A LiDAR-driven urban energy benchmarking framework for mapping building energy intensity and carbon emissions in hyper-arid cities

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Rapid urbanization and intensifying heat extremes are accelerating building energy demand and associated carbon emissions in hyper-arid cities. However, quantifying building-level energy performance at high spatial resolution remains challenging because building morphology, floor area, and energy-consumption data are rarely integrated within a comprehensive framework. This study develops an integrated geospatial framework that integrates LiDAR-derived three-dimensional building morphology, high-resolution building footprints, and electricity-consumption data to quantify building-level energy use intensity (EUI), operational carbon emissions, and energy-performance patterns across Qatar. LiDAR-derived building morphology, deep-learning-based building footprints from high-resolution Pléiades imagery, and 14,186 electricity meter records were integrated with forest-based regression to estimate EUI across the mapped building stock. The analysis reveals pronounced sectoral and spatial disparities in building energy performance. Residential villas account for 35.5% of estimated operational building carbon emissions, while commercial, industrial, and governmental buildings collectively contribute 56.02%. Between 2020 and 2024, electricity consumption increased by 8.52% in the residential sector and by 25.0% in commercial and industrial buildings. Annual ean EUI rose from 158 ± 15 to 206 ± 20 kWh/m 2 , and total operational CO 2 -equivalent emissions increased from 19.3 to 22.7 MtCO 2 e. Annual mean carbon emission intensity also rose from 111.5 to 130.7 kgCO 2 e/m 2 , a 17.22% increase. Building-level clustering identified substantial heterogeneity in energy performance, with only 18% of buildings classified in the lower-EUI group (Cluster 1), compared with 50% in the higher-EUI group (Cluster 3), which had a mean EUI of 455 kWh/m 2 /year. The results identify substantial opportunities for spatially targeted energy-efficiency interventions and carbon reduction across building sectors. The proposed framework provides a high-resolution baseline for building-energy benchmarking and supports evidence-based prioritization of energy-efficiency and decarbonization strategies in hyper-arid urban environments.