The　embedded　carbon　emissions　produced　by　human　consumption　are　an　important　part　of　residential　carbon　emissions.　With　large　sample　data　and　high-resolution　remote　sensing　images,　we　explored　the　spatial　differentiation　and　influencing　factors　of　household　embedded　carbon　emissions　within　the　city　fine　scale　using　the　EIO-LCA　model　and　quantile　regression.　The　results　indicate　that　the　factors　of　family　characteristics,　housing　characteristics,　lifestyles,　and　consumption　concept　have　significant　effects　on　the　embedded　carbon　emissions　of　each　person.　The　influencing　intensity　of　most　factors　showed　an　increasing　trend　with　increased　carbon　emissions.　The　application　of　the　quantile　regression　(QR)　method　can　more　accurately　reveal　the　different　mechanisms　of　various　influencing　factors　under　different　distribution　functions　and　overcome　the　estimation　error　of　the　least　square　method　on　the　mean　level.　The　estimated　results　show　that　the　influence　strength　of　the　remaining　factors　on　embedded　carbon　emissions　per　capita　showed　a　trend　of　increase　with　the　increase　in　carbon　emissions,　but　the　influencing　strength　of　housing　types　and　frugal　purpose　for　saving　money　have　the　tendency　of　zigzag　fluctuation.　To　a　certain　extent,　the　result　emphasized　the　necessity　of　specific　emissions　reduction　measures　for　high　carbon　emissions　groups.　Copyright　(C)　2018　Elsevier　Ltd.　All　rights　reserved.