High Spatiotemporal Resolution Inventory Finely Depicts the Rebound Characteristics of Greenhouse Gas Emissions from Vehicles in Beijing after the COVID-19 Epidemic

Abstract

Urban transportation systems are one of the major contributors to greenhouse gas (GHG) emissions. China’s COVID-19 lockdown greatly suppressed these emissions, yet the spatiotemporal patterns of emissions may have shifted as restrictions eased. Here, we developed hourly link-level inventories of GHG (CO₂, CH₄, and N₂O) emissions from on-road vehicles in Beijing for 2022 and 2023, leveraging extensive observational traffic data and machine learning models. Our analysis revealed that vehicle activity in Beijing greatly increased in 2023 following the relaxation of pandemic control measures, particularly on expressways and during weekends. This resulted in 21%, 22%, and 24% increases in CO₂, CH₄, and N₂O emissions, respectively, compared to 2022. The rebound was concentrated in two short periods of the strict 2022 lockdowns, accounting for over two-thirds of the total increase in 2023, underscoring the impact of human activity. Furthermore, we found that vehicle fleet updates in 2023 helped slightly mitigate the rebound in CO₂, CH₄, and N₂O emissions, reducing their total emissions by 8.4%, 6.8%, and 5.1%, respectively. This study precisely quantifies the postpandemic rebound effects, identifies the underlying causes, and provides a framework that can be applied to analyze the emission reduction effects of various local policies.