Global　warming　has　imposed　substantial　global　negative　impacts　on　different　sectors　of　human　societies,　such　as　extreme　weather　events.　In　this　sense,　it　is　imperative　to　ascertain　whether　the　rise　in　global　temperature　will　accelerate　carbon　emissions　simultaneously.　The　land　surface　temperature　(LST)　serves　as　a　common　indicator　to　represent　the　spatial　temperature.　In　addition,　urban　areas　account　for　a　majority　portion　of　global　emissions.　To　fill　this　gap,　selecting　the　central　urban　area　of　Fuzhou　City　as　a　study　case,　this　paper　aims　to　examine　the　potential　correlation　between　LST　and　overall　carbon　emissions,　based　on　the　land　use　and　cover　change　(LUCC)　data　and　nighttime　lighting　remote　sensing　data　in　three　years　(2012,　2016,　and　2020).　A　spatially　explicit　distribution　model　of　carbon　source　is　presented　in　this　paper.　Based　on　remote　sensing　data　and　land　use　and　cover　change　data,　this　model　used　inverse　distance　weighting　spatial　interpolation　to　calculate　urban　carbon　emissions　and　retrieve　LST.　Moreover,　the　potential　statistical　correlation　between　land　surface　temperature　(LST)　and　urban　carbon　emissions　is　explored　by　both　polynomial　and　spline　regressions　and　a　potential　positive　statistical　correlation　between　LST　and　carbon　emissions　is　observed　in　this　case.urban　carbon　emissions　generally