Transportation is an important part of social and economic development and is also a typical high-energy and high-emissions industry. Achieving low-carbon development in the transportation industry is a much-needed requirement and the only way to achieve high-quality development. Therefore, based on the relevant data of 30 provinces in China from 2010 to 2018, this research uses the static panel model, panel threshold model and spatial Durbin model to conduct an empirical study on the impact and mechanism of digital innovation on carbon emissions in the transportation industry, and draws the following conclusions. (1) Carbon emissions in the transportation industry have dynamic and continuous adjustment characteristics. (2) There is a significant inverted U-shape non-linear relationship between the level of digital innovation and carbon emissions in the industry. In regions with a low level of digital innovation, the application of digital technology increases carbon emissions in this industry, but as the level of digital innovation continues to increase its application suppresses carbon emissions, showing an effect of carbon emission reduction. (3) The impact of digital innovation on carbon emissions in the transportation industry has a spatial spillover effect, and its level in one province significantly impacts carbon emissions in other provinces’ transportation industry through the spatial spillover effect. Therefore, it is recommended to further strengthen the exchange and cooperation of digital innovation in the transportation industry between regions, improve the scale of digitalization in this industry, and accelerate its green transformation through digital innovation, thus promoting the green, low-carbon, and sustainable development of China’s economy.

In order to achieve its 2030 carbon emission peak target, China needs to adjust and allocate energy consumption and initial carbon emission allowances for each province in a phased and planned manner. Thus, this study applied an improved dynamic undesirable zero-sum-gains slacks-based-measure (ZSG-SBM) model to evaluate provincial CO2 emission reduction scenarios and energy allocation for 2015–2019 and calculate the optimal allocation values of carbon emission allowances for each province in 2030. The results showed that China’s allocation efficiency values for total energy exhibited rising and then declining trends during 2015–2019 and that most input–output term efficiency values had room for improvement. Furthermore, after four adjustment iterations of the improved dynamic undesirable ZSG-SBM model, the modeled China achieved optimal carbon emission efficiency for the whole country. In the final model, 19 provinces were allowed to increase their carbon emissions in 2030, while the remaining 11 provinces needed to reduce their emissions. The findings of this paper can help regulators to establish fairer and more effective policy solutions to promote regional synergistic emission reduction, achieve the national goal of peak total carbon emissions, and promote the green, coordinated, and sustainable development of China’s economy.