Abstract: As an important strategic and demonstration area for national development, scientific monitoring and analysis of carbon emission changes and associate influencing factors is of great significance to the high-quality development of the Yangtze River Basin. In this paper, the long time series DMSP/OLS and NPP/VIIRS nighttime light datasets were constructed to estimate carbon emissions in the Yangtze River Basin. The spatial autocorrelation analysis and GTWR model were used to explore the spatio-temporal pattern and spatial heterogeneity of carbon intensity. The results showed that:(1)The goodness of fit of the two types of night-light data fusion models was 0.93, at the significance level of P<0.001, which met the accuracy requirements; The carbon emission estimation models for provinces had a fitting accuracy of 0.85 and an  average relative error of 14.54%, at the significance level of P<0.001, which met the requirements of estimation accuracy.(2)The global autocorrelation analysis found that the global Moran's I of the municipal-scale carbon emissions from 2000 to 2021 were all greater than 0, at significance level of 1%. The municipal-scale carbon intensity had a significant spatial positive correlation. (3) In the local autocorrelation analysis, a significant aggregation at the municipal scale was found to decrease from 51.82% in 2000 to 43.52% in 2021, with certain spatial aggregation of carbon intensity. (4) The regression results of the GTWR model showed that the influence of land use on carbon intensity was the largest and had positive effects. The influence of industrial structure was the second largest, the population density had a bidirectional influence, and the level of economic development had the lowest and negative effects. The results of the study showed that it was of great significance to give full play to the demographic and economic advantages of the Yangtze River Basin in terms of rational planning of land use, promotion of low-carbon transformation of industries and upgrading of the industrial structure, in order to encompass the ecological environment and to accelerate the realization of the goal of "dual-carbon".