In　this　paper,　we　present　a　comprehensive　optimization　framework　that　identifies　renovation　plans　to　minimize　half-life　cycle　carbon　emissions,　investment　payback　period,　and　indoor　discomfort　hours.　The　framework　consists　of　four　stages.　First,　relevant　data　were　collected,　building　models　were　established,　and　the　renovation　scope　and　preliminary　parameters　were　determined.　Second,　a　sensitivity　analysis　of　the　initial　parameter　set　was　conducted,　and　important　parameters　were　selected　and　input　into　a　back-propagation　neural　network　model　for　prediction.　Finally,　an　optimal　renovation　plan　was　obtained　through　multi-objective　optimization　and　the　technique　for　order　of　preference　by　similarity　to　the　ideal　solution　(TOPSIS)　decision-making.　To　illustrate　the　framework＇s　feasibility,　it　was　applied　to　a　building　as　an　example.　Remarkably,　carbon　emissions　were　reduced　by　82.2　%,　and　zero　carbon　was　achieved　during　the　half-life　cycle.　Moreover,　this　achievement　resulted　in　a　relatively　swift　payback　period　of　3.9　years,　coupled　with　a　commendable　30　%　decrease　in　indoor　discomfort　hours.　Hence,　the　framework　is　effective　in　optimizing　building　renovation　objectives,　yielding　a　more　harmonious　and　ideal　building　renovation　strategy,　and　can　be　widely　utilized　to　enhance　building　performance.　©　2024　Elsevier　Ltd