Robotic　assembly　lines　are　widely　applied　in　the　manufacturing　sector　to　produce　a　wide　range　of　products　because　of　their　efficiency　and　multifunctionality.　The　robotic　assembly　line　balancing　problem　(RALBP)　is　a　combinatorial　optimization　problem　where　the　decision　variables　are　task　assignment　and　robot　allocation.　However,　RALBP　considering　carbon　footprint,　which　is　a　very　significant　environmental　concern,　has　scarcely　been　studied　in　the　literature　and　a　practical　＇cross-station＇　design　is　never　mathematically　formulated.　In　this　paper,　a　mixed-integer　programming　model　is　proposed　to　optimize　the　two　objectives　according　to　the　Pareto　principle.　A　particle　swarm　algorithm　(PSO)　with　some　improvement　rules　is　designed　to　solve　the　problem.　To　examine　the　efficiency　of　the　algorithm,　computational　experiments　including　five　medium-sized　and　five　large-sized　datasets　are　conducted.　The　results　show　that　the　efficiency　of　PSO　is　better　than　that　of　four　other　classic　algorithms　in　terms　of　three　evaluation　metrics.　Further,　the　production　manager　and　assembly　line　designer　can　choose　the　appropriate　production　plan　and　upgrade　the　line　configuration.　©　2024　Elsevier　Ltd