Reduction in energy use and carbon emissions is essential for achieving resilient, green, and smart cities. Understanding the various patterns of energy changes in cities can serve as a foundation for planning the strategies related to mitigation of carbon emissions. It has been reported that carbon emissions were reduced during the global COVID-19 pandemic. However, this reduction pattern in cities has not been investigated using spatially-temporally fine-resolutions due to data unavailability. Therefore, this study aims to estimate reduction rates of carbon emissions, especially those that are related to transport activity, which is a key factor for building resilient, green, and smart cities. This was accomplished by using the urban carbon mapping approach with big data. Our target city was Tokyo 23 wards, Japan and the study was conducted for a time frame of January to June 2020. The results show that carbon emissions and transport activities in May decreased by approximately 40% when compared to those in January.
Keywords COVID-19, carbon mapping, mobile GNSS/GPS data, electric vehicle, machine learning