Driving factors and interactions of urban transportation carbon emissions: A case study of China
将中国城市分为六类，用地理探测器和LMDI模型分析交通碳排放的驱动因素及交互作用，并构建新模型评估各因素对脱钩的贡献，为不同城市制定低碳交通政策提供依据。
Regional disparities among different types of cities pose significant challenges to reducing carbon emissions from urban transportation. This research classifies cities into six categories and examines the factors driving urban transportation carbon emissions, along with their interactions, using the Geographical Detector Model (GDM) and the Logarithmic Mean Divisia Index (LMDI). Building on this foundation, a novel decoupling effort model is developed to assess the contributions of each driving factor to the decoupling process. The findings highlight that economic growth is a major driver of emissions in large cities like Shanghai and Chengdu, while industrial structure plays a key role in large coastal cities such as Yantai. In contrast, urban public transportation participation and carrying capacity are pivotal factors in large inland cities like Shijiazhuang and medium-sized cities like Sanya and Jinhua. Notably, the study reveals a post-2016 challenge in balancing public transportation engagement and carrying capacity, emphasizing the need for tailored low-carbon policies in urban transport planning. These insights provide valuable guidance for cities worldwide facing similar transportation carbon emission challenges, offering a framework for more context-specific and effective carbon reduction strategies.