A new approach for the bottom-up calculation of global road transport emissions

Abstract

Road transport is responsible for two-thirds of transport related greenhouse emissions. To assess the impact of road transport on climate change, different model approaches have been applied. For global emissions, integrated assessment models (IAMs) have been established to asses the impact of different sectors on climate change. IAMs are capable of linking technical and socioeconomic development as well as policy decisions to emission scenarios. However, they offer limited differentiation in analyzing specific subsectors, transport modes, countries, or vehicle technologies. To address this gap, our aim is to calculate emissions from different road transport modes bottom-up for various world regions for the reference year 2019. The established models for determining transport activities and emissions consider different transport modes like passenger car, various truck classes, and two- and three-wheelers. Drivetrain and country specific emission factors are derived and subsequently aggregated according to the stock fleet in 2019. Different approaches and data sources are considered for estimating the drivetrain specific emission factors of each country analyzed. For giving a comprehensive overview of emissions, twenty species have been calculated, including CO2, CH4, CO, N2O, NMVOC, NO2, PM10, PM2.5, SO2 etc. Additionally, non-exhaust emissions have been analyzed. In this paper, we present the methodology and results of the emission calculations for the reference year 2019. Regarding the passenger car transport activity, the data for 2019 is determined based on historical motorization rates for representative countries. Gompertz functions are estimated that represent the relationship between economic development and car ownership. The result is motorization rates in number of cars per 1000 inhabitants for each country. Together with population data, average annual mileages per vehicle and occupancy rates, the annual car traffic demand is calculated. The transport performance of the 2- and 3-wheeler, rail and bus modes is calculated in relation to car transport performance on the basis of modal split assumptions. For the commercial vehicles, less statistical data is available. Therefore, for non-OECD countries where the transport activity in ton-km is not available, a similarity analysis has been performed to derive an approximate behavior. The 2019 commercial transport activity was mapped using fixed effects models. Data up to 2013 was used as training data for the regression. In order to obtain modeled results, economic and population data from 2019 was used in the model. For the spatial distribution of emissions, a new approach based on traffic data counts is presented. This enables a more precise allocation of emissions, which is important for certain pollutants. With this approach, we achieve a spatial resolution of 0.1°. The resulting emission inventory for road transport provides additional information of uncertainty factors along the entire modelling chain and allows a detailed evaluation of the results for climate modelers and practitioners. Moreover, the models developed in with this approach allow the creation of scenarios for the future trend for road traffic emissions. These scenarios can take into account specific technological developments and measures for individual modes of transport and countries.