Smart data, defined as digital traces that people leave behind during their daily activities, has an underexplored potential to estimate intra-urban traffic emissions and their implications for environmental justice. Here, we incorporate fine-grained mobility flows from the App (Huq) into the Spatial Weight Matrix (SWM) to predict traffic CO 2 in Glasgow City, UK. The results demonstrate that models based on customized SWM with real mobility better predict traffic CO 2 than traditional distance-based models. According to model results, income and car ownership rates are dominant factors associated with traffic CO 2 . Noticeably, traffic CO 2 emissions are closely related to incoming mobility flows from neighborhoods with high income and car ownership rates. Moreover, the top 20% areas by income and car ownership account for 37.21% and 49.52% of total traffic CO 2 , respectively, indicating that disadvantaged groups bear the costs of emissions disproportionately generated by residents of wealthier areas. Finally, urban planners should not only consider reducing traffic emissions but also ensure that disadvantaged residents will not be affected by affluent communities to mitigate emission inequality. This study provides insightful solutions for urban planning policies to reduce traffic emissions and to reveal environmental injustices, thereby achieving just urban transitions in global cities. • Incorporating the fine-grained mobility flows from the mobile app could better predict intra-urban traffic CO 2 emissions • Income and car ownership rates are dominant factors associated with traffic CO 2 in Glasgow, UK • The top 20% of high income and high car ownership communities are responsible for 37.21% and 49.52% of total traffic CO 2 , respectively • Policy needs to ensure disadvantaged groups do not bear the costs of emissions generated by residents of wealthier areas
Tian et al. (Sun,) studied this question.