Developing　low-carbon　logistics　requires　an　understanding　of　the　driving　factors　for　carbon　emissions.　We　employed　the　Comparative　Study　on　Urban　Transport　and　the　Environment　(CUTE)　framework　to　identify　the　driving　factors　of　China＇s　logistics　carbon　emissions.　Then,　the　Generalized　Fisher　Index　Decomposition　(GFID)　model　was　adopted　to　decompose　the　effects　of　the　driving　factors.　Finally,　we　tracked　the　spatial　dynamics　trajectory　of　each　driving　effect　based　on　the　gravity　model.　Our　results　showed　that　technical　intensity　and　transport　structure　promoted　carbon　emissions,　while　technical　efficiency　and　agglomeration　curbed　carbon　emissions.　The　gravity　centers　of　transport　structure　and　technical　efficiency　converged　with　that　of　carbon　emissions,　whereas　the　gravity　centers　of　technical　intensity　and　industry　agglomeration　diverged　from　that　of　carbon　emissions.　The　driving　effects　showed　an　obvious　spatial　heterogeneity,　which　indicated　that　carbon　reduction　policies　should　be　formulated　according　to　the　local　situation.