The　urbanization　and　expansion　of　megalopolises　have　led　to　concerns　on　traffic,　energy　crisis　and　deteriorated　green-house　gas　emissions,　and　thus　the　electric　vehicles　(EVs)　are　expected　to　be　an　essential　role　in　alleviating　these　problems.　In　this　study,　a　flexible-possibilist　chanced　constraints　programming　(FCCP)　model　is　developed　to　plan　low-carbon　energy-transportation　systems　at　the　metropolitan　scale　(METS),　which　can　incorporate　multiple　uncertainties　in　both　the　soft　constraints　and　objective　function.　By　integrating　the　possibilist　programming　with　fuzzy　sets　and　chanced　constraint,　the　FCCP　could　tackle　multiple　complexities　such　as　the　combination　of　vague　possibilities,　flexibilities　and　probabilities,　hence　is　superior　to　conventional　approaches.　The　FCCP　model　is　then　applied　for　the　planning　METS　in　Beijing,　and　solutions　are　obtained　under　different　satisfactory　degrees　and　confidence　levels.　The　results　reveal　that:　1)　the　power　demand　will　be　increasingly　dependent　on　the　imported　power　and　renewable　energy　in　Beijing;　2)　the　mass　roll-out　of　EVs　will　reduce　6.7　million　tonnes　of　CH,　44.7　million　tonnes　of　CO　and　1.08　x　105　million　tonnes　of　CO2　respectively,　while　the　need　of　battery　supply　facilities　will　cost　approximately　4　x　109　dollars;　3)　the　carbon　emissions　will　decrease　with　the　growing　number　of　EVs,　the　upgraded　power　supply　pattern　and　the　stringent　policies.　These　findings　could　support　decision-makers　to　plan　the　METS　system　when　faced　with　multiple　uncertainties.　(c)　2022　Published　by　Elsevier　Ltd.