As climate change intensifies and urbanization accelerates, megacities in the Global South face unprecedented energy insecurity, infrastructure fragility, and socio-economic disparities. This research presents a transformative, Artificial Intelligence-driven Hybrid Renewable and Waste-to-Energy System (AI-HRES) tailored for climate-resilient, equitable, and decentralized urban energy infrastructures, with Dhaka, Bangladesh serving as a case exemplar. Integrating Long Short-Term Memory (LSTM) networks for spatiotemporal demand forecasting, Deep Reinforcement Learning (DRL) for real-time energy dispatch, digital twins for dynamic grid simulation, and blockchain-enabled microgrid sovereignty, the framework holistically addresses resilience, sustainability, transparency, and social justice. Simulations using ward-level data reveal a >76% reduction in blackout frequency, improved Mean Time to Recovery (0.9 hours), and enhanced storage utilization (88%) under volatile climate conditions. The AI-HRES hybrid portfolio, comprising solar PV, wind turbines, Waste-to-Energy (WtE), and bioenergy, achieved a 56% increase in renewable output and an annual CO2-equivalent reduction of 7,210 metric tons, translating into tangible climate mitigation gains. A real-time Life Cycle Assessment (LCA) module embedded within the digital twin ecosystem tracked cradle-to-grave environmental impacts, enabling dynamic recalibration of embodied energy, carbon payback, and water usage across system components. The blockchain layer operationalized peer-to-peer energy trading, WtE credit tokenization, and decentralized ownership via smart contracts, fostering local trust and market democratization, especially in informal settlements. Gender-responsive energy governance was embedded through disaggregated modeling, participatory control layers, and equity key performance indicators (KPIs), ensuring that technological gains translated into procedural, distributional, and recognition justice. Benchmarking against global leaders- Singapore, Germany, and California- highlights Dhaka’s AI-HRES as a pioneering Southern model that uniquely combines resilience-focused AI dispatch, digital twin orchestration, and inclusive token economies. A Transferability Index (TI) of 0.45 (scalable to 0.70) demonstrates high replicability across other climate-exposed megacities. This research advances a new paradigm of intelligent, inclusive, and regenerative urban energy systems by fusing AI, climate science, blockchain governance, and social equity. It provides a replicable architecture for policymakers, engineers, and urban planners seeking to transition from vulnerable, fossil-dependent grids toward adaptive, low-carbon infrastructures. The AI-HRES framework is not merely a technical solution- it is a blueprint for resilient, just, and climate-smart urban futures.
Islam et al. (2025) studied this question.