The　forecasting　of　carbon　price　plays　a　significant　role　in　gaining　insight　into　the　dynamics　of　carbon　market　around　the　world　and　assigning　quota　about　carbon　emissions.　Many　studies　have　shown　that　decomposing　the　original　data　into　several　components　with　similar　attributes　is　a　widely　accepted　method　addressing　highly　complex　data.　The　resulting　issue　is　that　the　high　complexity　of　some　components　obtained　is　still　tricky.　This　paper　develops　a　new　secondary　decomposition　strategy,　which　employs　the　complementary　ensemble　empirical　mode　decomposition　(CEEMD)　and　the　variational　mode　decomposition　(VMD)　to　decompose　the　original　series　and　the　acquired　intrinsic　mode　functions　(IMFs)　with　maximum　sample　entropy　value,　respectively.　All　components　are　forecasted,　including　these　generated　by　the　first　and　secondary　decomposition.　The　final　results　are　obtained　by　synthesizing　the　predictions　of　all　components.　The　experimental　study　states　clearly　that　the　established　approach　is　superior　to　all　benchmark　models　in　terms　of　multistep　horizons　forecasting,　and　can　provide　the　reliable　and　convincing　results.　(C)　2020　Elsevier　B.V.　All　rights　reserved.