PurposeThe　growing　demand　for　rare-earth　elements　has　led　to　interest　in　assessing　the　environmental　impacts　of　their　production.　However,　most　existing　procedures　only　consider　a　small　part　of　the　life　cycle　of　rare-earth　elements.　Therefore,　this　study　proposed　an　allocation　model　for　the　resource　consumption　of　multi-output　products　in　hydrometallurgical　systems　based　on　input-output　physicochemical　parameters.MethodsThe　basic　methodological　idea　of　this　study　is　that　resource　inputs　to　a　multi-output　hydrometallurgical　process　should　be　allocated　based　on　how　they　are　consumed　in　the　process.　The　resources　consumed　by　the　multi-output　hydrometallurgical　process　were　analyzed　according　to　the　form　of　consumption.　Then,　the　basis　for　the　allocation　of　the　various　types　of　resource　consumption　was　determined　by　the　physicochemical　reaction　between　resource　input　and　metal　output.　The　allocation　model　was　applied　to　the　production　of　ion　adsorption-type　rare-earth　oxides,　and　the　results　of　the　allocation　were　compared　with　the　conventional　mass　allocation　method.Results　and　discussionThe　results　indicated　that,　whether　the　allocation　results　were　obtained　based　on　the　physicochemical　reaction　model　or　the　mass　model,　resources　consumed　of　Y2O3　accounted　for　the　largest　proportion,　with　differences　of　4.81-6.01%　between　the　two　allocation　results.　In　addition,　the　allocation　factor　for　Nd2O3　production　ranged　from　17-18%,　which　was　approximately　1.5%　lower　than　the　allocation　factor　based　on　the　mass　model.　The　assignment　results,　except　for　Y2O3,　were　higher　when　based　on　the　mass　model　than　when　based　on　the　physicochemical　reaction　model　for　all　eight　environmental　impact　types.　The　smallest　changes　were　observed　in　La2O3　(3.75-5.74%)　and　Nd2O3　(7.72-9.13%).　The　largest　change　was　observed　for　Er2O3　(15.02-20.43%).ConclusionsIn　a　multi-input/output　system,　the　allocation　of　life　cycle　inventory　for　single　rare-earth　oxides　or　metals　has　an　important　impact　on　the　accuracy　of　the　environmental　impact　assessment　results.　Data　deviations　in　the　life　cycle　inventory　of　a　certain　production　process　may　be　magnified,　impacting　decision-making　results.　The　model　can　help　solve　the　problem　of　co-occurring　metal　inventory　allocation　based　on　physicochemical　parameters　and　improve　the　accuracy　of　rare-earth　product　inventories.