A　scientific　environmental　investment　prediction　plays　a　crucial　role　in　controlling　environmental　pollution　and　avoiding　the　blind　investment　of　environmental　management.　However　effective　environmental　investment　prediction　usually　has　to　fact　three　challenges　about　diversiform　indicators,　insufficient　data,　and　the　reliability　of　prediction　models.　In　the　present　study,　a　new　prediction　model　is　proposed　using　the　extended　belief　rule-based　system　(EBRBS)　and　evidential　reasoning　(ER)　rule,　called　ensemble　EBRBS　model,　with　the　aim　to　overcome　the　above　challenges　for　better　environmental　investment　prediction.　The　proposed　ensemble　EBRBS　model　consists　of　two　components:　1)　multiple　EBRBSs,　which　are　constructed　on　the　basis　of　not　only　using　various　feature　selection　methods　to　select　representative　indicators　but　also　data　increment　transformation　to　enrich　the　training　data;　2)　an　ER　rule-based　combination　method,　which　utilizes　the　ER　rule　to　accommodate　the　weights　and　reliabilities　of　different　EBRBSs　with　the　predicted　outputs　of　these　EBRBSs　to　have　an　integrated　environmental　investment　prediction.　A　detailed　case　study　is　then　provided　for　validating　the　proposed　model　via　extensive　experimental　and　comparison　analysis　based　on　the　real-world　environmental　data　about　25　environmental　indicators　for　31　provinces　in　China　ranged　from　2005　to　2018.　The　results　demonstrate　that　the　ensemble　EBRBS　model　can　be　used　as　an　effective　model　to　accurately　predict　environmental　investments.　More　importantly,　the　ensemble　EBRBS　model　not　only　obtains　a　high　accuracy　better　than　some　existing　prediction　models,　but　also　has　an　excellent　robustness　compared　with　others　under　the　situations　of　excessive　indicators　and　insufficient　data.　©　2020　Elsevier　Ltd