Real-time logistics transport emission monitoring-Integrating artificial intelligence and internet of things

The lack of a globally recognized measurement technique combined with a limited ability to comprehend the actual level of GHG emissions in intricate logistics operations causes significant obstacles for firms in assessing the magnitude of their environmental footprint. Nevertheless, linking, upkeeping, and managing gas detectors on mobile vehicles under varying road and weather circumstances present an expensive solution for predicting GHG emissions. This article presents the development and evaluation of a reliable and accurate real-time technique for capturing GHG emissions using the Internet of Things (IoT) and Artificial Intelligence (AI). The findings indicate that the integration of gradient-boosting models (LightGBM, xGBoost, and gradient-boosting decision trees) via ensemble learning enhances the precision of CO2 emission predictions. The weighted ensemble method attains an RMSE of 1.8625, surpassing the performance of individual models. Visualizations validated a robust correlation between anticipated and actual CO2 concentrations, illustrating the model's precision and negligible prediction errors.