Abstract: In order to promote the civil aviation industry to win the battle of energy conservation and emission reduction and promote the high-quality development of green and low-carbon transportation, based on the dual units of domestic and international routes, the IPCC inventory algorithm was used to analyze the emission characteristics of multiple greenhouse gases (GHGs) such as CO₂, N₂O, and CH₄ from energy consumption in China’s civil aviation industry from 2015 to 2022 and identify the key emission sources. The LMDI decomposition model was used to empirically analyze the differential effects of energy consumption intensity, transportation economic benefit, internal and overall industrial structure, economic development level, and population size level on carbon emissions. The results showed that from 2015 to 2022, the GHG emissions showed an overall trend of first increasing and then decreasing. From the perspective of route types, the dominant position of revenue tonne-kilometers (RTK) or revenue passenger-kilometers (RPK) of domestic routes drived high-level GHGs emissions. From the perspective of gas species, CO₂ emissions contributed the most. The effect of energy consumption intensity was an important factor in curbing carbon emissions. The impact of transportation economic benefit varied with the level of transportation income and had become a key factor in slowing down the growth of carbon emissions since 2021. The internal structural effect of the industry was relatively synchronized with the change of carbon emission, and the main reason was that the increase in the proportion of output value of the civil aviation industry would drive an increase in the total consumption of transportation energy. The overall structure effect of the industry changed from an incentive factor of carbon emissions to an inhibitory factor, which was consistent with the changing trend of the proportion of the tertiary industry. The effect of economic development level was a key factor in promoting the growth of carbon emissions. The population size level showed a positive driving effect through the combined effect of population size and age structure.