Bridging data gaps in building LCA: A hybrid archetype–empirical approach to embodied carbon in Saudi residential buildings

Mir, B.A., Nurdiawati, A., Kumar, D., Halder, N., Akin, Ş., Hertwich, E., & Al-Ghamdi, S.G. (2026). Bridging data gaps in building LCA: A hybrid archetype–empirical approach to embodied carbon in Saudi residential buildings. Case Studies in Construction Materials 25 e06413. 10.1016/j.cscm.2026.e06413.

Preview

Text 1-s2.0-S2214509526006650-main.pdf - Published Version Available under License Creative Commons Attribution. Download (12MB) | Preview

Abstract

The share of embodied carbon (EC) in a building’s total emissions is increasing as operational emissions decline in energy-efficient buildings, underscoring the need for its measurement and mitigation. In Saudi Arabia, where rapid urbanization and large-scale housing developments are central to Vision 2030 and net-zero 2060, understanding EC in residential buildings is critical. This study aims to evaluate and compare the EC of predominant residential housing using two bottom-up approaches: empirical (EMP) and archetype-based (ARC). In EMP, life cycle assessment (LCA) of upfront EC (A1–A3) was conducted across thirteen buildings (26,100 m2; 121 dwellings) using building information modeling. The normalized global warming potential (GWP) results vary from 570 to 836 kg CO2e/m2 shared mainly by concrete, steel and aluminium. In earlier work, the ARC approach estimated material intensities through energy simulations of representative Saudi buildings, which were coupled with emission factors (EF) to calculate emissions. To evaluate the extent to which the ARC reproduces the results of EMP, material intensities obtained from both were statistically compared revealing that ARC over-predicts relative to EMP by 11.74%. Findings support the use of ARC as a valid reference estimator in contexts where empirical data is limited. Although this overprediction bias requires correction using preliminary calibration factors. Sensitivity analysis further highlights the impact of EF datasets from environmental product declarations and commercial databases, that leads to variability in LCA results, reinforcing the use of quality data. The findings provide a foundation for EC benchmarks, their integration into policy and material choices for decarbonization pathways.

Actions (login required)

View Item