A sustainable industrial waste control with AI for predicting CO2 for climate change monitoring

As the challenge of climate change continues to grow, we need creative solutions to predict better and track industrial waste carbon emissions, focusing on sustainable waste management practices. The present study proposes a state-of-the-art Metaverse framework that puts artificial intelligence into action in predicting carbon emissions using energy use patterns and industrial social factors. At the heart of this framework lies a hybrid deep learning model combining convolutional neural networks and Long-term, short-term memory to model complicated spatial and temporal dependencies inherent in data. Further, gradient-boosting machines have been added to improve predictive performance by modeling the nonlinear relationship and interaction between features. The Metaverse environment enables a dynamic and interactive platform for real-time climate monitoring, allowing users to visualize and analyze the impacts of different energy and socio-economic scenarios on carbon emissions. Instead of traditional models, the Metaverse provides an immersive experience with deep knowledge of complex spatial relationships. This interactive capacity allows users to engage with the data more in an adaptable way. The proposed hybrid model achieves 99.5 % predictive accuracy, R2 = 0.995 for carbon emissions, and 99.2 % R2=0.992 for energy consumption compared to traditional methods. Such high accuracy underlines how effective deep learning techniques are combined with ensemble methods in capturing multifaceted climate data. Therefore, the outcome that brings out this AI-driven Metaverse is a potent tool for policymakers and researchers to make informed decisions to mitigate the impact of climate change. This framework consolidates diverse data sources in an immersing virtual environment, making it a very advanced tool in the climate science landscape by providing a comprehensive solution for predicting and monitoring carbon emissions.