Carbon　trading　is　a　vital　market　mechanism　to　achieve　carbon　emission　reduction.　The　accurate　prediction　of　the　carbon　price　is　conducive　to　the　effective　management　and　decision-making　of　the　carbon　trading　market.　However,　existing　research　on　carbon　price　forecasting　has　ignored　the　impacts　of　multiple　factors　on　the　carbon　price,　especially　climate　change.　This　study　proposes　a　text-based　framework　for　carbon　price　prediction　that　considers　the　impact　of　climate　change.　Textual　online　news　is　innovatively　employed　to　construct　a　climate-related　variable.　The　information　is　combined　with　other　variables　affecting　the　carbon　price　to　forecast　the　carbon　price,　using　a　long　short-term　memory　network　and　random　forest　model.　The　results　demonstrate　that　the　prediction　accuracy　of　the　carbon　price　in　the　Hubei　and　Guangdong　carbon　markets　is　enhanced　by　adding　the　textual　variable　that　measures　climate　change.　(c)　2022　Economic　Society　of　Australia,　Queensland.　Published　by　Elsevier　B.V.　All　rights　reserved.