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

Abstract

Rapid urbanization and climate change severely strain energy and waste management systems, positioning Artificial Intelligence (AI) and cloud computing as transformative tools for smart city sustainability. Despite AI's potential, significant research and implementation gaps persist, particularly in climate-vulnerable and resource-constrained urban environments. Existing optimization models predominantly target data-rich, developed nations, often failing to address infrastructural limitations, grid instability, and heterogeneous waste profiles such as high-moisture organic waste. Furthermore, current literature frequently prioritizes short-term operational efficiency while overlooking the substantial energy consumption and carbon footprint associated with training and deploying complex AI models. There is also a pronounced deficit in comprehensive Life Cycle Assessments (LCA) and frameworks addressing socio-economic barriers, including policy fragmentation, informal sector labor displacement, and algorithmic bias.

This study aims to investigate the effectiveness of an integrated, socio-technical framework for AI-driven urban energy and Waste-to-Energy (WtE) optimization tailored to developing smart cities. The objectives are to identify context-aware AI solutions, establish energy-efficient computational models, and determine the feasibility of sustainable WtE integration in low-resource settings. The research adopts a mixed-methods approach, combining simulation-based modeling and secondary data analysis, with a representative dataset of urban energy consumption and waste generation patterns. Machine learning techniques, including Gradient Boosting for residential load forecasting, Long Short-Term Memory (LSTM), and Reinforcement Learning (RL) for smart grid optimization, as well as predictive analytics for WtE process control, are evaluated.

However, limitations include reliance on simulated datasets, limited real-world deployment, and potential variability in data availability across regions. The findings suggest that modular, context-aware AI architectures combined with Green AI techniques—such as model compression, neuromorphic computing, and carbon-aware scheduling—can reduce computational energy consumption by up to 45% with minimal loss in accuracy.

The study highlights the importance of integrating technical innovation with inclusive governance and policy frameworks. It implies that adopting scalable, energy-efficient AI systems can enhance urban energy security, support circular economy transitions, and strengthen climate adaptation strategies in developing cities.

Keywords: Artificial Intelligence; Smart Cities; Waste-to-Energy; Green AI