Climate change is a pressing global challenge that requires immediate action. To address this issue effectively, it is essential to engage and empower the younger generation who will shape the future. This abstract presents the experience of Mohamed Bin Zayed University for Humanities (MBZUH) in UAE in promoting climate action through youth empowerment and environmental education.MBZUH has recognized the significance of incorporating environmental education into its curriculum to foster a generation of environmentally conscious individuals. Through a multidimensional approach, the university has developed innovative strategies to empower students, enabling them to become active participants in addressing climate change. These strategies encompass both formal and informal education, leveraging various platforms and partnerships to create a comprehensive learning environment.This study delves into the initiatives undertaken by MBZUH to empower youth in climate action. It explores the incorporation of environmental education across disciplines, integrating sustainability principles into existing courses, and offering specialized programs focused on environmental science and climate studies. Additionally, it highlights the university's efforts in promoting hands-on learning experiences, such as field trips, research projects, and community engagement, to deepen students' understanding of climate issues and inspire practical action.Furthermore, the study examines the role of MBZUH's collaboration with local and international organizations, governmental bodies, and the wider community in fostering youth empowerment and climate action. It showcases successful partnerships that have resulted in impactful initiatives, including awareness campaigns, capacity-building workshops, and youth-led environmental projects.By sharing the experience of MBZUH, this study aims to provide valuable insights and best practices for promoting climate action through youth empowerment and environmental education. It underscores the importance of empowering the next generation with the knowledge, skills, and motivation to become effective agents of change in addressing climate challenges.

Gamification is an active methodology of great value that, in a quality educational environment, provides students with the necessary motivation to participate in their teaching-learning process. An emerging active methodology, which is based on the use of information and communication technologies (ICT) and requires an educational space that guarantees greater flexibility in the pedagogical dynamics in favor of academic achievement. This increase in interest in active methodologies, and specifically in gamification, has raised doubts about whether current educational spaces are prepared to host a renewal in methodology or if, on the contrary, they could undermine the attitude of change. For this reason, this research seeks to analyze whether current educational spaces are facilitating elements for the incorporation of gamification in the classroom. The methodological cut of the research is quantitative, specifically in two phases. On the one hand, a descriptive analysis of the results is carried out, obtaining information on the trend of each item. On the other hand, an inferential analysis is carried out around different variables to verify their possible influence on the evaluations of the participants. The results obtained, in the sample made up of 210 teachers distributed in the different centers and who carry out their educational activity from 3rd to 6th grade of primary school, indicate that teachers believe it is relevant to take into account the educational space when incorporating active methodologies in class.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.