Predicting　the　cost　of　environmental　governance　is　an　essential　process　in　environmental　protection.　However,　the　existing　cost　prediction　methods　face　several　challenges,　including　the　necessity　of　considering　the　causality　of　environmental　governance,　the　importance　of　distinguishing　environmental　indicators,　and　the　difficulty　of　collecting　environmental　data.　In　order　to　address　these　challenges,　a　novel　rule-based　system,　called　the　extended　belief　rule-based　(EBRB)　system,　is　first　introduced　to　establish　the　basic　framework　of　cost　prediction.　Then,　a　combination　of　structure　learning　and　parameter　learning,　or　joint　learning,　is　developed　to　improve　the　performance　of　the　EBRB　system.　Finally,　a　new　cost　prediction　method　based　on　the　improved　EBRB　system　is　proposed　for　environmental　governance.　To　verify　the　effectiveness　of　the　new　cost　prediction　method,　an　experimental　study　is　carried　out　to　compare　the　predicted　cost　of　environmental　governance　in　29　provinces　of　China.　The　comparative　analyses　demonstrate　that　the　new　cost　prediction　method　can　not　only　provide　a　desired　level　of　accuracy,　but　also　exhibit　excellent　robustness　that　makes　it　better　than　some　existing　cost　prediction　methods.