The transport sector is responsible for a significant and growing proportion of greenhouse gas emissions.  The urgent actions are required to take in the transport sector facing the challenge of growing global change. The major trends, including global urbanization, widespread application of digital technologies, and broad demand for sustainable development, have provided new opportunities for data-driven smart mobility in the future. This research aims to explore potentials of data-driven smart mobility in achieving Sustainable Development Goal 11.2, “provide access to safe, affordable, accessible and sustainable transport systems for all,” and Sustainable Development Goal 13.2, “take urgent action to combat climate change and its impacts” and “integrate climate change measures into national policies, strategies and planning” reducing greenhouse gas emissions every year. In order to meet this aim, this research explores the understandings and innovations of data-driven smart mobility in achieving decarbonization in urban, as well as barriers during the current practices. Hangzhou, as the capital city in Zhejiang Province in China, has been selected for the case study to examine data-driven smart mobility approaches. The research results show that the potentials of the data to tackle climate issues lie in the efficient transport operation and travel behaviors change. Data technologies have been widely applied to improve the integration of travel modes and the efficiency of transport management to reduce greenhouse gas emissions in road traffic. However, there are few drivers to mine data resources for travel behavior change. Moreover, data-driven smart mobility initiatives applied in urban areas involve multiple stakeholders but with limited access to data sharing and opening. Considering disruptive effects and potential promises brought by the big data technologies, the implementation of smart mobility requires for public data strategy with a holistic view of the complex urban challenges and global climate change.