Integrating fourth industrial revolution (4IR) technologies with green energy systems: A framework for AI-driven smart grid optimization and carbon footprint reduction

The convergence of Fourth Industrial Revolution (4IR) technologies with green energy systems presents a transformative pathway to address escalating energy demands and climate change. This paper proposes a novel framework integrating federated learning, lightweight blockchain, and multi-agent reinforcement learning (MARL) to optimize smart grid efficiency and sustainability. The three-layer architecture enables privacy-preserving energy demand forecasting (92.3% accuracy via federated LSTM), decentralized peer-to-peer trading (20.1% cost savings using Hyperledger Fabric), and dynamic grid control (98% renewable utilization). Validated through synthetic datasets and digital twin simulations, the framework achieves 25.1% energy efficiency gains and 15.8% CO2 emission reductions compared to conventional systems. By aligning with GDPR standards and reducing blockchain energy consumption by 40%, the work bridges technological innovation with ethical and regulatory imperatives. The results demonstrate scalable solutions for smart cities, directly supporting United Nations Sustainable Development Goals (SDGs) 7 (Affordable Energy), 9 (Industry Innovation), and 13 (Climate Action). This research provides policymakers and engineers a replicable model for decarbonizing energy infrastructure while democratizing access through decentralized markets.