Clarifying　the　provincial　spatial　correlation　network　of　municipal　solid　waste　(MSW)　carbon　emissions　and　its　influencing　factors　is　significant　for　reducing　MSW　carbon　emissions　and　for　ensuring　cross-regional　coordinated　emission　reduction.　This　study　used　a　modified　gravity　model　and　social　network　analysis　to　explore　the　structural　characteristics　of　the　spatial　correlation　network　of　MSW　carbon　emissions.　The　quadratic　assignment　procedure　was　then　used　to　examine　the　influencing　factors.　The　results　are　as　follows:　(1)　From　2005　to　2020,　the　MSW　carbon　emissions　spatial　correlation　network　showed　an　intuitive　and　complex　network　structure.　The　tightness　of　the　network　increased　with　fluctuations　but　still　needs　improvement,　while　the　network　structure　exhibited　good　accessibility　and　stability.　(2)　The　spatial　network　exhibited　a　significant　＂core-edge＂　distribution,　with　Beijing,　Tianjin,　Shanghai,　Jiangsu,　Zhejiang,　and　Fujian　being　at　the　center　of　the　spatial　correlation　network　and　playing　leading　roles.　(3)　The　spatial　correlation　network　of　MSW　carbon　emissions　was　divided　into　four　parts,　with　the　four　parts　having　few　internal　connections　but　with　the　relationships　between　them　being　close.　(4)　Geographical　adjacency,　economic　development　level,　science　and　technology　level,　and　MSW　treatment　structure　significantly　influenced　spatial　correlations,　while　the　urbanization　level　and　degree　of　government　intervention　had　no　significant　influence　on　spatial　correlations.