LAUSR

Abstract Considering the complexity and dynamicity of national energy-related CO2 emissions, it is necessary to analyse the influencing factors and evaluate the possible impact of relevant emission reduction policies before formulating them. Based on the system dynamics (SD) method, this study proposed a research framework and established a multi-level SD model to comprehensively analyse national energy-related carbon emissions, considering the relationship between factors of society, economic, energy and carbon emissions. The method was applied to the case of China. Firstly, it simulated China's historical emissions and predicted future baseline from 2005 to 2050. Then, based on the sensitivity analysis, the key influencing factors were discerned. Finally, by setting scenarios and introducing causal chains, the model was used to test the effectiveness of different emission reduction measures. The results show that GDP, energy structure and industrial structure have significant impact on energy-related CO2 emissions. The impact of the population is limited, while that of industrial energy intensity varies in different periods. Without carbon constraints, China's carbon emissions will peak by 2043 with the value of 15.2 Gt/year. Enhancing technological innovation in energy efficiency improvement and non-fossil energy is a better choice to reduce carbon emissions at current stage. An integrated measure combining technological innovation, infrastructure construction, resident behaviour improvement and adjustment of industrial structure can effectively advance the carbon peak to 2028, and reduce carbon intensity by 94% by 2050 compared to 2005, which will achieve China's national determined contribution goals. Such information will be important for China's future energy low-carbon transition and will provide suggestions for policy-making.