Abstract: The carbon footprint depth index represents the extent of the consumption of regional stock capital. It is of great significance for regionally differentiated carbon emission control. The carbon footprint depth of the capital cities in the middle reaches of the Yangtze River was calculated by referring to the improved three-dimensional carbon footprint model. This paper used nighttime light data to fit the carbon footprint depth index. In addition, the spatio-temporal evolution and distribution characteristics of the carbon footprint depth of the capital cities in the middle reaches of the Yangtze River were analyzed using hot spot analysis and trend surface. The spatial quantile model was used to study the influencing factors of carbon footprint depth. The results shows that: (1) From 2010 to 2019, the carbon footprint depth indices of Wuhan, Nanchang and Changsha all showed an upward trend. In 2010, the normalized carbon footprint depth index of the capital cities in the middle reaches of the Yangtze River showed the characteristics of Wuhan>Nanchang>Changsha. In 2015 and 2019, the normalized carbon footprint depth index of the capital cities in the middle reaches of the Yangtze River was presented in the form of Wuhan>Changsha>Nanchang. The high value ranges of normalized carbon footprint depth in Wuhan, Nanchang and Changsha all expand from the central urban area of the city to the surrounding areas; (2) From 2010 to 2019, the normalized carbon footprint depth indices of Wuhan, Nanchang, and Changsha all clustered at high values at the 1% significance level. From the spatial trend plane, it can be seen that the normalized carbon footprint depth index of the capital cities in the middle reaches of the Yangtze River is "high in the middle—low on both sides" in the east—west direction. In the north—south direction, the distribution pattern developed from "high in the north and low in the south" to "low in the middle—high on both sides", and the north was significantly higher than the south. (3) Influencing factors such as population density, total industrial output value, total energy, and per capita carbon emissions all have positive effects on carbon footprint depth, and the correlation coefficients of each influencing factor at different quantiles of carbon footprint depth are significantly different. Wuhan, Nanchang, and Changsha put forward differentiated proposals respectively.