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Achieving carbon peaking and carbon neutrality goals is a fundamental requirement for advancing high-quality development in China. Carbon emission efficiency (CEE) indicates the proportion of optimal emissions to actual emissions in the production system, providing a basis for reasonable carbon emission reductions. To facilitate the realisation of China's goals, this paper analyses CEE and the carbon emission reduction potential in China from 2001 to 2020. Considering the heterogeneity of both regions and industries, this paper proposes a three-hierarchy meta-frontier directional distance function (DDF) method to measure CEE under convex and non-convex assumptions of production possibility set (PPS). The empirical results show that the assumption of convex or non-convex axiom on the innermost PPS has a great impact on efficiency distribution, and the distribution of technology gap rate (TGR) is more affected by the selection of convex or non-convex assumption compared with the distribution of CEE. Currently, China's carbon emission efficiency remains low, mostly due to management inefficiency. The primary industry has more advanced emission reduction technologies than the other two industries. The eastern region has a large potential for carbon emission reduction because of its high carbon emission base, despite its high efficiency. [ABSTRACT FROM AUTHOR]

Critics opine that full expensing of research and development (R&D) depresses near-term profits and incentivizes myopic managers to under-invest in R&D, compromising firm efficiency. Advocates of the expensing rule argue that little rigorous research evidence supports the claimed adverse consequences. We examine the impact of the R&D expensing rule on firm efficiency by exploiting an exogenous shock: a shift in the accounting regime in Germany from full expensing to partial capitalization of R&D when it mandated International Financial Reporting Standard (IFRS) adoption in 2005. We employ Stochastic Frontier Analysis and Data Envelopment Analysis to estimate efficiency for the same German firms before and after the IFRS adoption. We find robust evidence of efficiency improvement in the post-period relative to the pre-period for German R&D firms that report R&D expenditures, and for both early adopters and timely adopters. We also document that financially constrained firms and firms experiencing rapid R&D growth prior to the IFRS adoption show greater efficiency improvement. Moreover, we conduct three falsification tests to make sure our results are not attributable to other accounting changes associated with the IFRS adoption, and find no efficiency improvement for the three control groups (German "no-R&D" sample, U.K. firms, and Australian firms), respectively. We conclude that the change in the R&D reporting rule is the likely catalyst for improvements in efficiency of German R&D firms. [ABSTRACT FROM AUTHOR]

This study investigates the existence of solutions for the key plant capacity utilisation (PCU) concepts using general nonparametric technologies. This is done via a theoretical review of existing and some new PCU concepts. Focusing on short-run and long-run output-oriented, attainable output-oriented, and input-oriented PCU notions, we first investigate the existence of solutions at the firm level. Under mild axioms, this question regarding the existence of solutions for these PCU concepts at the firm level is affirmatively answered under variable and constant returns to scale as well as under convex and nonconvex assumptions. However, short-run and long-run output-oriented and attainable output-oriented PCU concepts may not be implementable depending on certain conditions. There are no such reservations for the input-oriented PCU. Then, for this same range of PCU concepts, we explore the more difficult question as to the existence of solutions at the industry level. The output-oriented and attainable output-oriented PCU exist at the industry level under strict conditions: existence and attainability are interwoven at this level. The industry input-oriented PCU is always feasible at the industry model. This theoretical review is supplemented by a semi-systematic empirical review, and an empirical application. We conclude that input-oriented PCU is clearly the best concept. [ABSTRACT FROM AUTHOR]

Numerous studies have focused on digital twins (DTs) and their applications. However, a common method for evaluating the performance of DTs is lacking. Digital twins performance assessment mechanism is crucial when improving or monitoring any process or system in a business, such a strategy could serve as a guide to help researchers and practitioners create more effective digital twins. Guided by these challenges, we propose a structured method for measuring the performance and flexibility of digital twins, and our study provides a procedure for quantitative calculation of the performance of DTs based on four key performance indicators (KPIs). Moreover, we propose a new KPI, DTflex, for determining the flexibility of digital twins. We evaluated the performance of DTflex as a KPI to demonstrate its use as a simple tool for designers and practitioners to quickly compare different DT methodologies, which might provide valuable input for the iterative improvement of DTs to increase their efficiency in future applications. The knowledge provided along with the method proposed in this paper is expected to help researchers and practitioners working in different fields to compare DT methodologies and DT performance which will lead to higher efficiency DT solutions and increased re-usage of existing DTs. [ABSTRACT FROM AUTHOR]

To formulate multi-tier supply chain management strategies with global impact, understanding the roles of various supply chain partners in disseminating sustainability along the supply chain is crucial. We analyze the influence of different supply chain positions on sustainability and financial outcomes of Turkish manufacturing companies listed in İstanbul Stock Exchange (BIST). We propose a sustainability measure, and rank firms according to their sustainability practices in relation to their financial outcomes using data envelopment analysis (DEA). We estimate Seemingly Unrelated Regression (SUR) equations to control for the common financial shocks that can affect sustainability and financial outcomes simultaneously. Our analysis reveals that first-tier and sub-supplier (second-tier and lower-tiers) firms' financial performance is substantially better compared to Original Equipment Manufacturer (OEM) firms while their sustainability outcomes stay behind. We find that this latter effect is more profound for first-tier firms. Although the supplier firms generally comply with the OEM's product standards, our results show that they are not aligned with the OEM in terms of the environmental and social impact of their operations. Given the crucial role of first-tier suppliers in sustainability dissemination along the supply chain, policies should be formulated in this industry amid these characteristics of the sustainability dissemination. [ABSTRACT FROM AUTHOR]

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]

The Yellow River Basin serves as a vital ecological shield for China, and enhancing the urban ecological efficiency (UEE) is essential for the region's ecological civilization and high-quality development. This research employs the slacks- based measure-data envelopment analysis (SBM-DEA), which incorporates undesirable outputs, and the Malmquist index to measure the UEE of 65 cities in the Yellow River basin from 2008 to 2017, described its spatio-temporal evolution characteristics, and utilizes its influencing factors using the Bootstrap truncated regression model. The main findings are as follows. 1 The UEE in the Yellow River Basin has seen a steady, albeit low, increase from 2008 to 2017, with notable disparities existed in the upper, middle and lower reaches. Low pure technical efficiency is the main factor limiting the improvement of UEE in the Yellow River Basin. 2 Cities with higher ecological efficiency in the Yellow River Basin generally exhibit a spatial distribution pattern of "lower reaches >middle reaches > upper reaches," and it shows that cities with growing UEE are continuously shifting to the middle and lower reaches. 3 Environmental regulation, scientific and educational investment, economic scale, and greening level significantly promote the improvement of UEE in the Yellow River Basin, but population density and industrial structure lead to a decline in UEE. However, this result is heterogeneous between the upper, middle, and lower reaches, such as environmental regulation hindering the growth of UEE in the upper reaches, scientific and educational investment hindering the improvement of UEE in the downstream areas, and industrial structure significantly enhancing UEE in the midstream areas. These findings offer valuable insights for policymakers aiming to address ecological challenges and promote sustainable development within the Yellow River Basin, highlighting the need for targeted strategies that consider regional differences and the unique characteristics of each reach. [ABSTRACT FROM AUTHOR]

The study aims to compare the environmental efficiency within the group of European Union countries that joined the European Union in 2004 and later, and to identify the common circular economy determinants of efficiency. For this purpose, we performed Data Envelopment analysis and correlation analysis. We applied both constant and variable returns to scale models. Findings reveal that countries with a significant focus on services, particularly tourism, are more environmentally efficient. However, most countries are still heavily industryoriented, with Bulgaria, Romania, and Croatia being the least efficient. The study also highlights the need for significant efforts to reduce greenhouse gas emissions. Interestingly, a positive correlation was found between resource productivity and the circular materials used, suggesting the importance of circular economy tools in improving the environment. Despite having a higher material and consumption footprint, these countries still produce a relatively high product and relatively low CO2 emissions. Based on these analyses, we found regional differences and the need for setting cluster policies within the circular economy at the European Union level. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

Hierarchical data envelopment analysis (DEA) models evaluate the efficiency of decision-making units (DMUs) with a hierarchical group structure, where sub-DMUs are grouped into main DMUs. Contrary to early hierarchical DEA models, which were based on crisp input and output data, the model adopted in this study considers fuzziness attributes in the input and output data. The efficiency analysis was mainly based on the optimistic frontier in the early conventional fuzzy DEA models. To get a full view of the efficiency of the DMUs, we incorporated a second frontier based on the pessimistic assumption. In this study, two levels of analysis were implemented for the sub-DMUs and main DMUs. A combination of the optimistic and pessimistic efficiency ratings for both levels of analysis was done to get the overall performance measures. We applied the proposed approach to analyse the mobile money agents' locations. Aggregating sub-DMUs with ratings of the main DMUs improved the segregation of sub-DMUs in different groups. However, within groups, most sub-DMUs had similar ratings, making it difficult to distinguish the performance of the DMUs. To further improve the segregation of the DMUs, we applied the technique for order preference by similarity to the ideal solution (TOPSIS) based on the overall performance measures. The performance of 75% of the sub-DMUs was completely segregated, whilst 100% was achieved for the main DMUs. The final TOPSIS results were validated using the principal component analysis (PCA). A Spearman correlation of 50% was observed between the TOPSIS and the PCA results. The accuracy of the district location TOPSIS rankings on the final suburb location TOPSIS rankings was 72%. [ABSTRACT FROM AUTHOR]

This study explores the impact of environmental, social, and governance (ESG) factors on corporations by investigating how board gender diversity moderates the relationship between ESG practices and corporate performance across different regions. Using traditional data envelopment analysis (DEA) and chance‐constrained DEA models, the study analyzes 225 financial service firms over an 11‐year period (2011–2021). Results indicate that high (low) board gender diversity levels significantly influence the connection between the environmental (social) aspects of ESG practices and corporate performance. Thus, the roles of stakeholder engagement and regional distinctions are highlighted in achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive measure of fuel performance, considering economic and environmental factors using Data Envelopment Analysis (DEA). The study uses decentralized production approach, modeling two technology specifications, one with abatement and one without. The study focuses on the Indian public bus transport industry, treating carbon emissions as bad outputs with joint disposability alongside fossil fuels. Additionally, it examines fuel performance drivers through bootstrapped truncated regression. The findings reveal that maintenance practices and vehicle age negatively affect fuel performance, with the latter showing a non‐linear influence. As fuel prices rise, bus company managers increasingly adopt fuel‐saving practices. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has affected various industries in different ways depending on their type of business. We attempt to understand the impact of the pandemic on the performance of 26 major container carriers. To capture performance differences before and during the pandemic, we collect data the years 2019 and 2020. Further, we divide carriers' business processes into two stages, i.e., asset acquisitions and transport operations. We employ relevant data envelopment analysis (DEA) models to this effect. The mean efficiency score in 2020 was higher than in 2019. Overall, container carriers increased their performance during the COVID-19 pandemic. However, seven companies showed lower efficiency in 2020. Similarly, eight companies were efficient in their operations but not in asset acquisition decisions. Our results show that firm strategy is important in coping with external shocks. The contribution of the paper is in measuring the impact of external shocks, such as the COVID-19 pandemic, on the performance of container carriers, both in regard of their acquisitions and their operations. [ABSTRACT FROM AUTHOR]

Several countries have focused on achieving optimal economic growth in the past decades, which has caused diverse environmental concerns among policymakers and managers. The present study aims to introduce new metric using mathematical modeling to examine strategies to promote environmental performance in OECD countries. Thus, we propose a target-oriented Distance Friction Minimization (TO-DFM) model to design a practical improvement strategy for low-efficiency DMUs. In this regard, considering the efficient and inefficient frontiers, we present two models, IDMU and ADMU, respectively, based on TO-DFM and DFM models. Then, the distance scores yielded by the two models are combined to evaluate the efficiency of each DMU using the hybrid index of relative closeness (RC). The results reveal that the proposed model has a better distinctive potential compared to the standard TO-DFM model. Overall, implementing the proposed model on a set of sustainability data, for OECD countries, resulted in a more logical performance in classifying countries regarding their distance from the efficient and inefficient frontiers. Therefore, less developed countries should pay considerable attention to implementing environmental policies to improve their performance and control environmental pollution with a reliable and healthy economy. For instance, the implementation of such policies in countries such as Turkey, Estonia, and Hungary, with lower efficiency scores, can affect the sustainability of their development performance. [ABSTRACT FROM AUTHOR]

The aim of this to study is to examine how non-performing loans on the balance sheets of Japanese banks affect their performance by adopting a material balance principle. The paper outlines how the material balance conditions can be applied when modelling banks' production process in the presence of non-performing loans. The paper utilizes the generalized weak G-disposability principle which accounts for the heterogeneity among banks' input quality. We test how an input-oriented model (non-performing loans are treated as an input), the weak disposability assumption and the adopted material balance approach, affect banks' performance levels. We apply our test on a sample of Japanese banks over the period 2013 to 2019. Our findings indicate that the input-oriented model and the material balance estimator even if they present similar distributions, they account differently the effect of non-performing loans' fluctuations over the examined period. In addition, the results under the weak disposability assumption are found to be different compared to the material balance measures and less sensitive to banks' non-performing loans variation levels. We also provide evidence that the generalized weak G-disposability assumption captures better banks' performance fluctuations that has been caused by the restructuring of the Japanese banking industry. [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]

The determination of reasonable improvement direction is the key difficulty of directional distance functions in data envelopment analysis target setting method, but there is no consensus direction determination method at present. This paper introduces the concept of minimizing the distance between decision-making units (DMUs) and production frontier for constructing a new endogenous mechanism of improvement direction, and the corresponding target setting method is constructed to setting target for DMUs, which is used to minimize the total projection distance between DMUs and their targets. In addition, the free direction and unit invariant of improvement direction are discussed for meeting the diverse needs of decision makers. [ABSTRACT FROM AUTHOR]

Financial and credit institutions need to evaluate and rank their subsidiaries to control and improve their performances. There are several methods to evaluate the performance of such branches. In order to take advantage of the strengths of each of these methods and cover some of the limitations that exist in each of these methods alone, in this study, an algorithm which is a combination of multi-criteria decision-making methods, statistical analysis, and data envelopment analysis is proposed. The location of each of the methods mentioned in the steps of the algorithm, and its simulation to a standard linear programming model in MATLAB software, is the main research problem that is designed and presented for fuzzy type uncertain data. The proposed algorithm was used for 1736 branches of a certain bank in banking sector of Iran with uncertain data. Analysis of the results for different alpha-cuts and testing them with SPSS software show that with increasing the range of fuzzy numbers, the number of efficient branches increases and also affect the ranking. Nevertheless, there is still a significant correlation even in the alpha-cut changes in the ranking results. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis is applied to a sample of IT projects. The application is based on a discussion concerning the choice of inputs and outputs in the case when the units being evaluated are projects. This discussion takes into account various understandings of project success and thus project desired outputs, as well as the ambiguity and a strong situation dependence concerning the choice of project inputs, understood as variables that should be minimized. The sample consists of over 80 IT projects implemented in Poland. The applied DEA model has permitted to identify the projects that clearly stand out from the rest of the sample as less efficient. Their analysis made it possible to draw conclusions on the choice of inputs and outputs. A set of indications on the context-specific choice of inputs and outputs were formulated. [ABSTRACT FROM AUTHOR]

The European Green Deal and similar strategies seek to improve sustainability of the agricultural sector via public support programmes. It is important to assess the costs of sustainable energy use in agriculture by exploiting the shadow price approach. However, the earlier literature often ignored the fact that shadow price analysis may be sensitive to the assumed direction of optimization. This paper seeks to disentangle the major patterns in energy-related greenhouse gas (GHG) emission performance in the selected European Union countries by assuming different optimization directions. The country-level data are used to construct the environmental production technology by means of the data envelopment analysis. The different directional output distance functions (aggregate, unit and radial) for the weak disposability data envelopment analysis models are used to quantify the shadow prices of the energy-relevant GHG emission and construct the marginal abatement cost curves. The results indicate spatial and temporal variation in the environmental performance that can be addressed by adjusting the support programmes. [ABSTRACT FROM AUTHOR]

The efficient development of the agricultural product circulation industry, as a crucial component of agricultural growth, directly impacts the successful realization of the country's rural revitalization strategy. Despite the favorable trend in the development of China's agricultural product circulation industry, with continuous growth in the volume of agricultural product circulation and overall logistics turnover, challenges persist. There remains a prevalent emphasis on "heavy production and light circulation" in agricultural development, leading to high circulation costs and notably low efficiency. This situation necessitates imminent reforms within the agricultural product circulation industry. In response to emerging contradictions in the contemporary agricultural product circulation domain, the country has strategically advocated for digital development. The advancement of next-generation information technology has propelled the reforms within the agricultural product circulation industry and the exploration of intelligent technology applications in this field aligns with practical developmental needs. Firstly, this article constructs an evaluation framework for agricultural product circulation efficiency based on the perspective of dual online and offline circulation channels. Utilizing the data envelopment analysis (DEA) method, the study calculates the static efficiency of agricultural product circulation in China. Through regional comparisons, it is revealed that the overall efficiency of agricultural product circulation is highest in the eastern region, followed by the central and northeastern regions, with the western region lagging behind. Secondly, employing the Malmquist index model, the study investigates the changes in total factor productivity within agricultural product circulation. The findings indicate the following trends during the statistical period: the circulation of agricultural products in the eastern region has maintained a substantial growth rate primarily driven by technological advancements; in the northeastern region, the overall total factor productivity in agricultural product circulation shows an increasing trend but displays an M-shaped variation; the central region witnesses relatively low average annual growth in total factor productivity, characterized by significant fluctuations primarily influenced by variations in pure technical efficiency; the western region experiences a continuous decline in scale efficiency and pure technical efficiency compared to the previous year, with only a rise in technological progress index supporting a slight increase in total factor productivity. [ABSTRACT FROM AUTHOR]

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

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

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

This is a pioneering study that undertakes a comparative analysis assessing the annual intermediation efficiency of public versus private banks in Egypt. Moreover, liquidity risk is a major threat facing banks in their efforts to sustain financial stability. Thus, this study is the first to model the determinants of liquidity risk in public and private banks in Egypt while examining the impact of banks' intermediation efficiencies on their liquidity risk levels. The study employs advanced nonparametric econometric approaches on a sample of Egyptian public and private banks from 2014 to 2022. The data envelopment analysis is used in estimating banks' intermediation efficiency scores, while the quantile regression analysis is applied to examine the impact of bank intermediation efficiency on liquidity risk under different liquidity risk quantiles. The findings indicate that public banks show consistent superiority in terms of their financial intermediation efficiency levels compared to private banks. Moreover, the paper findings demonstrate the negative significant relationship between bank intermediation efficiency and liquidity risk while highlighting the higher significant positive impact of intermediation efficiency on reducing the liquidity risk of banks that are characterized by undertaking high liquidity risk levels. Furthermore, contrary to general assumptions, this study's findings demonstrate that the significance of micro- and macro-level determinants of a bank's liquidity risk is dependent on its prevailing liquidity risk level. Hence, the positive impact of equity capital, asset concentration, size, and growth in gross domestic product and the negative effect of asset quality on bank liquidity risk vary under banks' different liquidity risk quantiles. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

Warehouses represent key links in domestic and international commodity flows. The increasing shortage of workers and increasing costs on the one hand, and the increasing number and stricter demands of users on the other hand lead warehouse managers to realize their operations as efficiently as possible. A proposed model has an objective of enabling companies to monitor warehouse performance in an authoritative, reliable, and simple way and define appropriate corrective measures accordingly. The proposed empirical research consists of three stages, where in the first stage a combination of Principal Component Analysis-Data Envelopment Analysis methods was applied in order to determine efficient warehouses based on 90 decision making units. In the second phase, a completely new method called Interval Fuzzy Rough Pivot Pair-wise Relative Criteria Importance Assessment method used for determining criteria weights was developed and applied, which is one of the most important novelties of this study. In the last phase, the Interval Fuzzy Rough Measurement of Alternatives and Ranking according to the Compromise Solution method was applied to rank the alternatives. Twelve criteria were observed to evaluate 21 alternatives. Based on the results, it was concluded that salary stood out as the most important criterion, while amortization stood out as the least significant criterion. On the other hand, alternatives A9 and A10 stood out as the best-ranked alternatives while A1, A2, and A3 stood out as the least efficient ones. The paper provides clear scientific contributions that are reflected in the reduction of the gap that was observed after reviewing the literature where there is a lack of papers dealing with this task. Also, the combination of methods applied in the paper has not been used so far, so it can be said that this paper represents an excellent basis for further research. The model has practical contributions as it allows decision-makers to make quality decisions regarding the operation of their warehouses in different time periods or observation periods, as well as it represents a decision support tool that can be used for better warehouse management. • New model for warehouse performance evaluation has been proposed. • An integrated PCA-DEA-IFR PIPRECIA-IFR MARCOS Model was developed. • New approach IFR PIPRECIA was developed and presented in literature for first time. • The model enables more accurate and precise decision-making in logistics. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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