Selecting　the　right　renewable　energy　resources　(RESs)　is　emerging　as　a　solution　to　alleviate　energy　crisis　and　environmental　pollution.　Due　to　the　limitation　of　human　knowledge　and　the　complexity　of　reality,　the　selection　process　usually　involves　multiple　uncertainties.　In　this　paper,　an　interval　type-2　fuzzy　evidential　reasoning　approach　is　proposed　to　solve　the　RESs　evaluation　problem　with　uncertain　information.　First,　the　linguistic　terms　involved　in　the　RESs　evaluation　process　are　encoded　into　the　interval　type-2　fuzzy　sets　(IT2FSs).　Second,　a　new　interval　type-2　fuzzy　distance　model　is　developed　to　measure　the　distance　between　the　IT2FSs.　After　obtaining　the　distance,　two　new　information　transformation　techniques　are　respectively　defined　to　transform　the　IT2FSs　and　the　crisp　numbers　into　the　interval　belief　structures.　Then,　an　interval　type-2　fuzzy　entropy　measure　is　proposed　to　determine　the　weights　of　attributes　and　the　corresponding　axioms　are　proved　mathematically.　Finally,　the　interval　expected　utility　of　each　alternative　is　generated　by　a　pair　of　nonlinear　optimization　models　and　then　ranked　by　an　enhanced　minimax　regret　approach.　A　case　study　about　the　RESs　evaluation　is　provided　to　illustrate　the　effectiveness　of　the　proposed　approach,　comparisons　and　discussions　are　also　conducted　to　show　the　superiority.　©　2021　Elsevier　Inc.