The formulation of carbon reduction policy is one of the important measures to reduce greenhouse gases. The STIRPAT model has been one of the main methods used to analyse the impact factors of carbon emissions in recent years. The variable parameters in this model include the impacts of the environment, population, GDP per capita, energy intensity, and urbanisation rate on carbon emissions from energy consumption [20]. In this study, the indicators in the model were replaced with anthropogenic source carbon emission variables, resident population, and carbon intensity to obtain the mathematical expression of the extended STIRPAT model.

where

Y

is the total amount of carbon emissions,

X
1

,

X
2

,

X
3

,

X
4

,

and
 

X
5

are the drivers of carbon emissions, representing the transport mode, carbon intensity, urbanisation level, energy mix, and industrial mix respectively, and

α
1

,

α
2

,

α
3

,

α
4

,

and
 

α
5

are the elasticity factors of the indicator, and

t

is the year of time. The proportion of factors influencing the level of carbon emissions varies from city to city, which in turn requires that the carbon emission analysis is tailored to the local and temporal context. The interactions among different driving factors can effectively reveal the dynamic pattern change of urban agglomeration carbon emission efficiency [21]. In this study, spatial correlation coefficients were introduced for the analysis of carbon emission efficiency, with Moran scatter plots representing the spatial correlation of the data, where the correlation and aggregation dynamics of the variable data are represented by the spatial distributivity and correlation analysis. The mathematical expression is shown in Equation (2).

where

I

 
and
 

I
i

are the spatial autocorrelation and local autocorrelation, respectively,

n

is the number of spatial cells,

x
i

and
 

x
y

are the carbon emissions of the city unit

i
,
 
j

and

x
¯

are the mean values of the variables,

W

i
j

is the contiguous space weight …