Artificial Intelligence-Driven Smart Waste-to-Energy Networks for Climate-Resilient Circular Resource Management in Vulnerable Megacities

Abstract

Climate-vulnerable megacities like Dhaka, Bangladesh, face escalating challenges in managing mounting volumes of municipal solid waste (MSW), exacerbated by rapid urbanization, climate shocks, and inadequate resource recovery systems. This research proposes an advanced AI-driven Smart Waste-to-Energy (AI-CIR-WtE) framework designed to transform linear waste systems into adaptive, circular, and climate-resilient urban infrastructure. Integrating artificial intelligence, life cycle modeling, digital twins, and blockchain, the framework offers a comprehensive pathway to optimize waste valorization, emissions reduction, and sustainable energy generation in resource-constrained settings. The proposed system leverages Long Short-Term Memory (LSTM) networks for forecasting waste generation by ward and season, coupled with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization of waste routing, energy efficiency, and environmental impact. An AI-LCA engine, developed using OpenLCA and TensorFlow, dynamically quantifies GHG emissions, carbon offsets, and energy returns under multiple WtE configurations. Simulations are embedded within a 3D digital twin of Dhaka, constructed in Unity/Unreal Engine, enabling real-time modeling of disaster impacts (e.g., monsoon flooding, urban heatwaves) on infrastructure and service delivery. To ensure transparency and verifiability in carbon credit mechanisms, a blockchain-enabled MRV (Monitoring, Reporting, and Verification) layer tracks waste origin, conversion outputs, and emission reductions across the value chain. The framework incorporates climate equity through a gender and social inclusion lens, offering AI-based training modules and digital participation platforms for women, youth, and informal waste workers. Results show a projected 27–35% increase in circular material recovery, up to 41% reduction in lifecycle emissions, and 18% rise in decentralized energy yields under optimized conditions. The AI-CIR-WtE model demonstrates strong alignment with UN SDGs, Verra’s Verified Carbon Standard, and investment criteria from the Green Climate Fund (GCF) and World Bank climate finance facilities. By converging data-driven optimization, immersive simulation, and climate-just governance, this research offers a scalable blueprint for circular economy transition in megacities under climate threat. The framework is replicable in other Global South contexts and serves as a digital, equitable infrastructure roadmap toward net-zero urban futures.