[Spatio-temporal Trajectory and Driving Factors of Carbon Emissions Based on Bayesian Hierarchical Spatio-temporal Model].

t in 2021， with the rate of increase in carbon emissions slowing down from 1.95% to 1.61%. The Wuhan urban agglomeration was the core area for carbon emissions， accounting for 47.48% of the total emissions. The difference in carbon emissions in the Poyang Lake urban agglomeration was the largest； the focus of carbon emissions changed from south to north. ② The carbon emissions of the urban agglomeration in the middle reaches of the Yangtze River exhibited significant spatial correlation， primarily manifesting as high-high or low-low clustering types， and displayed a spatial distribution characteristic of higher emissions in the west and lower emissions in the east. ③ The degree of influence of driving factors on carbon emissions was ranked as follows： urbanization rate>industrial structure>level of economic development>actual use of foreign capital>energy efficiency>expenditure on science and technology>total population. The positive impact of urbanization rate， economic development level， actual utilization of foreign capital， and energy efficiency on carbon emissions was gradually increasing， the positive impact of industrial structure on carbon emissions was gradually weakening， and the positive impact of science and technology expenditure and total population on carbon emissions was fluctuating. The local changes in carbon emissions in the study area were obviously different， and the overall performance was 'weak in the upper part and strong in the lower part'. The hot spots were mainly concentrated below the study area， the local trend of carbon emissions in the study area was obviously different， and the rapid growth area was mainly distributed in Wuhan urban agglomeration. The study results are significant for understanding the spatiotemporal characteristics of carbon emissions and their driving variables and hold important theoretical and practical implications for the subsequent application of Bayesian hierarchical spatiotemporal models in the field of carbon emissions.