A validated kaya-based building emission model (MBEAM): Sensitivity analysis and net-zero scenario assessment across six typologies and five climate zones

Existing building-scale CO ₂ assessment tools trade accuracy for deployability or physics for parsimony; no validated, analytically closed-form framework simultaneously satisfies regulatory, portfolio, and research demands. Buildings account for ∼37% of global energy-related CO ₂ emissions, yet existing quantification tools remain fragmented: physics-based models require extensive data and expertise; statistical methods lack generalizability; lifecycle assessments conflate operational and embodied emissions. A transparent, analytically tractable, and deployable framework is absent. This paper develops and validates the Multivariable Building Emission Assessment Model (MBEAM), a building-scale adaptation of the Kaya Identity disaggregating operational CO ₂ into six drivers: building morphology, envelope performance, system efficiencies, occupancy patterns, renewable offset, and grid/fuel carbon intensity. Grounded in ISO 52000, ASHRAE 90.1, and IPCC Tier 1–2 methods, MBEAM uses 21 input variables derived from building databases across Europe, North America, and the Middle East. Validated on 96 buildings (six typologies, five climate zones), MBEAM achieves 5.8% MAPE and R ² = 0.93, outperforming benchmark models. Sobol’ sensitivity analysis identifies grid carbon intensity (S _T = 0.31), wall thermal transmittance (S _T = 0.22), and occupancy schedule (S _T = 0.18) as dominant drivers, explaining 71% of output variance. Counterfactual scenarios that combine grid decarbonization (2035 targets), deep envelope retrofits, and high-performance heat pumps reduce emissions by 61–78% relative to 2020 levels, making net-zero operational carbon achievable for most typologies.