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While there is a broad agreement in academia and the business community that hydrogen (H2) could significantly contribute to energy policy goals, there is no single shared vision of a sustainable hydrogen supply chain (HSC). The large number of sources for hydrogen production (various fossil fuels, biomass, water) and the different methods for extracting, distributing, and storing it make creating an efficient supply chain a challenge. The commonly used optimization techniques based on linear programming require a new mathematical problem to be formulated and solved for each case. Instead, this study aims to develop an innovative optimization approach for the hydrogen supply chain that does not require complete knowledge of the end-user hydrogen demand and applies to any region. Based on the Network Data Envelopment Analysis (NDEA) method, hybrid Life Cycle Analysis (LCA) and Value Sensitive Design (VSD) approach, the integral eco-efficiency indicator constructed by this approach is a weighted linear combination of economic parameters (CAPEX and OPEX) and environmental parameters (ecotoxicity, oxidation potential, eutrophication potential and climate change). The proposed approach has been tested on 22 different hydrogen supply chain options, confirming its efficiency and ability to inform national and international decision-making. For example, a pan-European decision support system could be based on continuously updated data from the EcoInvent database on environmental parameters of hydrogen production, storage, and transport technologies, and updated data on economic parameters from the European Hydrogen Observatory. [Display omitted] • We propose a new multi-criteria technique to optimise the eco-efficiency of the hydrogen supply chain. • We propose a Value Sensitive Design (VSD) approach to environmental impact selection. • Hydro/Wind/Nuclear energy – PEM electrolysis are the most eco-efficient hydrogen supply chain options. • Thermal generation based on coal – PEM/AWE/SOE electrolysis are the least eco-efficient options for the hydrogen supply chain. [ABSTRACT FROM AUTHOR]

China has steadfastly pledged its dedication to accomplishing carbon peaking by 2030 and achieving carbon neutrality before 2060, a commitment widely recognized as the Dual Carbon Goal (DCG). The proposition of China's DCG presents formidable challenges for the advancement of the transport sector due to its significant contribution to carbon emissions. Consequently, new carbon abatement constraints have been enforced on carbon emissions within the Chinese transport sector. In pursuit of the carbon peaking goal, this research introduces the environmental context-dependent data envelopment analysis approach. The approach is employed to identify sustainable performance targets for the Chinese transport sector at the city level, while considering carbon abatement constraints. These sustainable performance targets not only provide insights on enhancing the environmental performance of these cities with minimum effort, but also offer a comprehensive pathway towards carbon peaking cities in the Chinese transport sector. • A pathway towards carbon peaking cities in the Chinese transport sector is examined. • An environmental context-dependent data envelopment analysis approach is proposed. • Policy implications for the Chinese transport sector are presented. [ABSTRACT FROM AUTHOR]

In this article, we analyse India's transport sector policy. The study examines the two important modes of transport, viz., road and rail, and analyses the efficiency of transport infrastructure from 2011 to 2019. Through Data Envelopment Analysis (DEA), we evaluate modes in terms of the capacity of registered road goods vehicles and railway goods wagons, truck km/train km and energy consumption and corresponding freight traffic. The analysis gives valuable insight into the fact that rail infrastructure is more efficient than road infrastructure. Still, current transport sector policies promote energy-intensive carbon-fuel road vehicles rather than energy-efficient alternative transport modes. The study suggests the changes that may be incorporated in inputs to improve the efficiency of the road infrastructure. The Data Envelopment Analysis (DEA)-Malmquist Index analysis reports the need for total factor productivity improvement in rail transport and the introduction of technological innovations in road and rail transport sector management. The study employs a well-to-wheel (WTW) based index to ascertain the energy consumed and pollutants emitted by freight-carrying road and rail modes between two leading business hubs of India, i.e., Ludhiana and Mumbai. The paper identifies policy gaps and suggests a holistic approach for need-based roadway investment and a time-bound development of railway infrastructure in India. Our study generates valuable implications for India's policy planners and government authorities and provides new insights to other countries. • The study examines road and rail transport, and analyses the efficiency and energy consumption of these modes. • The study suggests the changes to improve the efficiency of the road infrastructure. • The Data Envelopment Analysis (DEA)-Malmquist Index analysis and well-to-wheel (WTW) based index have been employed for the analysis. • The paper suggests a need-based roadway investment approach and a time-bound development of railway infrastructure. [ABSTRACT FROM AUTHOR]

The evaluation research on integrated transport has mainly focused on urban agglomeration or cities. Yet the integration of various modes of transportation is increasingly being seen at smaller scales, such as counties, due to population concentration and rising affluence levels. Consequently, it is becoming necessary to evaluate the efficiency and convenience of transportation services for people at each location in the county space. This study has evaluated the efficiency and equity of integrated transport from a county perspective. With respect to efficiency indexes, in addition to transportation infrastructure investment, transportation energy consumption, transportation turnover, and other general indicators, we also take into account the accessibility of people to various types of hubs in the county. We use the BCC (Banker, Charnes, Cooper) model in DEA (Data Envelopment Analysis) for efficiency evaluation. Besides, we evaluate transport equity by using the Gini coefficient to reflect the population distribution of the county and GIS-based spatial accessibility of each hub site. Finally, we establish an evaluation matrix of efficiency and equity; we employ a spatial overlay method to classify research counties into different quadrants. By comprehensively considering the relationship between efficiency and equity in integrated transport, this study can identify areas for improvement in transportation development. The case study evaluation in Ningbo counties, using data from 2017 to 2020, provides practical questions and constructive suggestions for integrated transport development in these areas. Overall, this research has important implications for local transportation development and could inform policy decisions related to integrated transport. • The research is focused on evaluating the efficiency and equity of integrated transportation. • We expand the evaluation work to include a county-level perspective. • We consider the accessibility of transportation services for residents. • We utilize an efficiency-equity matrix and a DEA model to conduct the evaluation. • This study can assist decision-makers in gaining a comprehensive understanding of the transportation system's advantages and disadvantages at the county level. [ABSTRACT FROM AUTHOR]

• Municipal solid waste management performance of 167 NUTS-2 regions in EU states. • Performance measurement developed through 4 BoD models inspired by the DEA approach. • Best performers are registered in Central and Northern EU NUTS-2 regions. • Eastern and Southern EU NUTS-2 regions performed generally worse. Achieving a Circular Economy requires effective waste management. Monitoring the transformation path taken by the European Union (EU) and its Member States is crucial for assessing the impact and effectiveness of current European waste legislation. Even though numerous studies have shown positive performance and convergence in waste treatment, regional efficiency evaluation studies are still lacking. This research aims to benchmark the performance of municipal solid waste management in 167 regions – at level 2 of the Nomenclature of Territorial Units for Statistics - across European Member States during the period from 2008 to 2013. It looks to assess whether there is any positive convergence among these regions. The efficiency assessment employs four different Data Envelopment Analysis models. One of these models, the Benefit-of-Doubt, is a non-parametric approach and represents a special case within the Data Envelopment Analysis framework. Our findings indicate a yearly reduction in the coefficient of variation of 3.6%. Despite this progressive convergence, substantial differences in municipal solid waste management performance exist between EU-20 regions, even within the same country. Particularly, the analysis demonstrates that the best performers are registered in the Central and Northern EU regions, while the Eastern and Southern EU regions performed worse. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is regarded as a key path to tackling global warming and achieving the Sustainable Development Goals (SDGs). In 2020, the energy consumption of the world's ten major energy-consuming countries accounted for 66.8% of the global total. This paper applied data envelopment analysis (DEA) to calculate these ten major energy-consuming countries' total-factor energy efficiency (TFEE) at national and sectoral levels from 2001-2020, and explored the influencing factors of total-factor energy efficiency with the Tobit regression model. The results showed significant difference in the ten countries' energy efficiency. The United States and Germany topped the list for total-factor energy efficiency, while China and India were at the bottom. Meanwhile, the energy efficiency of the industrial subsector has increased significantly over the past two decades, while that of the other subsectors has been relatively flat. The industrial structure upgrading, per capita GDP, energy consumption structure, and foreign direct investment had significant impacts on energy efficiency with national heterogeneity. Energy consumption structure and GDP per capita were determinative factors of energy efficiency. [ABSTRACT FROM AUTHOR]

• Introduces in-depth key operating parameters and evaluates technical efficiency. • Dosage of urea, calcium hydrate and lye have relatively high improvement ratio. • Suggestions to promote the disposal facilities. Hazardous waste rotary kiln incineration, as the most effective and comprehensive technology to reduce and detoxify waste, generally faces problems such as low load rate and short continuous operating periods. However, there are few studies on the actual operation of such facilities and evaluation of their technical efficiency. Based on the 77-week time-series data of the case company, this study introduces in-depth key operating parameters and evaluates long-term technical efficiency through the data envelopment analysis (DEA) method. The results show that the continuous operating period of the rotary kiln incineration facility can reach more than half a year, with an average load rate of 91.7%. In the analysis of 9 input indicators, the amount of injected activated carbon could not be effectively evaluated due to the lack of relevant standards and online real-time monitoring of dioxins, which might become a weak link in the control of flue gas pollution. The average comprehensive technical efficiency of rotary kiln incineration facilities was 0.939, of which the average pure technical efficiency was 0.949 while the average scale efficiency was 0.989. With 33 of the 77 decision-making units being invalid, there is scope for improvement. The amount of incineration could be increased by 5.34%, and among the input variables, dosage of urea, calcium hydroxide and lye with a relatively high improvement ratio. Based on the results, targeted suggestions were proposed to advance the scientific and precise compatibility of hazardous waste, strengthen the control of dioxin emissions, and promote the intelligent control of the entire process. [ABSTRACT FROM AUTHOR]

• 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]

This study evaluates whether a motorist's choice over green cars (i.e., cars with featuring low C O 2 emissions) and non-green cars (i.e., cars featuring high C O 2 emissions) is efficient. To this end, we present a micro-founded model of motorist choice incorporating the impact of eco-incentives and taxation on C O 2 emissions. Subsequently, we implement data envelopment analysis on a large database of new cars registered in 2019 to evaluate efficiency. The results show that the motorist's optimal choice (green car) does not coincide with the efficient choice. Moreover, motorist choice is mainly guided by the high price of green cars. On average, the efficient choice coincides with cars that end up in the dark area (they do not benefit from eco-incentives and do not pay the pollution tax), being the cheapest, the lightest, the least powerful (low Kw) and petrol fuelled. On average, these elements pertain to Italian motorists with respect to their tastes; most importantly, their budgets do not go well with the high costs of green cars. • We present a model of motorists' optimal choice. • We verify if a motorist's optimal choice coincides with their efficient choice. • Eco-bonus is not sufficient because the optimal choice coincides with the efficient one. • We implement an efficiency analysis via data envelopment analysis. • Price is an important factor regarding the choice of the car. [ABSTRACT FROM AUTHOR]

With economic and social development, the demand for railway transport is increasing, and improving its efficiency is essential for sustainable growth of the sector worldwide. This study investigated railway transport in 16 countries from 2010 to 2018 via a three-stage data envelopment analysis modelling approach to empirically evaluate the corresponding efficiencies while eliminating the influence of the external environment and statistical noise. The results show that Switzerland (0.993), the UK (0.966), and the USA (0.966) top the first-stage efficiency (named comprehensive efficiency) calculation, and the comprehensive efficiency of railway transport has continuously increased over the past years for most countries. The external environment affects the efficiency of railway transport; however, the strength and direction of its impact vary. Railway density improves efficiency, while road density has the opposite effect. In the third stage, China (0.973), the USA (0.969), and the UK (0.906) presented the highest efficiency (real efficiency) estimation, which was further decomposed into pure and scale efficiency. The 16 countries considered present similar pure efficiencies, but significantly different scale efficiencies, implying a relatively low potential for efficiency enhancement via technological improvement. Policy opportunities to promote the railway sector's efficiency lie in implementing real efficiency-oriented governance strategies, piloting resource inputs by differentiating the influence of the external environment, and strengthening the competitiveness of railway transport to achieve economies of scale. • Railway transport efficiency is affected by external environment and internal factors. • We measure the efficiency of railway transport in 16 countries from 2010 to 2018. • Three-stage data envelopment analysis (DEA) modeling approach is employed. • The 16 countries present similar pure efficiency, but different scale efficiency. • Scale efficiency promotion should be prioritized to improve global railway efficiency. [ABSTRACT FROM AUTHOR]

• analyze the energy efficiency of Beijing's industrial sectors from 2002 to 2018. • model industry heterogeneity of Beijing's industrial sectors accurately. • low energy efficiency of Beijing's industrial sectors due to management inefficiency. • the energy efficiency of heavy industry is higher than that of light industry. • explore the influencing factors of energy efficiency of Beijing's industrial sectors. Improving energy efficiency is one of the effective means to achieve China's carbon peaking and carbon neutrality goals. As the capital of China, Beijing should improve energy efficiency and has a primary responsibility in achieving the goals. Considering the heterogeneity of industrial sectors, this paper proposes a data envelopment analysis (DEA) approach to analyze the total factor energy efficiency and its influencing factors of 31 industrial sectors in Beijing from 2002 to 2018. We show that the low energy efficiency of Beijing's industrial sectors, mainly due to management inefficiency, is not conducive to the realization of the goals. Besides, the energy efficiency of heavy industry is higher than that of light industry. The expansion of enterprise scale has a positive impact on energy efficiency, while the property right structure, foreign trade and energy consumption structure have a negative impact on energy efficiency. Finally, we put forward some policy recommendations. [ABSTRACT FROM AUTHOR]

• Monitoring all comparable factors to prevent risky driving is a challenging task. • Star rating is reliable to identify factors associated with risky driving behaviors. • Star rating of the driver's behavior provides credible in-depth analysis of behavior. • The most significant factors determine the monitoring stages at the territory level. • Effects of the goal-oriented actions depend on the most contributing factors. Driver behavior is a key determinant of road safety. Risky behaviors can be measured and quantified using traditional and modern methods, which enables the assessment of the driver's behavior. Following lessons from safety science, it is important to analyse and manage risks factors to optimize interventions in the transport context at the territory. The main objective of the paper is to explore the star rating of the driver's behavior to provide credible road safety monitoring and identification of the factors that most contribute to risky driving behaviors. The Data Envelopment Analysis method for selection of most significant factors is used for star rating of driver's behavior. The subject of the analysis encompasses 18 factors for 27 police administration units (PAUs) in the Republic of Serbia. The results are analysed regarding the following aspects: 1) star rating of driver's behavior and 2) defining the stages of the periodic factors monitoring for each PAU analysed. Using a mobile phone while driving, not using child restraints system, alcohol drunk driving during the night and seat belt use at rear seats of passenger vehicles were identified as the most significant factors associated with risky driving behaviors. Monitoring a broader set of factors helps to identify the strengths and weaknesses of a territory's road safety system. Star rating of driver's behavior as a tool intended for decision-makers ensures monitoring, management, the exchange of evidence-based and customized best practices and defining earlier goal-oriented actions to manage driver's behavior and to prevent risky driving. [ABSTRACT FROM AUTHOR]

The aim of this study is to measure the current state of the trucking business and its performance in existing literature and transportation management system. The research question is to what extent the performance measurement systems contribute in preference-based strategic decision-making in trucking industry and draws a path to their desired levels of success. This study integrates Balance Score Card (BSC) with Imprecise data envelopment analysis (IDEA). A systematic review of academic literature, trade journal, and the magazine has been conducted to develop the key performance measures based on four perspectives of the Balance Score Card (BSC): financial; customer; internal processes; learning, and growth. This study explored the different inputs and outputs to measure the performance and subsequently, data have been collected from the eighteen transporters, and the preference degree approach is used for comparison and ranking of the efficiency intervals of the transporters. The results show that the financial perspective requires special attention, which indicates that the operational cost due to the competitive market and underutilization of fleets contributes to the lower financial performance of the transporters. Furthermore, the study also reveals that there is considerable scope in customer service improvement. These findings will help practitioners to analyze their business from different perspectives and give suggestions for taking timely corrective action when necessary. • Analyzing how many performance measures help with preference-based strategy adoption for micro-truck owners. • Also, this study will help to understand what measures should be considered to analyze the performance of micro-truck owners.· We use Preference degree approach for comparison and ranking of the interval efficiency.· This study reveals that that no micro truck operators are efficient with regard to all perspectives, no one is perfect.· This study guides in strategic, operation and tactical planning through consideration of opportunities for reciprocal learning among transporters. • We use Preference degree approach for comparison and ranking of the interval efficiency • This study reveals that that no micro truck operators are efficient with regard to all perspectives, no one is perfect • This study guides in strategic, operation and tactical planning through consideration of opportunities for reciprocal learning among transporters [ABSTRACT FROM AUTHOR]

Currently, China is at a critical stage of economic transformation and pollution control. The environmental pollution control efficiency (PCE) deserves more in-depth study. This paper adopts a three-stage global data envelopment analysis (DEA) model and a global Malmquist index model to measure the PCE and the sources of its changes in China from 1991 to 2019. Study shows that: (1) the overall level of PCE has remained low over the past 29 years, and it has a slowly fluctuating upward trend, with an average annual growth rate of only 0.92 %; (2) the lower pure technical efficiency hinders the effectiveness of environmental pollution control, which is mainly affected by the slow technological progress; and (3) at the provincial level, the overall efficiency of Shanghai and Guangxi has long been above 0.9, while the efficiency of other provinces has been below 0.9 for quite a long time. At the regional level, the Yangtze River Delta is the most effective area, while the Yellow River Basin is the most ineffective area. In consideration of this, we provide specific strategies for improving PCE of each province, from three aspects of increasing the capital input, optimizing the scale structure, and upgrading technology level. [Display omitted] [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]

[Display omitted] • Assessment of the status and trends of PV DG in Brazil based on big data processing; • Application of machine learning algorithms to estimate the installed power of individual systems; • Application of forecasting models to predict the installed power of PV DG over time in Brazil and its regions; • Application of the DEA method to rank Brazilian states in terms of efficiency in PV DG deployment; • The paper is expected to be valuable for regulatory agencies, researchers, and PV aggregator companies. The deployment of photovoltaic (PV) distributed generation (DG) has been increasing substantially in Brazil. In this context, the present paper exploits a robust dataset provided by the Brazilian Electricity Regulatory Agency (ANEEL) to evaluate the status and trends concerning PV DG. As of Nov/2022, such a dataset consists of more than 1,4 million lines (one for each system) and thirty columns (various information concerning the systems). For an in-depth assessment, three items are addressed: (i) the application of machine learning algorithms to estimate the installed power of individual systems based on other information available in the dataset, (ii) the application of forecasting models to predict the installed power of PV DG over time in Brazil and its regions, and (iii) the application of the data envelopment analysis (DEA) method to rank Brazilian states in terms of efficiency in PV DG deployment. While items (i)-(iii) present distinct specific goals, they use the same dataset and provide essential insights concerning PV DG in Brazil. In item (i), elastic net (EN), decision tree (DT), random forest (RF), extra tree (ET), AdaBoost (AB), and gradient boosting (GB) are applied to select the most accurate algorithm. In item (ii), autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing (HWES), Bass diffusion model (BDM), and multilayer perceptron artificial neural network (MLP-ANN) are applied. In item (iii), output-oriented DEA CCR and BCC are applied. Concerning item (i), results demonstrate that estimating the installed power of individual systems is not very simple. Nonetheless, the machine learning algorithms imply a significant accuracy increase compared to taking the dataset's average as the estimation, especially the DT and RF algorithms with a coefficient of determination of 29%. Regarding item (ii), results demonstrate that the installed capacity in Brazil and its regions is expected to increase approximately linearly in the horizon of months but with weekly seasonal characteristics. Moreover, the most accurate model depends on the region, i.e., the ANN is superior for the Northeast (MAPE of 3.25%) and South (MAPE of 1.28%) regions, the HWES method is superior for Brazil (MAPE of 0.51%) and the Southeast region (MAPE of 2.26%), and the BDM is superior for the Mid-west (MAPE of 1.95%) and North (MAPE of 3.37%) regions. Concerning item (iii), results demonstrate that DEA CCR is more representative of the Brazilian reality since DEA BCC assigns several states as efficient, even if they showcase a very low installed capacity, particularly small states. DEA CCR indicates that Minas Gerais, Rio Grande do Sul, and Mato Grosso are the most efficient states in PV DG deployment relative to their inputs, closely followed by São Paulo with an efficiency of 95%. Furthermore, states located in the North and Northeast region require more attention from ANEEL since their efficiencies are generally low. A literature review demonstrated that this paper is the first of its kind in exploiting so thoroughly and robustly the dataset provided by ANEEL. Therefore, the paper is expected to assist ANEEL in the task of properly regulating the sector and also assist researchers and PV aggregator companies through in-depth insights. Moreover, the conducted procedures can also be applied to other countries with available datasets, thus ensuring high applicability for the research. [ABSTRACT FROM AUTHOR]

This study uses three data envelopment analysis models to determine the production efficiency of biohydrogen which is produced from macroalgae and other sources by dark fermentation. The efficiency of macroalgae is greatest in batch mode for S. Japonica using a sDFMEC process at pH 5.3, 35 °C, 1 g COD/L and a hydrogen production rate (HPR) of 0.34 L/L/h. The highest efficiency is using an internal circulation batch reactor in continuous mode for beverage waste water. The HPR and substrate concentration are the most important factor of biohydrogen efficiency, and efficiency and temperature are the most important factors of HPR. Malaysia and India are the two economies that most benefit from increased production efficiency due to the use of macroalgae. Increasing biohydrogen yield efficiency will improve macroeconomic growth and establish a renewable hydrogen and biohydrogen industry, which is especially efficient related to the economic recovery during the COVID-19 pandemic. • Determining production efficiency by dark fermentation of bioH2 from macroalgae •S. Japonica/sDFMEC/1g COD/g/pH 5.3/35 °C is the most efficient combination •The HPR and SC are the most important factor of biohydrogen efficiency •Improved efficiency will give Malaysia and India the greatest benefit in bioH2 •Investment on bioH2 efficiency technology will increase bioH2 output [ABSTRACT FROM AUTHOR]

• We theoretically analysis and empirically examine the causal relationship between DFI and EEP. • DFI has a solid U-shaped impact on the EEP instead of a linear one. • A battery of robustness and endogeneity checks supports our conjecture. • Household consumption stimulation and green innovation promotion are two plausible economic channels to explain this U-effect. • Rich implications are provided to advance sustainable development with digital financial policies. Digital financial inclusion (DFI) is newly cited as a significant driver of sustainable development in developing and transition economies, and it is thus likely that DFI affects energy-environment performance (EEP). Here, different from recent studies documenting monotonic links between them, we first theoretically reconcile the underlying U-shaped impact of DFI on EEP using two competing hypotheses: consumption stimulation versus innovation promotion effect. Subsequently, we extend non-radial direction distance function specifications within a biennial data envelopment analysis framework to assess EEP, and then re-examine the causal relationship between DFI and EEP using panel data covering 282 prefecture-level cities in China during 2011–2019. The empirical results show that DFI has a solid U-shaped impact on the EEP instead of a linear one. This effect holds up to various robustness and endogeneity checks, including the spatial econometric approach that captures cross-sectional dependence, the geographical distance as an instrument variable, the dynamic threshold regression approach, and the difference-in-difference approach. Further mechanism tests show that household consumption stimulation and green innovation promotion are two plausible economic channels to explain this U-effect, supporting our theoretical conjecture. Moreover, the U-effect is more pronounced for cities with relatively low levels of economic development and environmental regulation, including central and western cities, as well as smaller, non-low-carbon pilot and non-carbon emission trading pilot cities. However, we find that the U-effect is disproportionately stronger in southern cities with higher financial marketization levels than in northern cities. Overall, these findings shed more lights on the DFI-EEP nexus and provide new insights into understanding the real effects of DFI on sustainable development. [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 aim of this study was to assess the environmental impact of hydroelectric power generation projects and classify them according to their scale of environmental impact. To achieve this objective, the combination of Data Envelopment Analysis (DEA) and Artificial Neural Networks (ANN) techniques was applied to 53 hydroelectric power plant projects in the evaluation phase in Brazil. The main results were: a) the proposed index indicates that 7 of the 10 worst hydroelectric projects are of the Large Hydropower Plant (LHP) type; b) the neural model for predicting the environmental impact of hydroelectric projects has an error of less than 0.001; c) the neural model for classifying hydroelectric projects in terms of their environmental impact reached a performance of 99.0% accuracy. In general, this study contributes to the use of a hybrid decision-making approach based on a combination of DEA-ANN for energy policies, in addition to enabling an improvement in the evaluation of hydroelectric generation projects. [Display omitted] • The proposed index indicates that projects have some type of environmental impact. • Large Hydropower Plant (LHP) projects have the highest environmental impacts. • DEA-ANN approach compares hydropower projects and predicts their local impact. [ABSTRACT FROM AUTHOR]

Airports play an important role in the economic development of countries. Efficiency analyses of airports are crucial for providing an important roadmap to airport authorities and for helping to benchmark their airports with respect to a role model. Accordingly, in this study, an integrated analysis comprising a super-efficient data envelopment analysis (DEA) and an eigenvector centrality analysis is conducted. Additionally, a Simar–Wilson bootstrapping approach was used to identify the important factors influencing this efficiency. As a case study, the efficiencies of Turkey's 39 international airports were analyzed, and strategic recommendations were suggested. According to the results of the super-efficient DEA analysis and eigenvector centrality analysis, Istanbul Atatürk (closed as of 2019), Adana, and Diyarbakır airports are the "shining stars." These airports are operationally efficient and act as role models for other airports. Moreover, the hypothesis testing conducted based on the Simar–Wilson bootstrapping approach shows that the efficiency of airports varies by region. The efficiency of airports in the Mediterranean region, which is used as a reference, was found to be significantly higher than that of airports in Eastern Anatolia, Southeast Anatolia, Central Anatolia, and Marmara regions. There also exists a difference between the efficiencies of private and public terminal operations in the favor of public terminal operations. The efficiency of an airport is also positively influenced by the tourist population and by an increase in the gross domestic product per capita of the corresponding city. • This study proposes a super-efficient DEA and an eigenvector centrality analysis to analyze the efficiency of airports. • Simar-Wilson bootstrapping approach is also used to specify the essential environmental factors influencing this efficiency. • The efficiency of Turkey's 39 international airports is evaluated as a case study, and strategic suggestions are made. • A rational policy may be to close some inefficient regional airports and to consolidate traffic in larger airports. • Additional years of experience at an airport increase its efficiency due to the learning curve effect. [ABSTRACT FROM AUTHOR]

Incentive bonus payments for terminal operators are mostly based on the size of throughput growth rate. In most cases, this does not encourage terminal operators to effectively utilize the terminal facilities and equipment. Therefore, it is necessary to establish efficiency and scale of operation as the basis for incentive bonus allocations. The importance of this issue is that in some port governance models, concessions are static agreements where fees and other conditions generally do not change over time. This paper introduces the possibility and advantage of data envelopment analysis (DEA)-based incentive bonus allocations that can ensure that the incentive bonuses allocated to terminal operators are based on both the amount of throughput/throughput increment and production efficiency. To this end, three DEA-based incentive bonus allocation models are proposed for different output items. The results show that the incentive bonus allocation approach proposed in this study effectively takes into account the magnitude of the throughput/throughput increment and production efficiency of terminal operators. • We proposed a DEA-based method to allocate incentive bonuses to container terminal operators. • Our method considers both the efficiency and scale of container terminal operators. • The proposed approach is applied to an empirical study of container terminal operators in Kaohsiung, Taiwan. [ABSTRACT FROM AUTHOR]

• Incorporates the technological development into dynamic efficiency analysis. • Develops a new dynamic DEA model with carry-over and accumulation. • Evaluates the provincial industrial production of China. • Studies the spatial correlation of industrial production in China. In actual industrial production, technology shows an accumulative effect that exceeds the corresponding factor endowment in each period. This accumulation effect is accompanied by the whole industrial production and plays an important role in the evolution of industrialization as a bridge. In order to identify the status quo of industrial production more comprehensively, explore the role of technological accumulation in industrial production, and further promote industrialization, this paper constructs a new multi-period dynamic data envelopment analysis model reflecting the effect of technological accumulation. Furthermore, a spatial autocorrelation analysis is conducted to determine the spatial correlation of regional industrial production and to provide a reference for the coordinated development of industry among regions. A case study of China's industries was conducted, and the main results show the following: (1) The average industrial production efficiency is 0.806, and 17% of provinces have a score of less than 0.65. (2) There is an obvious positive spatial autocorrelation evolution trend in regional industrial production; the eastern coastal area is dominated by High-High clustering, and Low-Low clustering is concentrated in the northwest. (3) Technology accumulation aggravates the dispersion degree of regional industrial production efficiency. Finally, policy implications are put forward to promote industrialization and the coordinated development of interregional industry. [ABSTRACT FROM AUTHOR]

This paper studies the relationship between logistics, intermodality and airport efficiency. To this end, using Data Envelopment Analysis (DEA), the efficiency of the 21 largest European airports has been estimated over the 2009–2014 period. In a second stage, this research analyses the effect of Logistics and Modal Integration on airport efficiency. The airport's ability to compete globally depends, greatly, on the passengers and cargo' access to efficient networks. The results show evidence of the positive impact of the quality of the national logistics system on Airport efficiency. The effect of Logistics on overall airport efficiency is estimated at 0.15% for every 1% increase in the Logistic Performance (LPI), ceteris paribus. Furthermore, airports that have a direct link to a High-Speed Rail (HSR) network are expected to perform, on average, 27 per cent above the rest of the airports. Finally, airports that offer more facilities to optimise the interface between airport and aircraft are expected to achieve higher efficiency levels. • Airport's ability to compete globally depends on the access to efficient networks. • This paper find evidence of the positive impact of the quality of the national logistics system on Airport efficiency. • The effect of Logistics on airport efficiency is estimated at 0.15% for every 1% increase in the Logistic Performance. • Having Direct link to a High-Speed Rail (HSR) is crucial for hub airports efficiency. [ABSTRACT FROM AUTHOR]

• The circular economy performance of Chinese cities is measured. • The effect of environmental regulation on circular economy performance is studied. • The decomposition values of circular economy growth rate are measured. • The "catch-up effect" is a major influencing mechanism. While achieving rapid economic growth, the pressure caused by environmental pollution and resource depletion has increasingly become a bottleneck in China's economic development, making the development of a circular economy particularly important. The extant literature has not focused on the influence of environmental regulation on a circular economy performance. This study uses the metafrontier global direction distance function (Metafrontier-Global-DDF) super-efficiency data envelopment analysis (DEA) model to estimate the circular economy performance and decomposition values of circular economy growth rate in 286 prefecture-level cities in China from 2003 to 2018. It further tests the influences of environmental regulations on circular economy performance and its influencing mechanism. The results show that environmental regulation can play a linear role in promoting the performance of the circular economy, mainly through the "catch-up effect," while "innovation effect" and "demonstration effect" have not yet played an effective role. This study provides evidence for the performance evaluation of the circular economy in China and the relationship between environmental regulations and circular economy performance. The future development of a circular economy still needs the active development of circular economy technology in each city. The role of the "innovation effect" and "demonstration effect" in improving the performance of the circular economy should be further enhanced. [ABSTRACT FROM AUTHOR]

A key goal in sustainable supply chain management is the minimisation of risk across supply chains. However, this is jeopardised by underdeveloped aspects such as social risk management, especially in the case of energy systems as they involve complex supply chains. This article constitutes the first time that Social Life Cycle Assessment (S-LCA) is used to lay the foundation for a methodological framework to define, assess and prioritise strategies oriented towards the minimisation of social life-cycle impacts across the supply chain of energy products. This framework combines S-LCA, a novel approach to the definition of alternative supply chain strategies, and multi-criteria decision analysis (MCDA). It was demonstrated through a case study of bioelectricity in Portugal by defining and assessing fifteen strategies on the specific supply chains of oil and fertilisers to check their suitability to enhance the system's social life-cycle performance. The weighted sum method (WSM) and Data Envelopment Analysis (DEA) were used as MCDA tools to further support decision-making by prioritising strategies. According to the results for a set of six social indicators, the strategies proposed on the supply of oil and nitrogen-based fertilisers were deemed suitable trade-off solutions to mitigate the social life-cycle impact of the bioelectricity system. [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]

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]

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]

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]

Monitoring canopy water content in alpine scrub is crucial for hydrological and ecological studies in the Qilian Mountains. However, traditional measurement methods are time-consuming and inconvenient to implement due to the harsh climate and lack of transportation in alpine regions. In our study, a new method was proposed to estimate the canopy water content of alpine shrub using spectral data. The water content data for samples with alpine scrub canopy reflectance spectra in the range of 350–2500 nm was collected. Then, a lightweight Transformer regression model was built based on three different feature selection methods (the successive projections algorithm, genetic algorithm, and principal component analysis) to predict the canopy water content of shrubs. The proposed model was compared with three traditional machine learning models: random forest, support vector regression, and back-propagation neural network. The results reveal that the lightweight Transformer model demonstrates strong fitting and generalization capabilities in using only full-band spectra as input, which may be attributed to Transformer's own self-attention mechanism. From cross-validation, the RMSE and R2 of the model on the training (validation) set are 0.0201 (0.0233) and 0.968 (0.965), respectively. Furthermore, the performance of all regression models is enhanced after feature selection. On the validation set, PCA-Transformer has the smallest RMSE (0.0147) and largest R2 (0.985), and its running efficiency is improved by 81.69 % relative to the full-band spectra as input. In the additional test set, the predictive ability of the models remain stable. SPA-Transformer has the smallest RMSE (0.0111) and largest R2 (0.986). These suggest the excellent performance of lightweight Transformer on the task of estimating scrub water content and the ability of feature selection methods to optimize models. The results show that PCA-Transformer is an accurative and effictive tool to estimate plant water content, and offer a foundation for monitoring large-scale canopy water content in alpine regions using remote sensing technology. [ABSTRACT FROM AUTHOR]

The impact of organizational characteristics of waste collection services on efficiency was analyzed. The characteristics under scrutiny included the contractual status of personnel, the age and type of waste collection vehicles employed, and the management modality of the system. A dataset encompassing 1783 municipal waste collection systems in Mexico was analyzed utilizing double bootstrap data envelopment analysis. Findings suggest that the contractual status of personnel significantly influences efficiency, with systems characterized by greater employment flexibility having higher efficiency levels. Furthermore, the characteristics of the vehicles employed impact efficiency, with the utilization of compactor and newer vehicles displaying a positive correlation with efficiency. Additionally, the managerial modality emerged as a determinant factor in waste collection performance, with privately managed systems exhibiting superior efficiency compared to fully public systems or public-private partnerships. Conversely, waste collection systems operated by non-governmental organizations exhibited comparatively lower levels of efficiency. • Waste collection efficiency is positively correlated to employment flexibility. • Waste collection efficiency is affected by the characteristics of the vehicles used. • Fully private waste collection systems are more efficient than public and public-private systems. • Waste collection systems fully or partially operated by non-governmental organizations are less efficient than public ones. [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]

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]

Nightlife attractiveness plays a significant role in the local economy as it contributes to various sectors such as hospitality, entertainment, and tourism. Millennials and Generation Z (Gen MZ) are the primary leaders of nightlife activities. This study aims to evaluate the nightlife attractiveness of Seoul focusing on Gen MZ all-nighters. Specifically, the nightlife attractiveness is evaluated based on data envelopment analysis (DEA) using smart card data and open data sources from Seoul. Nightlife attractiveness is defined as the number of Gen MZ floating population and their duration of stay, compared to the number of nightlife venues, i.e., bars, clubs, restaurants, karaokes, and cafes. The DEA model and Tobit regression model are used to evaluate the nightlife attractiveness of 161 Dong unit areas in Seoul and explore Gen MZ's preference for hotspots, respectively. The results show that, on average, the attractiveness score is estimated to be 0.87, suggesting a 0.13 increase in outputs to be the most attractive hotspot. Regarding individual preferences, Gen MZ all-nighters residing in rich suburban areas tend to spend their all-night at these hotspots. These findings provide insights into existing hotspots and areas with the potential to become hotspots in the future. • The nightlife attractiveness score of Seoul was estimated to be 0.87, on average. • Areas with a high Gen MZ population and long durations had high attractiveness scores. • Gen MZ, who resides in areas with good transportation, prefer attractive hotspots despite long travel times and distances. • The nightlife score provides insights into both existing hotspots and areas with the potential to become attractive areas. [ABSTRACT FROM AUTHOR]

Balancing economic growth with environmental protection is a global priority. Both developed and developing nations face the challenge of optimizing renewable energy use to achieve sustainable development while minimizing ecological harm. This study employs the Data Envelopment Analysis (DEA) model, incorporating undesirable output, to assess the efficiency of renewable energy consumption across 21 member countries of the Asia-Pacific Economic Cooperation (APEC) from 2011 to 2020. The main variables include foreign direct investment, total energy consumption, total renewable energy capacity as inputs, and gross domestic product growth and greenhouse gas emissions as desired outputs. Results reveal varying levels of renewable energy consumption efficiency, with Brunei, Japan, South Korea, and Hong Kong exhibiting the highest efficiency, while Russia, China, and Vietnam demonstrate the lowest. Notably, developed nations display greater efficiency in utilizing renewable energy compared to their developing counterparts. Moreover, optimizing foreign direct investment, renewable energy consumption, and total renewable energy capacity can enhance overall energy efficiency. By leveraging empirical evidence and estimation techniques, the study provides actionable recommendations for policymakers, emphasizing the pivotal role of financial development and green finance initiatives in promoting sustainable energy development across APEC member countries. [ABSTRACT FROM AUTHOR]

• A thermal–hydraulic-mechanical-chemical model is established for EGS. • Orthogonal test is employed to conduct sensitivity and data envelopment analysis. • Production potential of EGS is evaluated by a dimensionless criterion. • Buoyancy effect is most significant for heat extraction, and momentum diffusion for power generation. Hot dry rock (HDR) has attracted much attention and it is considered as one of the alternatives to fossil fuels. The enhanced geothermal system (EGS) has been considered as an effective method to develop the deep geothermal resource in HDR. A significant effect of the interaction of thermal–hydraulic-mechanical-chemical (THMC) should be considered in geothermal reservoirs. To evaluate the production potential of EGS, a coupled THMC model with non-Darcy's law using supercritical carbon dioxide (ScCO 2) as the geothermal fluid is established. Through the Data Envelopment Analysis (DEA), the effect of THMC interactions on the performance of EGS is investigated based on series dimensionless parameters. A novel dimensionless criterion for evaluating production potential is obtained by the results of DEA and validated. The DEA results show that the inhomogeneous temperature distribution is exacerbated by buoyancy effects in fractures. For the performance of heat extraction, the most significant mechanism in THMC is the buoyancy effect, followed by convection heat transfer, momentum diffusion, inertial force, and mass diffusion. For each 0.1 decrease in effectiveness θ under DEA, the R s + of Re, Pe, Pr, Le and Ri are increased by 0.40 %, 0.66 %, 0.62 %, 1.53 × 10-11 % and 5.31 %, respectively. While the momentum diffusion is the most significant mechanism for the performance of power generation, followed by convection heat transfer, inertial force, mass diffusion, and buoyancy effect. For each 0.1 decrease in effectiveness θ , the R s + of Re, Pe, Pr, Le and Ri are increased by 0.58 %, 0.83 %, 0.86 %, 3.14 × 10-11 % and 10.00 %, respectively. [ABSTRACT FROM AUTHOR]

This paper delves into the complexities of assessing educational performance and efficiency in the context of education governance. We propose a novel perspective on how governing features of educational systems influence their efficiency, focusing on the key features of public bodies responsible for local education management in Peru, known as UGELs. We characterize the educational production function using three inputs: planning conditions, human resources, and accountability mechanisms; and two outputs: student achievement and educational progress. Our efficiency estimation utilizes Robust Free Disposal Hull, Robust Data Envelopment Analysis, and Stochastic Non-parametric Envelopment approaches, leveraging unique government data from 2014. Additionally, we explore potential factors like infrastructure and internet access that could improve UGELs' efficiency through conditional DEA analysis. Our findings highlight the need for nuanced methodologies in evaluating educational performance and reveal a significant gap in existing literature. This paper addresses this gap by offering a comprehensive evaluation framework, emphasizing the importance of thorough assessments for gauging educational institution efficiency. As policymakers increasingly rely on evidence-based decision-making, our research provides valuable insights that can inform and shape education policy decisions, making it a significant contribution to the field of education policy and governance. • Aim : Assessing educational performance and efficiency from governance perspective. • Focus : Local bodies (UGELs) in Peru. • Analysis : Inputs (planning, resources, accountability) & outputs (achievement, progress). • Advanced methods : FDH, DEA, StoNED & conditional DEA. • Impact : Comprehensive evaluation framework for educational governance assessment. [ABSTRACT FROM AUTHOR]

Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased. • This paper adopted a resource allocation model in DEA with undesired outputs. • Our resource allocation model is sensitive to uncertainty of the data. • A stochastic resource allocation model is given when we have random data and undesirable products. • An application on 21 electricity production & distribution companies for eight years is given. [ABSTRACT FROM AUTHOR]

The Cross-Efficiency (CE) method, based on Data Envelopment Analysis (DEA), is a commonly used approach to measure the efficiency of Decision-Making Units (DMUs). Various secondary goal models can yield different efficiency results. The recently developed Game CE (GCE) method aims to address this issue and achieve convergence efficiencies. However, the conventional GCE was developed under the unrealistic assumption that Decision Makers (DMs) are perfectly rational, as it relies on the arithmetic mean aggregation method. Additionally, the GCE evaluates DMUs based on efficient reference points, disregarding the pessimistic perspective. The study introduces a new aggregation method called Regret-Consensus Aggregation (RCA) that captures the subjective preferences of DMs. GCE-RCA scores are obtained by incorporating RCA into the GCE. A new method called Game Cross In-Efficiency (GCIE) with RCA (GCIE-RCA) has been developed to incorporate anti-efficient reference points into the evaluation process. A Double-Frontier GCE based on the RCA (DFGCE-RCA) is developed from two perspectives. The DFGCE-RCA is implemented to evaluate the performance of the Iranian Inter-City Road Passenger Transportation (IC-RPT) system based on six key performance indicators. DFGCE-RCA has several advantages in addition to those of GCE. Firstly, considering two perspectives provides more comprehensive results. Additionally, it can capture the subjective perspectives of DMs. Lastly; it enables the aggregation process to reach a consensus. • Game Cross Inefficiency (GCIE) method was originally developed. • The Regret-Consensus-Aggregation (RCA) method is a cross-evaluation aggregation method that has been developed. • RCA is incorporated into GCE and GCIE methods to capture the subjective preferences of decision-makers. • The double-frontier GCE with RCA incorporates both optimistic and pessimistic perspectives into the evaluation process. • The evaluation of Iranian Inter-City Road Passenger Transportation (IC-RPT) has been conducted using the proposed technique. [ABSTRACT FROM AUTHOR]

This article proposes nonparametric and parametric methods for conducting meaningful evaluations of academic programs. In particular, it proposes the use of data envelopment analysis (DEA) along with regression analysis for the evaluation of Spanish public universities' undergraduate curricula. Unlike the radial DEA models, which assume an equiproportional expansion of all outputs to achieve efficiency, the current study also relies on the use of non-radial DEA models for assessing the efficiency of Spanish public universities. A non-radial DEA measure (the so-called Russell measure) allows for unlike proportional augmentations in each positive output. In this study, non-radial DEA measures allow us to identify different levels of inefficiency for each output considered (social sciences and non-social sciences degrees). Specifically, the results show that the inefficiency in the production of scientific and technical degrees is greater than that in the production of humanities and social sciences degrees, although there are differences among institutions. Bachelor's degree production time is also estimated, and a duration analysis explains the time to degree. A worrying result is the "excessive" time it takes for a university to produce four-year undergraduate degree programs. The mean graduation time is 5.7 years. The Cox proportional hazards regression shows that a shorter time to graduation is associated with higher teaching quality as well as a higher faculty-to-student ratio. Parametric survival analysis using a lognormal distribution with gamma frailty also verifies these latter results. • Data envelopment analysis (DEA) is proposed for the evaluation of Spanish public universities' undergraduate curricula. • Radial and non-radial DEA models are used to assess the teaching performance of universities. • Non-radial models allow for identifying inefficiencies in the production of social sciences and non-social sciences degrees. • Bachelor's degree production times are also estimated, and a duration analysis explains the time-to-degree. [ABSTRACT FROM AUTHOR]

Due to the challenges of implementing environmental responsibility, sustainable development is often criticized for its promise to "square the circle". This paper incorporates sociological theories into operational research to enhance our understanding of the issue. It proposes a framework for evaluating eco-efficiency that captures the decision-making process from a macro perspective rather than focusing on a single technique or employing a multimethodology. By utilizing 272 Chinese cities as case studies, the evaluation process is effectively implemented. From three perspectives - region, city size, and policy - we draw some interesting conclusions. Finally, a vision for future research is presented in the concluding section. • This study incorporates sociological theories into operational research to enhance the understanding of eco-efficiency evaluations. • By utilizing 272 Chinese cities as case studies, we implement a comprehensive and effective evaluation process. • We propose a visionary framework and four potential directions for further exploration. [ABSTRACT FROM AUTHOR]

While resilience analysis can provide important insights into risk management strategies, research gaps exist in terms of logistics efficiency, regional economic environment, and spatial clustering patterns. Therefore, our study capitalizes on the dramatic variations in logistics activities among regions during COVID-19 to examine regional logistics resilience in the disaster and explore potential spatial clustering patterns. We measure regional logistics resilience through changes in performance outcomes, specifically efficiency fluctuation. To analyze spatial clustering patterns, (regional heterogeneity) presented by logistics efficiency, we propose a new multi-stage approach that integrates the data envelopment analysis with the K-medoids algorithm and analyzes data from the Chinese logistic industry. The results reveal that efficiency fluctuations exhibit distinct spatial distribution characteristics, with more pronounced negative fluctuations in areas of important logistics activities nodes and islands, and more widespread negative trends in coastal areas. Moreover, regions with high freight volumes and logistics specialization demonstrate a sustained high level and quality of logistics efficiency. Furthermore, consumption capacity and economic development appear to positively influence fluctuations in logistics efficiency. The findings hold implications for enhancing regional logistics resilience in the disaster and contribute valuable insights to regional logistics risk management. • We leverage the dramatic variations in logistics activities during COVID-19. • We integrate the data envelopment analysis model with the K-medoids algorithm. • We explore the resilience of regional logistics through efficiency fluctuations. • We investigate the spatial clustering patterns in logistics efficiency. • Implications of the multi-model method and regional logistics resilience. [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]