Abstract
This paper investigates the relationship between students' performance and the type of school attended during upper secondary education. The performance of three different types of schools (Liceo, Technical and Professional schools) in four Italian macroregions (North West, North East, Centre, South & Islands) is investigated. A benchmarking analysis of the variability in students' performance among regions (within macroregions) for cohorts of students attending Liceo is also conducted. The data was collected at the student level from the Italian Institute for the Evaluation of Education System (INVALSI), for the academic year 2017/18. Families with higher socio-economic status may self-select into Liceo, so a direct comparison with vocational schools could lead to biased conclusions regarding the impact of school type on student performance. To overcome this limitation, we used a Propensity Score Matching approach prior to the estimation of efficiency. A pseudo-Malmquist index, based on a metafrontier and satisfying the circular property, is developed. It enables comparing the location of the best-practice frontier for each type of school and the spread in the educational efficiency of the students attending each type of school. Thus, best performance of a given school type corresponds to the combined effect of these two aspects. This study is an interesting starting point to challenge the stereotypes that persist in Italy, especially concerning general and vocational studies and geographic differences in educational achievements.

Abstract
Economic efficiency assessments based on Data Envelopment Analysis are scarce compared to technical efficiency studies, even in for-profit firms. Some aspects justify this scarcity, such as the difficulty to estimate accurate prices, given their variability over time. In many situations, external benchmarking is hindered due to organizations' unique nature and the barriers to sharing information considered critical to competitiveness. The use of internal benchmarking can overcome some of these difficulties. This study conducted an internal benchmarking analysis of a broiler production system, focusing on cost efficiency. We conducted longitudinal case-based research over six years (2014-2019). The concepts of throughput accounting of the Theory of Constraints were applied to structure the DEA model (inputs, prices, and output). The Critical Incident Technique was used to explore the effects of interventions on the production system's cost efficiency. The results show that the broiler production system could reduce 32% of the total cost per unit of production if the balance of inputs suggested by the DEA evaluation was used. This work contributes to the literature by showing the potential of internal benchmarking to explore the evolution of cost efficiency over time. From a practical perspective, this study is important for managers by showing how to measure the impact of management actions on performance, providing valuable information to guide continuous improvement.

Abstract
Grasping the intricacy and diversity of complex systems dealing with ever-growing amounts of data is essential to public and private institutions' continuous improvement. Composite indicators (CIs) emerge as aggregators of key performance indicators, providing a single measure that reflects those multidimensional performance aspects. One way to build such measures is based on the use of data envelopment analysis (DEA). Several DEA models can be used to generate CIs. Still, not many of them can deal concurrently with desirable and undesirable outputs, and incorporate the decision-making actors' preference information. Based on the directional 'Benefit-of-the-Doubt' model, we propose a novel approach consisting of the simultaneous use of weight restrictions and an artificial target reached via a range directional vector. The resulting CI assesses the Portuguese public hospitals' performance under two perspectives of hospital activity: users and providers. In the end, managerial and policy implications are withdrawn from the results of this study conducted in cooperation with the Portuguese Ministry of Health.

Abstract
Convergence in productivity examines if entities in an industry get closer to the best practices or if the gap between the frontiers of the best and worst performers decreases over time. In a multi-input multioutput setting, the assessment of sigma- and beta-convergence can be measured with the use of non-parametric frontier techniques, such as data envelopment analysis. We propose an innovative approach to estimate convergence in the context of performance assessments resting on composite indicators, accounting for desirable and undesirable indicators. This methodology rests on 'Benefit-of-the-Doubt' models, specified with a directional distance function. It is applied to the Member States of the World Health Organization (WHO) in order to study their convergence in terms of the United Nations' Sustainable Development Goal (SDG) 'Good health and well-being'. We collected data for all years since the proposal of the SDGs, covering the period between 2016 and 2020. The results show that all WHO regions are (beta) over cap -divergent, especially because of the generalised decline of the Worst Practice Frontier (WPF), alongside an improvement at a lower rate of the Best Practice Frontier (BPF). The regional analysis also revealed (sigma) over cap -convergence in the Region of the Americas and the Eastern Mediterranean Region; the South-East Asia and African Regions exhibited (sigma) over cap -divergence; the Western Pacific and European Regions remained stable in terms of the performance spread regarding the BPF. At the worldwide level, we also observed an increase of the gap between the BPF and the WPF, although the performance spread around the worldwide BPF remained relatively stable.

Abstract
Technological developments related to renewable energy led to a decrease on the prices of generation and allowed the penetration of distributed energy resources in power systems. This context, combined with other factors, such as the development of electric vehicles, enabled the rapid evolution of Smart Grids. As a consequence, Distribution System Operators (DSOs) have been investing in this field to keep up with its deployment. This work presents a case study that compares a set of European DSOs regarding their investment in Smart Grid projects. The methodology underlying this study is based on the construction of composite indicators using the Data Envelopment Analysis technique. Furthermore, we evaluate the evolution in the DSOs performance between 2013 and 2017 using a Malmquist index. The results are discussed in the light of their contribution to the definition of public policies in the energy field. © 2021, Springer Nature Switzerland AG.

Abstract
Weather conditions have a major impact on citizens’ daily mobility. Depending on weather conditions trips may be delayed, demand may be changed as well as the modal shift. These variations have a major impact on the use and operation of public transport, particularly in transport systems that operate close to capacity. However, the influence of weather conditions on transport demand is difficult to predict and quantify. For this purpose, an artificial neural network model – the Multilayer Perceptron – is used as a regression model to estimate the demand of urban public transport buses based on weather conditions. Transit bus ridership and weather conditions were collected along a year from a medium-size European metropolitan area (Oporto, Portugal) and linked under the assumption that individuals choose the travel mode based on the weather conditions that are observed during the departure hour, the hour before and two hours before. The transit ridership data were also labelled according to the hour, day of the week, month, and whether there was a strike and/or holiday or not. The results demonstrate that it is possible to predict the demand of public transport buses using the weather conditions observed two hours before with low error for the entire network (MAE = 143 and RMSE = 322). The use of weather conditions allow to decreases the error of the prediction by ~8% for the entire network. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.