Multi-factor decomposition and multi-scenario prediction decoupling analysis of China's carbon emission under dual carbon goal

Comprehensively clarifying China's carbon emission factors and formulating effective strategies are essential and significant for achieving the “30–60” dual carbon target. This manuscript proposed a novel hierarchical framework of multi-factor decomposition, comprehensive evaluation, prediction, and decoupling analysis of the carbon emission. The multi-factor decomposition model from the perspectives of energy, economy, and society based on the expanding the Kaya Identity and LMDI decomposition method can provide the quantification results. On this basis, this manuscript applies the entropy weight method to construct the evaluation system and generate the index from the environment, energy, and economy dimensions for China's six power generation modes. Furthermore, the carbon emission dynamics model is built based on the carbon emission data in the past 40 years and used to predict China's carbon emission in the next 40 years under multi scenarios combined with Tapio's decoupling theory. The results show that income per capita and thermal power generation result in carbon emission, while energy price and intensity are decreasing. Moreover, reducing energy consumption and increasing the proportion of renewable energy are effective ways to make China's carbon emission peak in 2030, with a peak value of 12.276 billion tons. Eventually, with policies implemented, carbon emission, economic growth, and social development are predicted to reach a strong decoupling state, indicating long-lasting negative correlations. In summary, this study will provide a comprehensive analytical solution for factor decomposition, integrated assessment, and predictive decoupling of carbon emission from a national level, aiming to provide scientifically reasonable suggestions for policies and regulations for the “dual carbon” goal. [Display omitted] •Provided a comprehensive multi-factor decompose solution of China’s carbon emission.•Quantified the contribution of different factors and constructed a comprehensive evaluation model to assess different power generation modes.•Established and verified a system dynamics model for carbon emission prediction.•Predict and Analyzed relationship between carbon emission and GDP, HDI by decoupling theory.