AI Driven Urban Energy Optimization in Emerging Megacities: Addressing Scalability, Lifecycle Impacts, and Socio-Technical Gaps

Abstract

Rapid urbanization and climate change strain energy and waste systems, positioning AI and cloud computing as vital tools for smart city sustainability. However, significant gaps persist in resource-constrained environments where existing models often overlook infrastructural limitations and AI’s own carbon footprint.

This study evaluates a socio-technical framework for AI-driven urban energy and Waste-to-Energy (WtE) optimization using machine learning techniques like LSTM and Reinforcement Learning. Based on theoretical synthesis of current literature, findings indicate that Green AI techniques can theoretically reduce computational energy consumption by up to 45%. Ultimately, adopting scalable, energy-efficient AI integrated with inclusive governance enhances urban energy security and climate adaptation in developing cities.