Real-Time Carbon Footprint Monitoring and Predictive Reporting Cloud Solutions for Sustainability

Climate change demands time-sensitive carbon footprint computations that need systems for continuous observation and predictive analytics. The paper builds a time-sensitive cloud infrastructure using artificial intelligence and data analytical techniques, enabling carbon footprint observation alongside predictive reporting features. Industrial carbon emission monitoring is possible through IoT sensors that process big data while applying machine learning models for permanent observation of inter-industry emissions to generate accurate predictions for better decision outcomes. The system depends on the extensive data collection capabilities of a scalable cloud infrastructure to process information without interruption. Organizations achieve carbon prevention goals and sustainable objectives and decrease energy expenses by implementing AI-based predictive analysis. Cloud-based carbon monitoring demonstrates its ability to effectively reduce emissions, improve procedural standards, and enable environment-conscious decision support. The current sustainable computing research reveals a powerful real-time solution to environmental issues.