This research studies the efficiency of Flipped Classroom (FC) and Challenge-Based Learning (CBL) as an innovative methodology to facilitate the learning of concepts involving sustainability and the circular economy by higher education students. This study, conducted as part of various innovative education projects, was applied in different courses (Environmental Management, Environmental Engineering and Industrial Ecology) taught to different engineering majors (Degree in Geomatics and Survey Engineering and Master’s Degree in Chemical Engineering) at the Universidad Politécnica de Madrid (Spain). The main aim is to enhance the environmental competences of students by means of unconventional learning, increasing the motivation, participation and grades of the students in the course. Our results indicate that, in general terms, the students rated the efficiency of the new learning methodologies as satisfactory; however, a more detailed analysis of the results reveals some aspects that need special consideration, such as an assessment of specific and cross-cutting competences, in addition to the lack of previous soft skills, such as teamwork, creativity and work planning.

Diversification of fisheries has one of its main lines of development in tourism, especially in countries as touristic as Spain. An important part of the European Fisheries Funds (EFFs) has subsidized, through the Fisheries Local Action Groups, local development projects in fishing zones with a large part of these projects related to tourism, including nautical tourism. In this new context it is possible to quantify and analyse the subsidized projects’ typology and the relationship between recreational and professional fishing. This work analyses 705 projects financed by European funds, mainly the EFF, of which 172 are related to tourism and 48 to nautical tourism. The objective is to categorize and characterize the subsidized projects and, subsequently, to verify the degree of relationship, if any, between the projects of nautical tourism and professional fishing.

The development of geographic information systems has grown significantly over the past decade. Simultaneously, the concept of smart cities based on the management of large volumes of data has also spread worldwide. The digital twin concept has recently been incorporated into the technological domain of urban management. However, currently, phases such as technological integration, standardization, data and process interconnection, the development of tools and utilities, professional training, and the application of digital urban development in real-world situations are converging. This paper presents the experience developed on a university campus, detailing each of the phases carried out from the initial design to a fully operational pilot phase model. The article highlights the importance of certain aspects to consider in each phase, demonstrating that there are barriers and limitations and at the same time, great strengths and opportunities in applying the digital twin model in urban management, considering aspects such as mobility, accessibility, energy management, and involving students and university administrators in the process.

Property valuation studies often use classical statistics techniques. Among these techniques, the Artificial Neural Networks are the most applied, overcoming the inflexibility and the linearity of the hedonic models. Other researchers have used Geostatistics techniques, specifically the Kriging Method, for interpreting spatial-temporal variability and to predict housing unit prices. The innovation of this study is to highlight how the Kriging Method can help to better understand the urban environment, improving the results obtained by classical statistics. This study presents two different methods that share the general objective of extracting information regarding a city’s housing from datasets. The procedures applied are Ordinary Kriging (Geostatistics) and Multi-Layer Perceptron algorithm (Artificial Neural Networks). These methods were used to predict housing unit prices in the municipality of Pozuelo de Alarcon (Madrid). The implementation of both methods provides us with the urban characteristics of the study area and the most significant variables related to price. The main conclusion is that the Ordinary Kriging models and the Neural Networks models, applied to predicting housing unit prices are necessary methodologies to improve the information obtained in classical statistical techniques. Abbreviations: ANN: Artificial Neural Networks; OK: ordinary Kriging; MLP: multi-layer perceptron

Creativity and innovation are crucial skills to face challenges in economy, environment and social context today, especially next decade with 2030 Agenda for Sustainable Development adopted by all United Nations Member States. European Higher Education System considers STEM studies play a key role to lead the global labor market and address our economic system towards more sustainability and equality model. Innovative educational projects developed at the Universidad Politécinca de Madrid have identified lack of students skills in creativity and innovation to apply challenge based-learning and others methodologies in classrooms. Hovewer Higher Education institutions need a whole approach to include creativity in university curricula (graduate and postgraduate programs), at the same time, professors claim support to embebed innovative methodologies in their subjects. CHET Project is designed as a solid strategy aimed at developing an innovative process to modernization of Higher Education System in Europe. Step by step, the project begins by defining the learning environment, then developing creativity techniques and tools, and finally validating methodologies and processes. All this supported by free access online platform.