Reducing carbon emissions has become an urgent task in China. As the category with the largest economic and emissions contribution to the industry, the carbon emissions research of the manufacturing industry is particularly important. This paper uses the LMDI method to decompose manufacturing carbon emissions into seven influencing factors (i.e., population, urbanization, economic development, investment share, energy intensity, energy structure and emission intensity), in order to explore the factors driving manufacturing carbon emissions during 2003–2018. Then, the paper analyzes the decoupling relationship between manufacturing investment and carbon emissions in 30 provinces. Finally, three scenarios are developed to project future manufacturing emissions at the provincial level up to 2035, and whether manufacturing emissions in 30 provinces can realize peak is discussed. The paper results in three main findings. First, we find that energy intensity played the most important role in decreasing the manufacturing emissions during the whole study period, while the economic development and investment share were the main effect promoting manufacturing carbon emissions. Second, China experienced a process from weak decoupling to strong decoupling between manufacturing invest and emissions. Third, China's manufacturing carbon emissions can only achieve the carbon peaking target in 2030 under the High scenario, and 7 provinces cannot reach the peak before 2035 under the three scenarios. Supplementary Information The online version contains supplementary material available at 10.1007/s10668-023-03047-w.

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In order to support the emissions reduction options in manufacturing industry effectively, it is necessary to quantify the final demand embedded manufacturing consumption (DEMC) emissions which can be estimated by converting intermediate manufacturing consumption into all final demand categories. Here, we quantify the DEMC emissions in China’s 30 provinces during 2007–2017 using a multi-regional input-output (MRIO) model and the modified Hypothetical Extraction Method (HEM). Then, we analyze impacts of four factors (including emissions multipliers, consumption structure, investment efficiency and investment scale) on the DEMC emissions. Finally, considering a large driving effect of investment scale on manufacturing emissions, we conduct four scenarios to quantify the mitigation potential of DEMC emissions during 2020–2035. We find that from 2007 to 2012, the DMEC emissions increased doubled, while during 2012–2017, it decreased from 1217 Mt to 634 Mt. The capital-intensive manufacturing and the labor-intensive manufacturing industries were main sources of intra- and inter-sectoral emissions, respectively. Investment scale was the main driver of the growth in DEMC emissions during 2007–2015, while it led to a reduction of DEMC emissions during 2015–2017. Emission multipliers was the largest inhibiting factor for DEMC emissions during the whole period. Consumption structure had the positive effect on DEMC emissions during 2007–2012, while with the consumption structure shift towards knowledge-intensive manufacturing industry, it induced a reduction of DEMC emissions during 2012–2017. Moreover, implementing an integrated mitigation measures (including reducing emissions multipliers, decreasing investment efficiency, and adjusting consumption structure) could help China to realize the emissions peaking target. However, there are still 8 provinces whose DEMC emissions are unlikely to peak before 2030.