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.