Abstract
Conventional GHG accounting approaches typically average outcomes across entire transportation networks, limiting their sensitivity to intra-urban variations in urban form and travel behavior. As a result, emission-intensive clusters characterized can be overlooked,making it difficult to attribute emissions to specific spatial units and to design targeted planning interventions. This study develops and applies a high-resolution, bottom-up methodological framework to estimate and analyze district-level transport-related carbon emissions in Singapore’s integrated urban typologies. Leveraging mobile phone GPS data, smart card public transit records, and spatial datasets, the research constructs origin-destination matrices and quantifies travel activity across car, bus, and metro modes. Emissions are estimated using detailed vehicle and passenger activity modules, integrated with emission factor databases, and results are spatially disaggregated to reveal intra-district variation. The analysis demonstrates that while one-north records higher total emissions due to its larger commuting population, it achieves greater emissions efficiency per user and per passenger-kilometer compared to Science Park I & II. Conversely, Science Park exhibits higher emissions intensity specifically for public buses. Comparative findings highlight the limitations of relying solely on land-use integration for emissions reduction, emphasizing the need for a balanced mix of employment and residential uses and coordinated public transit planning. The study’s approach and results offer insights for urban planners and policymakers seeking to optimize the environmental performance of integrated districts and inform sustainable urban development strategies in rapidly growing cities