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Urban transportation is a key challenge for achieving climate neutrality. However, effective intervention is hindered by a lack of granular data to identify spatially heterogeneous emission hotspots, as scalable approaches for jointly estimating private car and public transport (PT) emissions at high resolution remain scarce. This study introduces a hybrid bottom-up framework to address this issue. By integrating mobile phone-based travel demand data, unified multimodal routing and Life Cycle Assessment (LCA) emission factors, daily travel related CO 2 emissions is mapped at high spatial resolution in the Helsinki Metropolitan Area. The results reveal pronounced spatial disparities: while the inner-city core exhibits high emission intensity across all modes, car emissions in the suburban periphery are found to exceed public transport emissions by over 50 times. Inspecting the emission patterns against underlying urban structures, the key findings show that high-emission clusters align with areas of high income and low job density, with the highest total emissions occurring in a suburban zone 10–20 km from the Helsinki city centre. The emission estimates were validated against municipal inventories, showing an overall agreement with the annual totals. The presented approach offers a diagnostic tool that can be beneficial for transport planning and policy to target decarbonization interventions and efforts to areas where they have the most impact.
Dey et al. (Thu,) studied this question.