Existing　methods　of　measuring　energy　consumption　require　complex　statistics　and　computing.　A　real-time　and　globally　applicable　approach　for　comparing　energy　consumption　across　different　cities　is　still　lacking.　Additionally,　the　nonlinear　relationships　and　varying　thresholds　of　energy　consumption　in　relation　to　economic　activities　and　urbanization　remain　unconfirmed.　This　study　aims　to　fill　these　gaps　by　utilizing　Suomi　National　Polar-orbiting　Partnership　Visible　Infrared　Imaging　Radiometer　Suite　(NPP-VIIRS)　nighttime　light　data　in　2015　and　a　top-down　approach　based　on　a　multiple　regression　model　to　examine　energy　consumption　in　global　cities　employing　a　redefined　urban　boundary.　It　also　explores　the　accurate　relationship　between　energy　consumption,　population　density　(as　a　proxy　of　urbanization),　and　per　capita　gross　domestic　product　(GDP)　across　different　regions　and　urban　sizes　using　generalized　additive　models　and　regression　models.　High-resolution　gridded　population　and　GDP　datasets　covering　the　entire　planet　are　utilized　for　this　purpose.　The　study　also　estimates　the　development　potentiality.　The　study　yields　followings　outcomes:　Firstly,　the　top　30　cities　with　the　highest　per　capita　energy　consumption　account　for　over　0.66%　of　the　total　per　capita　energy　consumption　of　all　cities.　Secondly,　in　East　Asia　(EA)　and　Southeast　Asia　(SEA),　the　per　capita　energy　consumption　decreases　when　per　capita　GDP　reaches　$40,000　and　$75,000,　respectively,　while　it　remains　stable　in　cities　located　in　Western　Europe　(WE)　and　North　America　(NA)　as　per　capita　GDP　increases.　Thirdly,　the　per　capita　energy　consumption　declines　with　increasing　urban　population　density　until　reaching　10,000　person/km2,　22,000　person/km2,　and　4000　person/km2　in　EA,　SEA,　and　NA,　respectively.　Fourthly,　in　Central　Asia　(CA),　megacities　can　save　over　100　Mbtu/population　when　per　capita　GDP　increases　by　$1000　compared　to　big　cities.　This　pioneering　study　provides　a　comparable　investigation　of　energy　consumption　at　the　global　city　level,　exploring　its　relationship　with　urbanization　and　economy　by　employing　a　unified　calculation　standard.　It　will　facilitate　long-term　energy-saving　policies　and　urban　planning　strategies.　©　2024　The　Authors