A　new　stepwise　regression　method　was　proposed　in　this　study　to　develop　a　high-resolution　emission　inventory.　Utilizing　PM10　emission　inventory　as　an　example,　a　group　of　regression　models　for　various　industrial　and　non-industrial　sectors　were　developed　based　on　an　emission　case　study　of　Handan　region　in　northern　China.　The　main　data　requirements　of　the　regression　models　for　industrial　sectors　were　coal　consumption,　electricity　consumption,　other　solid　fuel　consumption,　and　annual　operating　cost　of　exhaust　gas　control　devices.　The　data　requirements　for　non-industrial　sector　emission　estimations　were　the　area　of　construction　sites,　the　length　of　transportation　routes,　the　vehicle　population,　and　the　cultivated　land　area.　The　models　were　then　applied　to　Tangshan　region　in　northern　China,　and　the　results　revealed　that　the　developed　regression　models　had　relatively　satisfactory　performance.　Modeling　error　at　the　regional　level　and　county　level　was　17.0%　and　30.4%,　respectively.　The　regression　models　were　also　applied　to　other　regions　in　northern　China.　The　results　indicated　that　the　new　method　could　generate　emission　estimations　with　significantly　lower　error　than　found　in　previous　emission　inventory　studies.　The　modeling　uncertainty　due　to　the　allocation　of　modeling　input　parameter　value,　from　regional　level　to　county　level,　was　also　discussed　in　this　study.　It　was　concluded　that　the　new　statistical　method　presented　is　a　promising　technique　for　the　development　and　updating　of　high-resolution　emission　inventories　based　on　easily　obtained　statistical　data.　It　can　be　performed　with　data　available　from　the　current　statistical　reporting　system　in　China.　It　does　not　require　a　detailed　data　investigation　and　survey,　as　is　necessary　by　conventional　＂bottom-up＂　emission　inventory　investigation　approach.　(c)　2012　Elsevier　Ltd.　All　rights　reserved.