Abstract: Airports are the primary venues for aircraft operations during the Landing and Take-off (LTO) phase and constitute a major component of aviation’s overall carbon emissions. By combining the International Civil Aviation Organization (ICAO) standard emission model with an emission inventory compiled according to the proportion of aircraft takeoff and landing operations at airports, this study calculates a long-term series of carbon emissions for all operating airports in China (excluding Hong Kong, Macao, and Taiwan) from 2005 to 2022. Using the Kernel Density Analysis tool in ArcGIS 10.6, the study identifies the agglomeration characteristics and evolutionary trends of airport carbon emissions within the jurisdictions of China’s seven air traffic control regions. Furthermore, by integrating the Stochastic Impacts by regression on Population, Affluence, and Technology model with a spatial panel regression model, the influencing factors and spatial effects of airport carbon emissions in China are examined. The findings are as follows. 1) From 2005 to 2022, carbon emissions from China’s civil aviation airports generally showed a trend of steady growth followed by fluctuating decline; specifically, it increased at an average annual rate of 9.01% from 2005 to 2019 and decreased at an average annual rate of 11.27% from 2020 to 2022. Among China’s seven air traffic control regions, the growth rate of airport carbon emissions was more significant in the Southwest Region, Xinjiang Region, Northwest Region, and Northeast Region, while the growth was relatively slow in the three traditional hub airport-concentrated regions (Northern Region, Eastern Region, and Central and Southern Region). 2) With 2020 as the dividing line, the proportion of Level I and Level II airports in terms of carbon emissions among the seven air traffic control regions first decreased and then increased, while the proportion of Level III, Level IV, and Level V airports first increased and then decreased; Level I airports had the largest distribution quantity, and Level V airports had the smallest quantity. 3) The number of medium-high-density core areas of airport carbon emissions in China increased from 8 to 14, with a relatively stable agglomeration trend. These core areas are mainly distributed east and south of the Hu Huanyong Line, exhibiting a distinct pattern of scattered point distribution. 4) Six indicators exert a positive impact on airport carbon emissions in a region, namely urban population density, air passenger traffic by region, per capita GDP, per capita social consumer goods retail sales, per capita disposable income, and regional international tourism income. In contrast, five indicators exert a negative impact, including the unemployment rate, regional general public budget expenditure, regional general science and technology budget expenditure, R&D expenditure, and number of patent applications. The number of regular undergraduate and junior college enrollments by region generates a significant positive spatial spillover effect on airport carbon emissions in adjacent regions. Meanwhile, three indicators, air passenger traffic by region, per capita social consumer goods retail sales and foreign trade dependency ratio, produce a significant negative spatial spillover effect in this regard. This study breaks through the limitation of solely using the ICAO model for research, comprehensively presents the trends and spatiotemporal heterogeneity of China’s airport carbon emissions over a medium and long time period, and conducts a comparative analysis of the differences before and after the epidemic. It has theoretical guiding significance for comprehensively grasping the overall trend of aviation carbon emissions and positive practical significance for promoting the green development of the civil aviation industry.