has been promoted worldwide to increase farm productivity, including in salt farming. This research aims to determine the determinants of adoption decisions for salt production technology and estimate the adoption impact on technical efficiency. The data in this study are cross-sectional from 215 small-scale salt farmers on Madura Island, East Java, Indonesia. The data were analyzed using logistic regression to identify which factors influenced farmers' decisions to use geomembranes. The influence of adoption on farmers' technical efficiency was then assessed using propensity score matching (PSM) and data envelopment analysis (DEA). The findings indicate that age and the dummy variables of gender, land ownership, profit-sharing involvement, and membership in the People's Salt Business Group (KUGAR) all had a significant impact on adoption rates. The findings of controlling matched samples using the PSM process reveal that geomembrane application improves and greatly increases farmers' technical efficiency. Those who used geomembranes displayed greater technical efficiency than those who did not. These findings imply that salt production technology should be promoted more to increase productivity, especially geomembrane adoption, through outreach and dissemination of information, including for landowners involved in the profit-sharing system. The government should keep supporting salt farmers and motivate them to adopt geomembrane technology to ensure the sustainability of salt production in the coastal communities on Madura Island. [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]

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]