The　increasing　severity　of　global　climate　change　has　made　reductions　in　carbon　emissions　an　urgent　global　issue.　The　relative　lack　of　carbon　footprint　analyses　of　urban　public　transportation　systems　(UPTS)　is　therefore　surprising,　given　that　UPTS　is　an　important　component　of　urban　transportation　and　one　that　may　play　a　crucial　role　in　carbon　emission　reduction.　This　study　conducts　a　spatio-temporal　analysis　of　carbon　footprints　for　UPTS　during　the　COVID-19　pandemic　based　on　smart　card　data　in　Beijing.　Since　the　core　of　carbon　footprint　calculation　is　to　estimate　travellers＇　trip　trajectories　and　the　ridership　of　urban　rail　transit　(URT)　and　buses,　we　construct　a　novel　multi-layer　urban　rail　network　model　to　calculate　passenger　volume　and　travellers＇　trajectories　through　a　traffic　assignment　model.　Furthermore,　we　utilize　the　Generalized　Additive　Model　(GAM)　to　analyse　the　correlation　relationship　between　the　carbon　footprints　of　buses　and　URT.　Additionally,　we　conduct　statistical　analysis　of　the　carbon　footprint　of　UPTS.　The　results　of　the　spatio-temporal　analysis　of　carbon　footprints　for　UPTS　show　significantly　lower　carbon　emissions　during　holidays　compared　to　those　on　working　days,　and　emissions　during　peak　hours　contribute　approximately　half　of　the　total　daily　UPTS　emissions,　while　there　are　notable　variations　in　the　distribution　of　the　carbon　footprint　among　different　districts.　Moreover,　our　analysis　reveals　a　positive　correlation　between　the　carbon　footprints　of　buses　and　URT.　The　statistical　analysis　reflects　different　patterns　of　carbon　footprint　distribution　on　different　dates　during　the　pandemic,　but　the　carbon　footprint　distributions　on　selected　dates　all　follow　a　power-law　distribution.　This　study　may　facilitate　the　understanding　to　the　impacts　of　UPTS　on　the　environment　during　the　COVID-19　pandemic,　and　also　provide　important　guidance　and　reference　for　the　development　of　carbon　emission　reduction　strategies.