This research aims to test the effect that the implementation of green practices at a major sport tourism event, the Badminton World Championships in Huelva (Spain), has on the future intention of spectators to return to similar sport events. A total of 523 spectators who attended the event were randomly selected and self-administered in the presence of the interviewer. A confirmatory factor analysis of the model and a multi-group analysis were carried out. Sporting events have a great impact on the environment in which they are organised, mainly when they are linked to tourism, whether at an economic, social or environmental level. The results indicated that green practices indirectly influence spectators’ future intentions through emotions and satisfaction, direct antecedents. In addition, green practices directly affect both image and trust, and indirectly affect satisfaction. In conclusion, green practices are a variable to be taken into account when planning the organisation of a sporting event that aims to consolidate itself in the tourism and sports services market.

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.