Low-Carbon Sustainable Construction in KSA

Abstract

This research focuses on Saudi Arabia's energy-intensive sectors and presents a data-driven method for accurately forecasting CO₂ emissions from industrial facilities. By combining temporal variables, production metrics, energy consumption data, and operational efficiency indicators, the proposed model employs gradient boosting regression (XGBoost) to capture complex, non-linear connections between input features and emissions output. Compared to traditional statistical methods, this machine learning-based technique offers enhanced projected accuracy and responsiveness, making it suitable for real-time monitoring and decision-making.

Besides emissions projections, the model incorporates an adaptive control component that facilitates near-real-time operational adjustments. These adjustments are enhanced by a reinforcement learning loop, which provides continuous optimization and ongoing learning from prior decisions and outcomes. By employing scenario simulation, stakeholders can assess financial implications and develop environmentally friendly policies. Experiments demonstrate that the proposed framework outperforms traditional approaches in prediction accuracy and operational flexibility, providing an effective strategy for sustainable industrial operations in Saudi Arabia