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This paper aims to explore structural and social-economic determinants of China's transport CO 2 emissions (TCEs) from 2004 to 2016, by using logarithmic mean Divisa index (LMDI). Compared with existing studies, two new factors, i.e., spatial pattern and age structure, that should have impacts on TCEs, are considered in this study. Results show that during 2004–2016: (1) transportation demand and urbanization were dominant in TCEs growth, while energy intensity and industrial structure were the key factors of CO 2 emissions reduction. (2) The effects exerted by age structure and CO 2 emission factor change were relatively mild. The former one has a stable promoting effect, while the latter one inhibits TCEs growth. Additionally, the influence of spatial pattern on the growth of CO 2 emissions from transportation was limited. (3) Energy structure and energy intensity played more important roles in reducing emissions of the transport sector in the eastern region. The effects of population size on the eastern region were much greater than those of the central and western regions, presenting the phenomenon of "eastern agglomeration". (4) In Tianjin, energy intensity and energy structure did not perform well for TCEs. • This paper analyzed the nine drivers of China's transport CO 2 emissions from 2004 to 2016. • Urbanization and transportation demand were the main drivers of CO 2 emissions. • Energy intensity and industrial stnucture were the key emissions reduction factors. • The effects of age structure had a stable promoting effect. • Energy structural and energy intensity did not perform well for TCEs in all provinces. [ABSTRACT FROM AUTHOR]

China's transportation industry, with its typical characteristics of "high energy consumption and high emissions", is a key point for China's CO 2 emission reduction. An investigation of the driving factors of China's transport energy-related CO 2 emissions has great importance for clarifying the "gains" and "losses" as well as the future focuses of CO 2 emissions emission reduction in this sector. Existing studies on the driving factors of transport CO 2 emissions in China fail to incorporate effects of technology and efficiency into decomposition analysis framework. Based on the provincial panel data of China's transportation industry for the period of 2004–2016, this paper uses a comprehensive decomposition framework, which combines the logarithmic mean Divisia index method (LMDI) and production-theoretical decomposition analysis (PDA), to decompose China's transport CO 2 emissions into nine components: emission factor effect, energy structure effect, scale effect, regional structure effect, energy-saving technology effect, production technology effect, energy efficiency effect, production efficiency effect, and potential energy intensity effect. The main results indicate that: (1) during the sample period, China's transport industry witnessed 498.998 million tons (Mt) CO 2 emissions growth. Scale effect was the largest contributor, followed by production technology, energy-saving technology, and energy structure; (2) the improvement of energy efficiency and production efficiency, the decline of electricity's emission factor, and regional structure adjustment played active roles in the CO 2 emission reduction of transportation industry; (3) the performance of various driving factors varies greatly in different provinces. Local governments should establish and implement policies tailored to their characteristics. • A production-theoretical approach was used for China's transport CO 2 emissions. • It can decompose CO 2 emissions from technology and efficiency perspectives. • Results show that during 2004–2016, China's transport CO 2 emissions increased by 498.998 Mt. • Output effect was the largest contributor to this remarkable increase of transport CO 2 emissions. • Technology and efficiency factors cumulatively reduced 242.1 Mt transport CO 2 emissions. [ABSTRACT FROM AUTHOR]