Global greenhouse gas emissions have received widespread attention and the full ole life cycle carbon emissions of electric power equipment cannot be ignored. In this paper

the amorphous oil-immersed distribution transformer

a typical power equipment

is taken as the research object. Based on the life cycle assessment method

a full life cycle carbon footprint accounting model

including the stage of raw material acquisition

production

distribution

use and the end-of-life-cycle

is set up to analyze the contribution of the power structure

materials

energy

transportation to the carbon footprint of transformer. Moreover

a sensitivity analysis of the various inventory data is performed and the uncertainty of the carbon footprint of transformer is assessed by using the Taylor series expansion method. The results show that the carbon footprint of unit function amorphous oil-immersed transformer is 602 731 kgCO

2

e

of which the use stage accounts for as high as 99.45%. The high proportion of carbon footprint in this stage is determined by the transformer's power loss and power structure. With the clean development of power structure

it is p

redicted that the carbon footprint in the use stage can be reduced by about 65.39%. In addition to the analysis of the carbon footprint results in the use stage

it can be seen that the reduction of raw materials or the use of low carbon material

improvement of power utilization efficiency and replacement of electric vehicle transport are the key points to reduce the carbon footprint of the transformer. Moreover

it is concluded by sensitivity analysis that the recycling/substitution of materials is the priority for the low-carbon design of the transformer. The uncertainty of the carbon footprint results is expressed by the relative geometric standard deviation at 95% confidence level

i.e.

the

GSD

is 1.064

which is a credible result of the carbon footprint results. The uncertainty mainly originates from the database carbon footprint factor

which indicates the direction for further optimization of the model data quality.