Smart circular economy designed for the decarbonization of waste from industry to promote net zero and sustainable development goals

Industrial decarbonization tasks face essential challenges due to the inefficiencies in waste management and operational variations. Traditional methods struggle with real-time adjustments, which are necessary to manage waste volumes and emissions. As a result, these methods are insufficient to meet the Net Zero targets and the Sustainable Development Goals (SDGs). To address these complexities, the proposed study introduces a Smart Circular Economy (SCE) framework that leverages the advantages of Fuzzy Hybrid Deep Multi-Neural Networks (FH-DMNN) to enhance decision-making and performance in industrial decarbonization. The proposed model utilizes fuzzy logic to manage uncertainty, in conjunction with deep learning, to strengthen waste-to-energy (W2E) conversion, carbon capture, and recycling processes. The simulation of the model is performed under Singapore Industrial CO2 emission data. It proves its efficacy with 25% improvement in waste processing efficiency and shows its ability in reducing emissions up to 80% by 2060, by supporting SDG 12 and SDG 13. Therefore, the proposed model makes a significant contribution to achieving net-zero emissions in industrial sectors.