Global Road Transport Emissions: Bottom-Up Insights for Inventory Improvement

Abstract

At the global level, land transport emission inventories based on models and scenarios like integrated assessment models (IAMs) offer limited detail and differentiation for country, vehicle category, vehicle technology-based emissions and emission factors. The challenge in collating and harmonizing emission and vehicle fleet and traffic datasets for the global scope is well acknowledged (Lekaki, et al., 2024). In the current work, we present the results of a bottom-up based methodology for improved global transport emission inventory generation for the reference year of 2019 and future scenarios. As a further addition to the extent and quality of transport inventories, both exhaust and non-exhaust pollutant species are included. The improved modeling approach was supplemented by new methods for uncertainty quantification and spatial distribution