Environmental　investment　prediction　is　an　effective　solution　to　reduce　the　wasteful　investments　of　environmental　management.　Since　environmental　management　involves　diverse　environmental　indicators,　investment　prediction　modeling　usually　causes　the　curse　of　dimensionality　and　uses　irrelevant　indicators.　A　common　solution　to　solve　these　problems　is　the　use　of　indicator　selection　methods　to　select　representative　indicators.　However,　different　indicator　selection　methods　have　their　relative　strengths　and　weaknesses,　resulting　in　different　selected　indicators　and　information　loss　of　real　representative　indicators.　Hence,　in　the　present　work,　a　new　environmental　investment　prediction　model　is　proposed　on　the　basis　of　extended　belief　rule-based　(EBRB)　model　along　with　the　indicator　ensemble　selection　(IES)　and　is　called　IES-EBRB　model.　The　EBRB　model　is　a　white-box　designed　decision-making　model　and　has　the　specialty　on　using　prior　knowledge　to　enhance　data　analytics　for　autonomous　decision　making;　and　the　IES　is　an　extension　of　ensemble　learning　to　cooperatively　integrate　different　kinds　of　indicator　selection　methods　for　selecting　representative　indicators.　In　a　case　study,　the　real　world　environment　data　from　2005　to　2018　of　31　provinces　in　China　are　applied　to　verify　the　effectiveness　and　accuracy　of　the　IES-EBRB　model.　Results　show　that　the　IES-EBRB　model　not　only　can　obtain　desired　environmental　investments,　but　also　produces　satisfactory　accuracy　compared　to　some　existing　investment　prediction　models.　©　2020　Elsevier　Inc.