Big data provide valuable insights by offering diverse information and sophisticated analysis through advanced algorithms. However, its huge volume, variety, and speed present significant challenges for effective computing. To address these, this study applies a Multi-Criteria Decision-Making (MCDM) framework to manage spatial big data, specifically in new green applications. The paper introduces a robust MCDM framework using big data, designed to address renewable energy challenges within the environmental sector. This framework systematically prioritizes and evaluates large environmental datasets, incorporating economic, environmental, and social factors. This framework is especially efficient and reliable for green energy initiatives. Moreover, a pre-processing step extracts key features to enable high-performance efficient analysis and visualization. Results show that the framework improves accuracy by 18% compared to conventional single-criterion data analysis approaches in a large-scale case study and provides system managers with an interactive 3D visualization tool to enhance decision making process in big data environmental management.