At　the　2020　United　Nations　Climate　Summit,　China　officially　announced　the　goal　to　achieve　carbon　peaking　by　2030.　Exploring　whether　it　is　possible　to　reach　the　peak　of　carbon　emissions　earlier　necessitates　an　urgent　and　imperative　need　for　precise　long-term　forecasting　of　China＇s　carbon　emissions　dynamics.　However,　the　current　carbon　peaking　predictions　mostly　depend　on　mechanical　or　mathematical　models,　which　failed　to　consider　the　interdependence　between　carbon　emissions　and　the　time　series-based　patterns　existed　in　carbon　emission　data.　Therefore,　this　study　presents　a　novel　carbon　peaking　prediction　method　based　on　the　data-driven　rule-base　model,　which　is　implemented　by　the　adaption　of　the　extended　belief　rule　base　(EBRB)　model　for　time　series　forecasting　(TSF),　and　thus　the　proposed　method　is　referred　to　as　TSF-EBRB　model.　The　TSF-EBRB　model　not　only　captures　and　measures　the　temporal　correlations　within　the　data　throughout　the　processes　of　modeling　and　inference,　but　also　consists　of　a　novel　parameter　optimization　model　based　on　the　temporal　correlations.　The　study　collected　carbon　emission　data　from　30　provinces　in　China　for　empirical　analysis.　It　computed　and　predicted　the　carbon　peaking　trajectories　of　each　province　under　three　different　scenarios　from　2022　to　2030,　validating　the　effectiveness　and　superiority　of　the　TSF-EBRB　model　better　than　other　existing　carbon　peaking　prediction　methods.　The　results　indicated　that,　with　policy　interventions,　the　majority　of　provinces　are　projected　to　reach　carbon　peaking　before　2030.