Toward harmonized aviation emission policies: Forecasting CO2 correlation structures across G10 countries

This study investigates the correlation structure of CO2 emissions from domestic air travel across developed markets. We use a forecasting model based on Principal Component Analysis (PCA) to improve the accuracy of predicting these emission correlations. The model leverages the dominant eigenstructure of the correlation matrix to capture systematic variation and generate forward-looking estimates. Empirical findings demonstrate that the PCA-based model significantly outperforms two widely used benchmark approaches (the Martingale model and the Dynamic Conditional Correlation (DCC) model) in terms of predictive accuracy and stability. Our model provides more precise and timely forecasts of cross-country emission co-movements, particularly during periods of market volatility. The proposed correlation index serves as a valuable policy tool for assessing the synchronicity of domestic aviation emissions, offering practical guidance for the implementation of coordinated carbon reduction strategies. By capturing interdependencies in national emission behaviors, this research contributes to more informed climate governance and supports the development of globally consistent tourism policies aimed at mitigating aviation's environmental impact.