Environmental　investment　prediction　has　attracted　much　attention　in　the　last　few　years.　However,　there　are　still　great　challenges　in　investment　prediction　modeling,　e.g.,　1)　effective　environmental　indicators　must　be　accurately　selected　to　avoid　the　curse　of　dimensionality;　2)　effective　environmental　data　must　be　reasonably　selected　to　downsize　the　scale　of　historical　data;　3)　the　higher　interpretability　and　lower　complexity　of　prediction　models　must　be　considered.　To　address　the　above　three　challenges,　a　new　environmental　investment　prediction　model　using　fuzzy　rule-based　system　(FRBS),　evidential　reasoning　(ER)　approach,　and　subtractive　clustering　(SC)　algorithm　is　proposed　in　the　present　work,　called　FRBS-ERSC.　In　this　new　model,　the　FRBS　is　the　core　component　for　the　modeling　of　environmental　investment　prediction　and　therefore　provides　good　interpretability　and　complexity　to　environmental　managers.　Meanwhile,　the　ER　approach　is　used　as　an　improvement　technique　of　the　FRBS　to　combine　the　strengths　of　different　feature　selection　methods　for　better　indicator　selection,　and　the　SC　algorithm　is　used　as　another　improvement　technique　of　the　FRBS　to　select　effective　environmental　data.　An　empirical　case　of　environmental　investment　prediction　is　studied　based　on　data　on　31　provinces　in　China　ranged　from　2005　to　2018.　The　experimental　results　show　that　the　proposed　FRBS-ERSC　not　only　provides　interpretable　and　scalable　environmental　investment　prediction　based　on　effective　indicator　selection　and　data　selection,　but　also　produces　satisfactory　accuracy　compared　to　some　existing　models.　(c)　2021　Elsevier　B.V.　All　rights　reserved.