Purpose　The　purpose　of　this　paper　is　to　solve　new　pricing　issues　faced　by　low-carbon　companies　in　the　Yellow　River　Basin,　which　is　caused　by　the　change　of　key　pricing　factors　in　the　mixed　appliance　background　of　Big　Data　and　blockchain,　such　as　product　quality　and　carbon-emission　reduction　CER　level　(hereafter,　CER　level).　Design/methodology/approach　We　choose　a　low-carbon　supply　chain　with　a　low-carbon　manufacturer　and　a　retailer　as　our　research　object.　Then,　we　propose　that　using　the　ineffective　effect　of　the　CER　level　and　the　quality　and　safety　level　to　reflect　the　relationships　among　the　CER　level,　the　quality　and　safety　level　and　the　market　demand　is　more　suitable　in　the　new　environment.　Based　on　these,　we　revise　the　demand　equation.　Afterwards,　by　using　Stackelberg　game,　four　cost-sharing　situations　and　their　pricing　rules　are　analyzed.　Findings　Results　indicated　that　in　the　four　cost-sharing　situations,　the　change　trends　and　the　magnitudes　of　the　best　retail　prices　were　not　affected　by　the　changes　of　the　inputs　of　the　demand　information　and　the　traceability　services　costs　(hereafter,　DITS　costs),　the　proportion　about　retailer＇s　DITS　costs　undertaken　by　the　manufacturer,　the　ineffective　effect　coefficient　of　the　CER　level　and　the　quality　and　safety　level　and　the　cost　optimization　coefficient.　However,　the　cost-sharing　situations　could　affect　the　change　magnitudes　of　the　best　revenues.　Originality/value　This　paper　has　two　main　contributions.　First,　this　paper　proposes　a　demand　function　that　is　more　suitable　for　the　mixed　appliance　background　of　Big　Data　and　blockchain.　Secondly,　this　paper　improves　the　cost-sharing　model　and　finds　that　demand　information　sharing　and　traceability　service　sharing　have　different　impacts　on　key　pricing　factors　of　low-carbon　product.　In　addition,　this　research　provides　a　theoretical　reference　for　low-carbon　supply　chain　members　to　formulate　pricing　strategies　in　the　new　background.