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Environmental Assessment is a globally mandated tool for helping to deliver sustainable development, yet decision makers frequently use it to legitimise trade offs between socio-economic gains and environmental losses. As a result, environmental assessment is frequently criticised for its inability to prevent incremental environmental degradation. However, new frameworks stipulating what can be deemed as 'sustainable investment' or 'sustainable economic activity' for financing under sustainable finance frameworks are being developed. These are known as taxonomies of sustainable investments, and they have the potential to radically change the environmental outcomes of decision making, based on a 'significant contribution' and 'do no significant harm' approach to critical environmental components. We illustrate how they can change the mindset for the sustainable development expectations associated with policy tools like environmental assessment. Further, we demonstrate that emerging taxonomies can benefit from integration with existing environmental assessment systems. Conversely, an appropriate use of taxonomies of sustainable investments in environmental assessment systems can further strengthen the existing EA systems and allow them to better address the environmental sustainability priorities of the 21st century.

One of the most common errors in research on sustainable development is to analyse a set of features describing this development within one set of diagnostic features. Such an approach does not allow for examining the real changes taking place within each area of sustainable development. These changes may have a completely different direction in the case of indicators describing, for example, the economic area or the environmental area of sustainable development. The solution is to consider the indicators separately for each area and then compare the results obtained. In this work, multi‑criteria taxonomy was used for this purpose. The study used indicators published by Eurostat to monitor progress in implementing the Agenda for Sustainable Development 2030 from 2008 and 2016. The results presented in the paper confirmed the considerable diversity of the EU countries in each area of sustainable development and their large heterogeneity. ; Jednym z najczęściej popełnianych błędow podczas badań nad zrownoważonym rozwojem jest rozpatrywanie zbioru cech opisujących ten rozwoj w ramach jednego zbioru cech diagnostycznych. Takie podejście nie pozwala na zbadanie rzeczywistych zmian zachodzących w ramach poszczegolnych ładow zrownoważonego rozwoju. Zmiany te mogą mieć zupełnie inny przebieg w przypadku wskaźnikow opisujących np. wymiar gospodarczy czy środowiskowy zrownoważonego rozwoju. Rozwiązaniem jest rozpatrywanie wskaźnikow oddzielnie dla każdego ładu, a następnie porownywanie uzyskanych wynikow. W artykule zastosowano w tym celu taksonomię wielokryterialną. Do badania wykorzystano publikowane przez Eurostat wskaźniki monitorujące postęp we wdrażaniu Agendy na rzecz zrownoważonego rozwoju 2030 z lat 2008 i 2016. Otrzymane wyniki potwierdziły znaczne zrożnicowanie badanych krajow UE w zakresie poszczegolnych ładow i duże ich rozwarstwienie.

Educational institutions are undergoing an internal process of strategic transformation to adapt to the challenges caused by the growing impact of digitization and the continuous development of student and labor market expectations. Consequently, it is essential to obtain more accurate knowledge of students to improve their learning experience and their relationship with the educational institution, and in this way also contribute to evolving those students' skills that will be useful in their next professional future. For this to happen, the entire academic community faces obstacles related to data capture, analysis, and subsequent activation. This article establishes a methodology to design, from a business point of view, the application in educational environments of predictive machine learning models based on Artificial Intelligence (AI), focusing on the student and their experience when interacting physically and emotionally with the educational ecosystem. This methodology focuses on the educational offer, relying on a taxonomy based on learning objects to automate the construction of analytical models. This methodology serves as a motivating backdrop to several challenges facing educational institutions, such as the exciting crossroads of data fusion and the ethics of data use. Our ultimate goal is to encourage education experts and practitioners to take full advantage of applying this methodology to make data-driven decisions without any preconceived bias due to the lack of contrasting information.

This study provides a taxonomy of measures-of-fit that have been used for evaluating risk-equalization models since 2000 and discusses important properties of these measures, including variations in analytic method. It is important to consider the properties of measures-of-fit and variations in analytic method, because they influence the outcomes of evaluations that eventually serve as a basis for policymaking. Analysis of 81 eligible studies resulted in the identification of 71 unique measures that were divided into 3 categories based on treatment of the prediction error: measured based on squared errors, untransformed errors, and absolute errors. We conclude that no single measure-of-fit is best across situations. The choice of a measure depends on preferences about the treatment of the prediction error and the analytic method. If the objective is measuring financial incentives for risk selection, the only adequate evaluation method is to assess the predictive performance for non-random groups.

One of the most common errors in research on sustainable development is to analyse a set of features describing this development within one set of diagnostic features. Such an approach does not allow for examining the real changes taking place within each area of sustainable development. These changes may have a completely different direction in the case of indicators describing, for example, the economic area or the environmental area of sustainable development. The solution is to consider the indicators separately for each area and then compare the results obtained. In this work, multi‑criteria taxonomy was used for this purpose. The study used indicators published by Eurostat to monitor progress in implementing the Agenda for Sustainable Development 2030 from 2008 and 2016. The results presented in the paper confirmed the considerable diversity of the EU countries in each area of sustainable development and their large heterogeneity. ; Jednym z najczęściej popełnianych błędow podczas badań nad zrownoważonym rozwojem jest rozpatrywanie zbioru cech opisujących ten rozwoj w ramach jednego zbioru cech diagnostycznych. Takie podejście nie pozwala na zbadanie rzeczywistych zmian zachodzących w ramach poszczegolnych ładow zrownoważonego rozwoju. Zmiany te mogą mieć zupełnie inny przebieg w przypadku wskaźnikow opisujących np. wymiar gospodarczy czy środowiskowy zrownoważonego rozwoju. Rozwiązaniem jest rozpatrywanie wskaźnikow oddzielnie dla każdego ładu, a następnie porownywanie uzyskanych wynikow. W artykule zastosowano w tym celu taksonomię wielokryterialną. Do badania wykorzystano publikowane przez Eurostat wskaźniki monitorujące postęp we wdrażaniu Agendy na rzecz zrownoważonego rozwoju 2030 z lat 2008 i 2016. Otrzymane wyniki potwierdziły znaczne zrożnicowanie badanych krajow UE w zakresie poszczegolnych ładow i duże ich rozwarstwienie.