Understanding urban morphology is crucial for identifying energy inefficiencies and improving sustainable energy planning, especially in rapidly urbanizing cities. Urban morphology influences how electricity is distributed and consumed, while energy efficiency is a core target of Sustainable Development Goal 7 (SDG-7). This study examined the relationship between urban morphology and electrification efficiency, using night-time light imagery as a proxy for energy distribution. NTL data combined with population data from WorldPOP were processed through zonal statistics in GIS to calculate energy consumption per morphology level. Electrification data from the national statistics agency were correlated with light intensity to validate NTL as a reliable proxy. The results reveal significant variations in electrification efficiency across urban morphologies. The City Center category demonstrates the highest efficiency at 59.96%, indicating optimized electricity use relative to its dense infrastructure and population. In contrast, the Urban Fringe category has the lowest efficiency, at 4.30%, suggesting an imbalance between electricity supply and actual utilization. Additionally, the Inner Urban category (14.59%) and the Sub Urban category (5.64%) exhibit intermediate efficiency levels, while the Rural Hinterland category (8.24%) and the Urban Periphery category (7.28%) indicate that a portion of their electricity consumption may be allocated for non-residential activities, such as street lighting and infrastructure operations rather than for direct household use. These findings underscore the importance of spatially-informed electrification planning to optimize energy distribution based on urban structure and population needs. By incorporating data-driven strategies and smart grid technologies, policymakers can enhance energy efficiency, improve electrification access, and support more sustainable urban energy policies aligned with SDG-7.
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Afrianto et al. (2025) studied this question.
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