Research on flight emissions at airports is very important for environmental policymaking. This study analysed the trend of aircraft air pollutant emissions at mainland China airports from 1990 to 2017, mainly focusing on standard landing and take-off (LTO) cycles. Total flight movements increased 29-fold from 1990 to 2017 at Chinese airports. Over the same period, the emissions of NO_x, SO_x, CO, hydrocarbons (HC), and particulate matter (PM) increased 46, 27, 12, 5, and 4 times, respectively. Emissions at the 216 public airports showed a growth trend. It was estimated that in 2017, Chinese airports emitted 12,875 kilotons of CO₂, 59 kilotons of NO_x, 3 kilotons of SO_x, 38 kilotons of CO, 5 kilotons of HC, and 0.4 kilotons of PM. The largest 30 airports produced 68.2% of the total emissions. Emissions from B737-800, A320, A321, B737-700, A330-300, and A319 aircraft accounted for more than 75% of aircraft LTO emissions at Chinese airports in 2017. Results show that average emissions per passenger have decreased for CO₂, CO, HC, SO_x, PM, and NO_xfrom 1990 to 2017.

Carbon emission intensity is an important index reflecting an entity’s low-carbon competitiveness. This paper presents an extended logarithmic mean divisia index (LMDI) model to dissect carbon intensity within China’s civil aviation from 1998 to 2019, revealing a significant reduction in CO₂ emissions per air transport revenue. It attributes this decrease to technological advancements, optimized fleet structures, and improved operational efficiencies, highlighting the impact of larger, more efficient aircraft and enhanced load factors. The study also explores economic factors influencing carbon efficiency, suggesting a comprehensive approach encompassing technological innovation and strategic operational improvements for sustainable aviation development.

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.