Integrating Specific Emission Factors and Spatial Autocorrelation Analysis to Evaluate Urban Carbon Emissions in Residential and Waste Sectors: A CaseStudy of Mojokerto City, Indonesia - Taufiq Hidayat; Imam Santoso; Maharani Pertiwi Koentjoro (Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB), 2026)
The rise of urban carbon emissions is being seen as an important global climate change mitigation target, especially for medium-sized cities during rapid urbanization including Mojokerto in Indonesia. Yet almost all the existing studies rely on the basis of macro-scale inventories and overlook micro-spatial variations between urban villages, which cause very little insights into how residential and waste activities drive localized emissions patterns. This study fills this gap through a synergy of the Specific Emission Factor (SEF) method combined with spatial autocorrelation analysis to quantify and map carbon emissions sub-district wise. Empirically generated SEF indicators using the IPCC (2006) Tier 2 method demonstrate 0.278 tons CO₂ /household/year for residential emissions and 0.000579 tons CO₂-eq/person/year for waste emissions. This, was associated with the use of spatial statistical methods (Global Moran’s I, Local Moran’s I (LISA), and Getis–Ord Gi*) to characterize clusters and hotspots. The findings reveal total annual emissions amounting to 13,439.87 tons CO₂, primarily the former related to the residential sector. A Moran’s I value of 0.24 (p = 0.0127) indicated that there was a strong positive spatial autocorrelation, Wates and Kedundung were indicated the emission hotspots and Purwotengah and Sentanan were the cold spots. Results show that Mojokerto carbon emissions are spatially clustered and geographically concentrated, which is indicative of population density and household energy use intensity differences. The findings emphasise the potential of spatially specific mitigation approaches, suggesting that multisectoral and spatio-temporal analyses are necessary to enhance adaptive, context sensitive low carbon urban planning.