Abstract: [Purposes] This paper aims to reveal the influencing factors of carbon emissions in the Yangtze River Delta region and improve the accuracy of carbon emission prediction. [Methods] This paper constructs a spatio-temporal dataset of carbon emissions in the Yangtze River Delta based on multi-source data from 2010 to 2023.The characteristics of land use change are extracted by using CLCD images,and the ARIMA-Transformer dynamic weighted hybrid prediction model is constructed to predict the carbon emission trend.The stability of the model is tested through cross-validation of the rolling window.Meanwhile,regression analysis is adopted to explore the impact of land use change and other factors on carbon emissions. [Findings] The prediction accuracy of the hybrid model is superior to that of the single model.The R² of the test set is 0.88 and the MAPE is 4.2%,which shows good stability and generalization ability.The forecast results show that the growth rate of carbon emissions in the Yangtze River Delta region has slowed down.Among them,the expansion of impermeable surfaces significantly promotes the growth of carbon emissions,while the expansion of forest land to a certain extent restrains the increase of carbon emissions. [Conclusions] Urban spatial expansion is the main driving factor for the growth of carbon emissions in the Yangtze River Delta.Optimizing the land use structure and promoting the construction of green cities are of great significance for the regional low-carbon transformation.

Key words: the Yangtze River Delta region, carbon emissions, ARIMA-Transformer hybrid model, spatio-temporal prediction, land use