Artificial Intelligence-Based Carbon Emission Prediction and Sustainable Resource Optimization for Smart Cities

Abstract

Smart cities increasingly rely on intelligent technologies to achieve environmental sustainability while accommodating growing urban populations. Accurate prediction of carbon emissions enables policymakers to implement efficient resource allocation strategies. This paper proposes an artificial intelligence-based framework for urban carbon emission prediction using machine learning, satellite observations, weather information, transportation data, and energy consumption records. Ensemble learning models integrate Gradient Boosting, Random Forest, and Deep Neural Networks to estimate emission levels with high accuracy. A multi-objective optimization algorithm subsequently recommends sustainable resource allocation strategies for transportation, electricity distribution, and renewable energy utilization. Experimental evaluation demonstrates improved prediction accuracy and reduced environmental impact compared with existing forecasting methods. The proposed framework supports data-driven decision-making for sustainable smart city development.