Carbon　price—as　a　critical　component　in　the　functioning　of　carbon　market　mechanisms—plays　an　indispensable　role　in　policy　formulation,　market　development,　and　societal　progress.　Thus,　accurately　predicting　carbon　prices　is　of　paramount　importance.　This　study　aims　to　comprehensively　investigate　the　impact　of　unconventional　events　(e.g.,　political　conflicts　and　extreme　weather)　and　mixed-frequency　data　(e.g.,　daily　high-frequency　financial　information　and　monthly　low-frequency　macroeconomic　data)　on　carbon　price　forecasting;　to　this　end,　it　introduces　the　novel　Prophet-Backpropagation　Neural　Network-Reverse　(Unrestricted)　Mixed　Data　Sampling　model,　which　innovatively　integrates　the　following　three　key　advantages:　the　quantification　of　irregular　events　using　Prophet,　nonlinear　pattern　recognition　through　a　back-propagation　neural　network,　and　frequency　alignment　via　reverse　mixed　data　sampling.　Applied　to　daily　carbon　price　prediction　in　the　Hubei　carbon　market,　this　model　is　statistically　validated　to　significantly　outperform　other　models,　as　demonstrated　by　the　Diebold-Mariano　test.　This　study＇s　results　underscore　the　model＇s　superior　predictive　capability　and　elucidate　the　key　drivers　of　carbon　prices　and　their　nonlinear　impact　mechanisms.　©　2025　Elsevier　Ltd