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• A total-factor framework is developed for measuring safety performance. • A global DEA model is established. • The construction safety performance in Australia has been promoted by pure technical elements. • The scale factor is not an important force driving safety performance. • The safety performance improvement model is developed. Introduction: Safety is one of the critical and persistent challenges in the construction industry. Measuring safety performance could allow decision-makers to check safety production processes and enhance the health and safety environment. Method: This study developed a total-factor framework based on the global Data Envelopment Analysis (DEA) method to measure safety performance. The performance trend and influencing factors of pure technical efficiency and scale efficiency were separately investigated. The safety performance of construction employees in Australia was measured as a case study. Results: The results demonstrate that the safety performance in the Australian construction industry has been evidently enhanced, which is mainly promoted by the progress of pure technical elements. The scale factors did not play a positive and important role in driving the performance. Conclusions: The increasing regional differences in safety performance could be reduced by learning the practices from the benchmark construction industry, such as a young worker program, small business safety program, workplace mental health program, and advanced construction technologies. Practical Applications: The method can be utilized to measure safety performance and investigate the pathways to enhance performance without influencing production inputs and outcomes. The model and experiences of how to promote safety performance on the policymakers and employers were recognized. [ABSTRACT FROM AUTHOR]

Corrected milk equations were developed in attempts to bring milk weights to a standardized basis for comparison by expressing the weight and composition of milk as corrected to the energy content of milk of a specific composition. Expressed as milk weights familiar on farm and in commerce, this approach integrates energy contributions of the dissimilar components to make the mass units more comparable. Such values are applied in evaluating feed efficiency, lactation performance, and global milk production, as functional units for lifecycle assessments, and in translation of research results. Corrected milk equations are derived from equations relating milk gross energy to milk composition. First, a milk energy equation is used to calculate the energy value of the milk composition to correct to (e.g., 0.695 Mcal/kg for milk with 3.5% fat, 3.05% true protein, and 4.85% lactose). That energy value is divided into the energy equation to give the corrected milk equation. Confusion has arisen, as different equations purport to correct to the same milk composition; their differences are based on uses of different energy equations or divisors. Accuracy of corrected milk equations depends on the accuracy of the energy equations used to create them. Energy equations have evolved over time as different milk component analyses have become more available. Inclusion of multiple milk components more accurately predicts milk energy content than does fat content alone. Omission of components from an equation requires the assumption that their content in milk is constant or highly correlated with an included component. Neither of these assumptions is true. Milk energy equations evaluated on a small data set of measured milk values have demonstrated that equations that incorporate protein, fat, and lactose contents multiplied by the gross energy of each component more closely predict milk energy than equations containing fewer components or regression-derived equations. This provides a tentative recommendation for using energy equations that include the 3 main milk components and their gross energy multipliers for predicting milk energy and deriving corrected milk equations. Accuracy of energy equations is affected by the accuracy of gross energy values of individual components and variability of milk composition. Lactose has consistent reported gross energy values. In contrast, gross energy of milk fat and protein vary as their compositional profiles change. Future refinements could assess accuracy of milk fat and protein gross energy and whether that appreciably improves milk energy predictions. Fat gross energy has potential to be calculated using the milk fatty acid profile, although the influence on gross energy may be small. For research, direct reporting of milk energy values, rather than corrected milk, provides the most explicit, least manipulated form of the data. However, provision of corrected milk values in addition to information on components can serve to translate the energy information to a form familiar to and widely used in the field. When reporting corrected milk data, the corrected milk equation, citation for the energy equation used, and composition and energy contents of the corrected milk must be described to make clear what the values represent. [ABSTRACT FROM AUTHOR]

Dairy cow responses to dietary crude protein (CP) may depend on stage of lactation. The primary objective of this study was to evaluate the effects of 4 concentrations of dietary CP on dry matter intake (DMI), production performance, net energy for lactation (NE L) output in milk, feed efficiency (FE: milk NE L /DMI), and nitrogen use efficiency (100 × milk protein-N/N intake) when fed to cows grouped as early, mid-early, mid-late, and late lactation. Our secondary objective was to determine the range of CP concentration at which production responses were not negatively affected across days in milk (DIM). Multiparous Holstein cows (n = 64) were stratified by DIM [initial average ± standard deviation: 86 ± 14.9 (early), 119 ± 10.0 (mid-early), 167 ± 22.2 (mid-late), and 239 ± 11.1 (late)] and then randomly assigned within DIM group to receive 1 of 4 total mixed rations containing 13.6, 15.2, 16.7, and 18.3% CP (dry matter basis) according to a 4 × 4 factorial arrangement of treatments. Cows were individually fed a covariate diet for 14 d, followed by 56 d of treatment diets. Milk yield and DMI were recorded daily and milk components were analyzed weekly for 2 consecutive days at 3 daily milkings. Data were analyzed using a categorical mixed-effect model to evaluate the effects of CP concentration and DIM using linear, quadratic, and cubic contrasts, and their interactions. Additionally, a mixed-effect cubic regression model was fit with DIM, dietary CP concentration, and their interaction as continuous independent variables. Dietary CP concentration deemed optimal across DIM was determined as the range of CP for which the dependent responses did not differ from the predicted maximum. With advancing stage of lactation, DMI, milk NE L output, and FE decreased linearly (from 30.4 to 28.4 kg/d for DMI, from 33.2 to 23.3 Mcal/d for NE L output, and from 1.09 to 0.82 Mcal milk NE L /kg DMI for FE for early and late lactation cows, respectively). Responses to dietary CP concentration were linear, quadratic, and cubic with the greatest values observed when cows were fed the 16.7% CP diet across DIM (30.8 kg/d, 31.0 Mcal/d, and 1.01 Mcal/kg for DMI, milk NE L output, and FE, respectively). There was an interaction between dietary CP concentration and stage of lactation for DMI, milk NE L output, milk component yield, and FE, which was due to the decline in response to additional CP as lactation progressed. Compared with the 16.7% CP diet, feeding the 18.3% CP diet decreased milk NE L 0.81 and 5.3 Mcal/d for early and late lactation cows, respectively, indicating that feeding a higher CP concentration in late lactation had a negative effect on cow performance. Nitrogen use efficiency declined linearly with increasing CP concentration and DIM. Regression analysis suggested that dietary CP ranging from 16.3 to 17.4% maintained production in early and mid-early lactation. However, dietary CP could be reduced to between 15.7 and 17.1% in late lactation. This research suggested that there are distinct ranges of dietary CP concentrations that maintain cow performance at each stage of lactation. [ABSTRACT FROM AUTHOR]

Dairy cow responses to dietary crude protein (CP) may depend on stage of lactation. The primary objective of this study was to evaluate the effects of 4 concentrations of dietary CP on dry matter intake (DMI), production performance, net energy for lactation (NE L) output in milk, feed efficiency (FE: milk NE L /DMI), and nitrogen use efficiency (100 × milk protein-N/N intake) when fed to cows grouped as early, mid-early, mid-late, and late lactation. Our secondary objective was to determine the range of CP concentration at which production responses were not negatively affected across days in milk (DIM). Multiparous Holstein cows (n = 64) were stratified by DIM [initial average ± standard deviation: 86 ± 14.9 (early), 119 ± 10.0 (mid-early), 167 ± 22.2 (mid-late), and 239 ± 11.1 (late)] and then randomly assigned within DIM group to receive 1 of 4 total mixed rations containing 13.6, 15.2, 16.7, and 18.3% CP (dry matter basis) according to a 4 × 4 factorial arrangement of treatments. Cows were individually fed a covariate diet for 14 d, followed by 56 d of treatment diets. Milk yield and DMI were recorded daily and milk components were analyzed weekly for 2 consecutive days at 3 daily milkings. Data were analyzed using a categorical mixed-effect model to evaluate the effects of CP concentration and DIM using linear, quadratic, and cubic contrasts, and their interactions. Additionally, a mixed-effect cubic regression model was fit with DIM, dietary CP concentration, and their interaction as continuous independent variables. Dietary CP concentration deemed optimal across DIM was determined as the range of CP for which the dependent responses did not differ from the predicted maximum. With advancing stage of lactation, DMI, milk NE L output, and FE decreased linearly (from 30.4 to 28.4 kg/d for DMI, from 33.2 to 23.3 Mcal/d for NE L output, and from 1.09 to 0.82 Mcal milk NE L /kg DMI for FE for early and late lactation cows, respectively). Responses to dietary CP concentration were linear, quadratic, and cubic with the greatest values observed when cows were fed the 16.7% CP diet across DIM (30.8 kg/d, 31.0 Mcal/d, and 1.01 Mcal/kg for DMI, milk NE L output, and FE, respectively). There was an interaction between dietary CP concentration and stage of lactation for DMI, milk NE L output, milk component yield, and FE, which was due to the decline in response to additional CP as lactation progressed. Compared with the 16.7% CP diet, feeding the 18.3% CP diet decreased milk NE L 0.81 and 5.3 Mcal/d for early and late lactation cows, respectively, indicating that feeding a higher CP concentration in late lactation had a negative effect on cow performance. Nitrogen use efficiency declined linearly with increasing CP concentration and DIM. Regression analysis suggested that dietary CP ranging from 16.3 to 17.4% maintained production in early and mid-early lactation. However, dietary CP could be reduced to between 15.7 and 17.1% in late lactation. This research suggested that there are distinct ranges of dietary CP concentrations that maintain cow performance at each stage of lactation. [ABSTRACT FROM AUTHOR]

The Malmquist index constructed from distance functions is a useful tool for measuring productivity growth. In this paper, we assume the presence of random noise in empirical data and enhance the literature by introducing a panel-data model in stochastic nonparametric envelopment of data (StoNED) for the consistent estimation of the Malmquist-type index. The method we propose initially employs directional distance functions to evaluate both within and intertemporal efficiencies of Malmquist-type indices based on StoNED. A major feature of our approach is that it measures productivity changes over time while capturing both inefficiency and noise in a nonparametric multiple-input multiple-output setting. As an illustrative application, we investigate the productive performance of Japanese regional banks using a balanced panel dataset spanning 2008 to 2017. • We propose a stochastic nonparametric estimation of the Malmquist-type indices. • We establish a consistent estimator for measuring intertemporal efficiencies. • The proposed approach measures productivity changes under noise. • The results reveal the drivers of productivity growth of Japanese regional banks. [ABSTRACT FROM AUTHOR]

Waste disposal systems are crucial components of environmental management, and focusing on this sector can contribute to the development of various other sectors and improve social welfare. Urban waste is no longer solely an environmental issue; it now plays a significant role in the economy, energy, and value creation, with waste disposal centers (WDCs) being a key manifestation. The purpose of this study is to measure the performance of WDCs in the state of Nuevo León, Mexico, with the aim of developing environmental, social, and governance (ESG) strategies to strengthen and prepare the WDCs for the industrial developments in this state. By identifying environmental variables and undesirable factors, the efficiency and managerial capacity of 32 WDCs were assessed. The analysis revealed that 9 out of the 32 WDCs are technically efficient, while the remaining 23 require significant improvements. Using the Data Envelopment Analysis (DEA) technique, an average efficiency score of 0.91 was found, with a standard deviation of 0.08. The managerial capacity analysis indicated that the highest-ranked WDC achieved an efficiency score of 1, whereas the lowest-ranked WDC scored 0.67. Finally, an operational map of development strategies was developed using the Interpretive Structural Modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) approach. The results indicate that four phases of development should be followed for real development and maturity of development in these WDCs, including Groundwork, Structuring, Development and Growth, and Smart Maturity. [Display omitted] • Study measures WDC performance in Nuevo Leon, Mexico for ESG strategies. • DEA technique assesses 32 WDCs' efficiency with environmental factors. • A ranking conducted based on managerial capacity of each unit. • Development strategy map created using ISM and MICMAC approaches. • Four phases identified for WDC development and smart maturity. [ABSTRACT FROM AUTHOR]

• Examine the regional and temporal evolution of LCEE and its influencing factors. • Investigate LCEE on a global scale and analyze LCEE for each country. • Add carbon dioxide emissions as an undesired exportation to the LCEE evaluation indicator system. • Propose countermeasures to improve global LCEE. Nations worldwide must collaborate to tackle the challenge of improving carbon emission efficiency and optimizing land use effectively. This study extends the study area from provinces and cities, regional city clusters, and countries to 136 major countries in the world, and shows the differences between countries. The paper analyzed the spatiotemporal evolution characteristics and influencing factors of land use carbon emission efficiency (LCEE) in these 136 countries from 2000 to 2019 using the super-efficiency slacks-based measure data envelopment analysis (Super-SBM DEA) model, global malmquist index, spatial autocorrelation measurement tools and Spatial Durbin model. It can help to promote low-carbon and green development while simultaneously improving the global LCEE. The findings indicate that: (1) Over the study period, there is a trend toward a decline in the LCEE worldwide. (2) The primary cause of the worldwide LCEE's declining trend during the study period is the reduction in technical efficiency. (3) The low-low cluster type accounts for more, while the high-high agglomeration type accounts for less and exhibits a sluggish growth pattern. On the whole, there is a phenomenon of non-aggregation and non-uniformity. It is necessary to increase the driving effect of high-high cluster radiation. (4) Economic development level, investment level, population size, public service level and environmental sustainability level all have a significant impact on LCEE, while industrial development level has no significant impact on LCEE. From a global perspective, the radiation driving role of countries with high LCEE should be increased. Improve the efficiency and technological level of land use emission in light of local conditions, and promote independent innovation in green and low-carbon technologies. This is conducive to the establishment of long-term low-carbon growth. [ABSTRACT FROM AUTHOR]

The inverse data envelopment analysis (DEA) is an advanced complementary method for efficiency analysis using the classical DEA approach. One of the inverse DEA (InvDEA) method applications is the mergers and acquisitions problem. It can be used for analyzing any under evaluation mergers and acquisitions. The current article is the first attempt to propose a novel inverse structure for the DEA model using the multiplier forms. This eventuates the possibility of incorporating decision maker preferences within the merger analysis. Moreover, compared with the existing models in the literature, the proposed novel models are capable of analyzing multiple merger scenarios simultaneously in a single method based on the common set of weights (CSW) rather than a series of models for studying multiple scenarios of mergers and acquisitions. Practically, this property enables decision-makers to consider and analyze multiple mergers and find possible potentials at the same time. The applicability of the proposed model is investigated by using a real-world dataset in banking. [ABSTRACT FROM AUTHOR]

[Display omitted] • We adopts a perspective of equity and efficiency and constructing a framework to assess the improvement of ESV in China. • We reveals the regional unfairness between China's ESV and economic growth. • We evaluates the ESV improvement in China, considering both static and dynamic efficiency. • We provides policy suggestions for ecological environmental governance. Ecosystems provide the material and energy resources necessary for human development, improving people's quality of life and well-being. Thus, enhancing the effectiveness of ecosystem services valuation has become a focal point of concern. Here, we adopt a perspective of equality and efficiency and construct a framework based on the ecological Gini coefficient (E-Gini coefficient) and the three-stage Data Envelopment Analysis-Malmquist (DEA-Malmquist) model, by evaluating the input of financial investments and human resources and the output of ecosystem service value (ESV) production, to assess the improvement of ESV in China from 2008 to 2020. The research findings reveal that the E-Gini during the study period remained consistently above 0.54, which is categorized as "high inequality". ESV equality across different regions follows the order of northwest > south > north concerning economic and social development. Furthermore, through efficiency analysis, we find that there is room to improve the improvement efficiency of China's ESV. Static efficiency analysis indicates that the average improvement efficiency of China's ESV is low (0.331). This indicates that the input resources did not achieve the optimal ESV production. Dynamic efficiency analysis reveals that there is a 3.2 % decline in ESV improvement efficiency over the entire study period. Encouragingly, ESV improvement efficiency has been increasing annually since 2017. Moreover, there exists spatial heterogeneity in ESV improvement efficiency, with the northwest region showing significantly higher than the north and south. The research results can provide policy recommendations for ecological environmental governance, production efficiency, and ecological compensation. [ABSTRACT FROM AUTHOR]

Due to the complexity of the actual group decision-making environment, the probabilities with real values provided by multiplicative probabilistic linguistic term sets may not accurately reflect the decision maker's perspective on practical issues. To solve this issue, we first propose the multiplicative interval-probabilistic linguistic term set and multiplicative interval-probabilistic linguistic preference relation. It is widely acknowledged that group decision-making results derived from preference relations lacking an acceptable level of consistency are often considered unreasonable. Therefore, an optimal consistency adjustment strategy based on the prioritization algorithm is proposed to obtain the acceptable consistent multiplicative interval-probabilistic linguistic preference relations. Then, to derive the objective weights of decision makers, we design a nonlinear programming model based on the novel trust relationship. Furthermore, considering the benevolent mentality of decision makers, the benevolent multiplicative data envelopment analysis cross-efficiency model, which aims to minimize the sum of deviations between other decision-making units and the ideal point, is constructed to obtain the ranking of all alternatives. Finally, we give a case study on the branch location selection of a new energy vehicle company to demonstrate the validity of our suggested method. The merits of our proposed group decision-making method are validated through comparative analysis and discussion. [ABSTRACT FROM AUTHOR]

The performance evaluation method based on data envelopment analysis (DEA) is one of the important tools to measure the competitiveness and productivity of enterprises. However, the input and output of enterprises may contain negative data and the essence of DEA is an iterative optimization model, resulting in a low applicability of the DEA-based performance evaluation method in the real word, especially for the dilemma of evaluating enterprise performance within a limited time for new enterprises. Therefore, this study firstly develops a DEA model that can handle negative data for enterprise performance evaluation, and then further establishes a new method base on the extended belief rule-base (EBRB) model for enterprise performance online evaluation. A case study about 35 Chinese state-owned enterprises are conducted to verify the effectiveness of the proposed enterprise performance online evaluation method. Experimental results showed that the proposed method has capable of evaluating enterprise performance with accurate efficiency values better than some existing performance evaluation methods, and its computation time is significantly less than the DEA-based performance evaluation method, which guarantee that the proposed enterprise performance online evaluation method can serve as a reference for the promotion of enterprise productivity and sustainable economic development. [ABSTRACT FROM AUTHOR]

Evaluating the efficiency of electricity distribution companies (EDCs) accurately is one of the most important issues for regulators and policy makers. This research combines the results of data envelopment analysis (DEA) and corrected ordinary least squares (COLS) with machine learning techniques to evaluate a set of EDCs in the period 2011–2020. We propose a three-stage process. First, for each year, the efficiency scores of EDCs are measured using DEA and COLS methods. Then, this study applies support vector regression (SVR), a powerful machine learning technique, to estimate the efficient frontier and to calculate the efficiency of the EDCs. The efficiencies generated by DEA, COLS, and SVR are not the same and are used to construct fuzzy triangular numbers. Finally, the fuzzy efficiencies are considered as criteria for the technique for order performance by similarity to the ideal solution (TOPSIS), and the final efficiencies and ranks are obtained using the fuzzy TOPSIS (FTOPSIS) method. In addition, using the fuzzy C-means clustering (FCM) algorithm, the EDCs are clustered and discussed. The results show that there are increasing and decreasing trends for the selected EDCs in the period 2011–2022. In addition, some EDCs act in a poor situation and their performance should be improved. • Introducing a comprehensive approach to measure efficiency of EDCs. • Measuring efficiency of EDCs using DEA, COLS and SVR methods. • Combining the results of DEA, COLS and SVR by fuzzy TOPSIS method. • Clustering EDCs based on the scores generated by all methods. [ABSTRACT FROM AUTHOR]

Globally increasing concerns on climate change have garnered considerable attention toward green investments. Although the effects of green investments have been investigated, their productivity remains largely unexplored. This study proposes an initial discussion of green efficiency based on the cyclical relationship among green investments, technology, and national-level performance. This study applies a three-stage data envelopment analysis to compare green efficiency of 19 countries based on carbon dioxide emissions, green patents, and green-bond issuance data of 12 years starting from 2010 when national green-bond issuance was initiated. We use a logistic regression to derive policy implications by analyzing major factors affecting green efficiency. The results reveal that no country is efficient across all stages. Overall, the finding reveals which stage each country should focus on to improve green efficiency; suggests benchmarks and green efficiency strategies for inefficient countries; and highlights the appropriate level of research and development (R&D) expenditure and adoption support for solar/wind energy as part of the technology support policy. As the national green efficiency level is derived in stages, our approach contributes to forming green efficiency strategies at the national level in three stages. Moreover, this study presents the policy implications related to R&D expenditures and technology support to address the limited understanding of green efficiency management. • The three-stage DEA analysis for green efficiency strategy is proposed. • The proposed analysis consists of green performance, innovation, and investment. • Customized benchmark strategy fosters countries to improve national green activities. • Logistic regression is used to identify the policy factors affecting green efficiency. • Appropriate level of R&D expenditure and solar/wind energy is recommended. [ABSTRACT FROM AUTHOR]

Healthcare organisations have a significant carbon footprint due to their substantial energy consumption and waste generation. The existing literature has mostly focused on examining the isolated impact of specific green organisational practices on individual dimensions of environmental performance. Hence, policy makers and practitioners still lack a holistic view of how combinations of green organisational practices can mitigate the impact on climate change of healthcare organisations. Drawing on the practice-based view (PBV) of strategy, the study first examines the extent to which the adoption of imitable, individual green organisational practices impacts on the whole climate change mitigation performance of publicly owned hospitals. Second, it explores possible combinations of green organisational practices that can lead to high levels of climate change mitigation performance. Focusing on the illustrative case of publicly owned acute care hospitals in the English National Health Service and employing a two-stage Data Envelopment Analysis and a fuzzy set Qualitative Comparative Analysis (fsQCA), the analysis shows that the adoption of the practice of green energy procurement can, in isolation, positively lead to high climate change mitigation outcomes. Moreover, high performance levels can be achieved through multiple configurations of green organisational practices. Therefore, the findings suggest that the effectiveness of green organisational practices on climate change mitigation performance of hospitals depends on how practices are combined, rather than on their single effects, and indicate the presence of a complementary as well as a substitution effect between practices. [ABSTRACT FROM AUTHOR]

One of the challenges for suppliers is to increase their market share due to the limited target market. In other words, in the supply chain, the demand for suppliers' products is limited. Therefore, suppliers produce a determined share of the required amount by producers. However, when it comes to the share in the amount of output among suppliers, the output of suppliers in this indicator is interdependent. In the classical data envelopment analysis (DEA) models, there are no models that assess the suppliers according to the dependence of at least one output on each other. In this paper, a model is presented that can assess sustainable suppliers in the presence of interdependent output among suppliers by using DEA models and their extension. It can also determine the total benchmarks of decision making units (DMUs) in such a way that satisfies the interdependent output constraint. In other words, benchmarks are not determined independently. Ultimately, an approach is presented to determine efficient projections for inefficient DMUs by considering the concept of interdependent output. To represent the applicability of our proposed model, a dataset for two consecutive years including 32 sustainable suppliers with consideration of interdependent output has been implemented using the model presented in this paper. The resulting sustainability has been compared with a classical model. [ABSTRACT FROM AUTHOR]

The concept of green economic efficiency has attracted attention over the last decade. Several measurements of green economic efficiency and their various aspects have been proposed, and these have been the main focus in the literature. However, calculating the influence of each province on the country's green economic efficiency is essential to assist in targeted management and institutional design at the provincial level by the government. To perform such analysis, this study extended the data envelopment analysis model. Data from 30 provinces in China from 2010 to 2018 are used in the study. The results show that the provinces in the eastern and southern coastal regions have the greatest influence on the country's green economic efficiency, but the majority of the provinces in the Middle Yellow River region have the least influence on the entire green economy. Interestingly, high-efficiency provinces do not always exert the greatest influence, suggesting that even low-efficiency provinces can influence significantly on the economy. These findings can serve as a baseline for improving provincial influence and further promoting China's green economic efficiency. [Display omitted] [ABSTRACT FROM AUTHOR]

Esfordi phosphate mine is the largest phosphate mine in Iran, producing 360 thousand tons of phosphate ore annually. Currently, the mine blasting operation is facing problems such as back break, fines, and flyrock. The main purpose of this study is to propose an approach to achieve an optimal blasting pattern that, while it can produce the desired fragmentation, can also minimize adverse outcomes of blasting. In the first phase of the study, the blasting-related problems in the mine were investigated. Then, the mine was classified into three zones and blasting patterns were designed for each zone. In the second phase, the performance of designed patterns and previously implemented ones were examined using Data Envelopment Analysis (DEA) method. In the next stage, based on questionnaires filled out by experts and using multi-criteria decision making methods, the optimal blasting pattern for each zone of the mine was identified. Implementation of optimal patterns in the mine reduced fine generation by 17 percent and had a significant effect in reducing backbreak and flyrock. This is while there was a drop in drilling and blasting costs too, and a more uniform size of rock fragments was produced. The outcomes of this study indicated that the hybrid analysis of data envelopment analysis and multi-criteria decision making is a successful approach to optimize blasting operation in surface mines, especially where a limited number of input data sets are available. • DEA-MCDM hybrid method is an efficient approach for optimization of blasting. • Applying the proposed design can reduce the fines generation in blasting. •Proposed optimum design increased the uniformity of rock fragment size. • The optimum design reduced drilling and blasting costs and energy consumption. [ABSTRACT FROM AUTHOR]

Nowadays, health systems comprise a series of resources structured to provide healthcare services to meet our health needs. However, premature deaths still occur. To quantify and understand personal healthcare conditions affecting such amenable mortality, the Healthcare Access and Quality Index (HAQI) was put forward, evaluating 195 countries and territories since 1990. Nevertheless, the literature acknowledges a series of limitations of this framework, such as the drawbacks of using principal component analysis to aggregate individual indicators, the absence of control for financing and environmental conditions, and the presence of a substantial degree of data uncertainty. Accordingly, we propose a methodological alternative to the computation of the HAQI using a novel fuzzy Data Envelopment Analysis model to handle the aforementioned shortcomings. We also propose its extension towards the quantification of efficiency (E-HAQI) – in the sense of value for money – by incorporating financial aspects as modelling inputs. This way, we contribute with innovative modelling approaches that can also deal with the high degree of data uncertainty. Furthermore, in a second-stage analysis, the impact of key exogenous factors on healthcare access and quality is assessed via non-parametric hypothesis testing. Our results show positive and significant correlations of both the revisited HAQI and E-HAQI with the original HAQI 2016 dataset. They also reveal a better use of resources by European and Oceanian countries and territories than by Sub-Saharan African ones. Concerning contextual determinants, socio-demographic development, human development, and the type of health system were found to be statistically significant drivers of healthcare access and quality efficiency. • Revisitation of the 2016 'Healthcare Access and Quality Index'. • Novel fuzzy Data Envelopment Analysis approach for performance assessment. • Modelling of uncertainty and undesirable factors. • Incorporation of financial aspects for efficiency measurement. • Non-communicable diseases show the greatest potential for improvement. [ABSTRACT FROM AUTHOR]

The European Regional Development Fund devoted around 66 billion Euros to the financial support of innovation and productivity in European enterprises over the 2014–2020 programming period. In this framework, we assessed the implementation of the Operational Programmes dedicated to fostering research and innovation, particularly in small and medium-sized enterprises. With this aim, we used a network slack-based data envelopment analysis model paired with cluster analysis that encompasses a multitude of performance framework indicators to assess 53 Operational Programmes from 19 countries. Our findings suggest that compared to transition and less developed regions, more developed regions present a higher room for improvement. Also, less developed regions present a better performance when they employ their funding against more developed regions, suggesting that further funding should be channelled for leveraging research and innovation in the former regions. Finally, Operational Programme managers should focus on solving the problems both inherent to the poor outcomes in terms of enhancing the number of researchers working in improved research infrastructures and promoting the technology transfer between research institutions and enterprises. [ABSTRACT FROM AUTHOR]

The efficiency of decision-making units with multiple inputs and outputs can be evaluated using a non-parametric quantitative method called data envelopment analysis (DEA). DEA is a decision-making technique for determining the efficiency of similar units. As a result of its simplicity and applicability, DEA has attracted the attention of scientists and researchers working in various fields, which has contributed to a rich literature in terms of theoretical development and examples. The early models of DEA with crisp inputs and outputs were based on linear programming. As opposed to fuzzy theory and stochastic programming, uncertain belief theory can be applied in the absence of historical data for inputs and outputs. Therefore, this paper firstly studies the slacks-based measure model of DEA in an uncertain environment, where inputs and outputs are uncertain based on belief degrees. The problem is modeled mathematically, and some properties of the model are demonstrated. The slacks-based measure model with belief-degree input and output uncertainties is also examined for stability and sensitivity. The final step of the study involves reporting our experience with the new uncertain slack-based measure model and the stability radius for each decision-making unit. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is a mathematical programming model widely used for evaluating renewable energy performance. However, the presence of data uncertainty may make the constraints of traditional DEA infeasible, resulting in unreliable performance assessments. This paper proposes a robust DEA model with common weights to evaluate the renewable energy performance under data uncertainty. Additionally, a novel performance indicator, efficiency vulnerability, is designed to evaluate the ability of Decision-making units to withstand data uncertainty. The impact of data uncertainty on the renewable energy performance ranking is explored through a Monte Carlo simulation. Applying the proposed robust model, we empirically analyze the renewable energy performance of 38 OECD countries. The empirical results demonstrate the effectiveness of our approach in differentiating renewable energy performance. • Uncertainty should be involved in assessing renewable energy performance. • Evaluating the renewable energy performance using a robust DEA model with common weights. • Developing a robust DEA approach for performance evaluation. • Analyzing the renewable energy performance of OECD countries in terms of resilience to data uncertainty. [ABSTRACT FROM AUTHOR]

In group decision making (GDM), the real or virtual moderator usually provides the suggestion and compensation based on consensus improvement for the decision maker (DM) to promote group consensus. In order to ensure DMs own interests, there are the game among DMs and the game between DM and moderator to effect the consensus reaching. In this case, we analyze the inherent game reasons and investigate the game-driven consensus model. Firstly, based on the subjective trust of social network and the evaluation consensus of DMs, and inspired by the idea of data envelopment analysis (DEA), this paper explores the quantitative relation between subjective trust and the DMs' consensus to determine the DM's weights. Then, under the compensation of consensus improvement, we formulate game strategy between DMs and the moderator to ensure the DM's consensus, which impacts the cooperation behavior between DM and the moderator. Based on the game strategy, we further establish bi-level game consensus model and investigate Nash equilibrium of group consensus model. Finally, the experiments are used to illustrate the feasibility of the proposed method, and comparative analysis demonstrates its advantages. • Explore the relation between subjective trust information and the DM's consensus. • Investigates the intrinsic game mechanism and introduces the consensus recognition and trust evolution. • Constructs the game-driven adjustment model for the consensus. [ABSTRACT FROM AUTHOR]

With the rapid development of global shipping industry, the energy conservation and emission reduction issue of ships has received increasing attention from the international community. A series of regulations released by the International Maritime Organization (IMO) have greatly stimulated the related research. However, the mechanism of optimization design and evaluation of marine ship energy system at the whole ship level based on the comprehensive consideration of various performance of ships is still unclear. In the present study, an integrated modeling method of multi-objective optimization and evaluation for ship energy system are proposed to optimize the design of ship energy system. This paper constructs a technical selection, capacity allocation, and operational optimization model for ship energy systems using mixed-integer nonlinear programming (MINLP) method. Based on determining relevant constraints and multiple objective functions, the data envelopment analysis (DEA) method is introduced to evaluate and make decisions on the multi-objective optimization results, and compared with traditional decision-making method. The economic optimization results show that the net present cost of ship energy system can be reduced by 20%, while the NO x , SO x and CO 2 emissions of the system are reduced by 41%, 20% and 32%, respectively. Moreover, the energy efficiency of the optimized ship energy system is improved by 9% compared to the conventional ship energy system. In summary, the proposed method can effectively identify the advantages and disadvantages of different solutions, therefore provide more design options of sub-optimal schemes for decision makers. • Proposed an integrated model for optimizing and evaluating ship energy systems • Improve operational efficiency, and reduce greenhouse gas emissions of ship. • Evaluates the optimal design and configuration of ship energy systems effectively. • Analyze various factors affecting system economy and environmental sustainability. [ABSTRACT FROM AUTHOR]

The WHO has declared that the COVID-19 pandemic is a severe health crisis. Currently, variants of concern are delta and omicron, including sub-lineages of the omicron which are XBB and BQ.1 variants. Decision planning with situation awareness is important during the COVID-19 pandemic, especially demand planning for medical supplies following pandemic probability or severity via pandemic risk assessment. Therefore, this research proposes an intelligent risk assessment on the prediction of the COVID-19 pandemic using deep learning with deep neural network (DNN) and tunicate swarm algorithm (TSA). The results show the model can accurately predict the distance and elapsed time of the next COVID-19 case based upon the previous case and evaluate the associated risks. The contribution of this research, as prediction model is based upon a DNN, it has the ability to learn and by implementing the TSA, it can improve theoretically the performance of the DNN for more precise prediction and faster convergence to the optimal solution. The prediction results are practically expanded to analyze risk assessment using probability and the data envelopment analysis (DEA). The benefit of this research is that the proposed methodology demonstrates the prediction results using risks assessment based upon intelligent risk assessment charts. The Government or those involved can use the proposed methodology to achieve a better decision-making and management to control the COVID-19 pandemic in terms of supplying the medical supplies into pandemic areas. [ABSTRACT FROM AUTHOR]

Exogenous constraints such as fixed-sum output can affect the efficiency assessment of traditional two-stage data envelopment analysis (DEA) models. To address this issue, several studies have evaluated the DEA efficiencies for two-stage systems with fixed-sum output. They have proposed centralized models based on cooperative game theory and models under global mindset. However, in many cases, the first and second stages play a noncooperative game, and information on stage priority is often unobservable. In this study, we develop a noncooperative two-stage fixed-sum output DEA model with uncertain stage priority for calculating the overall system and stage efficiencies. Instead of stage priority as an endogenous variable, with aid of acceptaiblty analysis, we analyze the dominance relationship among decision-making units (DMUs) and provide a robust ranking by considering all possible stage priorities. We also conduct an empirical analysis with data from commercial banks to demonstrate the effectiveness of the developed model. After 10,000 groups of stage priority iterations, we report a robust ranking of commercial banks in which Hefei branch ranks first with 100 % probability. [ABSTRACT FROM AUTHOR]

• Theoretical mechanism of the non-uniqueness optimal solution for cross-efficiency is revealed. • Weight priority is constructed to reflect the comparative advantage of inputs and outputs. • Weight priority strategy can serve as both an independent strategy and a supplementary strategy. • The uniqueness of optimal result obtained by weight priority strategy is theoretically proved. Due to a large number of studies on cross-evaluation strategies, the theoretical problem of DEA cross-efficiency approach has been transformed from the non-uniqueness of the optimal solution to the relative uniqueness, because the existing cross-evaluation strategies can only reduce the possibility of non-unique solutions, but cannot prove the existence of a unique optimal solution. This paper illustrates the relative uniqueness of optimal solution of the cross-efficiency approach with traditional cross-evaluation strategies, and the graphic method and theoretical analysis are used to explain the reasons of relative uniqueness. Based on the data characteristic of inputs and outputs, the weight priority rule is made for constructing the new cross-evaluation strategy, and its optimal solution is proved to be unique. In addition, the weight priority strategy can also be introduced into traditional strategies as a supplementary strategy, and then the unique optimal solution can be obtained on the premise of preserving the characteristics of traditional strategies. In general, this paper solves the non-uniqueness and relative uniqueness problem of optimal solution in the DEA cross-efficiency approach, and then the reliability and robustness of the approach are thus improved. [ABSTRACT FROM AUTHOR]

Industrial clusters play a pivotal role in securing competitive advantages and achieving national strategic objectives within the new energy sector. The assessment of new energy industrial clusters is crucial for sustaining their swift development. However, prior research has rarely addressed this domain. In this study, a Multi-Criterion Decision-Making (MCDM) approach is introduced for the assessment of new energy industrial clusters. This approach employs the Data Envelopment Analysis (DEA) super efficiency method alongside the Grounding-Enterprise-Market-Surrounding (GEMS) model to evaluate the efficiency and competitiveness of new energy industrial clusters, respectively. Subsequently, this approach categorizes new energy industrial clusters based on their efficiency and competitiveness scores. Clusters exhibiting similar scores in these areas are grouped together for the identification of key new energy industrial clusters. Ultimately, the selection of key new energy industrial clusters is performed based on varying criteria. A case study on the selection of prominent solar energy industrial clusters is presented to demonstrate the efficacy of the proposed approach. The findings indicate that this approach facilitates an effective selection process for key new energy industrial clusters. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a technique that allows each decision-making unit (DMU) to calculate efficiency using the most favorable weights for inputs and outputs. The resulting efficiency scores are incomparable and difficult to discriminate. Cross-efficiency is a concept for solving the problem of incomparability among the efficiencies of a set of DMUs that are calculated from different weights and is helpful for ranking. In basic DEA, several cross-efficiency evaluation methods with various secondary purposes have been presented, however, the internal mechanism of the DMUs in cross-efficiency evaluation is often neglected. In this study, we use the composition approach for a basic two-stage structure, so that the efficiencies of the divisions are estimated first, and then the overall efficiency of the system is obtained. To solve the linear bi-objective model in the combination approach, we consider a compromise programming problem to find the solution that is as close as possible to the ideal point. Finally, a secondary objective model is proposed to select a weight among the optimal weights of the compromise model, which also guarantees the efficiency of the optimal weight for the bi-objective problem. We use the optimal solution obtained from this model to evaluate cross-efficiency and rank DMUs for a series two-stage system. The proposed approach enables us to rank the DMUs by assessing the performance of each DMU and each of its separate divisions. The validity of the suggested network cross-efficiency evaluation is demonstrated by a numerical example from the literature. [ABSTRACT FROM AUTHOR]

Efficient resource allocation in healthcare organizations remains a complex challenge, exacerbated by the intricate data structure and myriad variables involved. While traditional methods like Data Envelopment Analysis have been employed, these often lack the requisite discriminatory power for accurate evaluation. This study introduces the PDA methodology, a fusion of Principal Component Analysis, Data Envelopment Analysis, and Analysis of Variance algorithms, developed in a machine learning environment. To further optimize discriminative power, the Particle Swarm Optimization Algorithm was included. By applying the PDA methodology to hospitals in the Apulia region of Italy, we found that facilities with better perceived quality tend to be more efficient. Furthermore, we identified significant variations in efficiency based on network affiliation and hospital level. Machine learning algorithms, including linear regressions and neural networks, confirmed that our methodology outperforms traditional Data Envelopment Analysis models in precisely distinguishing between varying levels of hospital efficiency. In conclusion, this work provides a fresh perspective for future research and policy decisions in the healthcare domain, demonstrating how the use of advanced and machine learning algorithms can result in more accurate evaluations and targeted interventions. [ABSTRACT FROM AUTHOR]

Strategic alliances and partnerships based on mergers and acquisitions can be used to improve the performance of decision-making units (DMUs). In this paper, we present different types of strategic alliance cooperation and partnership between DMUs and their corresponding aggregation DMUs. We propose two different strategies, we were able to carry out the process of strategic alliances and partnerships in semi-additive production technology. In the first strategy, two partner DMUs can compensate for the lack of resources in some of their input components by using excess input resources in some of each other's input components. Thus these DMUs can improve their efficiency score among all DMUs. The modeling of strategic alliances and partnerships in the first strategy was based on data envelopment analysis (DEA) models. In the second strategy, the two DMUs selected as partners adjust their input and output levels with the help of each other in such a way that these two DMUs achieve a target efficiency level predetermined by the DM. The modeling of strategic alliances and partnerships in the second strategy was based on inverse DEA models. We also apply the proposed approach to show importance strategic alliances and partnerships in banking. In the following, we compared the results of the models in carrying out the partnership process in two different technologies. The results showed that in semi-additive production technology, we can achieve potential gains for banks. [ABSTRACT FROM AUTHOR]

The efficient management of water resources in Chinese agriculture is crucial for ensuring food security and mitigating environmental consequences such as water scarcity and pollution. Agricultural technological innovation is crucial in optimizing agricultural practices and making them more sustainable. To this end, this study investigates the dynamic relationship between agricultural technological innovation and total-factor agricultural water usage efficiency (TFAWUE) in Chinese provinces from 2000 to 2020. The study utilizes the Data Envelopment Analysis (DEA) Malmquist productivity index approach to measure the overall efficiency of water usage in agriculture, known as total-factor agricultural water usage efficiency (TFAWUE). The findings suggest that the mean TFAWUE score of Chinese provinces is 1.1356, surpassing a value of 1. It illustrates that Chinese provinces witnessed a growth of 13.56 in TFAWUE over the study period. Technological change is the primary determinant of growth in the TFAWUE, as technology change (TC) is higher than efficiency change (EC). Subsequently, by employing a rigorous econometrics series, this study provides valuable insights into the intricate dynamics of agricultural technological innovation and its impact on total-factor agricultural water usage efficiency. The study constructs a composite multidimensional index of agricultural technological development, encompassing various technologies pivotal to the agriculture sector. Analysis shows that agricultural technologies enable farmers to implement water conservation practices effectively to enhance agricultural water usage efficiency. However, the farm scale reduces the efficiency of agricultural water usage. Additionally, sprinkler technology positively enhances water usage efficiency in agriculture. These findings provide valuable insights for policymakers in the agricultural sector, offering guidance on sustainable practices and policies for managing water resources in conjunction with improvements in agricultural technologies. • The average total factor agricultural water usage efficiency of China is 1.1356. • Technological change is the growth determinant of the total factor agricultural water usage efficiency. • Agri-technology enables farmers to implement water-saving practices effectively. • Farm scale reduces the level of agricultural water usage efficiency. • Sprinkler technology significantly improves total factor agricultural water usage efficiency. [ABSTRACT FROM AUTHOR]

In the actual efficiency evaluation process, it frequently occurs that the quantitative input and output data of each decision-making unit is difficult to obtain due to the limitation in data measurement and the absence of prior information. Instead, the form of judgment information can be selected to express the input–output state of the evaluated decision-making units compared with the reference level. Traditional cross-efficiency evaluation methods do not involve this case and cannot be well used in this situation. Based on the above problems, a multiplicative cross-efficiency evaluation model with judgmental information is constructed, and the optimal adjustment model is provided by introducing deviation variables to deal with the inconsistent issue of data features. In addition, for the case of multiple optimal solutions, the maximizing and minimizing secondary objective models of the multiplicative cross-efficiency are established respectively to reasonably identify the corresponding optimal weights. Simultaneously, in order to effectively depict the interaction between decision-making units, the characteristic functions of JM-cross efficiency cooperative game and the multiplicative Shapley value are established based on benevolent and aggressive cross-efficiency matrices. Moreover, in order to avoid the extreme ranking results that are oriented by high impact decision-making units, a series of optimization coordination models are developed, and an optimal efficiency ranking vector generation method with strong applicability and decision flexibility is proposed. The optimal efficiency ranking vector not only creates a precise match with the given Shapley index of the multiplicative cross-efficiency game, but also bounds between the minimum and the maximum average efficiency. Finally, the validity and rationality of the discussed models are illustrated by a concrete example. [ABSTRACT FROM AUTHOR]

• An evaluation model is proposed by considering risk attitudes and aggregation method. • Our distance entropy function reduces the inconsistent degree of evaluation results. • Empirical example is performed to validate the applicability of the proposed model. • Sensitivity analysis is conducted to test the effect of parameter changes on results. Cross-efficiency evaluation with the data envelopment analysis (DEA) model is an effective way to assess performance and provide a complete ranking of decision-making units. However, it is generally assumed that decision makers are perfectly rational in the cross-efficiency model, which fails to consider the subjective preferences of decision-makers. Moreover, the arithmetic average method is usually adopted to aggregate efficiency scores in traditional cross-efficiency methods, which underestimates the importance of self-evaluation. To address these issues, we extend cross-efficiency with the DEA model by incorporating prospect theory and the distance entropy function. First, we calculate the prospect values of decision-making units to describe the non-rational subjective preferences under the risk of decision-makers. Second, based on prospect cross-efficiency, a new distance entropy function is developed to aggregate the ultimate prospect cross-efficiency values. More specifically, some traditional cross-efficiency evaluation models can be considered special cases of prospect cross-efficiency models with appropriate adjustments to the parameters. Finally, an empirical example is used to evaluate the prospect cross-efficiency results with the high-tech industries of 29 provinces in China to illustrate the applicability and effectiveness of the proposed model in ranking observations. [ABSTRACT FROM AUTHOR]

The institutions of world repute, like the United Nations, the World Bank, etc., assess and rank countries or firms based on various criteria pertaining to key areas to evaluate their performance and publish them as Indexes. These ranking tools aid decision-makers in formulating policies and procedures 4 4 PAP-Policies and procedures. (PAP) to achieve performance efficiency. In the real world, identifying the closest, most efficient country or firm whose PAP can be emulated with minimal effort is a management dilemma. These Indexes contain only output data, which compounds the problem. This paper uses Data envelopment analysis 5 5 DEA- Data envelopment analysis. (DEA) to resolve these issues. Each country or firm under evaluation is referred to as Decision-making unit 6 6 DMU-Decision-making unit. (DMU). The novelty of this study emanates from 1) segregating countries or firms into several frontiers using Pure Output Model-based Context-Dependent Data envelopment analysis 7 7 POMCDD-Pure output model-based context-dependent data envelopment analysis. (POMCDD), 2) proposing a technique to identify the most attractive nearest DMU that is performing better and is easy to emulate, 3) identifying the "Region of introspection" by using the translation technique of Pillar-Index scores concerning DMU under evaluation to demarcate reference DMU for the concerned Pillar, and 4) suggesting techniques such as "Rapid gain," "Best yield", and "Least effort" which are proposed to identify reference DMUs for the concerned Pillar. A case study analysis of the Logistics Performance Index 8 8 LPI-Logistics Performance Index. (LPI) is carried out to substantiate the contributions. The countries ranked in the Logistics Performance Index are segregated into several efficient frontiers using the POMCDD technique. The attractiveness of each country is evaluated relative to the frontiers of poorly performing countries. The translation of Pillar-Index scores concerning the country under evaluation is used to identify reference countries for that Pillar. The developed framework is helpful to the policy-makers in analyzing published Indexes. [ABSTRACT FROM AUTHOR]

Over recent decades, China's economy has continued to develop steadily and rapidly, contributing to a large environmental cost. Therefore, many researchers propose various methods to evaluate environmental efficiency to deal with these serious issues accurately. This paper is the first to evaluate environmental efficiency using a model featuring not only a parallel structure with three component processes (primary, secondary, and tertiary industries) in the first stage but also a sewage treatment process in the second stage. Simultaneously, we propose radial but also nonradial versions of the Environmental Multiple Variable Return to Scale (EMVRS) model. These two models both consider shared inputs as well as shared outputs, and the proportion of the shared inputs and shared outputs utilized in each component process is assumed to be unknown. Additionally, we further analyze the factors influencing environmental efficiency by utilizing the Tobit model. The results when EMVRS is applied to Chinese provinces can be divided into 3 parts: (1) Most provinces do not purify wastewater appropriately contributing to the bad environmental efficiency. (2) The efficiency values of the three industry types are similar or identical to each other in most provinces. (3) The population of people employed negatively impacts environmental efficiency. [ABSTRACT FROM AUTHOR]

In the era of economic globalization and heightened international competition, the emergence of free trade zones has significantly increased regional openness, introducing both new opportunities and challenges for ports. This paper proposes the enhancement of intelligent design in the maritime landscapes of smart ports, with a focus on optimizing internal pathways and developing a predictive model to gauge the impact of smart port landscapes. Furthermore, the study delves into the assessment of risks in smart port supply chains, employing an innovative approach that integrates rough set theory and neural networks. This novel model significantly improves the accuracy of risk evaluation in port supply chains, thereby providing crucial decision support for port management. Additionally, the economic efficiency of smart ports is scrutinized using the Data Envelopment Analysis (DEA) Malmquist index method. Through meticulous selection of research subjects, port efficiency indicators, and the DEA Malmquist index, this analysis offers a comprehensive insight into the economic operations of smart ports. This research not only offers guidance for port economic management but also establishes a framework for the sustainable development of smart ports, thereby ensuring their long-term viability. • The selection of research objects and port efficiency evaluation indicators, as well as the application of the DEA Malmquist index, can help to comprehensively understand the economic operation of smart ports. • This study not only guides the economic management of ports but also provides a reference basis for the sustainable development of smart ports. [ABSTRACT FROM AUTHOR]

Generating secure, affordable, and clean energy requires careful evaluation of the costs and associated risks of different energy generation sources. Portfolio optimisation models are commonly used in this regard to help diversify risks associated with generation sources. In recent times, energy policies often require the consideration of the environmental and social effects of such activity. Consequently, sustainability has become a key factor in making energy mix planning decisions. To incorporate sustainability considerations in energy mix planning, the conventional approach has been to add indicators for environmental and social costs to the total generation cost for each available technology in a portfolio optimisation model. However, this approach to developing a sustainable generation mix may not effectively address all dimensions of sustainability. In most cases, the economic dimension is prioritised over social and environmental factors. We examine how various aggregation methods impact the preference among the sources and the optimal portfolio mix and propose aggregation methods that effectively incorporate all sustainability dimensions. We observed that technology ranking based on multiplicative, pairwise interaction, and multilinear aggregation options aligns better with our sustainability goals than additive aggregation. By adopting these methods of aggregation, we were able to include more renewable and clean energy sources in our optimal portfolios. • Sustainable energy mix literature relies on the additive aggregation of costs. • Additive aggregation allows compensation across sustainability dimensions. • We examine various cost aggregation methods. • Various optimization models are developed to test aggregation methods • More sustainable portfolios are generated through multiplicative aggregation. [ABSTRACT FROM AUTHOR]

In traditional multi-attribute group decision-making (MAGDM) methods, the aggregation of expert evaluation information, the normalization of attribute scales, and the determination of attribute prior weights can lead to information loss and inconsistent results. In addition, these methods cannot identify and improve the shortcomings of non-optimal alternatives. Considering these, this paper proposes a MAGDM method based on data envelopment analysis (DEA) by setting up a parallel expert evaluation system, thus integrating the initial opinions of all experts to rank the alternatives from a global perspective. First, we construct the multi-expert evaluation process as a parallel system from the global perspective so that the decision-making process is based on the initial expert evaluation information. Then, a new directional distance function model based on endogenous projection directions is proposed to measure the alternatives under each expert subsystem, eliminating data processing and artificial weight assignment. The non-optimal alternatives can be improved to an efficient state by adjusting the attribute levels to target values. Next, we propose a DDF super-efficiency model to perform a full ranking of the alternatives. Finally, a case study is provided to demonstrate the rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The variable returns to scale data envelopment analysis (VRS-DEA) benchmarking models are developed by assuming convexity on technology. However, this assumption may be not consistent with the classical microeconomic theory where marginal product initially increases but diminishing returns eventually set in. Accordingly, the selected benchmarks may aggregate best practices, which are less attainable in performance improvement. This paper thus proposes a VRS-DEA benchmarking model with quasiconcave production function, consistent with classical microeconomic theory. A novel approach for determining the returns to scale of Decision-Making Units (DMUs) is developed. The hyperplane of the quasiconcave production function is characterized. Benchmarks are selected by minimizing the weighted distance between the evaluated DMU and the hyperplane. An empirical application of China's tourism sector is used to validate the approach. The empirical results show that Mergers and Acquisitions are beneficial for smaller hotels as the production of most regions in China's tourism sector is subject to increasing returns to scale. [ABSTRACT FROM AUTHOR]

The importance of reducing carbon emissions to safeguard the environment for generations is widely recognized worldwide. Countries are putting in efforts to lower their carbon footprint, aiming towards achieving carbon neutrality also termed as net zero emissions. This research seeks insights from manufacturing experts on how sustaileanant (sustainable + lean) innovations can support the journey towards net zero in manufacturing supply chains, making them more environmentally friendly. We initially determined the critical success factors (CSFs) of sustaileanant innovations that can help achieve net-zero emissions in manufacturing supply chains and validated through manufacturing experts using a validatory survey. Further, employing a robust two-step approach involving strong ordering data envelopment analysis (SODEA) and machine learning (ML) based Bayesian network (BN), we aimed to understand the significant CSFs driving this transition from traditional carbon-heavy manufacturing practices to a net zero objective. Our study involved gathering input from 125 professionals in the manufacturing sector for employing SODEA-BN methodologies. The findings highlight that incentive schemes for organizations, availability of professional expertise, and availability of funds for implementing sustaileanant practices are crucial for organizations to transform and achieve carbon neutrality. In this study, the recommendations provided in line with sustainable development goals (SDGs) will guide practitioners and academicians in understanding the critical sustaileanant factors for a successful net-zero achievement. • Identified 14 CSFs of sustainable and lean (sustaileanant) innovations for NZA. • Evaluated CSFs of sustaileanant innovations using integrated SODEA-BN methods. • Indian manufacturing supply chain context is considered for determining NZA. • Influence of CSFs of sustaileanant innovations on NZA is computed. • Implications and recommendations are provided in line with SDGs. [ABSTRACT FROM AUTHOR]

• A stochastic entropic analysis on the basis of ideal solutions is proposed. • The proposed model is applied to measure bank performance in Japan. • The proposed innovative model combines the advantages of DEA and TOPSIS. • Machine Learning methods are used to predict information gains. We propose a stochastic entropic analysis based on ideal solutions and apply it to evaluate the performance of Japanese banks. Not only is the odds ratio of the learning feedback modeled by Beta-distributed priors, but our innovative model also combines the advantages of Data Envelopment Analysis (DEA) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in performance evaluation. The information gains in terms of contextual variables are predicted using Machine Learning methods. Our findings demonstrate that our proposed method generates more comprehensive performance scores compared to DEA and TOPSIS. Additionally, we find that total liabilities, total assets, and total deposits, in that order, are the three most relevant inputs for understanding the outputs. Operating revenue, total income, and total loans are the output variables most pertinent to comprehending input behavior. We argue that while mutual information seems important in explaining efficiency variations, continuous improvement in the production process plays an even more crucial role in enhancing the performance of Japanese banks. [ABSTRACT FROM AUTHOR]

As an effective approach in ranking decision making units (DMUs), cross-efficiency evaluation is only extended to two basic network systems: series and parallel, which is not applicable to the common complex network system in reality. By using the directional distance function (DDF), this paper extends the cross-efficiency evaluation to a complex system called the parallel two-stage system, which consists of several independent parallel processes and each of them is a sub-system with a general two-stage series structure. The proposed method is able to decompose the cross-efficiency measure of the system into the weighted average of those of processes, stages and overall stages, respectively, and as a result, successfully achieves the layer-by-layer decomposition of cross-efficiency measures under complex nested networks. An empirical study on 40 open-ended equity funds in China from 2018 to 2020 verifies that the newly proposed method not only has a stronger performance discrimination power than self-evaluation, but also can identify the relationships among efficiencies of the system, processes and stages, and meanwhile it can reflect the effects of processes and stages on the system. More importantly, our method has a better practical value for actual fund investment selection. [ABSTRACT FROM AUTHOR]

In 2019, the aviation sector emitted about five percent of the total energy-related carbon dioxide equivalent (CO 2 e) emissions in the United States. The replacement of conventional aviation fuel with drop-in sustainable aviation fuel (SAF) derived from various biomass-based feedstocks is vital to reduce the overall CO 2 e emissions of the aviation sector. The SAF derived from Brassica carinata (carinata), an oilseed winter crop, could help the aviation sector mitigate CO 2 e emissions. Therefore, it is vital to develop a framework for assessing the eco-effectiveness of carinata in crop rotations by combining economic and environmental benefits. We identified 292 crop rotations (over a four-year rotation period with and without carinata) in South Georgia, a major agricultural region of the United States. We conducted a comprehensive life cycle assessment to determine the total carbon emissions of selected crop rotations. We combined the carbon emissions and profitability information using data envelopment analysis (DEA) to estimate the eco-efficiency score of each crop rotation. The results indicate that crop rotations with carinata emit less CO 2 e than crop rotations without it, especially when carinata replaces winter wheat. Carinata's profitability (net present value of $2996/acre) is highest in a "corn-corn-soybean" rotation at a contract price of $441 per metric ton. Finally, "cotton-carinata-cotton-fallow-soybean-fallow-cotton-carinata" is the most eco-efficient rotation at the same contract price. Overall, carinata increases eco-efficiency at the farm level and, thereby, has the potential to promote the bioeconomy in South Georgia and beyond. [ABSTRACT FROM AUTHOR]

To assess the efficiency of high-standard farmland construction (HSFC), this study utilized data spanning from 1998 to 2020 obtained from 30 provinces, autonomous regions, and municipalities in China. This assessment was conducted using a three-stage super-efficient slack-based measure data envelopment analysis(SBM-DEA)model. Furthermore, we investigated the spatial and temporal variances and convergence of HSFC efficiency using the Dagum Gini coefficient and convergence analysis. The results firstly show that from 2007 to 2020, China's HSFC efficiency displayed fluctuations and declines, significantly influenced by environmental and stochastic factors. Secondly, it showed that the gap in China's HSFC efficiency has widened, with super-variable density being demonstrated as the primary source of spatial imbalance in HSFC efficiency. Thirdly, it showed that the deviation in the efficiency of HSFC in the central region of China from the average level has shown a gradually declining trend, while the deviation in the efficiency of HSFC in the national, western, eastern, and northeastern regions has not presented a downward trend. Fourthly, it demonstrated that over time, the efficiency of building high-standard agriculture throughout the nation and its four main areas will converge to the same level. • The three-stage super efficiency model is used to measure the efficiency. • The efficiency of high-standard farmland construction is affected by external environmental factors. • The gap of construction efficiency in various parts of our country expands unceasingly. • Hypervariable density is the main source of spatial imbalance in the efficiency. • There was β absolute convergence and β conditional convergence across the country. [ABSTRACT FROM AUTHOR]

In the context of escalating global environmental concerns, the automotive industry faces pressing imperatives to align its growth with sustainable practices. This urgency, coupled with the need for a holistic evaluation framework, sets the stage for this study. Motivated by the gaps in quantitatively assessing sustainability, the study aims to discern and quantify the sustainability efforts of leading automotive manufacturers. The objective is to establish a robust, quantifiable link between operational efficiency and sustainability criteria that informs both business strategy and environmental stewardship. To achieve this, an innovative methodology combining the Malmquist Data Envelopment Analysis (Malmquist DEA) framework and the Ordinal Priority Approach (OPA) is employed. The approach analyzes efficiency changes, technological progress, and the industry's adoption of sustainable practices over a five-year period, concluding in the pre-pandemic year of 2020. The findings reveal a notable emphasis on productivity and market exposure, identifying them as pivotal to sustainable manufacturing. The OPA provides a weighted analysis of sustainability criteria based on expert evaluations, emphasizing the integration of environmental initiatives with core business strategies. The study's contribution is twofold: it introduces a novel, quantifiable approach to evaluating sustainability in the automotive industry, and it offers strategic insights for companies seeking to elevate their sustainability profile effectively. However, the reliance on pre-pandemic data and expert opinions marks the limitations of the study, indicating the necessity for further research. Future studies should aim to expand the temporal scope to include the latest industry shifts and to diversify the pool of sustainability criteria, capturing a broader spectrum of environmental and social dimensions. This continued research will enhance the industry's understanding of sustainable manufacturing in the evolving global context. • Innovatively combines Malmquist DEA and OPA to quantify automotive sustainability. • Identifies efficiency and market reach as central to sustainable automotive practices. • Employs expert insights to prioritize sustainability criteria, influencing industry strategies. • Stresses the integration of environmental goals with business operations for sustainable success. • Notes the need for updated post-pandemic research and broader sustainability metrics. [ABSTRACT FROM AUTHOR]

This study examines internal greener operations in Indian healthcare during COVID-19 pandemic. Specifically, the research explores the interrelationships among three parallel healthcare lines: general care, emergency care, and biomedical waste (BMW) management to fulfill greener healthcare's targeted sustainable development goals. We use the three parallel stage data envelopment analysis (DEA) approach model to assess efficiencies of the overall system and the individual parallel stages. We collected panel data for cities in the state of Maharashtra, India during the second wave of pandemic in 2021. We find that the effectiveness of general and emergency medical services depends on the requirements of healthcare resources such as beds and equipment in times of pandemics. By contrast, the capacity of individual BMW treatment processes such as incineration, autoclave, shredder, and deep burial would have a bearing on the efficiency of both BMW management and the total system for greener operations. In the second phase, we evaluated the impact of context-specific exogenous factors using a regression model. We found that literacy level was the most important factor at all three parallel stages separately. Additionally, the total population and number of female vaccinated persons were significant across the system while population density and number of male vaccinated persons were inconsequential factors. Interestingly, the Wilcoxon rank-sum hypothesis test offered no evidence to suggest that performance of general care and BMW management, and emergency care and BMW management were equivalent. Implications of these findings for academic theory, practitioners, and policy makers are offered. [ABSTRACT FROM AUTHOR]

This study offers a novel empirical application for assessing the impact of agri-environment schemes (AES) on the performance of farms. The existing evidence about the impact of these schemes considering environmental and economic aspects equally is still limited. Therefore, our objective is to contribute to the literature on the impact evaluation of AES by considering three important aspects in our empirical analysis. First, the performance of farms is proxied by an indicator that incorporates environmental externalities into production activities. Second, our empirical analysis focuses on a sample of Bavarian dairy farms covering the period 2013–2018, thus, we can evaluate the effectiveness of agri-environmental schemes in one of Germany's leading agricultural and forestry regions during the latest programming period. Finally, in an effort to increase robustness, we employ an improved version of the Malmquist-Luenberger productivity index, which enables us to get around some of the shortcomings of the original index. The obtained results suggest that agri-environment payments have a limited effect on improving farm-level green productivity. [ABSTRACT FROM AUTHOR]

Over the last decade, waste management and prevention have become important in the transition towards a circular economy (CE). This study explores how voluntary environmental policies, adopted at a regional level, contribute to green transition from the perspective of the CE. By considering general and region-specific trends in waste management and environmental certifications, we perform a two-step analysis on a panel of 20 Italian regions between 2015 and 2019: the first step extrapolates the relative efficiency by using a Data Envelopment Analysis (DEA), while the second step extrapolates the efficiency in terms of productivity changes supported by Malmquist Productivity Analysis (MPA). The DEA results show an overall rising trend of efficiency among the Italian regions, supported by the MPA. Furthermore, they reveal that technological innovation in Italian waste management contributes to a greater efficiency in socio-economic systems which are characterized by 'environmental federalism'. From a decision-making perspective, this study suggests that measures such as the incentivization of voluntary environmental policies should be proposed to promote techno-organizations' transition to CE, in coherence with regional economic/industrial specializations and strategies, possibly based on a diversified policy mix, setting a knowledge-based framework by using budgetary measures and policy actions at the regional level. • Circular Economy with resource recovery is a driving force to achieve green transition. • Increase in resource efficiency is an unexplored economic opportunity to support growth. • EMAS and UNI EN ISO certifications are proxies for the level of attention paid to the environment. • Voluntary environmental certifications facilitate the transition. • Choices made at the regional level contribute to the transition. [ABSTRACT FROM AUTHOR]