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

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]

• Assessing WEF nexus is crucial to ensure food security. • A novel approach is proposed embedding WEF nexus and circularity. • The percentage of circularity for each farm was measured using Circular Flow Tool. • The framework was applied to 30 dairy farms in Galicia (NW Spain). • Considering the whole dairy cooperative the circularity has increased significantly. The Water-Energy-Food (WEF) nexus approach is increasingly being used for supporting a transition to sustainable development, with initiatives involving the concept of circular economy (CE). In the agricultural sector in particular, assessing this nexus is crucial to ensure food security, control the consumption of key resources such as water and energy, as well as measure atmospheric emissions linked to climate change. This manuscript aims to propose a novel approach by coupling the WEF nexus with a circularity indicator, seeking to capture in a single index (the WEF+CEi) both performances in a sample of companies. The novel approach is applied to 30 dairy farms located in Galicia (NW Spain) to benchmark them in a holistic manner. To do this, the WEF nexus of each farm was represented through the following indicators: carbon footprint, water footprint, energy footprint, and food productivity. In addition, the percentage of circularity for each farm, and for the agro-industrial cooperative was measured thanks to the application of a circularity tool in percentage terms. Finally, the WEF+CEi indicator was obtained using the multicriteria mathematical tool of Data Envelopment Analysis (DEA). The results show that without considering the agro-industrial cooperative, the system is 51 % circular. On the other hand, considering the farms and the cooperative, the system goes up to 80 % of circularity. Finally, the proposed approach can support decision-making and provide insights for producers and stakeholders in the area. [ABSTRACT FROM AUTHOR]

This research aimed to observe the efficiency of productive units of broiler chickens using Data Envelopment Analysis (DEA) with the CCR model. This technique aims to define an efficiency frontier from the process's inputs and outputs, which can be used to evaluate the relative efficiency of the studied units and to define consistent goals to the units bellow the expected performance, comparing units of the same technology profile and scale. The study identified benchmark units as performance reference in order the improve the organization's results level, increasing productivity and reducing costs. Twenty-five production units were examined in aviaries with automated technology, located in the Center-West region of Brazil, of which 23 presented global inefficiency, with potential to increase the volume of meat produced with the same amount of inputs. The units that contained only male birds presented the highest global efficiencies, followed by the mixed units and, at last, the units that contained only female birds. The units with the lowest efficiency rates were the ones with the highest mortality rates (3.3 and 4.05%). Several other factors can contribute to these results, such as, possible human errors ant the equipment's adjustments and the employment of correct husbandry techniques. Investigating the low performance causes is necessary for the company to design a strategy of performance improvement. [ABSTRACT FROM AUTHOR]

This study is devoted to the analysis of the statistical relationship between the efficiency of an insurance company and a number of factors characterizing the structures of its assets. Efficiency in the study is considered as a metric that evaluates the quality of an organization's management, which is the one of the most important characteristic of its financial stability. The empirical analysis was carried out using data from the financial statements of insurance companies for the period from 2017 to 2020 of the group of leaders in terms of insurance premiums of the Expert RA rating agency based on the results of 2020. Life insurance companies, reinsurance companies and mutual insurance companies were excluded from this list due to the specifics of their business and financial statements. For the companies included in the final sample, an assessment of overall efficiency was built. The methodology of its calculation is formulated and developed in [3, 14, 15]. It is based on Data Envelopment Analysis (DEA). The partial efficiency indicators obtained for a certain set of DEA model specifications are aggregated into several indicators using the principal component analysis method (PCA). It is important that there is a positive correlation between the overall efficiency indicator and the partial efficiency indicators. The first principal component acts as an indicator of overall efficiency, the others allow one to identify its sources. This makes it possible to compare companies that have the same overall efficiency. Further, for all representatives of the final sample, a regression model of the statistical relationship of overall efficiency with the characteristics of the asset structure was built. The model specification was performed based on the MFP (Multivariable Model-Building with Fractional Polynomials) algorithm [16, 17], which allows to adapt the specification of the regression model to the properties of the data and identify nonlinear statistical relationships. The results of the evaluation of the model allow us to assert that the general and, accordingly, all partial performance indicators have a statistical relationship with the selected characteristics of the asset structure. The nature of the relationship is predominantly nonlinear. The results obtained in the work can be useful in forming a strategy for managing the financial stability of an insurance company, as well as for potential partners and investors in solving the task of benchmarking. [ABSTRACT FROM AUTHOR]

Post-pandemically, innovation-driven growth has become a new trend in the evolution of society. Under these circumstances, evaluating the significant power of the world economy becomes necessary. Thus, the main aim of the present research endeavor is to analyze the innovation efficiency or inefficiency of European member states. By conducting a Data Envelopment Analysis (DEA), which assumes an input-output calculation of relative efficiency scores and constant returns to scale (CRS), it was found that 9 out of 27 European member states are efficient in maximizing output for given inputs. In terms of innovation, the sample of efficient countries consists of nine EU member states: Netherlands, Italy, Bulgaria, Czech Republic, Malta, Romania, Germany, Sweden, and Slovakia. On the other hand, the most inefficient EU member states are Lithuania, Latvia, and Greece. Consequently, the model suggests that the inefficient EU countries can be seen to the most efficient countries as peers or benchmarks for improving their innovation level. [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]

This paper analyses the determinants of Portuguese Banking efficiency using a sample of 45 banks from 2005 to 2019. Simar and Wilson's two-stage approach [1] has been applied. Data envelopment analysis (DEA) was used in the first stage to estimate efficiency scores. In the second stage, a truncated regression was estimated with a double bootstrap to test the significance of the relationship between bank efficiency and its determinants. The results obtained indicate that, in general, Portuguese banking shows good levels of pure technical efficiency but poor results in scale efficiency, thus deteriorating the levels of total technical efficiency. After a decrease in total technical efficiency between 2005 and 2011, from 2012 onwards, it shows a positive trend. Our results also show that larger banks, better-capitalised, more profitable, with less credit risk and less diversified sources of income, are the most efficient. These results have implications for public policies adopted by the government and regulated by the competent authorities for the sector studied. Namely, creating large, well-capitalized banks should be promoted, preventing excess credit risk. [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]

The aim of this study is to model optimal hydrogen supply chain from the eco-efficiency point of view. The study uses capital expenditures (CAPEX) and operation and maintenance expenditures (OPEX) as economic indicators on each stage of supply chain and environmental impacts calculated based on life cycle inventory and analysis methodology as environmental indicators. Network Data Envelopment Analysis (NDEA) is used as a tool for evaluation of the mixed economic and environmental performance of supply chain. The study shows that most efficient chains according to the criterion of integrated environmental and economic efficiency are wind energy - PEM electrolysis, hydro energy - PEM electrolysis and coal gasification with carbon capture and storage. The least eco-efficient option for hydrogen supply chain are options with thermal generation based on coal - electrolysis, due to relatively high capital costs of electrolysis and high negative environmental effects of the first stage. [ABSTRACT FROM AUTHOR]

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

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]

This study presents an integrated multi-attribute decision-making (MADM) and data envelopment analysis (DEA) framework for solving problems with heterogeneous attributes. We classify the heterogeneous attributes into desirable and undesirable classes and provide a model for aggregating the attributes' weights and the alternatives' scores. The proposed model is initially designed as a Multiple Objective Decision Making (MODM) problem with a Data Envelopment Analysis (DEA) policy and then reformulated as a linear programming model tackled through a goal programming approach. We apply the proposed model to a set of European countries based on their fulfillment of the 17 Sustainable Development Goals (SDGs) defined by the United Nations. We show the proposed approach minimizes computational efforts and complexities and maximizes the participation and satisfaction of decision-makers. We compare the rankings derived from our model with those obtained from standard MADM techniques such as Euclid and TOPSIS. We illustrate how the different normalization methods are applied to condition the discrimination power of the models and analyze the reversals triggered by TOPSIS relative to the other techniques. We conclude by noting that our model does not rely on the weights defined by the experts to determine the ranking, which constitutes a significant advantage over the standard MADM techniques in strategic evaluation environments. • We present an integrated MADM and DEA model for problems with heterogeneous attributes. • Heterogeneous attributes are classified into desirable and undesirable categories. • Our model does not rely on the weights defined by the experts to determine the ranking. • The rankings derived remain stable independently of the normalization formula applied. • We study the fulfillment of Sustainable Development Goals among European countries. [ABSTRACT FROM AUTHOR]

China faces the challenge of balancing economic development with environmental sustainability as one of the largest economies and the world's top emitter of greenhouse gases. This study investigates the role of fiscal incentives and energy efficiency in fostering green economic growth in China. The Data Envelopment Analysis (DEA) model is used in the analysis, which spans the years 2010 to 2021, to assess the effectiveness of energy use and the influence of financial incentives on environmental sustainability. The results show that increasing energy efficiency reduces environmental deterioration and improves resource utilization, positively contributing to green economic growth. The study also demonstrates the critical role financial incentives, such as tax breaks and subsidies, play in encouraging investments in sustainable practices and clean technology development. These results underline how crucial it is to implement targeted fiscal policies and energy efficiency measures to promote green economic growth in China. [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]

This study investigates the green total factor productivity of urban agglomerations, a high-level urban organisational form, by estimating the green total factor productivity (GTFP) of 285 cities in China from 2003 to 2017 using global data envelopment analysis and a super-slack-based measure model with undesirable output. Based on these estimations, we obtain the GTFP of 19 urban agglomerations and analyse the spatiotemporal evolution, dynamic evolution of spatial distribution, and regional differences in the urban agglomeration GTFP using various analytical tools such as the Dagum Gini coefficient, subgroup decomposition, σ and β convergence analysis, and kernel density estimation. Additionally, we identify and analyse the driving factors of GTFP growth in urban agglomerations by employing a panel model from the perspective of improving the efficiency of cities within urban agglomerations and optimising the development of intercity industrial and spatial structures. The results are as follows: First, the GTFP of urban agglomerations generally exhibit a fluctuating upward trend with low values in northwest-central China and high values in the north-southeastern coastal areas. Second, the GTFP differences among the urban agglomerations present an overall convergent trend, with the smallest gap between the national-level urban agglomerations and a stable performance. The gaps between regional and local urban agglomerations undergo considerable narrowing of the evolutionary trend, whereas gaps between local urban agglomerations and other urban agglomerations keep expanding. Third, economic growth, environmental regulation, energy consumption, and foreign direct investment and technological progress are conducive to improving urban GTFP, and a U-shaped relationship exists between cooperative industrial agglomeration and urban agglomeration GTFP. We suggest that strengthening regional collaborative environmental governance can improve the GTFP in urban agglomerations. [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]

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]

The high-quality development of national high-tech zones is a matter of great concern for both the government and scholars as it serves as an important base for realizing scientific and technological innovation and industrialization of achievements. This paper constructs a conceptual model of the national high-tech zone innovation system by breaking it down into two subsystems: the innovation R&D subsystem and the innovation incubation subsystem. Through the use of a two-phase Data Envelopment Analysis (DEA) model, the innovation efficiency of each subsystem is measured for the 42 national high-tech zones in Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations. The research conclusion indicates that the majority of the high-tech zones in China are high in both research and development (R&D) efficiency and achievement conversion rate, indicating generally high overall innovation efficiency. However, there is a gap between the three urban agglomerations in terms of innovation resources and transformation environment, with Beijing-Tianjin-Hebei High-tech Zone having low transformation efficiency, and some areas of Yangtze River Delta and Pearl River Delta having low efficiency in technology R&D. Furthermore, the study finds that the innovation modes and paths of high-tech zones within urban agglomerations are quite different in the process of coordinated development of innovation. Therefore, the paper proposes two innovative development paths: the endogenous growth path and the exogenous driving path. The research results provide a tool for more objective evaluation of the innovation efficiency of national high-tech zones in different urban agglomerations in China, and provide a decision-making basis for different regions to implement corresponding innovative development paths. [ABSTRACT FROM AUTHOR]

This work presents an exploratory analysis concerning the understand of which Brazilian states have proven efficient in combating the Covid-19 pandemic, based on the treasury provided to them by the federal government, along with the understand of the association between input and output variables. As mathematical modeling support, the Data Envelopment Analysis (DEA) methodology is used as aid in the exploratory analysis. In this scenario, the states were analyzing based on the data of the treasury provided in the portal transparency of the federal government as well as the data of contamination and deaths available in the site informing health also of the government. The results show that with the CCR model, seven states are efficient while eleven are exposed in the BCC model. After the reduction in the number of inputs and outputs , the BCC model showed a decrease in the number of efficient states, being seven the new number to demonstrate an increase in inefficiency aspects. [ABSTRACT FROM AUTHOR]

The aim of this study is to delve into the interrelations between sustainability and the circular economy (CE) as it relates to the development of sustainable waste management (SWM). Innovative environmental policy pathways towards the CE are discussed as are the novel Environment Action Programme (EAP) and the new circular economy action plan which are intrinsic parts of the European Green Deal (EGD). Panel data analysis using the Malmquist Productivity Index (MPI) and the Malmquist–Luenberger productivity index (MLPI) is employed to evaluate the growth in total factor productivity in the European Union. The study incorporates as inputs: energy use, labour force, and gross fixed capital formation. Desirable output is described in terms of gross domestic product, and undesirable output – municipal waste generation – is examined using the MLPI model. The studied period is 1995–2019 and involves comparison between three periods: 1998–1999, 2008–2009, and 2018–2019. It is these periods in which TFP has been increasing in Europe in an era with active institutional actions on the environment. The average productivity growth based on MPI and MLPI is found to be 3.28%, and 0.1% respectively. However, we find that the incorporation of municipal waste generation – as an undesirable output – constitutes a significant factor in the lessening of TFP. This analysis provides a useful input for EU stakeholders involved in municipal solid waste management by highlighting methods of effectively waste generation and thus expanding the elements of the CE framework which will be reviewed in the upcoming Agenda 2030 and Agenda 2050 meetings. [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]

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]

• 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]

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]

This paper proposes a hybrid two-stage robustness approach to portfolio construction under data uncertainty. In the first stage, a stock's efficiency performance from candidate stocks is assessed and selected using an integrated dynamic slack-based measure data envelopment analysis model. We discuss the stability of efficiency estimates using the leave-one-out method. In the second stage, a "robust" stable and scaled mean-variance-Entropic Value-at-Risk model is used to determine the optimal weights allocated to qualified stocks in the presence of proportional transaction costs. The proposed method reduces computational complexity, increases robustness, and provides a comprehensive evaluation of stocks under different financial decisions, thereby increasing conservatism in the investment process. We demonstrate the applicability of the proposed hybrid two-stage approach to stock data from the Shenzhen and Shanghai Stock Exchanges. Results show that with increasing required returns, the proposed method improves the capital amount for investment and lowers transaction costs at the expense of additional risk. The study concludes by comparing the computational performance of the proposed approach to that of existing methods. [ABSTRACT FROM AUTHOR]

Carbon-reduction policy affects socio-economy and Water-Energy-Food (WEF) nexus contained therein. Existing studies concentrated on policy performances on resource consumption, but cannot quantify the impacts on resource utilization efficiency. To fulfill such gap, this study developed an integrated evaluation method through integrating computable general equilibrium model with data envelopment analysis to explore the impact of carbon tax on WEF environmental efficiency of China. Results show that increasing carbon taxes would improve the energy structure and reduce carbon emissions. While the increased cost of fossil fuels would have negative but limited impacts on economic output of water supply and food sectors through economic activity connections. Synergistic effect of WEF resource conservation could be observed in most production sectors, expect for clean energy sectors. Carbon tax would improve the WEF environmental efficiency expressed as the trade-offs between economic and carbon emission outputs and WEF resource inputs. There are significant differences in WEF efficiency and its growth rate among production sectors, with food sectors experiencing the most significant improvement in WEF efficiency at a carbon tax of 70 yuan/ton. The evaluation results raised the awareness of the performance of carbon tax on WEF nexus and provide a new perspective for promoting WEF nexus efficiency. • An integrated evaluation approach is developed with the combination of CGE and DEA models. • This method can assess the impacts of carbon tax policy on WEF environmental efficiency. • Carbon tax improves energy structure, but negatively affects water and food sectors' economic output. • Synergistic effects of WEF resource conservation exist in most production sectors. • Carbon tax would improve both the national and the sectoral WEF environmental efficiency. [ABSTRACT FROM AUTHOR]

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

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

In this paper, data-driven models for wind speed forecasting are established by incorporating atmospheric variables, such as temperature, humidity divergence and etc. as input, and using forecasted wind speed as the output. Input data, statistics in every 3-h interval, utilized in this study are from CERA-20C within a period from the year 2001–2010. In the models, a feature selection technique is proposed to identify the relative dependence of each input to the output. Through a comparison between results with and without the selection technique, it is observed that the technique helps to select the most pertinent inputs, and thus further improve the forecast performance, accuracy and efficiency. In addition, three data-driven models are further revealed to acquire the best forecast accuracy, by including temporal effect, spatial effect, or coupled temporal and spatial effects on atmospheric conditions, which are named as temporal-based, spatial-based and hybrid data-driven models, respectively. By comparing the forecast performance among different models, it is revealed that the newly proposed hybrid data-driven model with feature selection technique is more suitable in dealing with wind speed forecasting. Moreover, an additional case study further reinforces the feasibility of the newly proposed hybrid data-driven model for forecasting of wind direction. The proposed date-driven models in the paper provide a high-cost efficiency alternative for wind speed forecast using machine learning instead of physical models. This approach can assist engineers in making prompt and informed decisions even when resources are constrained during emergency conditions. • Cost-efficiency wind speed forecast methods are developed by incorporating atmospheric variables into data-driven models. • Data-driven models are generated by considering temporal and spatial effects of atmospheric conditions on wind fields. • The feature selection is utilized to select the appropriate inputs and to enhance the accuracy of wind speed forecasts. • The important atmospheric variables that significantly affect wind fields are identified. • The feasibility of the newly proposed hybrid data-driven model for wind direction forecasting is demonstrated. [ABSTRACT FROM AUTHOR]

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

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

The COVID-19 pandemic ushered in unforeseen consequences for urban public transportation systems, raising concerns about their efficiency in cities during pandemics. Using a two-stage bootstrapped Data Envelopment Analysis with a regression approach, this study investigates the changes in integrated transit station efficiency/performance before and during the COVID-19 pandemic in Seoul, South Korea, presenting a unique case study due to its mega city status and absence of a complete lockdown during the pandemic. Besides, it explores the shifting impacts of external environmental factors on station efficiency from 2019 to 2021. Findings from the first-stage analysis underscore a gradual decline in station efficiency, with a pronounced impact evident in 2021. Examining land-uses around the stations revealed that the presence of eateries and hotels in station catchment areas corresponded to enhanced performance. Interestingly, high performing stations with high ridership during the pandemic had low station areas and operating costs. The second-stage analysis pinpointed influential environmental factors, notably young pedestrian volume, which exerted varying influence on station efficiency across different periods. Vehicular accidents and rainfall in the vicinity of transit station catchment areas were consistently positively associated with station efficiency, irrespective of the study year. These findings carry substantial implications and insights for urban planners worldwide. • Variations in efficiency of stations before and during the COVID-19 pandemic are explored. • There is a discernible trend of diminishing station performance from 2019 to 2021. • Stations with restaurants and shops in their buffer areas demonstrated a progressive enhancement in efficiency. • Stations in residential areas and cultural centers experienced reduction in efficiency over time. • Young pedestrian volume exerted varying influences on station efficiency across different periods. [ABSTRACT FROM AUTHOR]

The rapid urbanization leading to extensive construction activities has significantly harmed the urban ecosystem. However, there remains a lack of systematic consideration regarding the interplay of energy and materials within construction projects. To bridge this gap, the present study employs emergy analysis and data envelopment analysis to comprehend the metabolic characteristics of construction projects. Specifically, 66 construction projects in China were selected for their comprehensive project-level data. The construction flows were categorized into renewable resources, local non-renewable resources, imported resources, exported products, and wastes. The results indicate that imported emergy comprises the majority of total emergy usage, with labor input being the primary contributing factor. Ecological benefit and efficiency analyses reveal that the emergy cost surpasses the emergy benefit. This inefficiency stems mainly from suboptimal utilization of imported resources and inadequate management of undesired outputs in construction projects. Furthermore, buildings in northern and central regions exhibit superior performance in total emergy use, emergy density, and emergy load ratio, whereas public and residential buildings exhibit higher environmental burdens and greater reliance on external resources. A statistically significant sublinear power-law relationship between the scale of construction projects and their metabolic rates was also identified, indicating a notable scale effect on energy and resource consumption during the building construction process. • A project-level analytical framework is built to understand the construction metabolism. • The overall metabolic performances of construction projects are measured. • The scaling law is demonstrated in Chinese construction practice. [ABSTRACT FROM AUTHOR]

This study aims to analyze the efficiency of global airlines by incorporating innovation capital into a two-stage dynamic network data envelopment analysis (DEA) framework. This paper addresses these concerns by developing a dynamic network DEA model that considers the interconnectedness between airlines' internal processes and the carry-over effects between time periods. It incorporates the dynamic impact of shared carry-over items, enabling accurate computation of efficiency scores for these two stages. The study divides the airline operation system into two stages: production stage and service stage, where intertemporal shared innovation capital occurs. The results reveal that, despite majority airlines prioritizing the service stage, production stage contributed most to overall efficiency. In terms of periodic results, the efficiency score of airlines in 2017 is marginally higher compared to 2016 and 2018, respectively. In general, airlines place a slightly greater emphasis on service stage than production stage. Innovation is essential for airlines to achieve a sustainable competitive advantage. Despite considerable interest in analyzing performance, this is one of the first studies to incorporate innovation capital in airline efficiency studies. The proposed model can help airline managers, regulators, and policymakers measure performance reliably and improve airline efficiency and sustainability. • A dynamic network DEA model integrating innovation capital is employed in the study. • There is a significant impact of the production stage on airline efficiency. • The results reveal a marginal rise in efficiency in 2017, followed by a slight dip in 2018. • The study emphasizes the vital role of innovation in sustaining airline competitiveness. [ABSTRACT FROM AUTHOR]

An increase in the population in pastoral regions and an improvement in living standards have increased livestock production; however, this has led, at least in part, to global grassland degradation. Consequently, the optimal pathway to mitigate trade-offs between livestock production and ecological functions of grassland is the key to achieve sustainable development in pastoral regions. Transformative adaptation is the recommended and feasible approach. However, most studies are not designed to determine which grassland management system addresses transformative adaptation and, therefore, do not provide options that can resolve the trade-offs. To fill this gap, we compared three grassland management systems, namely single, joint and cooperative. The grassland health index (GHI), data envelopment analysis (DEA), life cycle assessment (LCA), TOPSIS model and the multi-objective optimization model were employed to assess the productive and ecological benefits of the three systems. Cooperative management had the greatest comprehensive benefits when considering ecological functions, livestock production, carbon efficiency utilization and grassland area utilization efficiency, and had the greatest potential to achieve a balance between livestock production and ecological functions in the future. • The single management grassland system had the highest grassland health index. • The cooperative management system had the highest production efficiency and income. • The cooperative management grassland system emitted the most greenhouse gasses. • The cooperative management grassland system required the least grassland area. • The cooperative management grassland system had the highest comprehensive benefits. [ABSTRACT FROM AUTHOR]

• Developing a novel framework for appraising shared electric micro-mobility hubs in Dublin. • Employing DEA and negative binomial regression and using six months of real-world data. • Proposing an e-bike sharing scalability measure using returns-to-scale analysis. • Assessing eco-efficiency based on trip and user characteristics and emission offset. • High scalability suggests system's expansion and improving infrastructure can boost performance. In car-dominated urban areas, shared electric micro-mobility offers a sustainable alternative to decarbonise and reshape mobility paradigms. This paper presents a comprehensive framework for evaluating Dublin's e-bike sharing system, comprising 12 stations (eHUBs). Using six months of real-world riding data, it employs data envelopment analysis to assess the eco-efficiency of each eHUB in utilising nearby infrastructure, population in the catchment area, and location to achieve desired economic, social, and environmental outputs. Results indicate an upward trajectory for the system's eco-efficiency. The returns-to-scale analysis provides insights into the system's scalability, suggesting that expanding the e-bike sharing system, along with infrastructural enhancements, would significantly increase ridership. Analysing decarbonisation, usage patterns, and spatial factors of eHUBs reveals the substantial potential of shared e-bikes if optimally used. The research highlights the need to reevaluate car-centric policies in favour of more inclusive and environmentally sustainable alternatives and proposes actionable policy recommendations to achieve this transformation. [ABSTRACT FROM AUTHOR]

The high penetration of digitalization has emerged as a crucial driving force in enhancing financing efficiency across global enterprises. However, few studies have explored the potential effects of digital transformation on enterprises' financing efficiency, wherein the financing system is composed of fund procurement and application stages. Under such background, this study constructs a novel network DEA model with dominance probability and adjusted utility to analyze the multiple-dimensional performance of the financing system. Then an empirical econometric model is developed from the theoretical analysis and applied to Chinese energy enterprises for the period 2015-2021. The main findings are summarized as follows. First, our research demonstrates that digital transformation can significantly increase financing efficiency, with fund procurement identified as the lagging stage to efficiency improvement. Second, when considering the heterogeneous analysis, digital transformation is particularly effective in alleviating financing constraints and enhancing operating performance for clean energy enterprises. Finally, with regard to the mechanism's explorations, digital transformation increases enterprises' financing efficiency by reducing the ownership concentration and management expense rate. These findings provide an empirical foundation for the advancement of digital transformation, and also hold significant implications for narrowing financing efficiency gaps across energy enterprises. [Display omitted] • We propose a novel network model with dominance probability and adjusted utility. • The digital transformation has a significant role in promoting financing efficiency. • The stage of fund procurement is the main barrier to efficiency improvement. • Digitalization better improves financing performance for clean energy enterprises. • This study evaluates China's energy enterprises for the period 2015–2021. [ABSTRACT FROM AUTHOR]

• Analyzing reliability of a distribution company by data envelopment analysis. • Obtaining distribution companies' reliability targets using best past performance. • Estimation of reliability target level by machine learning methods. • Incorporation of outage characteristics for reliability target estimations. Electrical distribution system (EDS) reliability has always been a critical subject due to the considerable share of distribution systems in power interruptions to end-users. Meeting regulatory service quality standards requires distribution companies to achieve defined reliability levels for performance improvement. Conventionally, specialists determine target reliability based on experience, experimental knowledge, and rules of thumb. However, these methods are error-prone and may result in suboptimal investments, economic inefficiency, and inadequate resource allocation in EDS operation and planning. Accordingly, this paper presents a machine learning-based method to determine the target level for EDS's reliability indices based on its historical performance. The approach involves comprehensive analysis of historical EDS reliability data, followed by a data envelopment analysis (DEA) to assess performance across subdivisions like substations or feeders, identifying the most effective units. Subsequently, machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function (RBF) network—are trained on optimal subdivisions from the DEA stage. These models accurately estimate target reliability for all subdivisions, contributing to the overall determination of the EDS target reliability level. A three-scenario case study validates the proposed method, suggesting its potential applicability as a valuable tool for regulatory agencies in setting EDS reliability targets. While admitting the potential challenges, notably the method's reliance on the quality and completeness of the historical data collected by distribution companies, the paper emphasizes the importance of collaborative efforts to ensure data accuracy and sufficiency. The proposed method leads to a fair mechanism to determine the target level for a real EDS's reliability indices in comparison with a business-as-usual scenario. Moreover, it provides a reliability performance ranking for the EDS's subdivisions. [ABSTRACT FROM AUTHOR]

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