Carbon emissions from the transportation sector pose a significant challenge to environmental sustainability, requiring continuous monitoring and control. While methods such as machine learning techniques and regression models are widely used for emission monitoring, they often require large datasets, involve complex model tuning, and lack real-time adaptability, which reduces their practicality for continuous monitoring. Therefore, this study proposes an optimized economic design of the dual exponentially weighted moving average (DEWMA) chart. This scheme integrates two EWMA charts to enhance the detection of emission shifts, while minimizing costs related to emission quality and operational expenses in statistical process control (SPC). The proposed scheme enhances cost-efficient emission monitoring and enables the timely detection of excessive emissions. Comparative analyses reveal that the proposed optimal economic DEWMA chart is highly efficient and outperforms its conventional and other optimized counterparts. A case study using real-world marine transportation data from the United Arab Emirates (UAE) validates the effectiveness of the proposed monitoring scheme, demonstrating faster detection of emission shifts and significant cost savings. These findings offer decision-makers a practical and economically viable solution for emission control in the transportation sector, supporting global efforts to reduce greenhouse gas (GHG) emissions and achieve sustainability goals.