QAR-Based Modeling of Aircraft Ground-Taxi Fuel Consumption and CO2 Emissions: Determinants, Prediction and Operational Implications

The airport ground taxiing phase is identified as a significant contributor to fuel consumption and carbon emissions within the aviation transport system. However, refined management and research remain challenged by data gaps and model imprecision. A research framework is proposed, combining high-precision Quick Access Recorder (QAR) data, statistical analysis, and machine learning. Main-engine fuel consumption differences across aircraft types, airports, and taxi-in and taxi-out operations were analyzed to identify the principal determinants of taxiing fuel consumption. A direct taxi-time–fuel baseline and an Adaptive Mechanism Learning (AML) model were constructed to predict aircraft taxiing fuel consumption. The AML model achieved a test-set coefficient of determination (R2) of 0.9672, while aircraft-specific R2 values exceeded 0.97 for A320, A350, and B777 aircraft. The results highlight the importance of taxiing duration and engine operating state in estimating main-engine fuel consumption and associated CO2 emissions. The findings provide a basis for refined fuel-consumption assessment and operational decision support for greener airport operations.