Abstract

The coronavirus pandemic has severely impacted our day-to-day activities and brought about significant change in all major sectors, especially surface passenger transport. Lockdowns and stay-at-home restrictions have significantly reduced energy demand and consequently CO2 emissions of surface passenger transport. The change in CO2 emissions is calculated from near-real-time activity change data as a function of 3 confinement levels. The activity change and related emission trends reflect changes in the mode of transport during different waves, this can be used to understand mobility trends and patterns when stringent measures are imposed. Consequently, constructive use of this data can help prepare and develop the transport sector in case of another epidemic outbreak or other unprecedented calamities and to build a resilient transport infrastructure post-COVID-19. This study estimates and analyzes the changes in CO2 emissions associated with the public (bus and rail) and private surface passenger transport from March 1st, 2020 to Jan 31st, 2021 in 21 countries. The research period covers the 1st and the 2nd waves of COVID-19 in these countries. A higher activity reduction and consequently CO2 emission reduction is displayed during the 1st wave compared to the 2nd for most countries despite implementing stringent measures during both waves. This is in line with countries adapting to the ‘new normal’ and restarting socio-economic activities. Similarly, public transport recovery is slower than private transport recovery, making it essential to focus on reinforcement and adaptation of public transport infrastructure for the future. The results show that a cumulative 510 Mt CO2 has been reduced over 11 months in 21 countries, compared to pre-pandemic levels. This reduction brings about a 6% drop in transport CO2 emissions and a 1.5% drop in global CO2 emissions. This analysis sheds light on mobility trends and travel behavior of surface passenger transport modes and related CO2 emissions in different countries which can be used to exemplify the path to recovery based on near-real-time data.

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