The reduction of carbon emissions by airlines has become a crucial objective in achieving carbon neutrality. However, due to the existing benefits game between airlines and local governments, reducing carbon emissions in the air transport industry is a complex process. Previous research on reducing carbon emissions in the Chinese air transport industry has been divided on which approach to take. This study investigates the potential contribution of a carbon trading mechanism towards carbon emission reduction. Firstly, evolutionary game theory is employed to analyze the complex interactions between local governments and airlines, with the public will being introduced as a constraint in the proposed model. Secondly, the impact of the carbon trading mechanism on the dynamic evolution process and stakeholders is analyzed. Furthermore, the stability of the proposed evolutionary game model is verified through empirical analysis. The results show that the carbon trading mechanism plays a significant positive role in promoting both parties’ objectives, indicating that it is a feasible method for reducing emissions in China’s air transport industry.

Most previous studies on airline fleet planning have focused solely on economic considerations, neglecting the impact of carbon reduction. This paper presents a novel method for green fleet planning, using a bi-level programming model to balance conflicts among stakeholders while considering uncertain parameters such as demand and operating costs. The upper model aims to reduce carbon emissions by taking into account government constraints, such as carbon allowances and carbon prices, as well as airline satisfaction. The lower model seeks to maximize airline revenue using a space-and-time network model based on given airline flight schedules. To verify the game model, a case study utilizing randomly generated scenarios is employed within the context of China’s aviation-specific emissions trading scheme. Results show that: (1) compared to the scenario without a policy aiming at reducing carbon emissions, this method reduces carbon emissions by 23.03% at the expense of a 6.96% reduction in terms of the airline’s operating profit; (2) when passenger demand levels increase to 160%, the profitability of the proposed fleet increases by 50.83%, while there were only insignificant changes in carbon emissions; (3) the proposed methodology can assist the airlines systematically to reduce carbon emissions and provide valuable strategic guidance for policy makers.

Traditional CCRMs (Constrained Center-and-Range Methods) in solving the problem of interval regression could hardly make tradeoffs between the overall fitting accuracy and the coincidence degree between the observed and predicted intervals and could also hardly reduce the number of disjoint elements between the observed and predicted intervals, as well as raise the average ratio of all predicted intervals contained within their observed intervals. This paper constructed a nonlinear regression model based on center-and-range method, in which the maximization of coincidence degree for the sample with the worst coincidence degree between the observed and predicted interval was incorporated into the traditional CCRM model’s objective. This novel nonlinear programming model was proven to be a convex one that satisfied K-T condition. Monte Carlo simulation shows that the model is degenerated to the compared CCRM+ model as the objective only contains the minimization of the overall fitting accuracy for both center and range sample series. In this situation, it could obtain a better solution than the use of the compared CCRM model. In addition, when the proposed model only takes into account the maximization of coincidence degree for the sample with the worst coincidence degree between the observed and predicted interval, the model shows a better performance than the CCRM+ model in terms of the average ratio of all predicted intervals contained within their observed intervals, as well as the average number of forecasts with 0% accuracy.

With the rapid growth of the aviation industry, the issue of carbon emissions has become a substantial challenge for governments and airlines. This paper proposes a hybrid carbon emission reduction mechanism, including major airlines in the emission trading systems and implementing carbon tax for small and medium-sized airlines. First, a tripartite evolutionary game model is constructed to study strategic behaviors. Second, four scenarios of evolutionarily stable strategies (ESSs) are analyzed. Finally, the influencing parameters of players’ strategy choices are analyzed through simulations. The results show that: 1) the steady development scenarios (1, 1, 1) can be reached under the appropriate conditions; 2) the parameters such as carbon allowances and carbon tax prices significantly influence the evolutionary trend of stakeholders’ dynamic choices; 3) the implementation of a hybrid mechanism by the government could facilitate the choice of low carbon operation strategies for both types of airlines. Accordingly, a series of policy recommendations are proposed to promote carbon emission reduction in civil aviation. This study combines evolutionary game and scenario analysis methods in an attempt to provide a new perspective on carbon emission reduction governance, thereby promoting the effective development of carbon emission reduction in civil aviation in the future.

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