The　dispersion　of　PM2.5-related　pollutants　in　the　atmosphere　is　intricately　connected　to　the　natural　geographical　environment.　Nevertheless,　previous　evaluations　of　environmental　policies　regarding　urban　PM2.5　concentrations　have　rarely　taken　into　account　the　influence　of　the　natural　geographical　environment.　Utilizing　panel　data　and　DID　models　for　266　prefecture-level　cities　in　China　from　2006　to　2019,　this　study　thoroughly　examines　the　impact　of　geographical　environment　on　the　China＇s　Carbon　Emissions　Trading　Scheme　(ETS)　in　reducing　urban　PM2.5　concentrations.　The　results　suggest　that　(1)　Neglecting　the　impact　of　natural　geographical　environment,　although　the　ETS　effectively　reduces　the　PM2.5　concentration　in　the　pilot　cities　from　an　overall　perspective,　there　are　anomalies　in　the　robustness　test　and　the　heterogeneity　analysis,　which　are　mainly　reflected　in　the　decrease　in　the　significance　of　the　PSM-DID　results,　and　the　positive　haze-enhancement　effect　of　the　ETSs　of　Fujian,　Beijing,　Shanghai,　etc.　(2)　Considering　the　influence　of　geographic　environment,　combined　with　the　spatial　correlation　between　China＇s　topographic　distribution　and　PM2.5　concentration,　four　control　groups　of　Basin,　Coastal,　etc.　are　screened　for　group　regression,　and　the　results　show　that　geographic　environment　factors　significantly　affect　the　emission　reduction　effect　of　each　ETS　on　PM2.5　concentration.　(3)　The　regression　results　of　the　three　types　of　spatial　econometric　models　all　indicate　that　there　is　a　significant　spatial　spillover　effect　of　ETS　to　reduce　PM2.5　concentration.　Considering　the　influence　of　geographic　environment　on　the　spatial　spillover　effect　and　the　fact　that　China＇s　provincial　division　is　mostly　based　on　geographic　environment　factors,　in　order　to　more　reasonably　assess　the　effect　of　the　policy　on　the　PM2.5　concentration,　the　researcher　should　select　sample　cities　with　no　spatial　correlation　of　PM2.5　and　clustering　robust　standard　errors　to　the　provincial　level.