Air passenger carbon offset and carbon neutrality strategies: Implementation mechanism by convolutional neural...

To more effectively address the issue of carbon emissions in the aviation industry, this study first analyzes the current development status of carbon offset and carbon neutrality strategies in the aviation industry, as well as examines the existing relevant research findings. Then, optimizations are made to the Convolutional Neural Network to improve the accuracy and efficiency of the prediction model. These optimizations include architectural improvements, the use of attention mechanisms to more accurately focus on important features, as well as the adoption of multiscale feature extraction and advanced optimization algorithms to enhance the model's learning ability and convergence speed. These comprehensive improvements not only enhance the model's generalization ability but also significantly improve its applicability in complex environments. Finally, by comparing the performance of Transformer Networks, Graph Convolutional Networks, Capsule Networks, Generative Adversarial Networks, Temporal Convolutional Networks, and the proposed optimization algorithm on datasets of airline carbon emissions and fuel usage, the performance of the proposed optimization algorithm is validated through comparison of accuracy, precision, recall, and F1-score calculated from the data. Simultaneously, simulation experiments are conducted to validate the effectiveness and feasibility of the proposed optimization algorithm by comparing prediction stability, strategy adaptability, response time, and long-term effectiveness. The experimental results show that the accuracy, precision, recall, and F1-score of the proposed optimized model reach up to 0.942, 0.967, 0.951, and 0.934 respectively, all higher than those of the compared models, validating the good performance of the proposed optimized model. In the comparison of simulation experiments, the scores of prediction stability and strategy adaptability of the proposed optimized model reach up to 0.944 and 0.953 respectively, much higher than those of other models. The response time is only 0.04s when the data volume is 1000, and the computational advantage of the proposed optimized model becomes more apparent with the increase in data volume. In the comparison of long-term effectiveness, the advantage of the proposed optimized model in this aspect also becomes more obvious with the increase in data volume. Through simulation experiments, the performance of the model in actual application scenarios is further evaluated to ensure its practicability. Therefore, this study not only provides a new optimization tool for carbon emission strategies in the aviation industry but also has certain significance for research on environmental sustainability.