Carbon emission calculation is a fundamental prerequisite for achieving dual carbon goals and promoting green economic transformation. Currently

carbon emission calculation in the power system primarily focuses on the transmission network. However

measurement information in the distribution network is significantly less than that in the transmission network

posing an urgent challenge in calculating carbon emissions based on limited measurement information available in the distribution network. A carbon emission factor calculation model for the distribution network based on the graph convolutional network (GCN) is proposed. Firstly

based on the topological connection relationship and measurement data of each node in the distribution network

an adjacency matrix representing the topological relationship and an input matrix representing the electrical and carbon information of nodes are constructed. Secondly

by training GCN to fit and learn the carbon emission factor calculation function

the mapping relationship between carbon emission factors and electrical carbon information is established

thereby calculating carbon emission factors at each node in the distribution network. The calculation results show that the mean absolute error of the carbon emission factor calculation model proposed in this paper is about 0.0342 kgCO2/(kW•h)

and the mean absolute percentage error is about 9.55 %

which verifies the accuracy of the model. Based on the characteristics of carbon emission flow in the distribution network

the nodes with large errors are corrected

and the overall mean absolute error of the model is reduced by about 1.54%. Finally

the applicability of the model under network topology changes is studied.